Patent Application
20190109810
The present disclosure of invention relates generally to online networking systems and uses thereof.
The disclosure relates more specifically to Social-Topical/contextual Adaptive Networking (STAN) systems that, among other things, empower co-compatible users to on-the-fly join into corresponding online chat or other forum participation sessions based on user context and/or on likely topics currently being focused-upon by the respective users. Such STAN systems can additionally provide transaction offerings to groups of people based on system determined contexts of the users, on system determined topics of most likely current focus and/or based on other usages of the STAN system by the respective users. Yet more specifically, one system disclosed herein maintains logically interconnected and continuously updated representations of communal cognitions spaces (e.g., topic space, keyword space, URL space, context space, content space and so on) where points, nodes or subregions of such spaces link to one another and/or to cross-related online chat or other forum participation opportunities and/or to cross-related informational resources. By automatically determining where in at least one of these spaces a given user's attention is currently being focused, the system can automatically provide the given user with currently relevant links to the interrelated chat or other forum participation opportunities and/or to the interrelated other informational resources. In one embodiment, such currently relevant links are served up as continuing flows of more up to date invitations that empower the user to immediately link up with the link targets.
The following copending U.S. patent applications are owned by the owner of the present application, and their disclosures are incorporated herein by reference in their entireties as originally filed:
(A) Ser. No. 12/369,274 filed Feb. 11, 2009 by Jeffrey A. Rapaport et al. and which is originally entitled, ‘Social Network Driven Indexing System for Instantly Clustering People with Concurrent Focus on Same Topic into On Topic Chat Rooms and/or for Generating On-topic Search Results Tailored to User Preferences Regarding Topic', where said application was early published as US 2010-0205541 A1; and
(B) Ser. No. 12/854,082 filed Aug. 10, 2010 by Seymour A. Rapaport et al. and which is originally entitled, Social-Topical Adaptive Networking (STAN) System Allowing for Cooperative Inter-coupling with External Social Networking Systems and Other Content Sources.
The following copending U.S. provisional patent applications are owned by the owner of the present application, and their disclosures are incorporated herein by reference in their entireties as originally filed:
(A) Ser. No. 61/485,409 filed May 12, 2011 by Jeffrey A. Rapaport, et al. [atty docket: RAPA17334-1V US] and entitled Social-Topical Adaptive Networking (STAN) System Allowing for Group Based Contextual Transaction Offers and Acceptances and Hot Topic Watchdogging;
and
(B) Ser. No. 61/551,338 filed Oct. 25, 2011 [atty docket: RAPA17334-2V US] and entitled Social-Topical Adaptive Networking (STAN) System Allowing for Group Based Contextual Transaction Offers and Acceptances and Hot Topic Watchdogging.
The disclosures of the following U.S. patents or Published U.S. patent applications are incorporated herein by reference:
(A) U.S. Pub. 20090195392 published Aug. 6, 2009 to Zalewski; Gary and entitled: Laugh Detector and System and Method for Tracking an Emotional Response to a Media Presentation;
(B) U.S. Pub. 2005/0289582 published Dec. 29, 2005 to Tavares, Clifford; et al. and entitled: System and method for capturing and using biometrics to review a product, service, creative work or thing;
(C) U.S. Pub. 2003/0139654 published Jul. 24, 2003 to Kim, Kyung-Hwan; et al. and entitled: System and method for recognizing user's emotional state using short-time monitoring of physiological signals; and
(D) U.S. Pub. 20030055654 published Mar. 20, 2003 to Oudeyer, Pierre Yves and entitled: Emotion recognition method and device.
Imagine a set of virtual elevator doors opening up on your N-th generation smart cellphone (a.k.a. smartphone) or tablet computer screen (where N≤3 here) and imagine an on-screen energetic bouncing ball hopping into the elevator, dragging you along visually with it into the insides of a dimly lighted virtual elevator. Imagine the ball bouncing back and forth between the elevator walls while blinking sets of virtual light emitters embedded in the ball illuminate different areas within the virtual elevator. You keep your eyes trained on the attention grabbing ball. What will it do next?
Suddenly the ball jumps to the elevator control panel and presses the button for floor number 86. A sign lights up next to the button. It glowingly says “Superbowl™ Sunday Party Today”. You already had a subconscious notion that this is where this virtual elevator ride was going to next take you. Surprisingly, another, softer lit sign on the control panel momentarily flashes the message: “Reminder: Help Grandma Tomorrow”. Then it fades. You are glad for the gentle reminder. You had momentarily forgotten that you promised to help Grandma with some chores tomorrow. In today's world of mental overload and overwhelming information deluges (and required cognition staminas for handling those deluges) it is hard to remember where to cast one's limited energies (of the cognitive kind) and when and how intensely to cast them on competing points of potential focus. It is impossible to focus one's attentions everywhere and at everything. The human mind has a problem in that, unlike the eye's relatively small and well understood blind spot (the eye's optic disc), the mind's conscious blind spots are vast and almost everywhere except in the very few areas one currently concentrates one's attentions on. Hopefully, the bouncing virtual ball will remember to remind you yet again, and at an appropriate closer time tomorrow that it is “Help Grandma Day”. (It will.) You make a mental note to not stay at today's party very late because you need to reserve some of your limited energies for tomorrow's chores.
Soon the doors of your virtual elevator open up and you find yourself looking at a refreshed display screen (the screen of your real life (ReL) intelligent personal digital assistant (a.k.a. PDA, smartphone or tablet computer). Now it has a center display area populated with websites related to today's Superbowl™ football game (the American game of football, not British “football”, a.k.a. soccer). On the left side of your screen is a list of friends whom you often like to talk to (literally or by way of electronic messaging) about sports related matters. Sometimes you forget one or two of them. But your computer system seems not to forget and thankfully lists all the vital ones for this hour's planned activities. Next to their names are a strange set of revolving pyramids with red lit bars disposed along the slanted side areas of those pyramids. At the top of your screen there is a virtual serving tray supporting a set of so-called, invitation-serving plates. Each serving plate appears to serve up a stack of pancake-like or donut-like objects, where the served stacks or combinations of pancake or donut-like objects each invites you to join a recently initiated, or soon-to-be-started, online chat and where the user-to-user exchanges of these chats are (or will be) primarily directed to your current topic of attention; which today at this hour happens to be on the day's Superbowl™ Sunday football game. Rather than you going out hunting for such chats, they appear to have miraculously hunted for, and found you instead. On the bottom of your screen is another virtual serving tray that is serving up a set of transaction offers related to buying Superbowl™ associated paraphernalia. One of the promotional offerings is for T-shirts with your favorite team's name on them and proclaiming them the champions of this year's climactic but-not-yet-played-out game. You think to yourself, “I'm ready to buy that, and I'm fairly certain my team will win”.
As you muse over this screenful of information that was automatically served up to you by your wirelessly networked computer device (e.g., smartphone) and as you muse over what today's date is, as well as considering the real life surroundings where you are located and the context of that location, you realize in the back of your mind that the virtual bouncing ball and its virtual elevator friend had guessed correctly about you, about where you are or where you were heading, your surrounding physical context, your surrounding social context, what you are thinking about at the moment (your mental context), your current emotional mood (happy and ready to engage with sports-minded friends of similar dispositions to yours) and what automatically presented invitations or promotional offerings you will now be ready to now welcome. Indeed, today is Superbowl™ Sunday and at the moment you are about to sit down (in real life) on the couch in your friend's house (Ken's house) getting ready to watch the big game on Ken's big-screen TV along with a few other like minded colleagues. The thing of it is that today you not only have the topic of the “Superbowl™ Sunday football game” as a central focal point or central attention receiving area in your mind, but you also have the unfolding dynamics of a real life social event (meeting with friends at Ken's house) as an equally important region of focus in your mind. If you had instead been sitting at home alone and watching the game on your small kitchen TV, the surrounding social dynamics probably would not have been such a big part of your current thought patterns. However, the combination of the surrounding physical cues and social context inferences plus the main topic of focus in your mind places you in Ken's house, in front of his big screen, high definition TV and happily trading quips with similarly situated friends sitting next to you.
You surmise that the smart virtual ball inside your smartphone (or inside another mobile data processing device) and whatever external system it wirelessly connects with must have been empowered to use a GPS and/or other sensor embedded in the smart cellphone (or tablet or other mobile device) as well as to use your online digitized calendar to make best-estimate guesses at where you are (or soon will be), which other people are near you (or soon will be with you), what symmetric or asymmetric social relations probably exist between you and the nearby other people, what you are probably now doing, how you mentally perceive your current context, and what online content you might now find to be of greatest and most welcomed interest to you due to your currently adopted contexts and current points of focus (where, ultimately in this scenario; you are the one deciding what your currently adopted contexts are: e.g., Am I at work or at play? and which if any of the offerings automatically presented to you by your mobile data processing device you will now accept).
Perhaps your mobile data processing device was empowered, you further surmise; to pick up on sounds surrounding you (e.g., sounds from the turned-on TV set) or images surrounding you (e.g., sampled video from the TV set as well as automatically recognized faces of friends who happen to be there in real life (ReL)) and it was empowered to report these context-indicating signals to a remote and more powerful data processing system by way of networking? Perhaps that is how the limited computing power associated with your relatively small and low powered smartphone determined your most likely current physical and mental contexts? The question intrigues you for only a flash of a moment and then you are interrupted in your thoughts by Ken offering you a bowl full of potato chips.
With thoughts about how the computer systems might work quickly fading into the back of your subconscious, you thank Ken and then you start paying conscious attention to one of the automatically presented websites now found within a first focused-upon area of your smartphone screen. It is reporting on the health condition of your favorite football player, Joe-the-Throw Nebraska (best quarterback, in your humble opinion; since Joe Montana (a.k.a. “Golden Joe”, “Comeback Joe”) hung up his football cleats). Meanwhile in your real life background, the Hi-Def TV is already blaring with the pre-game announcements and Ken has started blasting some party music from the kitchen area while he opens up more bags of pretzels and potato chips. As you return focus to the web content presented by your PDA-style (Personal Digital Assistant type) smartphone, a small on-screen advertisement icon pops up next to the side of the athlete's health-condition reporting frame. You hover a pointer over it and the advertisement icon automatically expands to say: “Pizza: Big Local Discount, Only while it lasts, First 10 Households, Press here for more”. This promotional offering you realize is not at all annoying to you. Actually it is welcomed. You were starting to feel a wee bit hungry just before the ad popped up. Maybe it was the sound and smell of the bags of potato chips being opened in the kitchen or maybe it was the party music. You hadn't eaten pizza in a while and the thought of it starts your mouth salivating. So you pop the small teaser advertisement open to see even more.
The further enlarged promotional informs you that at least 50 households in your current, local neighborhood are having similar Superbowl™ Sunday parties and that a reputable pizza store nearby is ready to deliver two large sized pizza pies to each accepting household at a heavily discounted price, where the offered deal requires at least 10 households in the same, small radius neighborhood to accept the deal within the next 30 minutes; otherwise the deal lapses. Additional pies and other items are available at different discount rates, first not as good of a deal as the opening teaser rate, but then getting better and better again as you order larger and larger volumes (or more expensive ones) of those items. (In an alternate version of this hypothetical story, the deal minimum is not based on number of households but rather on number of pizzas ordered, or number of people who send their email addresses to the promoter or on some other basis that may be beneficial to the product vendor for reasons known to him. Also, in an alternate version, special bonus prizes are promised if you convince the next door neighbor to join in on your group order so that two adjacent houses are simultaneously ordering from the same pizza store.)
This promotional offering not only sounds like a great deal for you, but as you think on it some more, you realize it is also a win-win deal for the local pizza pie vendor. The pizza store owner can greatly reduce his delivery overhead costs by delivering in one delivery run, a large volume of same-time ordered pizzas to a same one local neighborhood (especially if there are a few large-sized social gatherings i.e., parties, in the one small-radiused neighborhood) and all the pizzas should be relatively fresh if the 10 or more closely-located households all order in the allotted 30 minutes (which could instead be 20 minutes, 40 minutes or some other number). Additionally, the pizza store can time a mass-production run of the pizzas, and a common storage of the volume-ordered hot pizzas (and of other co-ordered items) so they will all arrive fresh and hot (or at least lukewarm) in the next hour to all the accepting customers in the one small neighborhood. Everyone ends up pleased with this deal; customers and promoter. Additionally, if the pizza store owner can capture new customers at the party because they are impressed with the speed and quality of the delivery and the taste and freshness of the food, that is one additional bonus for the promotion offering vendor (e.g., the local pizza store).
You ask around the room and discover that a number of other people at the party (in Ken's house, including Ken) are also very much in the mood for some hot fresh pizza. One of them has his tablet computer running and he just got the same promotional invitation from the same vendor and, as a matter of fact, he was about to ask you if you wanted to join with him in signing up for the deal. He too indicates he hasn't had pizza in a week and therefore he is “game” for it. Now Jim chimes in and says he wants spicy chicken wings to go along with his pizza. Another friend (Jeff) tells you not to forget the garlic bread. Sye, another friend, says we need more drinks, it's important to hydrate (he is always health conscious). As you hit the virtual acceptance button within your on-screen offer, you begin to wonder; how did the pizza store, or more correctly your smartphone's computer and whatever it is remotely connected to; know this would happen just now—that all these people would welcome this particular promotional offering? You start filling in the order details on your screen while keeping an eye on an on-screen deal-acceptance counter. The deal counter indicates how many nearby neighbors have also signed up for the neighborhood group discount (and/or other promotional offering) before the offer deadline lapses. Next to the sign-up count there is a countdown timer decrementing from 30 minutes towards zero. Soon the required minimum number of acceptances is reached, well before the countdown timer reaches zero. How did all this come to be? Details will follow below.
After you place the pizza order, a not-unwelcomed further suggestion icon or box pops open on your screen. It says: “This is the kind of party that your friends A) Henry and B) Charlie would like to be at, but they are not present. Would you like to send a personalized invitation to one or more of them? Please select: 0) No, 1) Initiate Instant Chat, 2) Text message to their cellphones or tablets using pre-drafted invitation template, 3) Dial their cellphone or other device now for personal voice invite, 4) Email, 5) more . . . ”. The automatically generated suggestion further says, “Please select one of the following, on-topic messaging templates and select the persons (A,B,C, etc.) to apply it to.” The first listed topic reads: “SuperBowl Party, Come ASAP”. You think to yourself, yes this is indeed a party where Charlie is sorely missed. How did my computer realize this when it had slipped my mind? I'm going to press the number 2) “Text message” option right now. In response to the press, a pre-drafted invitation template addressed to Charlie automatically pops open. It says: “Charlie, We are over at Ken's house having a Superbowl™ Sunday Party. We sorely miss you. Please join ASAP. P.S. Do you want pizza?” Further details for empowering this kind of feature will follow below.
Your eyes flick back to the on-screen news story concerning the health of your favorite sports celebrity (Joe-the-Throw Nebraska—a hypothetical name). A new frame has now appeared next to it: “Will Joe Throw Today?”. You start reading avidly. In the background, the doorbell rings. Someone says, “Pizza is here!” The new frame on your screen says “Best Chat Comments re Joe's Health”. From experience you know that this is a compilation of contributions collected from numerous chat rooms, blog comments, etc.; a sort of community collection of best and voted most-worthy-to-see comments so far regarding the topic of Joe-the-Throw Nebraska, his health status and today's American football game. You know from past experience that these “community board” type of comments have been voted on, and have been ranked as the best liked and/or currently ‘hottest’ and they are all directed to substantially the same topic you are currently centering your attention on, namely, the health condition of your favorite sports celebrity's (e.g., “Is Joe well enough to play full throttle today?”) and how it will impact today's game. The best comments have percolated to the top of the list (a.k.a., community board). You have given up trying to figure out how your smartphone (and whatever computer system it is wirelessly hooked up to) can do this too. Details for empowering this kind of feature will also follow below.
As used herein, terms such as “cloud”, “server”, “software”, “software agent”, “BOT”, “virtual BOT”, “virtual agent”, “virtual ball”, “virtual elevator” and the like do not mean nonphysical abstractions but instead always entail a physically real and tangibly implemented aspect unless otherwise explicitly stated to the contrary at that spot.
Claims appended hereto which use such terms (e.g., “cloud”, “server”, “software”, etc.) do not preclude others from thinking about, speaking about or similarly non-usefully using abstract ideas, or laws of nature or naturally occurring phenomenon. Instead, such “virtual” or non-virtual entities as described herein are always accompanied by changes of physical state of real physical, tangible and non-transitory objects. For example, when it is in an active (e.g., an executing) mode, a “software” module or entity, be it a “virtual agent”, a spyware program or the alike is understood to be a physical ongoing process (at the time it is executed) which is being carried out in one or more real, tangible and specific physical machines (e.g., data processing machines) where the machine(s) entropically consume(s) electrical power and/or other forms of real energy per unit time as a consequence of said physical ongoing process being carried out there within. Parts or wholes of software implementations may be substituted for by substantially similar in functionality hardware or firmware including for example implementation of functions by way of field programmable gate arrays (FPGA's) or other such programmable logic devices (PLD's). When it is in a static (e.g., non-executing) mode, an instantiated “software” entity or module, or “virtual agent” or the alike is understood (unless explicitly stated otherwise herein) to be embodied as a substantially unique and functionally operative and nontransitory pattern of transformed physical matter preserved in a more-than-elusively-transitory manner in one or more physical memory devices so that it can functionally and cooperatively interact with a commandable or instructable machine as opposed to being merely descriptive and totally nonfunctional matter. The one or more physical memory devices mentioned herein can include, but are not limited to, PLD's and/or memory devices which utilize electrostatic effects to represent stored data, memory devices which utilize magnetic effects to represent stored data, memory devices which utilize magnetic and/or other phase change effects to represent stored data, memory devices which utilize optical and/or other phase change effects to represent stored data, and so on.
As used herein, the terms, “signaling”, “transmitting”, “informing” “indicating”, “logical linking”, and the like do not mean nonphysical and abstract events but rather physical and not elusively transitory events where the former physical events are ones whose existence can be verified by modern scientific techniques. Claims appended hereto that use the aforementioned terms, “signaling”, “transmitting”, “informing”, “indicating”, “logical linking”, and the like or their equivalents do not preclude others from thinking about, speaking about or similarly using in a non-useful way abstract ideas, laws of nature or naturally occurring phenomenon.
As used herein, the terms, “empower”, “empowerment” and the like refer to a physically transformative process that provides a present or near-term ability to a data producing/processing device or the like to be recognized by and/or to communicate with a functionally more powerful data processing system (e.g., an on network or in cloud server) where the provided abilities include at least one of: transmitting status reporting signals to, and receiving responsive information-containing signals from the more powerful data processing system where the more powerful system will recognize at least some of the reporting signals and will responsively change stored state-representing signals for a corresponding one or more system-recognized personas and/or for a corresponding one or more system-recognized and in-field data producing and/or data processing devices and where at least some of the responsive information-containing signals, if provided at all, will be based on the stored state-representing signals. The term, “empowerment” may include a process of registering a person or persona (real or virtual) or a process of logging in a registered entity for the purpose of having the functionally more powerful data processing system recognize that registered entity and respond to reporting signals associated with that recognized entity. The term, “empowerment” may include a process of registering a data processing and/or data-producing and/or information inputting and/or outputting device or a process of logging in a registered such device for the purpose of having the functionally more powerful data processing system recognize that registered device and respond to reporting signals associated with that recognized device and/or supply information-containing and/or instruction-containing signals to that recognized device.
The above identified and herein incorporated by reference U.S. patent application Ser. No. 12/369,274 (filed Feb. 11, 2009) and Ser. No. 12/854,082 (filed Aug. 10, 2010) disclose certain types of Social-Topical Adaptive Networking (STAN) Systems (hereafter, also referred to respectively as “Sierra#1” or “STAN_1” and “Sierra#2” or “STAN_2”) which empower and enable physically isolated online users of a network to automatically join with one another (electronically or otherwise) so as to form a topic-specific and/or otherwise based information-exchanging group (e.g., a ‘TCONE’—as such is described in the STAN_2 application). A primary feature of the STAN systems is that they provide and maintain one or more so-called, topic space defining objects (e.g., topic-to-topic associating database records) which are represented by physical signals stored in machine memory and which topic space defining objects can define (and thus model) topic nodes and logical interconnections (cross-associations) between, and/or spatial clusterings of those nodes and/or can provide logical links to forums associated with topics modeled by the respective nodes and/or to persons or other social entities associated with topics of the nodes and/or to on-topic other material associated with topics of the nodes. The topic space defining objects (e.g., database records, also referred to herein as potentially-attention-receiving modeled points, nodes or subregions of a Cognitive Attention Receiving Space (CARS), which space in this case is topic space) can be used by the STAN systems to automatically provide, for example, invitations to plural persons or to other social entities to join in on-topic online chats or other Notes Exchange sessions (forum sessions) when those social entities are deemed to be currently focusing-upon (e.g., casting their respective attention giving energies on) such topics or clusters of such topics and/or when those social entities are deemed to be co-compatible for interacting at least online with one another. (In one embodiment, co-compatibilities are established by automatically verifying reputations and/or attributes of persons seeking to enter a STAN-sponsored chat room or other such Notes Exchange session, e.g., a Topic Center “Owned” Notes Exchange session or “TCONE”.) Additionally, the topic space defining objects (e.g., database records) are used by the STAN systems to automatically provide suggestions to users regarding on-topic other content and/or regarding further social entities whom they may wish to connect with for topic-related activities and/or socially co-compatible activities.
During operation of the STAN systems, a variety of different kinds of informational signals may be collected by a STAN system in regard to the current states of its users; including but not limited to, the user's geographic location, the user's transactional disposition (e.g., at work? at a party? at home? etc.); the user's recent online activities; the user's recent biometric states; the user's habitual trends, behavioral routines, the user's biological states (e.g., hungry tired, muscles fatigued from workout) and so on. The purpose of this collected information is to facilitate automated joinder of like-minded and co-compatible persons for their mutual benefit. More specifically, a STAN-system-facilitated joinder may occur between users at times when they are in the mood to do so (to join in a so-called Notes Exchange session) and when they have roughly concurrent focus on same or similar detectable content and/or when they apparently have approximately concurrent interest in a same or similar particular topic or topics and/or when they have current personality co-compatibility for instantly chatting with, or for otherwise exchanging information with one another or otherwise transacting with one another.
In terms of a more concrete example of the above concepts, the imaginative and hypothetical introduction that was provided above revolved around a group of hypothetical people who all seemed to be currently thinking about a same popular event (the day's Superbowl™ football game) and many of whom seemed to be concurrently interested in then obtaining event-relevant refreshments (e.g., pizza) and/or other event-relevant paraphernalia (e.g., T-shirts). The group-based discount offer sought to join them, along with others, in an online manner for a mutually beneficial commercial transaction (e.g., volume purchase and localized delivery of a discounted item that is normally sold in smaller quantities to individual and geographically dispersed customers one at a time). The unsolicited and thus “pushed” solicitation was not one that generally annoyed the recipients as would conventionally pushed unsolicited and undesired advertisements. It's almost as if the users pulled the solicitation in to them by means of their subconscious will power rather than having the solicitations rudely pushed onto them by an insistent high pressure salesperson. The underlying mechanisms that can automatically achieve this will be detailed below. At this introductory phase of the present disclosure it is worthwhile merely to note that some wants and desires can arise at the subconscious level and these can be inferred to a reasonable degree of confidence by carefully reading a person's facial expressions (e.g., micro-expressions) and/or other body gestures, by monitoring the persons' computer usage activities, by tracking the person's recent habitual or routine activities, and so on, without giving away that such is going on and without inappropriately intruding on reasonable expectations of privacy by the person. Proper reading of each individual's body-language expressions may require access to a Personal Emotion Expression Profile (PEEP) that has been pre-developed for that individual and for certain contexts in which the person may find themselves. Example structures for such PEEP records are disclosed in at least one of the here incorporated U.S. Ser. No. 12/369,274 and Ser. No. 12/854,082. Appropriate PEEP records for each individual may be activated based on automated determination of time, place and other context revealing hints or clues (e.g., the individual's digitized calendar or recent email records which show a plan, for example, to attend a certain friend's “Superbowl™ Sunday Party” at a pre-arranged time and place, for example 1:00 PM at Ken's house). Of course, user permission for accessing and using such information should be obtained by the system beforehand, and the users should be able to rescind the permissions whenever they want to do so, whether manually or by automated command (e.g., IF Location=Charlie's Tavern THEN Disable All STAN monitoring”). In one embodiment, user permission automatically fades over time for all or for one or more prespecified regions of topic space and needs to be reestablished by contacting the user and either obtaining affirmative consent or permission from the user or at least notifying the user and reminding the user of the option to rescind. In one embodiment, certain prespecified regions of topic space are tagged by system operators and/or the respective users as being of a sensitive nature and special double permissions are required before information regarding user direct or indirect ‘touchings’ into these sensitive regions of topic space is automatically shared with one or more prespecified other social entities (e.g., most trusted friends and family).
Before delving deeper into such aspects, a rough explanation of the term “STAN system” as used herein is provided. The term arises from the nature of the respective network systems, namely, STAN_1 as disclosed in here-incorporated U.S. Ser. No. 12/369,274 and STAN_2 as disclosed in here-incorporated U.S. Ser. No. 12/854,082. Generically they are referred to herein as Social-Topical ‘Adaptive’ Networking (STAN) systems or STAN systems for short. One of the things that such STAN systems can generally do is to maintain in machine memory one or more virtual spaces (data-objects organizing spaces) populated by interrelated data objects stored therein such as interrelated topic nodes (or ‘topic centers’ as they are referred to in the Ser. No. 12/854,082 application) where the nodes may be hierarchically interconnected (via logical graphing) to one another and/or logically linked to topic-related forums (e.g., online chat rooms) and/or to topic-related other content. Such system-maintained and logically interconnected and continuously updated representations of topic nodes and associated forums (e.g., online chat rooms) may be viewed as social and dynamically changing communal cognition spaces. (The definition of such communal cognition spaces is expanded on herein as will be seen below.) In accordance with one aspect of the present disclosure, if there are not enough online users tethered to one topic node so as to adequately fill a social mix recipe of a given chat or other forum participation session, users from hierarchically and/or spatially nearby other topic nodes those of substantially similar topic may be automatically recruited to fill the void. In other words, one chat room can simultaneously service plural ones of topic nodes. (The concept of social mix recipe will be explained later below.) The STAN_1 and STAN_2 systems (as well as the STAN_3 of the present disclosure) can cross match current users with respective topic nodes that are determined by machine means as representing topics likely to be currently focused-upon ones in the respective users' minds. The STAN systems can also cross match current users with other current users (e.g., co-compatible other users) so as to create logical linkages between users where the created linkages are at least one if not both of being topically relevant and socially acceptable for such users of the STAN system. Incidentally, hierarchical graphing of topic-to-topic associations (T2T) is not a necessary or only way that STAN systems can graph T2T associations via a physical database or otherwise. Topic-to-topic associations (T2T) may alternatively or additionally be defined by non-hierarchical graphs (ones that do not have clear parent to child relationships as between nodes) and/or by spatial and distance based positionings within a specified virtual positioning space.
The “adaptive” aspect of the “STAN” acronym correlates in one sense to the “plasticity” (neuroplasticity) of the individual human mind and correlates in a second sense to a similar “plasticity” of the collective or societal mind. Because both individualized people and groups thereof; and their respective areas of focused attention tend to change with time, location, new events and variation of physical and/or social context (as examples), the STAN systems are structured to adaptively change (e.g., update) their definitions regarding what parts of a system-maintained, Cognitive Attention Receiving Space (referred to herein also as a “CARS”) are currently cross-associated with what other parts of the same CARS and/or with what specific parts of other CARS. The adaptive changes can also modify what the different parts currently represent (e.g., what is the current definition of a topic of a respective topic node when the CARS is defined as being the topic space). The adaptive changes can also vary the assigned intensity of attention giving energies for respective users when the users are determined by the machine means to be focused-upon specific subareas within, for example, a topics-defining map (e.g., hierarchical and/or spatial). The adaptive changes can also determine how and/or at what rate the cross-associated parts (e.g., topic nodes) and their respective interlinkings and their respective definitions change with changing times and changing external conditions. In other words, the STAN systems are structured to adaptively change the topics-defining maps themselves (a.k.a. topic spaces, which topic maps/spaces have corresponding, physically represented, topic nodes or the like defined by data signals recorded in databases or other appropriate memory means of the STAN_system and which topic nodes or groups thereof can be pointed to with logical pointer mechanisms). Such adaptive change of perspective regarding virtual positions or graphed interlinks in topic space and/or reworking of the topic space and of topic space content (and/or of alike subregions of other Cognitive Attention Receiving Spaces) helps the STAN systems to keep in tune with variable external conditions and with their variable user populations as the latter migrate to new topics (e.g., fad of the day) and/or to new personal dispositions (e.g., higher levels of expertise, different moods, etc.).
One of the adaptive mechanisms that can be relied upon by the STAN system is the generation and collection of implicit vote or CVi signals (where CVi may stand for Current (and implied or explicit) Vote-Indicating record). CVi's are vote-representing signals which are typically automatically collected from user surrounding machines and used to infer subconscious positive or negative votes cast by users as they go about their normal machine usage activities or normal life activities, where those activities are open to being monitored (due to rescindable permissions given by the user for such monitoring) by surrounding information gathering equipment. User PEEP files may be used in combination with collected CFi and CVi signals to automatically determine most probable, user-implied votes regarding focused-upon material even if those votes are only at the subconscious level. Stated otherwise, users can implicitly urge the STAN system topic space and pointers thereto to change (or pointers/links within the topic space to change) in response to subconscious votes that the users cast where the subconscious votes are inferred from telemetry gathered about user facial grimaces, body language, vocal grunts, breathing patterns, eye movements, and the like. (Note: The above notion of a current cross-association between different parts of a same CARS (e.g., topic space or some other Cognitive Attention Receiving Space) is also referred to herein as an IntrA-Space cross-associating link or “InS-CAX” for short. The above notion of a current cross-association between points, nodes or subregions of different CARS's is also referred to herein as an IntEr-Space cross-associating link or “IoS-CAX” for short, where the “o” in the “IoS-CAX” acronym signifies that the link crosses to outside of the respective space. See for example, IoS-CAX 370.6 of
Although not specifically given as an example in the earlier filed and here incorporated U.S. Ser. No. 12/854,082 (STAN_2), one example of a changing and “neuro-plastic” cognition landscape might revolve around a keyword such as “surfing”. In the decade of the 1960's, the word “surfing” may most likely have conjured up in the minds of most individuals and groups, the notion of waves breaking on a Hawaiian or Californian beach and young men taking to the waves with their “surf boards” so they can ride or “surf” those waves. By contrast, after the decade of the 1990's, the word “surfing” may more likely have conjured up in the minds of most up-to-date individuals (and groups of the same), the notion of people using personal computers and using the Internet and searching through it (surfing the net) to find websites of interest. Moreover, in the decade of the 1960's there was essentially no popular attention giving activities directed to the notion of “surfing” meaning the idea of journeying through webs of data by means of personally controlled computers. By contrast, beginning with the decade of the 1990's (and the explosive growth of the World Wide Web), it became exponentially more and more popular to focus one's attention giving energies on the notion of “surfing” as it applies to riding through the growing mounds of information found on the World Wide Web or elsewhere within the Internet and/or within other network systems. Indeed, another word that changed in meaning in a plastic cognition way is the word sounded out as “Google”. In the decade of the 1960's such a sounded out word (more correctly spelled as “Googol”) was understood to mean the number 10 raised to the 100th power. Thinking about sorting through a Googol-ful of computerized data meant looking for a needle in a haystack. The likelihood of finding the sought item was close to nil. Ironically, with the advent of the internet searching engine known as Google™, the probability of finding a website whose content matches with user-picked keywords increased dramatically and the popularly assumed meaning for the corresponding sound bite (“Googol” or “Google”) changed, and the topics cross-correlated to that sound bite also changed; quite significantly.
The sounded-out words, “surfing and “Google” are but two of many examples of the “plasticity” attribute of the individual human mind and of the “plasticity” attribute of the collective or societal mind. Change has and continues to come to many other words, and to their most likely meanings and to their most likely associations to other words (and/or other cognitions). The changes can come not only due to passage of time, be it over a period of years; or sometimes over a matter of days or hours, but also due to unanticipated events (e.g., the term “911”—pronounced as nine eleven—took on sudden and new meaning on Sep. 11, 2001). Other examples of words or phrases that have plastically changed over time include, being “online”, opening a “window”, being infected by a “virus”, looking at your “cellular”, going “phishing”, worrying about “climate change”, “occupying” a street such as one named Wall St., and so on. Indeed, not only do meanings and connotations of same-sounding words change over time, but new words and new ideas associated with them are constantly being added. The notion of having an adaptive and user-changeable topic space was included even in the here-incorporated STAN_1 disclosure (U.S. Ser. No. 12/369,274).
In addition to disclosing an adaptively changing topics space/map (topic-to-topic (T2T) associations space), the here also-incorporated U.S. Ser. No. 12/854,082 (STAN_2) discloses the notion of a user-to-user (U2U) associations space as well as a user-to-topic (U2T) cross associations space. Here, an extension of the user-to-user (U2U) associations space will be disclosed where that extension will be referred to as Social/Persona Entities Interrelation Spaces (SPEIS'es for short). A single such space is a SPEIS. However, there often are many such spaces due to the typical presence of multiple social networking (SN) platforms like FaceBook™, LinkedIn™, MySpace™, Quora™, etc. and the many different kinds of user-to-user associations which can be formed by activities carried out on these various platforms in addition to user activities carried out on a STAN platform. The concept of different “personas” for each one real world person was explained in the here incorporated U.S. Ser. No. 12/854,082 (STAN_2). In this disclosure however, Social/Persona Entities (SPE's) may include not only the one or different personas of a real world, single flesh and blood person, but also personas of hybrid real/virtual persons (e.g., a Second Life™ avatar driven by a committee of real persons) and personas of collectives such as a group of real persons and/or a group of hybrid real/virtual persons and/or purely virtual persons (e.g., those driven entirely by an executing computer program). In one embodiment, each STAN user can define his or her own custom groups or the user can use system-provided templates (e.g., My Immediate Family). The Group social entity may be used to keep a collective tab on what a relevant group of social entities are doing (e.g., What topic or other thing are they collectively and recently focusing-upon?).
When it comes to automated formation of social groups, one of the extensions or improvements disclosed herein involves formation of a group of online real persons who are to be considered for receiving a group discount offer (e.g., reduced price pizza) or another such transaction/promotional offering. More specifically, the present disclosure provides for a machine-implemented method that can use the automatically gathered CFi and/or CVi signals (current focus indicator and current voting indicator signals respectively) of a STAN system advantageously to automatically infer therefrom what unsolicited solicitations (e.g., group offers and the like) would likely be welcome at a given moment by a targeted group of potential offerees (real or even possibly virtual if the offer is to their virtual life counterparts, e.g., their SecondLife™ avatars) and which solicitations would less likely be welcomed and thus should not be now pushed onto the targeted personas, because of the danger of creating ill-will or degrading previously developed goodwill. Another feature of the present disclosure is to automatically sort potential offerees according to likelihood of welcoming and accepting different ones of possible solicitations and pushing the M most likely-to-be-now-welcomed solicitations to a corresponding top N ones of the potential offerees who are currently likely to accept (where here M and N are corresponding predetermined numbers). Outcomes can change according to changing moods/ideas of socially-interactive user populations as well as those of individual users (e.g., user mood or other current user persona state). A potential offeree who is automatically determined to be less likely to welcome a first of simultaneously brewing group offers may nonetheless be determined to more likely to now welcome a second of the brewing group offers. Thus brewing offers are competitively and automatically sorted by machine means so that each is transmitted (pushed) to a respective offerees population that is populated by persons deemed most likely to then accept that offer and offerees are not inundated with too many or unwelcomed offers. More details follow below.
Another novel use disclosed herein of the Group entity is that of tracking group migrations and migration trends through topic space and/or through other cognition cross-associating spaces (e.g., keyword space, context space, etc.). If a predefined group of influential personas (e.g., Tipping Point Persons) is automatically tracked as having traveled along a sequence of paths or a time parallel set of paths through topic space (by virtue of making direct or indirect ‘touchings’ in topic space), then predictions can be automatically made about the paths that their followers (e.g., twitter fans) will soon follow and/or of what the influential group will next likely do as a group. This can be useful for formulating promotional offerings to the influential group and/or their followers. Also, the leaders may be solicited by vendors for endorsing vendor provided goods and/or services. Detection of sequential paths and/or time parallel paths through topic space is not limited to predefined influential groups. It can also apply to individual STAN users. The tracking need not look at (or only at) the topic nodes they directly or indirectly ‘touched’ in topic space. It can include a tracking of the sequential and/or time parallel patterns of CFi's and/or CVi's (e.g., keywords, meta-tags, hybrid combinations of different kinds of CFi's (e.g., keywords and context-reporting CFi's), etc.) produced by the tracked individual STAN users. Such trackings can be useful for automatically formulating promotional offerings to the corresponding individuals. In one embodiment, so-called, hybrid spaces are created and represented by data stored in machine memory where the hybrid spaces can include but are not limited to, a hybrid topic-and-context space, a hybrid keyword-and-context space, a hybrid URL-and-context space, whereby system users whose recently collected CFi's indicate a combination of current context and current other focused-upon attribute (e.g., keyword) can be identified and serviced according to their current dispositions in the respective hybrid spaces and/or according to their current trajectories of journeying through the respective hybrid spaces.
It is to be understood that this background and further introduction section is intended to provide useful background for understanding the here disclosed inventive technology and as such, this technology background section may and probably does include ideas, concepts or recognitions that were not part of what was known or appreciated by others skilled in the pertinent arts prior to corresponding invention dates of invented subject matter disclosed herein. As such, this background of technology section is not to be construed as any admission whatsoever regarding what is or is not prior art. A clearer picture of the inventive technology will unfold below.
In accordance with one aspect of the present disclosure, likely to-be-welcomed group-based offers or other offers are automatically presented to STAN system users based on information gathered from their STAN (Social-Topical Adaptive Networking) system usage activities. The gathered information may include current mood or disposition as implied by a currently active PEEP (Personal Emotion Expression Profile) of the user as well as recently collected CFi signals (Current Focus indicator signals), recently collected CVi signals (Current Voting (implicit or explicit indicator signals) and recently collected context-indicating signals (e.g., XP signals) uploaded for the user and recent topic space (TS) usage patterns or hybrid space (HS) usage patterns or attention giving energies being recently cast onto other Cognitive Attention Receiving Points, Nodes or SubRegions (CAR PNoS's) of other cognition cross-associating spaces (CARS) maintained by the system or trends therethrough as detected of the user and/or associated group and/or recent friendship space usage patterns or trends detected of the user (where latter is more correctly referred to here as recent SPEIS'es usage patterns or trends {usage of Social/Persona Entities Interrelation Spaces}). Current mood and/or disposition may be inferred from currently focused-upon nodes and/or subregions of other spaces besides just topic space (TS) as well as from detected hints or clues about the user's real life (ReL) surroundings (e.g., identifying music playing in the background or other sounds and/or odors emanating from the background, such as for example the sounds and/or smells of potato chip bags being popped open at the hypothetical “Superbowl™ Sunday Party” described above).
In accordance with another aspect of the present disclosure, various user interface techniques are provided for allowing a user to conveniently interface (even when using a small screen portable device; e.g., smartphone) with resources of the STAN system including by means of device tilt, body gesture, facial expressions, head tilt and/or wobble inputs and/or touch screen inputs as well as pupil pointing, pupil dilation changes (independent of light level change), eye widening, tongue display, lips/eyebrows/tongue contortions display, and so on, as such may be detected by tablet and/or palmtop and/or other data processing units proximate to STAN system users and communicating with telemetry gathering resources of a STAN system.
Although numerous examples given herein are directed to situations where the user of the STAN_system is carrying a small-sized mobile data processing device such as a tablet computer with a tappable touch screen, it is within the contemplation of the present disclosure to have a user enter an instrumented room or other such area (e.g., instrumented with audio visual display resources and other user interface resources) and with the user having essentially no noticeable device in hand, where the instrumented area automatically recognizes the user and his/her identity, automatically logs the user into his/her STAN_system account, automatically presents the user with one or more of the STAN_system generated presentations described herein (e.g., invitations to immediately join in on chat or other forum participation sessions related to a subportion of a Cognitive Attention Receiving Space, which subportion the user is deemed to be currently focusing-upon) and automatically responds to user voice and/or gesture commands and/or changes in user biometric states.
In accordance with yet another aspect of the present disclosure, a user-viewable screen area is organized to have user-relevant social entities (e.g., My Friends and Family) iconically represented in one subarea (e.g., hideable side tray area) of the screen and user-relevant topical and contextual material (e.g., My Top 5 Now Topics While Being Here) iconically represented in another subarea (e.g., hideable top tray area) of the screen, where an indication is provided to the user regarding which user-relevant social entities are currently focusing-upon which user-relevant topics (and/or other points, nodes or subregions in other Cognitive Attention Receiving Spaces). Thus the user can readily appreciate which of persons or other social entities relevant to him/her (e.g., My Friends and Family, My Followed Influencers) are likely to be currently interested in what topics that are same or similar (as measured by hierarchical and/or spatial distances in topic space) to those being current focused-upon by the user in the user's current context (e.g., at a bus stop, bored and waiting for the bus to arrive) or in topics that the user has not yet focused-upon. Alternatively, when the on-screen indications are provided to the user with regard to other points, nodes or subregions in other Cognitive Attention Receiving Spaces (e.g., keyword space, URL space, content space) the user can learn of user-relevant other social entities who are currently focusing-upon such user-relevant other spaces (including upon same or similar base symbols in a clustered symbols layer of the respective Cognitions-representing Space (CARS)).
Other aspects of the disclosure will become apparent from the below yet more detailed description.
The below detailed description section makes reference to the accompanying drawings, in which:
Some of the detailed description found immediately below is substantially repetitive of detailed description of a ‘
Referring to
The resources of the schematically illustrated environment 400 may be used to define so-called, user-to-user association codings (U2U) including for example, so-called “friendship spaces” (which spaces are a subset of the broader concept of Social/Persona Entities Interrelation Spaces (SPEIS) as disclosed herein and as represented by data signals stored in a SPEIS database area 411 of the STAN_3 system portion 410 of
The present disclosure will show how various matrix-like cross-correlations between one or more SPEIS 411 (e.g., friendship relation spaces) and topic-to-topic associations (T2T, a.k.a. topic spaces) 413 and hybrid context associations (e.g., location to users to topic associations) 416 may be used to enhance online experiences of real person users (e.g., 431, 432) of the one or more of the sub-networks 410, 441, 442, . . . , 44X, etc. due to cross-correlating actions automatically taken by the STAN_3 sub-network system 410 of
Yet more detailed background descriptions on how Social-Topical Adaptive Networking (STAN) sub-systems may operate can be found in the above-cited and here incorporated U.S. application Ser. No. 12/369,274 and Ser. No. 12/854,082 and therefore as already mentioned, detailed repetitions of said incorporated-by-reference materials will not all be provided here. For sake of avoiding confusion between the drawings of Ser. No. 12/369,274 (STAN_1) and the figures of the present application, drawings of Ser. No. 12/369,274 will be identified by the prefix, “giF.” (which is “Fig.” written backwards) while figures of the present application will be identified by the normal figure prefix, “Fig.”. It is to be noted that, if there are conflicts as between any two or more of the two earlier filed and here incorporated applications and this application, the later filed disclosure controls as to conflicting teachings.
In brief, giF. 1A of the here incorporated '274 application shows how topics that are currently being focused-upon by (not to be confused with sub-portions of content being currently ‘focused upon’ by) individual online participants may be automatically determined based on detection of certain content sub-portions being currently and emotively ‘focused upon’ by the respective online participants and based upon pre-developed profiles of the respective users (e.g., registered and logged-in users of the STAN_1 system). (Incidentally, in the here disclosed STAN_3 system, the notion is included of determining what group offers a user is likely to currently welcome or not welcome based on a variety of factors including habit histories, trending histories, detected context and so on.)
Further in brief, giF. 1B of the incorporated '274 application shows a data structure of a first stored chat co-compatibility profile that can change with changes of user persona (e.g., change of mood); giF. 1C shows a data structure of a stored topic co-compatibility profile that can also change with change of user persona (e.g., change of mood, change of surroundings); and giF. 1E shows a data structure of a stored personal emotive expression profile of a given user, whereby biometrically detected facial or other biotic expressions of the profiled user may be used to deduce emotional involvement with on-screen content and thus degree of emotional involvement with focused upon content. One embodiment of the STAN_1 system disclosed in the here incorporated '274 application uses uploaded CFi (current focus indicator) packets to automatically determine what topic or topics are most likely ones that each user is currently thinking about based on the content that is being currently focused upon with above-threshold intensity. The determined topic is logically linked by operations of the STAN_1 system to topic nodes (herein also referred to as topic centers or TC's) within a hierarchical parent-child tree represented by data stored in the STAN_1 system.
Yet further and in brief, giF. 2A of the incorporated '274 application shows a possible data structure of a stored CFi record while giF. 2B shows a possible data structure of an implied vote-indicating record (CVi) which may be automatically extracted from biometric information obtained from the user. The giF. 3B diagram shows an exemplary screen display wherein so-called chat opportunity invitations (herein referred to as in-STAN-Vitations™) are provided to the user based on the STAN_1 system's understanding of what topics are currently of prime interest to the user. The giF. 3C diagram shows how one embodiment of the STAN_1 system (of the '274 application) can automatically determine what topic or domain of topics might most likely be of current interest for a given user and then responsively can recommend, based on likelihood rankings, content (e.g., chat rooms) which are most likely to be on-topic for that user and compatible with the user's current status (e.g., level of expertise in the topic).
Moreover, in the here incorporated '274 application, giF. 4A shows a structure of a cloud computing system (e.g., a chunky grained cloud) that may be used to implement a STAN_1 system on a geographic region by geographic region basis. Importantly, each data center of giF. 4A has an automated Domains/Topics Lookup Service (DLUX) executing therein which receives up- or in-loaded CFi data packets (Current Focus indicating records) from users and combines these with user histories uploaded form the user's local machine and/or user histories already stored in the cloud to automatically determine probable topics of current interest then on the user's mind. In one embodiment the DLUX points to so-called topic nodes of a hierarchical topics tree. An exemplary data structure for such a topics tree is provided in giF. 4B which shows details of a stored and adaptively updated topic mapping data structure used by one embodiment of the STAN_1 system. Also each data center of giF. 4A further has one or more automated Domain-specific Matching Services (DsMS's) executing therein which are selected by the DLUX to further process the up- or in-loaded CFi data packets and match alike users to one another or to matching chat rooms and then presents the latter as scored chat opportunities. Also each data center of giF. 4A further has one or more automated Chat Rooms management Services (CRS) executing therein for managing chat rooms or the like operating under auspices of the STAN_1 system. Also each data center of giF. 4A further has an automated Trending Data Store service that keeps track of progression of respective users over time in different topic sectors and makes trend projections based thereon.
The here incorporated '274 application is extensive and has many other drawings as well as descriptions that will not all be briefed upon here but are nonetheless incorporated herein by reference. (Note again that where there are conflicts as between any two or more of the earlier filed and here incorporated applications and this application, the later filed disclosure controls as to conflicting teachings.)
Referring again to
As used herein, the term, “local data processing equipment” includes data processing equipment that is remote from the user but is nonetheless controllable by a local means available to the user. More specifically, the user (e.g., 431) may have a so-called net-computer (e.g., 431a) in his local possession and in the form for example of a tablet computer (see also 100 of
Of course, certain resources such as the illustrated GPS-2 peripheral part of CPU-2 (in
It is to be understood that the CPU-1 device (431a) used by first user 431 when interacting with (e.g., being tracked, monitored in real time by) the STAN_3 system 410 is not limited to a desktop computer having for example a “central” processing unit (CPU), but rather that many varieties of data processing devices having appropriate minimal intelligence capability are contemplated as being usable, including laptop computers, palmtop PDA's (e.g., 199 of
It is within the contemplation of the present disclosure that alternatively or in addition to having an imaging device near the user and using an automated image/object categorizing tool such as GoogleGoggles™, IQ_Engine™, etc., other encoding detecting devices and automated categorizing tools may be deployed such as, but not limited to, sound detecting, analyzing and categorizing tools; non-visible light band detecting, analyzing, recognizing and categorizing tools (e.g., IR band scanning and detecting tools); near field apparatus identifying communication tools, ambient chemistry and temperature detecting, analyzing and categorizing tools (e.g., What human olfactorable and/or unsmellable vapors, gases are in the air surrounding the user and at what changing concentration levels?); velocity and/or acceleration detecting, analyzing and categorizing tools (e.g., Is the user in a moving vehicle and if so, heading in what direction at what speed or acceleration?); gravitational orientation and/or motion detecting, analyzing and categorizing tools (e.g., Is the user titling, shaking or otherwise manipulating his palmtop device?); and virtually-surrounding or physically-surrounding other people detecting, analyzing and categorizing tools (e.g., Is the user in virtual and/or physical contact or proximity with other personas, and if so what are their current attributes?).
Each user (e.g., 431, 432) may project a respective one of different personas and assumed roles (e.g., “at work” versus “at play” persona, where the selected persona may then imply a selected context) based on the specific environment (including proximate presence of other people virtually or physically) that the user finds him or herself in. For example, there may be an at-the-office or at-work-site persona that is different from an at-home or an on-vacation persona and these may have respectively different habits, routines and/or personal expression preferences due to corresponding contexts. (See also briefly the context identifying signal 316o of
When operating under this alternate persona (431u2), the first user 431 may choose (or pre-elect) to not be wholly or partially monitored in real time by the STAN_3 system (e.g., through its CFi, CVi or other such monitoring and reporting mechanisms) or to otherwise not be generally interacting with the STAN_3 system 410. Instead, the user 431 may elect to log into a different kind of social networking (SN) system or other content providing system (e.g., 441, . . . , 448, 460) and to fly, so-to-speak, STAN-free inside that external platform 441—etc. While so interacting in a free-of-STAN mode with the alternate social networking (SN) system (e.g., FaceBook™, MySpace™, LinkedIn™, YouTube™, GoogleWave™, ClearSpring™, etc.), the user may develop various types of user-to-user associations (U2U, see block 411) unique to that outside-of-STAN platform. More specifically, the user 431 may develop a historically changing record of newly-made “friends”/“frenemys” on the FaceBook™ platform 441 such as: recently de-friended persons, recently allowed-behind the private wall friends (because they are more trusted) and so on. The user 431 may develop a historically changing record of newly-made live-video chat buddies on the FaceBook™ platform 441. The user 431 may develop a historically changing record of newly-made 1st degree “contacts” on the LinkedIn™ platform 444, newly joined groups and so on. The user 431 may then wish to import some of these outside-of-STAN-formed user-to-user associations (U2U) to the STAN_3 system 410 for the purpose of keeping track of what topics in one or more topic spaces 413 (or other nodes in other spaces) the respective friends, non-friends, contacts, buddies etc. are currently focusing-upon in either a direct ‘touching’ manner or through indirect heat ‘touching’. Importation of user-to-user association (U2U) records into the STAN_3 system 410 may be done under joint import/export agreements as between various platform operators or via user transfer of records from an external platform (e.g., 441) to the STAN_3 system 410.
Referring next, and on a brief basis to
In the exemplary illustration, the displayed objects of screen 111 are clustered into major screen regions including a major left column region 101 (a.k.a. first axis), a topside and hideable tray region 102 (a second axis), a major right column region 103 (a third axis) and a bottomside and hideable tray region 104 (a fourth axis). The corners at which the column and row regions 101-104 meet also have noteworthy objects. The bottom right corner (first axes crossing—of axes 103 and 104) contains an elevator tool 113 which can be used to travel to different virtual floors of multi-storied virtual structure (e.g., building). Such a multi-storied virtual structure may be used to define a virtual space within which the user virtually travels to get to virtual rooms or virtual other areas having respective combinations of invitation presenting trays and/or such tools. (See also briefly,
Among the objects displayed in the left column area 101 are urgency valued or importance valued ones that collectively define a sorted list of social entities or groups thereof, such as “My Family” 101b (valued in this example as second most important/relevant after the “Me” entity 101a) and/or “My Friends” 101c (valued in this example as third in terms of importance/urgency after “Me” and after “My Family”) where the represented social entities and their positionings along the list are pre-specified by the current user of the device 100 or accepted as such by the user after having been automatically recommended by the system.
The topmost social entity along the left-side vertical column 101 (the sorted list of now-important/relevant social entities) is specially denoted as the current King-of-the-Hill Social Entity (e.g., KoH=“Me” 101a) while the person or group representing objects disposed below the current King-of-the-Hill (101a) are understood to be subservient to or secondary relative to the KOH object 101a in that certain categories of attributes painted-on or attached to those subservient objects (101b, 101c, etc.) are inherited from the KOH object 101a and mirrored onto the subservient objects or attachments thereof. (The KOH object may alternatively be called the Pharaoh of the Pyramids for reasons soon to become apparent.) Each of the displayed first items (e.g., social entity representing items 101a-101d) may include one or both a correspondingly displayed label (e.g., “Me”) and a correspondingly displayed icon (e.g., up-facing disc). Alternatively or additionally, the presentation of the first items may come by way of voice presentation. Different ones of the presented first items may have unique musical tones and/or color tones associated with them, where in the case of the display being used, the corresponding musical tones and/or color tones are presented as the user hovers a cursor or the like over the item.
In terms of more specifics, and referring also to
From usage of the system, it becomes understood to users of the system that the associated topic of each such histogram bar on the attached status pyramid (e.g., 101rb in
Therefore, if the social entity identified as “Me” by the top item of column 101 is King-of-the-Hill and the Top 5 Now Topics of “Me” are represented by bars on a face of the KOH's adjacent reporting pyramid 101ra, the same Top 5 Now Topics of “Me” will be represented by (mirrored by) respective locations of bars on a corresponding face of subservient reporting pyramids (e.g., 101rb). Accordingly, with one quick look, the user can see what Top 5 Now Topics of “Me” (if “Me” is the KOH) are also being focused-upon (if at all), and if so with what “heat” (emotional and/or otherwise) by associated other social entities (e.g., by “My Family” 101b, by “My Friends” 101c and so on).
The designation of who is currently the King-of-the-Hill Social Entity (e.g., KoH=“Me” 101a) can be indicated by means other than or in addition to displaying the KOH entity object 101a at the top of first vertical column 101. For example, KOH status may be indicated by displaying a virtual crown (not shown) on the entity representing object (e.g., 101a) who is King and/or coloring or blinking the KOH entity representing object 101a differently and so on. Placement at the top of the stack 101 is used here as a convenient way of explaining the KOH concept and also explaining the concept of a sorted array of social entities whose positional placement is based on the user's current valuation of them (e.g., who is now most important, who is most urgent to focus-upon, etc.). The user's data processing device 100 may include a ‘Help’ function (activated by right clicking to activate, or otherwise activating a context sensitive menu 111a) that provides detailed explanation of the KOH function and the sorted array function (e.g., is it sorting its items 101a-10d based on urgency, based on importance or based on some other metrics?). Although for sake of an easiest to understand example, the “Me” disc 101a is disposed in the KOH position, the representative disc of any other social entity (individual or group), say, “My Others” 101d can instead be designated as the KOH item, placed on top, and then the Top 5 Now Topics of the group called “My Others” (101d) will be mirrored onto the status reporting pyramids of the remaining social entity objects (including “Me”) of column 101. The relative sorting of the secondary social entities relative to the new KoH entity will be based on what the user of the system (not the KoH) thinks it should be. However, in one embodiment, the user may ask the system to sort the secondary social entities according to the way the KoH sorts those items on his computer.
Although
According to yet another variation (not shown), the arrayed first items 101a-101d of the first vertical column 101 may respectively represent different versions of the “Me” entity; as such for example “Me When at Home” (a first context); “Me When at Work” (a second context); “Me While on the Road” (a third context); “Me While Logged in as Persona#1 on social networking Platform#2” (a fourth context) and so on.
In one embodiment, the sorted first array of disc objects 101a-101d and what they represent are automatically chosen or automatically offered to be chosen based on an automatically detected current context of the device user. For example, if the user of data processing device 100 is detected to be at his usual work place (and more specifically, in his usual work area and at his usual work station), then the sorted first array of disc objects 101a-101d might respectively represent work-related personas or work-related projects. In an alternate or same embodiment, the sorted array of disc objects 101a-101d and what they represent can be automatically chosen or automatically offered to be chosen based on the current Layer-Vator™ floor number (as indicated by tool 113a). In an alternate or same embodiment, the sorted array of disc objects 101a-101d and what they represent can be automatically chosen or automatically offered to be chosen based on current time of day, day of week, date within year and/or current geographic location or compass heading of the user or his vehicle and/or scheduled events in the user's computerized calendar files.
Returning to the specific example of the items actually shown to be arrayed in first vertical column 101 of
As may be appreciated, the current “heat” reporting function of the status reporting objects in column 101r (they do not have to be pyramids) provides a convenient summarizing view, for example, for: (1) identifying relevant social-associates of the user (e.g., “Me” 101a), (2) for indicating how those socially-associated entities 101b-101d are grouped and/or filtered and/or prioritized relative to one another (e.g., “My Friends” equals only all my trusted, behind the wall friends of the past week plus my three bowling buddies); (3) for tracking some of their current activities (if not blocked by privacy settings) in an adjacent column 101r by indicating cross-correlation with the KOH's Top 5 Now Topics or by indicating “heat” cast by each on their own Top 5 Now Topics if there is no designated KOH.
Although in the illustrated example, the subsidiary adjacent column 101r (social radars column) indicates what top-5 topics of the entity “Me” (101a) are also being focused-upon in recent time periods (e.g., now and 15 minutes ago, see faces 101t and 101x of magnified pyramid 101rb in
Focused-upon “topics” or topic regions are merely one type of trackable thing or item represented in a corresponding Cognitive Attention Receiving Space (a.k.a. “CARS”) and upon which users may focus their attentions upon. As used herein, trackable targets of cognition (codings or symbols representing underlying and different kinds of cognitions) have, or have newly created for them, respective data objects uniquely disposed in a corresponding data-objects organizing space, where data signals representing the data objects are stored within the system. One of the ways to uniquely dispose the data objects is to assign them to unique points, nodes or subregions of the corresponding Cognitive Attention Receiving Space (e.g., Topic Space) where such points, nodes, or subregions may be reported on (as long as the to-be-tracked users have given permission that allows for such monitoring, tracking and/or reporting). As will become clearer, the focused-upon top-5 topics, as exemplified by pyramid face 101t in
In the simplified example of introductory
It is to be understood here that the illustrated screen layout of introductory
In one variation, the insides of a user's mouth are instrumented such that movement of the tip of the tongue against different teeth and/or the force of contact by the tongue against teeth and/or other in-mouth surfaces are used to signal conscious or subconscious wishes of the user. More specifically, the user may wear a teeth-covering and relatively transparent mouth piece that is electronically and/or optically instrumented to report on various inter-oral cavity activities of the user including teeth clenchings, tongue pressings and/or fluid moving activities where corresponding reporting signals are transmitted to the user's local data processing device for possible inclusion in CFi reporting signals, where the latter can be used by the STAN_3 system to determine levels of attentiveness by the user relative to various focused-upon objects.
In one embodiment, the user alternatively or additionally wears an instrumented necklace or such like jewelry piece about or under his/her neck where the jewelry piece includes one or more, embedded and forward-pointing video cameras and a wireless short range transceiver for operatively coupling to a longer range transceiver provided nearby. The longer range transceiver couples wirelessly and directly or indirectly to the STAN_3 system. In addition to the forward pointing digital camera(s), the jewelry piece includes a battery means and one or more of sound pickups, biological state transducers, motion detecting transducers and a micro-mirrors image forming chip. The battery means may be repeatedly recharged by radio beams directed to it and/or by solar energy when the latter is available and/or by other recharging means. The embedded biological state transducers may detect various biological states of the wearer such as, but not limited to, heart rate, respiration rate, skin galvanic response, etc. The embedded motion detecting transducers may detect various body motion attributes of the wearer such as being still versus moving and if moving, in what directions and at what speeds and/or accelerations and when. The micro-mirrors image forming chip may be of a type such as developed by the Texas Instruments™ Company which has tiltable mirrors for forming a reflected image when excited by an externally provided, one or more laser beams. In one embodiment, the user enters an instrumented area that includes an automated, jewelry piece tracking mechanism having colored laser light sources within it as well as an optional IR or UV beam source. If an image is to be presented to the user, a tactile buzzer included in the necklace alerts him/her and indicates which way to face so that the laser equipped tracking mechanism can automatically focus in upon the micro-mirrors based image forming device (surrounded by target patterns) and supply excitational laser beams safely to it. The reflected beams form a computer generated image that appears on a nearby wall or other reflective object. Optionally, the necklace may include sound output devices or these can be separately provided in an ear-worn BlueTooth™ device or the like.
Informational resources of the STAN_3 system may be provided to the so-instrumented user by way of the projected image wherever a correspondingly instrumented room or other area is present. The user may gesture to the STAN_3 system by blocking part of the projected image with his/her hand or by other means and the necklace supported camera sees this and reports the same back to the STAN_3 system. In one embodiment, the jewelry piece includes two embedded video cameras pointing forward at different angles. One camera may be aimed at a wall mounted mirror (optionally an automatically aimed one which is driven by the system to track the user's face) where this mirror reflects back an image of the user's head while the other camera may be aimed at projected image formed on the wall by the laser beams and the micro-mirrors based reflecting device. Then the user's facial grimaces may be automatically fed back to the STAN_3 system for detecting implicit or explicit voting expressions as well as other user reactions or intentional commands (e.g., tongue projection based commands). In one embodiment, the user also wears electronically driven shutter and/or light polarizing glasses that are shuttered and/or variably polarized in accordance with an over-time changing pattern that is substantially unique to the user. The on-wall projected image is similarly modulated such that only the spectacles-wearing user can see the image intended for him/her. Therefore, user privacy is protected even if the user is in a public instrumented area. Other variations are of course possible, such as having the cameras and image forming devices placed elsewhere on the user's body (e.g., on a hat, a worn arm band near the shoulder, etc.). The necklace may include additional cameras and/or other sensors pointing to areas behind the user for reporting the surrounding environment to the STAN_3 system.
Referring still to the illustrative example of
It is also within the contemplation of the present disclosure to use a scrolling reel format such as illustrated in
In
Incidentally, as used herein, the term “Cognition-Representing Objects Organizing Space” (a.k.a. CROOS) is to be understood as referring to a more generic form of the species, “Cognitive Attention Receiving Space” (a.k.a. CARS) where both are data-objects organizing spaces represented by data objects stored in system memory and logically inter-linked or otherwise organized according to application-specific details. When a person (e.g., a system user) gives conscious attention to a particular kind of cognition, say to a textual cognition; which cognition can more specifically be directed to a search-field populating “keyword” (which could be a simultaneous collection or a temporal clustering of keywords), then as a counterpart machine operation, a representing portion of a counterpart, conscious Cognition Attention Receiving Space (CARS) should desirably be lit up (focused-upon) in a machine sense to reflect a correct modeling of a lighting up of (energizing of) the corresponding cognition providing region in the user's brain that is metabolically being lit up (energized) when the user is giving conscious attention to that particular kind of cognition (e.g., re a “keyword”). Similarly, when a system user gives conscious attention to a question like, “What are we talking about?” and to its answer (“What are we talking about?”), that is referring to what in the machine counterpart system would be a lighting up of (e.g., activation of) a counterpart point, node or subregion in a system-maintained topic space (TS). Some cognitions however, do not always receive conscious attention. An example might be how a person subconsciously parses (syntactically disambiguates) a phonetically received sentence (e.g., “You too/two[?] should see/sea[?] to it[?]”) and decodes it for semantic sense. That often happens subconsciously. At least one of the data-objects organizing spaces discussed herein (
The present disclosure, incidentally, does not claim to have discovered how to, nor does it endeavor to represent cognitions within the human mind down to the most primitive neuron and synapse actuations. Instead, and as shall be detailed below, a so-called, primitive expressions (or symbols or codings) layer is contemplated within which is stored machine codes representing corresponding expressions, symbols or codings where the latter represent a meta-level of human cognition, say for example, a semantic sense of what a particular text string (e.g., “Lincoln”) represents. The meta-level cognitions can be combined in various ways to build yet more complex representations of cognitions (e.g., “Lincoln” plus “Abraham”; or “Lincoln” plus “Nebraska”; or “Lincoln” plus “Car Dealership”). Although it is not an absolute requirement of the present disclosure, preferably, the primitive expressions storing (and clustering) layer is a communally created and communally updated layer containing “clusterings” of expressions, symbols or codings where a relevant community of users implicitly determines what cognitive sense each such expression or clustering of expressions represents, where legacy “clusterings” of expressions, etc. are preserved and yet new “clusterings” of such expressions, etc. can be added or inserted as substitutes as community sentiments change with regard to such adaptively updateable, expressions, codings, or other symbols that implicitly represent underlying cognitions. More specifically, and as a brief example, prior to September 2011, the expression string” “911” may have most likely invoked the cognitive sense in a corresponding community of a telephone number that is to be dialed In Case of Emergency (ICE). However, after said date, the same expression string” “911” may most likely invoke the cognitive sense in a corresponding community of an attack on the World Trade Center in New York City. For that brief example, an embodiment in accordance with the present disclosure would seek to preserve the legacy cognitive sense while at the same supplanting it with the more up to date cognitive sense. Details of how this can be done are provided later below.
Still referring to
Additionally, in one embodiment and in response to activating the dialog expansion tool (e.g., starburst+), a current thread of discourse by the respective persona is displayed, where the thread typically is one inside an on-topic chat or other forum participation session for which a “heat of exchange” indication 101w″ is displayed on the forward turned (101u″) face (e.g., 101t″ or 101x″) of the heat displaying pyramid 101ra″. Here the “heat of exchange” indication 101w″ is not showing “heat” cast by a single person on a particular topic but rather heat of exchange as between two or more personas as it may relate to any corresponding point, node or subregion of a respective Cognitive Attention Receiving Space where the later could be topic space (TS) for example, but not necessarily so. Expansion of the social dynamics tree flag 101sh″ will show how social dynamics between the hotly involved two or more personas (e.g., debating persons) is changing while the “heat of exchange” indications 101w″ will show which amount of exchange heat and activation of the expansion tool (e.g., starburst+) on the face (e.g., 101t″) of the pyramid will indicate which topic or topics (or points, nodes or subregions (a.k.a. PNOS's) of another Cognitive Attention Receiving Space) are receiving the heat of the heated exchange between the two or more persons. It may be that there is no one or more points, nodes or subregions receiving such heat, but rather that the involved personas are debating or otherwise heatedly exchanging all over the map. In the latter case, no specific Cognitive Attention Receiving Space (e.g., topic space) and regions thereof will be pinpointed.
If the user of the data processing device of
Referring to
In summary, when a user sees an affiliated space flag (e.g., 101s′) atop an attributes mapping pyramid (e.g., 101ra′ of
In addition to displaying the so-called “heats” cast by different social entities on respective topic or other nodes, the exemplary screen of
Throughout the present disclosure, a so-called, starburst+ expansion tool is depicted as a means for obtaining more detailed information. Referring for example to
Still referring to
Referring back to the left side of
The tracked social entities of left column 101 do not necessarily have to be friends or family or other well-liked or well-known acquaintances of the user (or of the KoH entity; not necessarily same as the user). Instead of being persons or groups whom the user admires or likes, they can be social entities whom the user despises, or feels otherwise about, or which the first user never knew before, but nonetheless the first user wishes to see what topics are currently deemed to be the “topmost” and/or “hottest” for that user-selected header entity 101a (where KoH is not equal to “Me”) and further social entities associated with that user-selected KoH entity. Incidentally, in one embodiment, when the user selects a new KoH entity (e.g., new KoH=“Charlie”), the system automatically presents the user with a set of options: (a) Don't change the other discs in column 101; (b) Replace the current discs 101b-101d in column 101 with a first set of “Charlie”-associated other entity discs (e.g., “Charlie's Family”, “Charlie's Friends”, etc.); (c) Replace the current discs 101b-101d in column 101 with a second set of “Charlie”-associated other entity discs (e.g., “Charlie's Workplace Colleagues”, etc.) and (d) Replace the current discs 101b-101d in column 101 with a new third set that the user will next specify. Thus, by changing the designated KoH entity, the user may not only change the identification of the currently “hot” topics whose heats are being watched, but the user may also change, by substantially the same action, the identifications of the follower entities 101b-101d.
While the far left side column 101 of
In the exemplary case of
In one embodiment, the changing of designation of who (what social entity) is the KoH 101a automatically causes the system to present the user with a set of upper-tray modification options: (a) Don't change the serving plates on tray 102; (b) Replace the current serving plates 102a, 102b, 102c in row 102 with a first set of “Charlie”-associated other serving plates (e.g., “Charlie's Top 5 Now Topics”, “Charlie's Family's Top 5 Now Topics”, etc. where here the KoH is being changed from being “Me” to being “Charlie”); (c) Replace the current serving plates 102a, 102b, 102c in row 102 with a second set of “Charlie”-associated other serving plates (e.g., “Top N now topics of Charlie's Workplace Colleagues”, “Top M now keywords being used by Charlie's Workplace Colleagues”, etc.); and (d) Replace the current serving plates 102a, 102b, 102c in row 102 with a new third set of serving plates that the user will next specify. Thus, by changing the designated KoH entity, the user may not only change the identification of the currently “hot” topics (or other “hot” nodes) whose heats are being watched in reporting column 101r, but the user may also change, by substantially the same action, the identifications of the serving plates in the upper tray area 102 and the nature of the “touched” or to-be-“touched” items that they will serve up (where those “touched” or to-be-“touched” items can come in the form of links to, or invitations to, chat or other forum participation sessions that are “on-topic” or links to suggested other kinds of content resources that are deemed to be “on-topic” or links to, or invitations to, chat or other forum participation sessions or other resources that are deemed to be well cross-correlated with other types of ‘touched’ nodes or subregions (e.g., “Top M now keywords being used by Charlie's Workplace Colleagues”). At the same time the upper tray items 102a-102c are being changed due to switching of the KoH entity, the identifications of the corresponding follower entities 101b-101d may also be changed.
The so-called, upper serving plates 102a, 102b, 102c, etc. of the upper serving tray 102 (where 102c and the extendible others which may be accessible for enlarged viewing with use of a viewing expansion tool (e.g., clicking or otherwise activating the 3 ellipses 102c)). These upper serving plates are not limited to showing (serving up) an automatically determined set of recently ‘touched’ and “hot” nodes or subregions such as a given social entities' top 5 topics or top N topics (where N can be a number other than 5 here, and where automated determination of the recently ‘touched’ and “hot” nodes or subregions in a selected space (e.g., topic space) can be based on predetermined knowledge base rules). Rather, the user can manually establish how many ‘touched’-topics or to-be-‘touched’/recommended topics serving plates 102a, 102b, etc. (if any) and/or other ‘touched’/recommended node serving plates (e.g., “Top U now URL's being hyperlinked to by Charlie's Workplace Colleagues”, —not shown) will be displayed on the “hot” nodes or hot space subregions serving tray 102 (where the tray can also serve up “cold” items if desired and where the serving tray 102 can be hidden or minimized (via tool 102z)). In other words, instead of relying on system-provided templates (recommended) for determining which topic or collection of topics will be served up by each “hot” now topics serving plate (e.g., 102a), the user can use the setting tools 114 to establish his own, custom tailored, serving rules and corresponding plates or his own, custom tailored, whole serving trays where the items served up on (or by) such carriers can include, but are not limited to, custom picked topic nodes or subregions and invitations to chat or other forum participation sessions currently or soon to be tethered to such topic nodes and/or links to other on-topic resources suggested by (linked to by and rated highly by) such topic nodes. Alternatively or additionally, the user can use the setting tools 114 to establish his own, custom tailored, serving plates or whole serving trays where the items served on such carriers can include, but are not limited to, custom picked keyword nodes or subregions, custom picked URL nodes or subregions, or custom picked points, nodes or subregions (a.k.a. PNOS's) of another Cognitive Attention Receiving Space. The topics on a given topics serving plate (e.g., 102a) do not have to be related to one another, although they could be (and generally should be for ease of use).
Incidentally, the term, “PNOS's” is used throughout this disclosure as an abbreviation for “points, nodes or subregions”. Within that context, a “point” is a data object of relatively similar data structure to that of a corresponding “node” of a corresponding Cognitive Attention Receiving Space or Cognitions-representing Space (e.g., topic space) except that the “point” need not be part of a hierarchical tree structure whereas a “node” is often part of a hierarchical, data-objects organizing scheme. Accordingly, the data structure of a PNOS “point” is to be understood as being substantially similar to that of a corresponding “node” of a corresponding Cognitions-representing Space except that fields for supporting the data object representing the “point” do not need to include fields for specifying the “point” as an integral part of a hierarchical tree structure and such fields may be omitted in the data structure of the space-sharing “point”. A “subregion” within a given Cognitions-representing Space (e.g., a CARS or Cognitive Attention Receiving Space) may contain one or more nodes and/or one or more “points” belonging to its respective Cognitions-representing Space. A Cognitions-representing Space may be comprised of hierarchically interrelated “nodes” and/or spatially distributed “points” and/or both of such data structures. A “node” may be spatially positioned within its respective Cognitions-representing Space as well as being hierarchically positioned therein.
The term, “cognitive-sense-representing clustering center point” also appears numerous times within the present disclosure. The term, “cognitive-sense-representing clustering center point” (or “center point” for short) as used herein is not to be confused with the PNOS type of “point”. Cognitive-sense-representing clustering center points (or COGS's for short) are also data structures similar to nodes that can be hierarchically and/or spatially distributed within a corresponding hierarchical and/or spatial data-objects organizing scheme of a given Cognitions-representing Space except that, at least in one embodiment, system users are not empowered to give names to such center points (COGS's) and chat room or other forum participation sessions do not directly tether to such COGS's and such COGS's do not directly point to informational resources associated with them (with the COGS's) or with underlying cognitive senses associated with the respective and various COGS's. Instead, a COGS (a single cognitive-sense-representing clustering center point) may be thought of as if it were a black hole in a universe populated by topic stars, subtopic planets and chat room spaceships roaming there about to park temporarily in orbit about one planet and then another (or to loop figure eight style or otherwise simultaneously about plural topic planets). Each COGS provides a clustering-thereto cognitive sense kind of force much like the gravitational force of a real world astronomical black hole provides an attracting-thereto gravitational force to nearby bodies having physical mass. One difference though, is that users of the at least one embodiment can vote to move a cognitive-sense-representing clustering center point (COGS) from one location to another within a Cognitions-representing Space (or a subregion there within) that they control. When a COGS moves, the points, nodes or subregions (PNOS's) that were clustered about it do not automatically move. Instead the relative hierarchical and/or spatial distances between the unmoved PNOS's and the displaced COGS change. That change indicates how close in a cognitive sense the PNOS's are deemed to be relative to an unnamed cognitive sense represented by the displaced COGS and vice versa. Just as in the physical astronomical realm where it is not possible (according to current understandings) to see what lies inward of the event horizon of a black hole, according to one aspect of the present disclosure, it is generally not permitted to directly define the cognitive sense represented by a COGS. Instead the represented cognitive sense is inferred from the PNOS's that cluster about and nearby to the COGS. That inferred cognitive sense can change as system users vote to move (e.g., drift) the nearby PNOS's to newer ones of hierarchical and/or spatial locations, thereby changing the corresponding hierarchical and/or spatial distances between the moved PNOS's and the one or more COGS that derive their inferred cognitive senses from their neighboring PNOS's. The inferred cognitive sense can also change if system users vote to move the COGS rather than moving the one or more PNOS's that closely neighbor it. A COGS may have additional attributes such substitutability by way of re-direction and expansion by use of expansion pointers. However, such discussion is premature at this stage of the disclosure and will be picked up much later below. (See for example and very briefly the discussion re COGS 30W.7p of
In one embodiment, different organizations of COGS's may be provided as effective for different layers of cognitive sentiments. More specifically, one layer of cognitive sentiments may be attributed to so-called, central or main-stream ways of thinking by the system user population while a second such layer of cognitive sentiments may be attributed to so-called, left wing extremist ways of thinking and yet a third such layer may be attributed to so-called, right wing extremist ways of thinking (this just being one possible set of examples). If a first user (or first persona) who subscribes to main-stream way of thinking logs in, the corresponding central or main-stream layer of accordingly organized COGS's is brought into effect while the second and third are rendered ineffective. On the other hand, if the logging-in first persona self-identifies him/herself as favoring the left wing extremist ways of thinking, then the second layer of accordingly organized COGS's is brought into effect while the first and third layers are rendered ineffective. Similarly, if the logging-in first persona self-identifies him/herself as favoring the right wing extremist ways of thinking, then the third layer of accordingly organized COGS's is brought into effect while the first and second layers are rendered ineffective. In this way, each sub-community of users, be they left-winged, middle of the road, or right winged (or something else) can have the topical universe presented to them with cognitive-sense-representing clustering center points being positioned in that universe according to the confirmation biasing preferences of the respective user. As mentioned, the left versus right versus middle of the road mindsets are merely examples and it is within the contemplation of the present disclosure to have more or other forms of multiple sets of activatable and deactivatable “layers” of differently organized COGS's where one or more such layers are activated (brought into effect) for a given one mindset and/or context of a respective user. In one embodiment, different governance bodies of respective left, right or other mindsets are given control over the hierarchical and/or spatial postionings of the COGS's of their respectively activatable layers where the controlled postionings are relative to the hierarchically and/or spatially organized points, nodes or subregions (PNOS's) of topic space and/or of another applicable, Cognitions-representing Space. In one embodiment, the respective governance bodies of respective Wikipedia™ like collaboration projects (described below) are given control over the postionings of the COGS's that become effective for their respective B level, C level or other hierarchical tree (described below) and/or semi-privately controlled spatial region within a corresponding Cognitions-representing Space.
In one embodiment, in addition to having the so-called, cognitive-sense-representing clustering center points (COGS's) around which, or over which, points, nodes or subregions (PNOS's) of substantially same or similar cognitive sense may cluster, with calculated distance being indicative of how same or similar they in accordance with a not necessarily articulated sense, it is within the contemplation of the present disclosure to have cognitive-sense-representing clustering lines, or curves or closed circumferences where PNOS-types of points, nodes or subregions disposed on a one such line, curve or closed circumference share a same cognitive sense and PNOS's distanced away from such line, curve or closed circumference are deemed dissimilar in accordance with the spacing apart distance calculated along a normal drawn from the spaced apart PNOS to the line, curve of circumference. In one embodiment, and yet alternatively or additionally, so-called, repulsion and/or exclusion center points, lines, curves or closed circumferences may be employed where PNOS-types of points, nodes or subregions are repulsed from (according to a decay factor) and/or are excluded from occupying a part of hierarchical and/or spatial space occupied by a respective, repulsion and/or exclusion type of center point, line, curve or closed circumference. The repulsion and/or exclusion types of boundary defining entities may be used to coerce the governance bodies who control placement of PNOS-types of points, nodes or subregions to distribute their controlled PNOS's more evenly within different bands of hierarchical and/or spatial space rather than clumping all such controlled PNOS's together. For example, if concentric exclusion circles are defined, then governance bodies are coerced into placing their controlled PNOS's into one of several concentric bands or another rather than organizing them as one unidifferentiated clump in the respective Cognitions-representing Space.
The topic of COGS, PNOS's, repulsion bands and so forth was raised here because the term PNOS's has been used a number of times above without giving it more of definition and this juncture in the disclosure presented itself as an opportune time to explain such things. The discussion now returns to the more mundane aspects of
Referring to
The ability to track the top-N topic(s) that the user and/or other social entity is now focused-upon (giving cognitive attention to) or has earlier focused-upon is made possible by operations of the STAN_3 system 410 (which system is represented for example in
In one embodiment, each user of the STAN_3 system 410 can control his/her future share-out attributes so as to specify one or more of: (1) no sharing at all; (2) full sharing of everything; (3) limited sharing to a limited subset of associated other users (e.g., my trusted, behind-the-wall friends and immediate family); (4) limited sharing as to a limited set of time periods; (5) limited sharing as to a limited subset of areas on the screen 111 of the user's computer; (6) limited sharing as to limited subsets of identified regions in topic space; (7) limited sharing as to limited subsets of identified regions in other Cognitive Attention Receiving Spaces (CARs); (8) limited sharing based on specified blockings of identified points, nodes or regions (PNOS's) in topic space and/or other Cognitive Attention Receiving Spaces; (9) limited sharing based on the Layer-Vator™ (113) being stationed at one of one or more prespecified Layer-Vator™ floors, (10) limited sharing as to limited subsets of user-context identified by the user, and so on. If a given second user has not authorized sharing out of his attribute statistics, such blocked statistics will be displayed as faded out, grayed out screen areas or otherwise indicated as not available areas on the radar icons column (e.g., 101ra′ of |x x x x x”. If desired, the graphs can be more detailed to show how long and/or with what emotional intensities the various online or offline entities are/were online and/or for how long they in their current offline state.
Not all of
The word “context” is used herein to mean several different things within this disclosure. Unfortunately, the English language does not offer many alternatives for expressing the plural semantic possibilities for “context” and thus its meaning must be determined based on; please forgive the circular definition, its context. One of the meanings ascribed herein for “context” is to describe a role assigned to or undertaken by an actor and the expectations that come with that role assignment. More specifically, when a person is in the context of being “at work”, there are certain presumed “roles” assigned to that actor while he or she is deemed to be operating within the context of that “at work” activity. More particularly, a given actor may be assigned to the formal role of being Vice President of Social Media Research and Development at a particular company and there may be a formal definition of expected performances to be carried out by the actor when in that role (e.g., directing subordinates within the company's Social Media Research and Development Department). Similarly, the activity (e.g., being a VP while “at work”) may have a formal definition of expected subactivities. At the same time, the formal role may be a subterfuge for other expected or undertaken roles and activities because everybody tends to be called “Vice President” for example in modern companies while that formal designation is not the true “role”. So there can be informal role definitions and informal activity definitions as well as formal ones. Moreover, a person can be carrying out several roles at one time and thus operating within overlapping contexts. More specifically, while “at work”, the VP of Social Media R&D may drop into an online chat room where he has the role of active room moderator and there he may encounter some of the subordinates in his company's Social Media R&D Dept. also participating within that forum. At that time, the person may have dual roles of being their boss in real life (ReL) and also being room moderator over their virtual activities within the chat room. Accordingly, the simple term “context” can very quickly become complex and its meanings may have to be determined based on existing circumstances (another way of saying context). Other meanings for the term context as used herein can include, but are not limited to unless specifically so-stated: (1) historical context which is based on what memories the user currently has of past attention giving activities; (2) social dynamics context which is based on what other social entities the given user is, or believes him/herself to be in current social interaction with; (3) physical context which is based on what physical objects the given user is, or believes him/herself to be in current proximity with; and (4) cognitive state context, which here, is a catch-all term for other states of cognition that may affect what the user is currently giving significant energies of cognition to or recalling having given significant energies of cognition to, where the other states of cognition may include attributes such as, but not limited to, things sensed by the 5 senses, emotional states such as: fear, anxiety, aloofness, attentiveness, happy, sad, angry and so on; cognitions about other people, about geographic locations and/or places in time (in history); about keywords; about topics and so on.
One addition provided by the STAN_3 system 410 disclosed here is the database portion 416 which provides “Context” based associations and hybrid context-to-other space(s) associations. More specifically, these can be Location-to-User and/or Location-to-Topic and/or Location-to-Content and/or Place-in-Time-to-Other-Thing associations. The context; if it is location-based for example, can be a real life (ReL) geographic one and/or a virtual one of where the real life (ReL) or virtual user is deemed by the system to be located. Alternatively or additionally, the context can be indicative of what type of Social-Topical situation the user is determined by the machine system to be in, for example: “at work”, “at a party”, at a work-related party, in the school library, etc. The context can alternatively or additionally be indicative of a temporal range (place-in-time) in which the user is situated, such as: time of day, day of week, date within month or year, special holiday versus normal day and so on. Alternatively or additionally, the context can be indicative of a sequence of events that have and/or are expected to happen such as: a current location being part of a sequence of locations the user habitually or routinely traverses through during for example, a normal work day and/or a sequence of activities and/or social contexts the user habitually or routinely traverses through during for example, a normal weekend day (e.g., IF Current Location/Activity=Filling up car at Gas Station X, THEN Next Expected Location/Activity=Passing Car through Car Wash Line at same Gas Station X in next 20 minutes). Moreover, context can add increased definition to points, nodes or subregions in other Cognitive Attention Receiving Spaces; thus defining so-called, hybrid spaces, points, nodes or subregions; including for example IF Context Role=at work and functioning as receptionist AND keyword=“meeting” THEN Hybrid ContextualTopic#1=Signing in and Directing new arrivals to Meeting Room. Much more will be said herein regarding “context”. It is a complex subject.
For now it is sufficient to appreciate that database records (e.g., hierarchically organized context nodes and links which connect them to other nodes) in this new section 416 can indicate for the machine system, context related associations (e.g., location and/or time related associations) including, but not limited to, (1) when an identified social entity (e.g., first user) is present (virtually or in real life) at a given location as well as within a cross-correlated time period, and that the following one or more topics (e.g., T1, T2, T3, etc.) are likely to be associated with that location, that time and/or a role that the social entity is deemed by the machine system to probably be engaged in due to being in the given “context’ or circumstances; (2) when a first user is disposed at a given location as well as within a cross-correlated time period, then the following one or more additional social entities (users) are likely to be associated with (e.g., nearby to) the first user: U2, U3, U4, etc.; (3) when a first user is disposed at a given location as well as within a cross-correlated time period, then the following one or more content items are likely to be associated with the first user: C1, C2, C3, etc.; and (4) when a first user is disposed at a given location as well as within a cross-correlated time period, then the following one or more hybrid combinations of social entity, topic, device and content item(s) are likely to be associated with the first user: U2/T2/D2/C2, U3/T2/D4/C4, etc. The context-to-other (e.g., hybrid) association records 416 (e.g., X-to-U/T/C/D association records 416, where X here represents context) may be used to support location-based or otherwise context-based, automated generation of assistance information. In
Before providing a more concrete example of how a given user (e.g., Stan/Stew 431) may have multiple personas operating in different contexts and how those personas may interact differently based for example on their respective contexts and may form different user-to-user associations (U2U) when operating under their various contexts (currently adopted roles or models) including under the contexts of different social networking (SN) or other platforms, a brief discussion about those possible other SN's or other platforms is provided here. There are many well known dot.COM websites (440) that provide various kinds of social interaction services. The following is a non-exhaustive list: Baidu™; Bebo™; Flickr™; Friendster™; Google Buzz™; Google+™ (a.k.a. Google Plus™), Habbo™, hi5™; LinkedIn™; LiveJournal™; MySpace™; NetLog™; Ning™, Orkut™; PearlTrees™, Qzone™, Squidoo™, Twitter™; XING™; and Yelp™.
One of the currently most well known and used ones of the social networking (SN) platforms is the FaceBook™ system 441 (hereafter also referred to as FB). FB users establish an FB account and set up various permission options that are either “behind the wall” and thus relatively private or are “on the wall” and thus viewable by any member of the public. Only pre-identified “friends” (e.g., friend-for-the-day, friend-for-the-hour) can look at material “behind the wall”. FB users can manually “de-friend” and “re-friend” people depending on who they want to let in on a given day or other time period to the more private material behind their wall.
Another well known SN site is MySpace™ (442) and it is somewhat similar to FB. A third SN platform that has gained popularity amongst so-called “professionals” is the LinkedIn™ platform (444). LinkedIn™ users post a public “Profile” of themselves which typically appears like a resume and publicizes their professional credentials in various areas of professional activity. LinkedIn™ users can form networks of linked-to other professionals. The system automatically keeps track of who is linked to whom and how many degrees of linking separation, if any, are between people who appear to the LinkedIn™ system to be strangers to each other because they are not directly linked to one another. LinkedIn™ users can create Discussion Groups and then invite various people to join those Discussion Groups. Online discussions within those created Discussion Groups can be monitored (censored) or not monitored by the creator (owner) of the Discussion Group. For some Discussion Groups (private discussion groups), an individual has to be pre-accepted into the Group (for example, accepted by the Group moderator) before the individual can see what is being discussed behind the wall of the members-only Discussion Group or can contribute to it. For other Discussion Groups (open discussion groups), the group discussion transcripts are open to the public even if not everyone can post a comment into the discussion. Accordingly, as is the case with “behind the wall” conversations in FaceBook™, Group Discussions within LinkedIn™ may not be viewable to relative “strangers” who have not been accepted as a linked-in friend or as a contact for whom an earlier member of the LinkedIn™ system sort of vouches for by “accepting” them into their inner ring of direct (1st degree of operatively connection) contacts.
The Twitter™ system (445) is somewhat different because often, any member of the public can “follow” the “tweets” output by so-called “tweeters”. A “tweet” is conventionally limited to only 140 characters. Twitter™ followers can sign up to automatically receive indications that their favorite (followed) “tweeters” have tweeted something new and then they can look at the output “tweet” without need for any special permissions. Typically, celebrities such as movie stars output many tweets per day and they have groups of fans who regularly follow their tweets. It could be said that the fans of these celebrities consider their followed “tweeters” to be influential persons and thus the fans hang onto every tweeted output sent by their worshipped celebrity (e.g., movie star).
The Google™ Corporation (Mountain View, Calif.) provides a number of well known services including their famous online and free to use search engine. They also provide other services such a Google™ controlled Gmail™ service (446) which is roughly similar to many other online email services like those of Yahoo™, EarthLink™, AOL™, Microsoft Outlook™ Email, and so on. The Gmail™ service (446) has a Group Chat function which allows registered members to form chat groups and chat with one another. GoogleWave™ (447) is a project collaboration system that is believed to be still maturing at the time of this writing. Microsoft Outlook™ provides calendaring and collaboration scheduling services whereby a user can propose, declare or accept proposed meetings or other events to be placed on the user's computerized schedule. A much newer social networking service launched very recently by the Google™ Corporation is the Google Plus™ system which includes parts called: “Circles”, “Hangouts”, “Sparks”, and “Huddle”.
It is within the contemplation of the present disclosure for the STAN_3 system to periodically import calendaring and/or collaboration/event scheduling data from a user's Microsoft Outlook™ and/or other alike scheduling databases (irrespective of whether those scheduling databases and/or their support software are physically local within a user's computer or they are provided via a computing cloud) if such importation is permitted by the user, so that the STAN_3 system can use such imported scheduling data to infer, at the scheduled dates, what the user's more likely environment and/or contexts are. Yet more specifically, in the introductory example given above, the hypothetical attendant to the “Superbowl™ Sunday Party” may have had his local or cloud-supported scheduling databases pre-scanned by the STAN_3 system 410 so that the latter system 410 could make intelligent guesses as to what the user is later doing, what mood he will probably be in, and optionally, what group offers he may be open to welcoming even if generally that user does not like to receive unsolicited offers.
Incidentally, it is within the contemplation of the present disclosure that essentially any database and/or automated service that is hosted in and/or by one or more of a user's physically local data processing devices, or by a website's web serving and/or mirroring servers and data processing parts or all or part of a cloud computing system or equivalent can be used in whole or in part such that it is accessible to the user through one or more physical data processing and/or communicative mechanisms to which the user has access. In other words, even with a relatively small sized and low powered mobile access device, the user can have access to, not only much more powerful computing resources and much larger data storage facilities but also to a virtual community of other people even if each is on the go and thus can only use a mobile interconnection device. The smaller access devices can be made to appear as each had basically borrowed the greater and more powerful resources of cooperatively-connected-to other mechanisms. And in particular, with regard to the here disclosed STAN_3 system, a relatively small sized and low powered mobile access device can be configured to make use of collectively created resources of the STAN_3 system such as so-called, points, nodes or subregions in various Cognitive Attention Receiving Spaces which the STAN_3 system maintains or supports, including but not limited to, topic spaces (TS), keyword spaces (KwS), content spaces (CS), CFi categorizing spaces, context categorizing spaces, and others as shall be detailed below. More to the point, with net-computers, palm-held convergence devices (e.g., iPhone™, iPad™ etc.) and the like, it is usually not of significance where specifically the physical processes of data processing of sensed physical attributes takes place but rather that timely communication and connectivity and multimedia presentation resources are provided so that the user can experience substantially same results irrespective of how the hardware pieces are interconnected and located. Of course, some acts of data acquisition and/or processing may by necessity have to take place at the physical locale of the user such as the acquisition of user responses (e.g., touches on a touch-sensitive tablet screen, IR based pattern recognition of user facial grimaces and eyeball orientations, etc.) and of local user encodings (e.g., what the user's local environment looks, sounds, feels and/or smells like). And also, of course, the user's experience can be limited by the limitations of the multimedia presentation resources (e.g., image displays, sound reproduction devices, etc.) he or she has access to within a given context.
Accordingly, the disclosed system cannot bypass the limitations of the input and output resources available to the user. But with that said, even with availability of a relatively small display screen (e.g., one with embedded touch detection capabilities) and/or minimalist audio interface resources, a user can be automatically connected in short order to on-topic and screen compatible and/or audio compatible chat or other forum participation sessions that likely will be directed to a topic the user is apparently currently casting his/her attention toward such that the user can have a socially-enhanced experience because the user no longer feels as if he/she is dealing “alone” with the user's area of current focus but rather that the user has access to other, like-minded and interaction co-compatible people almost anytime the user wants to have such a shared experience. (Incidentally, just because a user's hand-held, local interface device (e.g., smartphone) is itself relatively small in size that does not mean that the user's interface options are limited to screen touch and voice command alone. As mentioned elsewhere herein, the user may wear or carry various additional devices that expand the user's information input/output options, for example by use of an in-mouth, tongue-driven and wirelessly communicative mouth piece whereby the user may signal in privacy, various choices to his hand-held, local interface device (e.g., smartphone).)
A more concrete example of context-driven determination of what the user is apparently focusing-upon may take advantage of the digressed-away method of automatically importing a user's scheduling data to thereby infer at the scheduled dates, what the user's more likely environment and/or other context based attributes is/are. Yet more specifically, if the user's scheduling database indicates that next Friday he is scheduled to be at the Social Networking Developers Conference (SNDC, a hypothetical example) and more particularly at events 1, 3 and 7 in that conference at the respective hours of 10:00 AM, 3:00 PM and 7:00 PM, then when that date and a corresponding time segment comes around, the STAN_3 system may use such information in combination with GPS or like location determining information (if available) as part of its gathered, hint or clue-giving encodings for then automatically determining what likely are the user's current situation, mood, surroundings (especially context of the user and of other people interacting with the user), expectations and so forth. For example, between conference events 1 and 3 (and if the user's then active habit profile—see
In order for the system 400 to appear as if it can magically and automatically connect all the right people (e.g., those with concurrent shared areas of focus in a same Cognitions-representing Space and/or those with social interaction co-compatibilities) at the right time for a power lunch in the locale of a business conference they are attending, the system 400 should have access to data that allows the system 400 to: (1) infer the likely moods of the various players (e.g., did each not eat recently and is each in the mood for and/or in the habit or routine a business oriented lunch when in this sort of current context?), (2) infer the current topic(s) of focus most likely on the mind of each individual at the relevant time; (3) infer the type of conversation or other social interaction each individual will most likely desire at the relevant time and place (e.g., a lively debate as between people with opposed view points, or a singing to the choir interaction as between close and like-minded friends and/or family?); (4) infer the type of food or other refreshment or eatery ambiance/decor each invited individual is most likely to agree to (e.g., American cuisine? Beer and pretzels? Chinese take-out? Fine-dining versus fast-food? Other?); (5) infer the distance that each invited individual is likely to be willing to travel away from his/her current location to get to the proposed lunch venue (e.g., Does one of them have to be back on time for a 1:00 PM lecture where they are the guest speaker? Are taxis or mass transit readily available? Is parking a problem?) and so on. See also
Since STAN systems such as the ones disclosed in here incorporated U.S. application Ser. No. 12/369,274 and Ser. No. 12/854,082 as well as in the present disclosure are repeatedly testing for, or sensing for, change of user context, of user mood (and thus change of active PEEP and/or other profiles—see also
In one embodiment, users are automatically and selectively invited to join in on a system-sponsored game or contest where the number of participants allowed per game or contest is limited to a predetermined maximum number (e.g., 100 contestants or less, 50 or less, 10 or less, or another contest-relevant number). The game or contest may involve one or more prizes and/or recognitions for a corresponding first place winning user or runner up. The prizes may include discount coupons or prize offerings provided by a promoter of specified goods and/or services. In one embodiment, to be eligible for possible invitation to the game or contest (where invitation may also require winning in a final invitations round lottery), the users who wish to be invited (or have a chance of being invited) need to pre-qualify by being involved in one or more pre-specified activities related to the STAN_3 system and/or by having one or more pre-specified user attributes. Examples of such activities/attributes related to the STAN_3 system include, but are not limited to: (1) participating in a chat or other forum participation session that corresponds to a pre-specified topic space subregion (TSR) and/or to a subregion of another system-maintained space (another CARS); (2) participating in adding to or modifying (e.g., editing) within a system-maintained Cognitive Attention Receiving Space (CARS, e.g., topic space), one or more points, nodes or subregions of that space; (3) volunteering to perform other pre-specified services that may be beneficial to the community of users who utilize the STAN_3 system; (4) having a pre-specified set of credentials that indicate expertise or other special disposition relative to a corresponding topic in the system-maintained topic space and/or relative to other pre-specified points, nodes or subregions of other system-maintained CARS's and agreeing to make oneself available for at least a pre-specified number of invitations and/or queries by other system users in regard to the topic node and/or other such CARS PNOS; (5) satisfying in the user's then active personhood and/or profiles of pre-specified geographic and/or other demographic criteria (e.g., age, gender, income level, highest education level) and agreeing to make oneself available for at least a pre-specified number of invitations and/or queries by other system users in regard to the corresponding demographic attributes, and so on.
In one embodiment, user PEEP records (Personal Emotion Expression Profiles) are augmented with user PHAFUEL records (Personal Habits And Favorites/Unfavorites Expression Logs—see
Automated life style planning tools such as the Microsoft Outlook™ product can be used to locate common empty time slots and geographic proximity because tools such as the Microsoft Outlook™ typically provide Tasks tracking functions wherein various to-do items and their criticalities (e.g., flagged as a must-do today, must-do next week, etc.) are recorded. Such data could be stored in a computing cloud or in another remotely accessible data processing system. It is within the contemplation of the present disclosure for the STAN_3 system to periodically import Task tracking data from the user's Microsoft Outlook™ and/or other alike task tracking databases (if permitted by the user, and whether stored in a same cloud or different resource) so that the STAN_3 system can use such imported task tracking data to infer during the scheduled time periods, the user's more likely environment, context, moods, social interaction dispositions, offer welcoming dispositions, etc. The imported task tracking data may also be used to update user PHAFUEL records (Personal Habits And Favorites/Unfavorites Expression Log) which indicate various life style habits of the respective user if the task tracking data historically indicates a change in a given habit or a given routine. More specifically with regard to current user context, if the user's task tracking database indicates that the user has a high priority, high pressure work task to be completed by end of day, the STAN_3 system may use this imported information to deduce that the user would not then likely welcome an unsolicited event offer (e.g., 104t or 104a in
In one embodiment, the CRM/calendar tool is optionally configured to just indicate to the STAN_3 system when free time is available but to not show all data in CRM/calendar system, thereby preserving user privacy. In an alternate embodiment, the CRM/calendar tool is optionally configured to indicate to the STAN_3 system general location data as well as general time slots of free time thereby preserving user privacy regarding details. Of course, it is also within the contemplation of the present disclosure to provide different levels of access by the STAN_3 system to generalized or detailed information of the CRM/calendar system thereby providing different levels of user privacy. The above described, automated generations and transmissions of suggestions for impromptu lunch proposals and the like may be based on automated assessment of each invitee's current emotional state (as determined by current active PEEP record) for such a proposed event as well as each invitee's current physical availability (e.g., distance from venue and time available and transportation resources). In one embodiment, a first user's palmtop computer (e.g., 199 of
As a yet better enhancer for likelihood of success, the system originated and corresponding group event offer (e.g., let's have lunch together) may be augmented by adding to it a local merchant's discount advertisement. For example, and with regard to the group event offer (e.g., let's have lunch together) which was instigated by the first user (the one whose CRM database was exploited to this end by the STAN_3 system to thereby automatically suggest the group event to the first user who then acts on the suggestion), that group event offer is automatically augmented by the STAN_3 system 410 to have attached thereto a group discount offer (e.g., “Note that the very nearby Louigie's Italian Restaurant is having a lunch special today”). The augmenting offer from the local food provider automatically attached due to a group opportunity algorithm automatically running in the background of the STAN_3 system 410 and which group opportunity algorithm will be detailed below. Briefly, goods and/or service providers can formulate discount offer templates which they want to have matched by the STAN_3 system with groups of people that are likely to accept the offers. The STAN_3 system 410 then automatically matches the more likely groups of people with the discount offers those people are more likely to accept. It is win-win for both the consumers and the vendors. In one embodiment, after, or while a group is forming for a social gathering plan (in real life and/or online) the STAN_3 system 410 automatically reminds its user members of the original and/or possibly newly evolved and/or added on reasons for the get together. For example, a pop-up reminder may be displayed on a user's screen (e.g., 111) indicating that 70% of the invited people have already accepted and they accepted under the idea that they will be focusing-upon topics T_original, T_added_on, T_substitute, and so on. (Here, T_original can be an initially proposed topic that serves as an initiating basis for having the meeting while T_added_on can be later added topic proposed for the meeting after discussion about having the meeting started.) In the heat of social gatherings, people sometimes forget why they got together in the first place (what was the T_original?). However, the STAN_3 system can automatically remind them and/or additionally provide links to or the actual on-topic content related to the initial or added-on or deleted or modified topics (e.g., T_original, T_added_on, T_deleted, etc.)
More specifically and referring to
In response, one of the group members notices the flashing (and optionally red colored) circle 102m on front plate 102a_Now of his tablet computer 100 and double clicks or taps the dot 102m open. In response to such activation, his computer 100 displays a forward expanding connection line 115a6 whose advancing end (at this stage) eventually stops and opens up into a previously not displayed, on-topic content window 117 (having an image 117a of the book included therein). As seen in
In one embodiment, after passage of a predetermined amount of time the My Top-5 Topics Now serving plate, 102a_Now automatically transforms into a My Top-5 Topics Earlier serving plate, 102a′_Earlier which is covered up by a slightly translucent but newer and more up to date, My Top Topics Now serving plate, 102a_Now. In the case where Tower-of-Hanoi stacked rings are used in an inverted cone orientation, the smaller, older ones of the top plate can leak through to the “Earlier” in time plate 102a′_Earlier where they again become larger and top of the stack rings because in that “Earlier” time frame they are the newest and best invitations and/or recommendations. If, after such an update, the user wants to see the older, My Top Topics Earlier plate 102a′_Earlier, he may click on, tap, or otherwise activate a protruding-out small portion of that older plate and stacked behind plate. The older plate then pops to the top. Alternatively the user might use other menu means for shuffling the older serving plate to the front. Behind the My Top Topics Earlier serving plate, 102a′_Earlier there is disposed an even earlier in time serving plate 102a″ and so on. Invitations (to online and/or real life meetings) that are for a substantially same topic (e.g., book club) line up almost behind one another so that a historical line up of such on-same-topic invitations is perceived when looking through the partly translucent plates. This optional viewing of current and older on-topic invitations is shown for the left side of plates stack 102b (Their Top 5 Topics). (Note: the references 102a′_Earlier and 102a′Earlier are used interchangeably herein.) Incidentally, and as indicated elsewhere herein, the on-topic serving plates, such as those of plate stack 102b need not be of the meet-up opportunity type, or of the meet-up opportunity only type. The serving plates (e.g., 102aNow) can alternatively or additionally serve up links to on-topic resources (e.g., content providing resources) other than invitations to chat or other forum participation sessions. The other on-topic resources may include, but not limited to, links to on-topic web sites, links to on-topic books or other such publications, links to on-topic college courses, links to on-topic databases and so on.
If the exemplary Book-of the-Month Club member had left window 117 open for more than a predetermined length of time, an on-topic event offering 104t may have popped open adjacent to the on-topic material of window 117. However, this description of such on-topic promotional offerings has jumped ahead of itself because a broader tour of the user's tablet computer 100 has not yet been supplied here and such a re-tour (return to the main tour) will now be presented.
Recall how the Preliminary Introduction above began with a bouncing, rolling ball (108) pulling the user into a virtual elevator (113) that took the user's observed view to a virtual floor of a virtual high rise building. When the doors open on the virtual elevator (113, bottom right corner of screen) the virtual ball (108″) hops out and rolls to the diagonally opposed, left upper corner of the screen 111. This tends to draw the user's eyes to an on-screen context indicator 113a and to the header entity 101a of social entities column 101. The user may then note that the header entity has been automatically preset to be “Me”. The user may also note that the on-screen context indicator 113a indicates the user is currently on a virtual floor named, “My Top 5 Now Topics” (which floor name is not shown in
Before moving on to next stopping point 108′, the virtual ball (also referred to herein as the Magic Marble 108) outputs a virtual spot light from its embedded virtual light sources onto a small topic space flag icon 101ts sticking up from the “Me” header object 101a. A balloon icon (not shown) temporarily opens up and displays the guessed-at most prominent (top) topic that the machine system (410) has determined to be the topic likely to be foremost (topmost) in the user's mind. In this example, it says, “Superbowl™ Sunday Party”. The temporary balloon (not shown) collapses and the Magic Marble 108 then shines another virtual spotlight on invitation dot 102i at the left end of the also-displayed, My Top Topics Now serving plate 102a_Now. Then the Magic Marble 108 rolls over to the right, optionally stopping at another tour point 108′ to light up, for example, the first listed Top Now Topic for the “Them/Their” social entity of plates stack 102b. Thereafter, the Magic Marble 108 rolls over further to the right side of the screen 111 and parks itself in a ball parking area 108z. This reminds the user as to where the Magic Marble 108 normally parks. The user may later want to activate the Magic Marble 108 for performing user specified functions (e.g., marking up different areas of the screen for temporary exclusion from STAN_3 monitoring or specific inclusion in STAN_3 monitoring where all other areas are automatically excluded).
Unseen by the user during this exercise (wherein the Magic Marble 108 is rolling diagonally from one corner (113) to the other (113a) and then across to come to rest in the Ball Park 108z) is that the user's tablet computer 100 is automatically watching him while he is watching the Magic Marble 108 move to different locations on the screen. Two spaced apart, eye-tracking sensors, 106 and 109, are provided along an upper edge of the exemplary tablet computer 100. (There could be yet more sensors, such as three at three corners.) Another sensor embedded in the computer housing (100) is a GPS one (Global Positioning Satellites receiver, shown to be included in housing area 106). At the beginning of the story (the Preliminary Introduction to Disclosed Subject Matter), the GPS sensor was used by the STAN_3 system 410 to automatically determine that the user is geographically located at the house of one of his known friends (Ken's house). That information in combination with timing and accessible calendaring data (e.g., Microsoft Outlook™) allowed the STAN_3 system 410 to automatically determine one or a few most likely contexts for the user and then to extract best-guess conclusions that the user is now likely attending the “Superbowl™ Sunday Party” at his friend's house (Ken's), perhaps in the context role of being a “guest”. The determined user context (or most likely handful of contexts) similarly provided the system 410 with the ability to draw best-guess conclusions that the user would soon welcome an unsolicited Group Coupon offering 104a for fresh hot pizza. But again the story given here is leap-frogging ahead of itself. The guessed at, social context of being at “Ken's Superbowl™ Sunday Party” also allowed the system 410 to pre-formulate the layout of the virtual floor displayed by way of screen 111 as is illustrated in
The display screen 111 may be a Liquid Crystal Display (LCD) type or an electrophoretic type or another as may be appropriate. The display screen 111 may accordingly include a matrix of pixel units embedded therein for outputting and/or reflecting differently colored visible wavelengths of light (e.g., Red, Green, Blue and White pixels) that cause the user (see 201A of
When earlier, in the introductory story, the Magic Marble 108 bounced around the screen after entering the displayed scene (of
Another sensor that the tablet computer 100 may include is a housing directional tilt and/or jiggle sensor 107. This can be in the form of an opto-electronically implemented gyroscopic sensor and/or MEMs type acceleration sensors and/or a compass sensor. The directional tilt and jiggle sensor 107 determines what angles the flat panel display screen 111 is at relative to gravity and/or relative to geographic North, South, East and West. The tilt and jiggle sensor 107 also determines what directions the tablet computer 100 is being shaken in (e.g., up/down, side to side, Northeast to Southwest or otherwise). The user may elect to use the Magic Marble 108 as a rolling type of cursor (whose action point is defined by a virtual spotlight cast by the internally lit ball 108) and to position the ball with tilt and shake actions applied to the housing of the tablet computer 100. Push and/or rotate actuators 105 and 110 are respectively located on the left and right sides of the tablet housing and these may be activated by the user to invoke pre-programmed functions associated with the Magic Marble 108. In an embodiment the Magic Marble 108 can be moved with a finger or hand gesture. These functions may be varied with a Magic Marble Settings tool 114 provided in a tools area of the screen 111.
One of the functions that the Magic Marble 108 (or alternatively a touch driven cursor 135) may provide is that of unfurling a context-based controls setting menu such as the one shown at 136 when the user depresses a control-right keypad or an alike side-bar button combination. (Such hot key combination activation may alternatively or additionally be invoked with special, predetermined facial contortions which are picked up by the embedded IR sensors.) Then, whatever the Magic Marble 108 or cursor 135 (shown disposed inside window 117 of
Let it be assumed for sake of illustration and as a hypothetical that when the user control-right clicks or double taps on or otherwise activates the key.a5 object, the My Family disc-like icon 101b glows (or otherwise changes). That indicates to the user that one or more keywords of the key.a5 object are logically linked to the “My Family” social entity. Let it also be assumed that in response to this glowing, the user wants to see more specifically what topics the social entity called “My Family” (101b) is now primarily focusing-upon (what are their top now N topics?). This cannot be done using the pyramid 101rb for the illustrated configuration of
That is one way of discovering what the top N now topics of the “My Family” entity (101b) are. Another way involves clicking or otherwise activating a flag tool 101s provided atop the 101rb pyramid as is shown in the magnified view of pyramid 101rb in
In addition to using the topic flag icon (e.g., 101ts) provided with each pyramid object (e.g., 101rb), the user may activate yet another topic flag icon that is either already displayed within the corresponding social entity representing object (101a, . . . , 101d) or becomes visible when the expansion tool (e.g., starburst+) of that social entity representing object (101a, . . . , 101d) is activated. In other words, each social entity representing object (101a, . . . , 101d) is provided with a show-me-more details tool like the tool 99+ (e.g., the starburst plus sign) that is for example illustrated in circle 101d of
By clicking or otherwise activating one of the platform icons (P1, P2, etc.) of greater details pane 101de, such action opens up more detailed information about where in the corresponding platform (e.g., FaceBook™, STAN3™, etc.) the corresponding topic nodes or subregions logically link to. Although not shown in the exemplary greater details pane 101de, yet further icons may appear therein that, upon activation, reveal more details regarding points, nodes or subregions (PNOS's) in other Cognitive Attention Receiving Spaces such as keyword space (KwS), URL space, context space (XS) and so on. And as mentioned above, some of the revealed more details can indicate how similar or dissimilar various PNOS's are in their respective Cognitions-representing Spaces. More specifically, cross-correlation details as between the current KoH entity (e.g., “Me”) and the other detailed social entity (e.g., “My Other” 101d) may include indicating what common or similar keywords or content sub-portions both social entities are currently focusing significant “heat” upon or are otherwise casting their attention on. These common keywords (as defined by corresponding objects in keyword space) may be indicated by other indicators in place of the “heat” indicators. For example, rather than showing the “heat” metrics, the system may instead display the top 5 currently focused-upon keywords that the two social entities have in common with each other. In addition to or as an alternative to showing commonly shared topic points, nodes or subregions and/or commonly shared keyword points, nodes or subregions, or how similar they are, the greater details pane 101de may show commonalities/similarities in other Cognitive Attention Receiving Spaces such as, but not limited to, URL space, meta-tag space, context space, geography space, social dynamics space and so on. In addition to or as an alternative to comparatively showing commonly shared points, nodes or subregions in various Cognitive Attention Receiving Spaces (CARS's) which are common to two or more social entities, the greater details pane 101de may show the top N points, nodes or subregions of just one social entity and the corresponding “heats” cast by that just one social entity (e.g., “Me”) on the respective points, nodes or subregions in respective ones of different Cognitive Attention Receiving Spaces (CARS's; e.g., topic space, URL space, ERL space (defined below), hybrid keyword-context space, and so on).
Aside from causing a user-selected hot key combination (e.g., control right click or double tap) to provide more detailed information about one or more of associated topic and associated social entities (e.g., friends), the settings menu 136 may be programmed to cause the user-selected hot key combination to provide more detailed information about one or more of other logically-associated objects, such as, but not limited to, associated forum supporting mechanisms (e.g., platforms 103) and associated group events (e.g., professional conference, lunch date, etc.) and/or invitations thereto and/or promotional offerings related thereto.
While a few specific sensors and/or their locations in the tablet computer 100 have been described thus far, it is within the contemplation of the present disclosure for the user-proximate computer 100 to have other or additional sensors. For example, a second display screen with embedded IR sensors and/or touch or proximity sensors may be provided on the other side (back side) of the same tablet housing 100. In addition to or as replacement for the IR beam units, 106 and 109, stereoscopic cameras may be provided in spaced apart relation to look back at the user's face and/or eyeballs and/or to look forward at a scene the user is also looking at. The stereoscopic cameras may be used for creating a 3-dimensional of the user (e.g., of the user's face, including eyeballs) so that the system can determine therefrom what the user is currently focused-upon and/or how the user is reacting to the focused-upon material.
More specifically, in the case of
Aside from GPS-like location identifying means and/or directional sound pick up means being embedded in the user's mobile device (e.g., 199) or being available in, and accessed by way of, nearby other devices and being temporarily borrowed for use by the user's mobile device (e.g., 199), the user's mobile device may include direction determining means (e.g., compass means and gravity tilt means) and/or focal distance determining means for automatically determining what direction(s) one or more of used cameras/directional microphones (e.g., 210) are pointing to and where (how far out) the focal point is of the directed camera(s)/microphones relative to the location of the of camera(s)/microphones. The automatically determined identity, direction and distance and up/down disposition of the pointed to object/person (e.g., 198) is then fed to a reality augmenting server within the STAN_3 system 410. The reality augmenting server (not explicitly shown, but one of the data processing resources in the cloud) automatically looks up most likely identity of the person(s) (based for example on automated face and/or voice recognition operations carried out by the cloud), most likely context(s) and/or topic(s) (and/or other points, nodes or subregions of other spaces) that are cross-associated as between the user (or other entity) and the pointed-at real life object/person (e.g., Ken's house 198/Ken). For example, one context plus topic-related invitation that may pop up on the user's augmented reality side (screen 211) may be something like: “This is where Ken's Superbowl™ Sunday Party will take place next week. Please RSVP now.” Alternatively, the user's augmented reality or augmented virtuality side of the display may suggest something like: “There is Ken in the real life or in a recently inloaded image and by the way you should soon RSVP to Ken's invitation to his Superbowl™ Sunday Party”. These are examples of context and/or topic space augmented presentations of reality and/or of a virtuality. The user is automatically reminded of likely topics of current interest (and/or of other focused-upon points, nodes or subregions of likely current interest in other spaces) that are associated with real life (ReL) objects/persons that the user aims his computer (e.g., 100, 199) at or associated with recognizable objects/persons present in recent images inloaded into the user's device.
As another example, the user may point at the refrigerator in his kitchen and the system 410 invites him to formulate a list of food items needed for next week's party. The user may point at the local supermarket as he passes by (or the GPS sensor 106 detects its proximity) and the system 410 invites him to look at a list of items on a recent to-be-shopped-for list. This is another example of topic and context spaces based augmenting of local reality. So just by way of recap here, it becomes possible for the STAN_3 system to know/guess on what objects and/or which persons are being currently pointed at by one or more cameras/microphones under control of, or being controlled on behalf of a given user (e.g., 210A of
Yet other sensors that may be embedded in the tablet computer 100 and/or other devices (e.g., head piece 201b of
In the STAN_3 system 410 of
If general welcomeness has been determined by the automated welcomeness filter 426 for certain general types of offers, the identification of the likely welcoming user is forwarded to the hybrid topic-context router 427 for more refined determination of what specific unsolicited offers the user (and current friends) are more likely to accept than others based on one or more of the system determined current topic(s) likely to be currently on his/their minds and current location(s) where he/they are situated and/or other contexts under which the user is currently operating. Although, it is premature at this point in the present description to go into greater detail, later below it will be seen that so-called, hybrid topic-context points, nodes or subregions can be defined by the STAN_3 system in respective hybrid Cognitive Attention Receiving Spaces. The idea is that a user is not just merely hungry (as an example of mood/biological state) and/or currently casting attention on a specific topic, but also that the user has adopted a specific role or job definition (as part of his/her context) that will further determine if a specific promotional offering is now more welcome than others. By way of a more specific example, assume that the hypothetical user (you) of the above Superbowl™ Sunday party example is indeed at Ken's house and the Superbowl™ game is starting and that hypothetical user (you) is worried about how healthy Joe-The-Throw Nebraska is, but also that one tiny additional fact has been left out of the story. The left out fact is that a week before the party, the hypothetical user entered into an agreement (e.g., a contract) with Ken that the hypothetical user will be working as a food serving and trash clean-up worker and not as a social invitee (guest) to the party. In other words, the user has a special “role” that the user is now operating under and that assumed role can significantly change how the user behaves and what promotional offerings would be more welcomed or less unwelcomed than others. Yet more specifically, a promotional offering such as, “Do you want to order emergency carpet cleaning services for tomorrow?” may be more welcomed by the user when in the clean-up crew role but not when in the party guest role. The subject of assumed roles will be detailed further in conjunction with
In the example above, one or more of various automated mechanisms could have been used by the STAN_3 system to learn that the user is in one role (one adopted context) rather than another. The user may have a task-managing database (e.g., Microsoft Outlook Calendar™) or another form of to-do-list managing software plus associated stored to-do data, or the user may have a client relations management (CRM) tool he regularly uses, or the user may have a social relations management (SRM) tool he regularly uses, or the user may have received a reminder email or other such electronic message (e.g., “Don't forget you have clean-up crew job duty on Sunday”) reminding the user of the job role he has agreed to undertake. The STAN_3 system automatically accesses one or more of these (after access permission has been given) and searches for information relating to assumed, or to-be-assumed roles. Then the STAN_3 system determines probabilities as between possible roles and generates a sorted list with the more probable roles and their respective probability scores at the top of the list; and the system prioritizes accordingly.
Assumed roles can determine predicted habits and routines. Predicted habits and routines (see briefly
Referring still to
Additionally, it is within the contemplation of the present disclosure to automatically collect implicit or explicit CVi's from permitting STAN users at the times that unsolicited event offers (e.g., 104t, 104a) are popped up on that user's tablet screen (or otherwise presented to the user). An example of an explicit CVi may be a user-activateable flag which is attached to the promotional offering and which indicates, when checked, that this promotional offering was not welcome or worse, should not be present again to the user and/or to others ever or within a specified context. The then-collected CVi's may indicate how welcomed or not welcomed the unsolicited event offers (e.g., 104t, 104a) are for that user at the given time and in the given context. The goal is to minimize the number of times that STAN-generated event offers (e.g., 104t, 104a) are unwelcomed by the respective user. Neural networks or other heuristically evolving automated models may be automatically developed in the background for better predicting when and under which contexts, various unsolicited event offers will be welcomed or not by the various users of the STAN_3 system 410. Parameters for the over-time developed heuristic models are stored in personal preference records (e.g., habit and routine records, see
Referring still to
Aside from these various kinds of social networking (SN) platforms (e.g., 441-448, 460), other social interactions may take place through tweets, email exchanges, list-serve exchanges, comments posted on “blogs”, generalized “in-box” messagings, commonly-shared white-boards or Wikipedia™ like collaboration projects, etc. Various organizations (dot.org's, 450) and content publication institutions (455) may publish content directed to specific topics (e.g., to outdoor nature activities such as those followed by the Field-and-Streams™ magazine) and that content may be freely available to all members of the public or only to subscribers in accordance with subscription policies generated by the various content providers. (With regard to Wikipedia™ like collaboration projects, those skilled in the art will appreciate that the Wikipedia™ collaboration project—for creating and updating a free online encyclopedia—and similar other “Wiki”-spaces or collaboration projects (e.g., Wikinews™, Wikiquote™, Wikimedia™, etc.) typically provide user-editable world-wide-web content. The original Wiki concept of “open editing” for all web users may be modified however by selectively limiting who can edit, who can vote on controversial material and so on. Moreover, a Wiki-like collaboration project, as such term is used further below, need not be limited to content encoded in a form that is compatible with early standardizations of HTML coding (world-wide-web coding) and browsers that allow for viewing and editing of the same. It is within the contemplation of the present disclosure to use Wiki-like collaboration project control software for allowing experts within different topic areas to edit and vote (approvingly or disapprovingly) on structures and links (e.g., hierarchical or otherwise) and linked-to/from other nodes/content providers of topic nodes that are within their field of expertise. More detail will follow below.)
Since a user (e.g., 431) of the STAN_3 system 410 may also be a user of one or more of these various other social networking (SN) and/or other content providing platforms (440, 450, 455, 460, etc.) and may form all sorts of user-to-user associations (U2U) with other users of those other platforms, it may be desirous to allow STAN users to import their out-of-STAN U2U associations, in whole or in part (and depending on permissions for such importation) into the user-to-user associations (U2U) database area 411 maintained by the STAN_3 system 410. To this end, a cross-associations importation or messaging system 432m may be included as part of the software executed by or on behalf of the STAN user's computer (e.g., 100, 199) where the cross-associations importation or messaging system 432m allows for automated importation or exchange of user-to-user associations (U2U) information as between different platforms. At various times the first user (e.g., 432) may choose to be disconnected from (e.g., not logged-into and/or not monitored by) the STAN_3 system 410 while instead interacting with one or more of the various other social networking (SN) and other content providing platforms (440, 450, 455, 460, etc.) and forming social interaction relations there. Later, a STAN user may wish to keep an eye on the top topics (and/or other top nodes or subregions of non-topic spaces) currently being focused-upon by his “friend” Charlie, where the entity known to the first user as “Charlie” was befriended firstly on the MySpace™ platform. (See briefly 484a under column 487.1C of
In this hypothetical example, the same first user 432 (USER-B) employs the username, “Tom” when logged into and being tracked in real time by the STAN_3 system 410 (and may use a corresponding Tom-associated password). (See briefly 484.1c under column 487.1A of
Despite the possibilities for such difference of persona and interests, there may be instances where user-to-user associations (U2U) and/or user-to-topic associations (U2T) developed by the Thomas persona (432u2) while operating exclusively under the auspices of the external SN system 44X environment (e.g., FaceBook™) and thus outside the tracking radar of the STAN_3 system 410 may be of cross-association value to the Tom persona (432u1). In other words, at a later time when the Tom/Thomas person is logged into the STAN_3 system 410, he may want to know what topics, if any, his new friend “Charlie” is currently focusing-upon. However, “Charlie” is not the pseudo-name used by the real life (ReL) personage of “Charlie” when that real life personage logs into system 410. Instead he goes by the name, “Chuck”. (See briefly item 484c under column 487.1A of
It may not be practical to import the wholes of external user-to-user association (U2U) maps from outside platforms (e.g., MySpace™) because, firstly, they can be extremely large and secondly, few STAN users will ever demand to view or otherwise interact with all other social entities (e.g., friends, family and everyone else in the real or virtual world) of all external user-to-user association (U2U) maps of all platforms. Instead, STAN users will generally wish to view or otherwise interact with only other social entities (e.g., friends, family) whom they wish to focus-upon because they have a preformed social relationship with them and/or a preformed, topic-based relationship with them. Accordingly, the here disclosed STAN_3 system 410 operates to develop and store only selectively filtered versions of external user-to-user association (U2U) maps in its U2U database area 411. The filtering is done under control of so-called External SN Profile importation records 431p2, 432p2, etc. for respective ones of STAN_3's registered members (e.g., 431, 432, etc.). The External SN Profile importation records (e.g., 431p2, 432p2) may reflect the identification of the external platform (44X) where the relationship developed as well as user social interaction histories that were externally developed and user compatibility characteristics (e.g., co-compatibilities to other users, compatibilities to specific topics, types of discussion groups etc.) and as the same relates to one or more external personas (e.g., 431u2, 432u2) of registered members of the STAN_3 system 410. The external SN Profile records 431p2, 432p2 may be automatically generated or alternatively or additionally they may be partly or wholly manually entered into the U2U records area 411 of the STAN_3 database (DB) 419 and optionally validated by entry checking software or other means and thereafter incorporated into the STAN_3 database.
An external U2U associations importing mechanism is more clearly illustrated by
In one embodiment, and for the case of accessing data of external sources (e.g., 432L1-432L6), cooperation agreements may be negotiated and signed as between operators of the STAN_3 system 410 and operators of one or more of the Out-of STAN other platforms (e.g., external platforms 441, 442, 444, etc.) or tools (e.g., CRM) that permit automated agents output by the STAN_3 system 410 or live agents coached by the STAN_3 system to access the other platforms or tool data stores and operate therein in accordance with restrictions set forth in the cooperation agreements while creating filtered submaps of the external U2U association maps and thereafter causing importation of the so-filtered submaps (e.g., reduced in size and scope; as well as optionally compressed by compression software) into the U2U records area 411 of the STAN_3 database (DB) 419. An automated format change may occur before filtered external U2U submaps are ported into the STAN_3 database (DB) 419.
Referring to
As a result, an identity cross-correlation and context cross-correlations can be established for the primary real life (ReL) person (e.g., 432 and having corresponding real user identification node 484.1R stored for him in system memory) and his various pseudonames (alter-ego personas, which personas may use the real name of the primary real life person as often occurs for example within the FaceBook™ platform). Also, cross-correlations between the different pseudonames and corresponding passwords (if given) may be obtained when that first person logs into the various different platforms (STAN_3 as well as other platforms such as FaceBook™, MySpace™, LinkedIn™, etc.). With access to the primary real life (ReL) person's passwords, pseudonames and/or networking devices (e.g., 100, 199, etc.), the STAN_3 BOT agents often can scan through the appropriate data storage areas to locate and copy external social entity specifications including, but not limited to: (1) the pseudonames (e.g., Chuck, Charlie, Charles) of friends of the primary real life (ReL) person (e.g., 432); (2) the externally defined social relationships between the ReL person (e.g., 432) and his friends, family members and/or other associates; (3) the externally defined roles (e.g., context-based business relationships; i.e. boss and subordinate) between the ReL person (e.g., 432) and others whom he/she interacts with by way of the external platforms; (4) the dates on when these social/other-contextual relationships were originated or last modified or last destroyed (e.g., by de-friending, by quitting a job) and then perhaps last rehabilitated, and so on.
Although
Referring to column 487.1A of the forefront pane 484.1 (Tom's pane), this one provides representations of user-to-user associations (U2U) as formed inside the STAN_3 system 410. For example, the “Tom” persona (432u1 in
The real life (ReL) personages behind the personas known as “Tom” and “Chuck” may have also collaborated within the domains of outside platforms such as the LinkedIn™ platform, where the latter is represented by vertical column 487.1E of
More specifically, and referring to magnified data storing area 487c of
Moreover, the present description of user-to-user associations (U2U) as defined through a respective Cognitive Attention Receiving Space (e.g., topic space per data area 487c.2) is not limited to individuals. The concept of user-to-user associations (U2U) also includes herein, individual-to-Group (i2G) associations and Group-to-Group (G2G) associations. More specifically, a given individual user (e.g., Usr(B) of
With regard to Group-to-Group (G2G) associations, the social entity identifications shown in
Relationships between social entities (e.g., real life persons, virtual persons, groups) may be many faceted and uni or bidirectional. By way of example, imagine two real life persons named Doctor Samuel Rose (491) and his son Jason Rose (492). These are hypothetical persons and any relation to real persons living or otherwise is coincidental. A first set of uni-directional relationships stemming from Dr. S. Rose (Sr. for short) 491 and J. Rose (Jr. for short) 492 is that Sr. is biologically the father of Jr. and is behaviorally acting as a father of Jr. A second relationship may be that from time to time Sr. behaves as the physician of Jr. A bi-directional relationship may be that Sr. and Jr. are friends in real life (ReL). They may also be online friends, for example on FaceBook™. They may also be topic-related co-chatterers in one or more online forums sponsored or tracked by the STAN_3 system 410. They may also be members of a system-recognized group (e.g., the fathers/sons get-together and discuss politics group). The variety of possible uni- and bi-directional relationships possible between Sr. (491) and Jr. (492) is represented in a nonlimiting way by the uni- and bi-directional relationship vectors 490.12 shown in
In one embodiment, at least some of the many possible uni- and bi-directional relationships between a given first user (e.g., Sr. 491) and a corresponding second user (e.g., Jr. 492) are represented by digitally compressed code sequences (including compressed ‘operator code’ sequences). The code sequences are organized so that the most common of relationships (as partially or fully specified by interlinkable/cascadable ‘operator codes’) between general first and second users are represented by short length code sequences (e.g., binary 1's and 0's). This reduces the amount of memory resources needed for storing codes representing the most common operative and data-dependent relationships (e.g., operatorFiF1=“former is friend of latter” combined with operatorFiF2=“under auspices of this platform:”+data2=“FaceBook™”; operatorFiF1+operatorFiF2+data2=“MySpace™”; operatorFiF3=“former is father of latter”, operatorFiF4=“former is son of latter”, . . . is brother of . . . , is husband of . . . , etc.). Unit 495 in
Jason Rose (a.k.a. Jr. 492) may not know it, but his father, Dr. Samuel Rose (a.k.a. Sr. 491) enjoys playing in a virtual reality domain, say in the SecondLife™ domain (e.g., 460a of
Jason Rose (a.k.a. Jr. 492) is not only a son of Sr. 491, he is also a business owner. Accordingly, Jr. 492 may flip between different roles (e.g., behaving as a “son”, behaving as a “business owner”, behaving otherwise) as surrounding circumstances change. In his business, Jr. 492 employs Kenneth Keen, an engineer (a.k.a. as KK 493). They communicate with one another via various social networking (SN) channels. Hence a variety of online relationships 490.23 develop between them as it may relate to business oriented topics or outside-of-work topics and they each take on different “roles” (which often means different contexts) as the operative relationships (e.g., 490.23) change. At times, Jr. 492 wants to keep track of what new top topics KK 493 is currently focusing-upon while acting in the role of “employee” and also what new top topics other employees of Jr. 492 are focusing-upon. Jr. 492, KK 493 and a few other employees of Jr. are STAN users. So Jr. has formulated a to-be-watched custom U2U group 496 in his STAN_3 system account. In one embodiment, Jr. 492 can do so by dragging and dropping icons representing his various friends and/or other social entity acquaintances into a custom group defining circle 496 (e.g., his circle of trust). In the same or an alternate embodiment, Jr. 492 can formulate his custom group 496 of to-be-watched social entities (real and/or virtual) by specifying group assemblage rules such as, include all my employees who are also STAN users and are friends of mine on at least one of FaceBook™ and LinkedIn™ (this is merely an example). The rules may also specify that the followed persons are to be followed in this way only when they are in the context of (in the role of) acting as an employee for example, or acting as a “friend”, or irrespective of undertaken role. An advantage of such rule based assemblage is that the system 410 can thereafter automatically add and delete appropriate social entities from the custom group and filter among their various activities based on the user specified rules. Accordingly, Jr. 492 does have to hand retool his custom group definition every time he hires a new employee or one decides to seek greener pastures elsewhere and the new employees do not have to worry that their off-the-clock activities will be tracked because the rules that Jr. 492 has formulated (and optionally published to the affected social entities) limit themselves to context-based activities, in other words, only when the watched social entities are in their “employee” context (as an example). However, if in one embodiment, Jr. 492 alternatively or additionally wants to use the drag-and-drop operation to further refine his custom group 496, he can. In one embodiment, icons representing collective social entity groups (e.g., 496) are also provided with magnification and/or expansion unpacking/repacking tool options such as 496+. Hence, anytime Jr. 492 wants to see who specifically is included within his custom formed group definition and under what contexts, he can do so with use of the unpacking/repacking tool option 496+. The same tool may also be used to view and/or refine the automatic add/drop rules 496b for that custom formed group representation.
Aside from custom group representations (e.g., 496), the STAN_3 system 410 provides its users with the option of calling up pre-fabricated common templates 498 such as, but not limited to, a pre-programmed group template whose automatic add/drop rules (see 496b) cause it to maintain as its followed personas, all living members of the user's immediate family while they are operating in roles that are related to family relationships. The relationship codes (e.g., 490.12) maintained as between STAN users allows the system 410 to automatically do this. Other examples of pre-fabricated common templates 498 include all my FaceBook™ and/or MySpace™ friends during the period of the last 2 weeks; my in-STAN top topic friends during the period of the last 8 days and so on. The rules can be refined to be more selective if desired; for example: all new people who have been granted friend status by me during the period of the last 2 weeks; or all friends I have interacted with during the period of the last 8 days; or all FaceBook™ friends I have sent an email or other message to in a given time period, and so on. As the case with custom group representations (e.g., 496), each pre-programmed group template 498 may include magnification and/or expansion unpacking/repacking tool options such as 498+. Hence, anytime Jr. 492 wants to see who specifically is included within his template formed group definition and what the filter rules are, he can with use of the unpacking/repacking tool option 498+. The same tool may also be used to view and/or refine the automatic add/drop rules (see 496b) for that template formed group representation. When the template rules are so changed, the corresponding data object becomes a custom one. A system provided template (498) may also be converted into a custom one by its respective user (e.g., Jr. 492) by using the drag-and-drop option 496a.
From the above examples it is seen that relationship specifications and formation of groups (e.g., 496, 498) can depend on a large number of variables. The exploded view of relationship specifying data object 487c at the far left of
Blank field 487c.3 is representative of many alternative or additional parameters that can be included in relationship specifying data object 487c. More specifically, these may include user(B) to user(C) shared platform codes for specific platforms such as FaceBook™, LinkedIn™, etc. In other words, what platforms do user(B) and user(C) have shared interests in, and under what specific subcategories of those platforms? These may include user(B) to user(C) shared event offer codes. In other words, what group discount or other online event offers do user(B) and user(C) have shared interests in? These may include user(B) to user(C) shared content source codes. In other words, what major URL's, blogs, chat rooms, etc., do user(B) and user(C) have shared interests in?
Relationships can be made, broken and repaired over the course of time. In accordance with another aspect of the present disclosure, the relationship specifying data object 487c may include further fields specifying when and/or where the relationship was first formed, when and/or where the relationship was last modified (and was the modification a breaking of the relationship (e.g., a de-friending?), a remaking of the last broken level or an upgrade to higher/stronger level of relationship). In other words, relationships may be defined by recorded data of one embodiment, not with respect to most recent changes but also with respect to lifetime history so that cycles in long term relationships can be automatically identified and used for automatically predicting future co-compatibilities and the like. The relationship specifying data object 487c may include further fields specifying when and/or where the relationship was last used, and so on. Automated group assemblage rules such as 496b may take advantage of these various fields of the relationship specifying data object 487c to automatically form group specifying objects (e.g., 496) which may then be inserted into column 101 of
While the user-to-user associations (U2U) space has been described above as being composed in one embodiment of tabular data structures such as panes 484.1, 484.2, etc. for respective real life (ReL) users (e.g., where pane 484.1 corresponds to the real life (ReL) user identified by ReL ID node 484.1R) and where each of the tabular data structures contain, or has pointers pointing to, further data structures such 487c.1, it is within the contemplation of the present disclosure to use alternate methods for organizing the data objects of the user-to-user associations (U2U) space. More specifically, an “operator nodes” method is disclosed here, for example in
“Operator nodes” (e.g., 487c.1N, 487c.2N) may point to other spaces aside from pointing to internal nodes of the user-to-user associations (U2U) space. More specifically, rather than having a specific operator node called “Is Member of My (FB or MS) Friends Group” as in the above example, a more generalized relations operator node may be a hybrid node (e.g., 487c.2N) called for example “Is Member of My (XP1 or XP2 or XP3 or . . . ) Friends Group” where XP1, XP2, XP3, etc. are inheritance pointers that can point to external platform names (e.g., FaceBook™) or to other operator nodes that form combinations of platforms or inheritance pointers that can point to more specific regions of one or more networks or to other operator nodes that form combinations of such more specific regions and by object oriented inheritance, instantiate specific definitions for the “Friends Group”, or more broadly, for the corresponding user-to-user associations (U2U) node.
Hybrid operator nodes may point to other hybrid operator nodes (e.g., 487c.2N) and/or to nodes in various system-supported cognition “spaces” (e.g., topic space, keyword space, music space, etc.). Accordingly, by use of object-oriented inheritance functions, a hybrid operator node in U2U space may define complex relations such as, for example, “These are my associates whom I know from platforms (XP1 or XP2 or XP3) and with whom I often exchange notes within chat or other Forum Participation Sessions (FPS1 or FPS2 or FPS3) where the exchanged notes relate to the following topics and/or topic space regions: (Tn11 or (Tn22 AND Tn33) or TSR44 but not TSR55)”. It is to be understood here that like XP1, XP2, etc., variables FPS1, etc.; Tn11, etc; TSR44, etc. are instantiated by way of modifiable pointers that point to fixed or modifiable nodes or areas in other cognition spaces (e.g., in topic space). Accordingly a robust and easily modifiable data-objects organizing space is created for representing in machine memory, the user-to-user associations similar to the way that other data-object to data-object associations are represented, for example the topic-node to topic-node associations (T2T) of system topic space (TS). See more specifically TS 313′ of
Referring now again to
As another example, the system 410 may have guessed wrong as to exact location and that may have led to erroneous determination of the user's current context. The user is not in Ken's house to watch the Superbowl™ Sunday football game, but rather next door, in the user's grandmother's house because the user had promised his grandmother he would fix the door gasket on her refrigerator that day. (This alternate scenario will be detailed yet further in conjunction with
If the mistaken location and/or context determining action by the STAN_3 system 410 is an important one, the user can optionally activate a “training” button (not shown) when taking the Layer-vator 113 to the correct virtual floor or system layer and this lets the system 410 know that the user made modification is a “training” one which the system 410 is to use to heuristically re-adjust its location and/or context determining decision makings on in the future.
Referring again to
In one embodiment, when users of the STAN_3 system categorize their imported U2U submaps of friends or other contacts in terms of named Groups, as for example, “My Immediate Family” (e.g., in the Circle of Trust shown as 101b in
Although throughout much of this disclosure, an automated plates-packing tool (e.g., 102aNow) having a name of the form “My Currently Focused-Upon Top 5 Topics” is used as an example (or “Their Currently Focused-Upon Top 5 Topics”, etc.) for describing what topic-related items can be automatically provided on each serving plate (e.g., 102b of
Yet another automated invitation generating tool that the user may elect to manually attach to one of his serving plates or to have the system 410 automatically attach onto one of the serving plates on a particular Layer-Vator™ floor he visits (see
An example of why a DIVERSIFIED Topics picker might be desirable is this. Suppose Entity(X) is Cousin Wendy and unfortunately, Cousin Wendy is obsessed with Health Maintenance topics. Invariably, her top 5 topics list will be populated only with Health Maintenance related topics. The user (who is an inquisitive relative of Cousin Wendy) may be interested in learning if Cousin Wendy is still in her Health Maintenance infatuation mode. So yes, if he is analyzing Cousin Wendy's currently focused-upon topics, he will be willing to see one sampling which points to a topic node or associated chat or other forum participation session directed to that same old and tired topic, but not ten all pointing to that one general topic subregion (TSR). The user may wish to automatically skip the top 10 topics of Cousin Wendy's list and get to item number 11, at which for the first time in Cousin Wendy's list of currently focused-upon topics, points to an area in topic space far away from the Health Maintenance subregion. This next found hit will tell the inquisitive user (the relative of Cousin Wendy) that Wendy is also currently focused-upon, but not so much, on a local political issue, on a family get together that is coming up soon, and so on. (Of course, Cousin Wendy is understood to have not blocked out these other topics from being seen by inquisitive My Family members.)
In one embodiment, two or more top N topics mappings (e.g., heat pyramids) for a given social entity (e.g., Cousin Wendy) are displayed at the same time, for example her Undiversified Top 5 Now Topics and her Highly Diversified Top 5 Now Topics. This allows the inquiring friend to see both where the given social entity (e.g., Cousin Wendy) is concentrating her focus heats in an undiversified one topic space subregion (e.g., TSR1) and to see more broadly, other topic space subregions (e.g., TSR2, TSR3) where the given social entity is otherwise applying above-threshold or historically high heats. In one embodiment, the STAN_3 system 410 automatically identifies the most highly diversified topic space subregions (e.g., TSR1 through TSR9) that have been receiving above-threshold or historically increased heats from the given social entity (e.g., Cousin Wendy) during the relevant time duration (e.g., Now or Then) and the system 410 then automatically displays a spread of top N topics mappings (e.g., heat pyramids) for the given social entity (e.g., Cousin Wendy) across a spectrum, extending from an undiversified top N topics Then mapping to a most diversified Last Ones of the Then Above-threshold M topics (where here M≤N) and having one or more intermediate mappings of less and more highly diversified topic space subregions (e.g., TSR5, TSR7) between those extreme ends of the above-threshold heat receiving spectrum.
Aside from the DIVERSIFIED Topics picker, the STAN_3 system 410 may provide many other specialized filtering mechanisms that use rule-based criteria for identifying nodes or subregions in topic space (TS) or in another system-supported space (e.g., a hybrid of topic space and context space for example). One such example is a population-rarifying topic-and-user identifying tool (not shown) which automatically looks at the top N now topics of a substantially-immediately contactable population of STAN users versus the top N now topics of one user (e.g., the user of computer 100). It then automatically determines which of the one user's top N now topics (where N can be 1, 2, 3, etc. here) is most popularly matched within the top N now topics of the substantially-immediately contactable population of other STAN users and it eliminates that popular-attention drawing topic from the list of shared topics for which co-focused users are to be identified. The system (410) thereafter tries to identify the other users in that population who are concurrently focused-upon one or more topic nodes or topic space subregion (TSRs) described by the pruned list (the list which has the most popular topic removed from it). Then the system indicates to the one user (e.g., of computer 100) how many persons in the substantially-immediately contactable population are now focused-upon one or more of the less popular topics, which topics (which nodes or subregions); and if the other users had given permission for their identity to be publicized in such a way, the identifications of the other users who are now focused-upon one or more of the less popular, but still worthy of attention topics. Alternatively or additionally, the system may automatically present the users with chat or other forum participation opportunities directed only to their respective less popular topics of concurrent focus. One example of an invitations filter option that can be presented in the drop down menu 190b of
The terminology, “substantially-immediately contactable population of STAN users” as used immediately above can have a selected one or more of the following meanings: (1) other STAN users who are physically now in a same room, building, arena or other specified geographic locality such that the first user (of computer 100) can physically meet them with relative ease; (2) other STAN users who are now participating in an online chat or other forum participation session which the first user is also now participating in; (3) other STAN users who are now currently online and located within a specified geographic region; (4) other STAN users who are now currently online; (5) other STAN users who are now currently contactable by means of cellphone texting or other forms of text-like communication (e.g., tablet texting) or other such socially less-intrusive-than direct-talking techniques; and (6) other STAN users who are now currently available for meeting in person or virtually online (e.g., video chat using a real body image or an avatar body image or a hybrid mixture of real and avatar body image—such as for example a partially masked image of the user's real face that does not show the nose and areas around the eyes) because the one or more other STAN users have nothing much to do at the moment (not keenly focused on anything), they are bored and would welcome communicative contact of a pre-specified kind (e.g., avatar based video chat) in the immediate future and for a predetermined duration. The STAN_3 system can automatically determine or estimate what that predetermined duration is by, for example, looking at the digitized calendars, to-do-lists, etc. of the prospective chatterers and/or using the determined personal contexts and corresponding PHAFUEL records (habits, routines) of the chatterers (where the habits, routines data may inform as to the typical free time of the user under the given circumstances).
It is within the contemplation of the disclosure to augment the above exemplary option of “The Least Popular 3 of My Top 5 Now Topics Among Other Users Within 2 Miles of Me” to instead read for example: “The Least Popular 3 of My Top 5 Now DIVERSIFIED Topics Among Other Users Within 10 Miles of Me” or “The Least Popular 2 of Wendy's Top 5 Now DIVERSIFIED Topics Among Other Users Now online”.
An example of the use of a filter such as for example “The Least Popular 3 of My Top 5 Now DIVERSIFIED Topics Among Other Users Attending Same Conference as Me” can proceed as follows. The first user (of computer 100) is a medical doctor attending a conference on Treatment and Prevention of Diabetes. His number one of My Top 5 Now Topics is “Treatment and Prevention of Diabetes”. In fact for pretty much every other doctor at the conference, one of their Top 5 Now Topics is “Treatment and Prevention of Diabetes”. So there is little value under that context in the STAN_3 system 410 connecting any two or more of them by way of invitation to chat or other forum participation opportunities directed to that highly popular topic (at that conference). Also assume that all five of the first user's Top 5 Now Topics are directed to topics that relate in a fairly straight forward manner to the more generalized topic of “Diabetes”. However, let it be assumed that the first user (of computer 100) has in his list of “My Top 5 Now DIVERSIFIED Topics”, the esoteric topic of “Rare Adverse Drug Interactions between Pharmaceuticals in the Class 8 Compound Category” (a purely hypothetical example). The number of other physicians attending the same conference and being currently focused-upon the same esoteric topic is relatively small. However, as dinner time approaches, and after spending a whole day of listening to lectures on the number one topic (“Treatment and Prevention of Diabetes”) the first user would welcome an introduction to a fellow doctor at the same conference who is currently focused-upon the esoteric topic of “Rare Adverse Drug Interactions between Pharmaceuticals in the Class 8 Compound Category” and the vise versa is probably true for at least one among the small subpopulation of conference-attending doctors who are similarly currently focused-upon the same esoteric topic. So by using the population-rarifying topic and user identifying tool (not shown), individuals who are uniquely suitable for meeting each other at say a professional conference, or at a sporting event, etc., can determine that the similarly situated other persons are substantially-immediately contactable and they can inquire if those other identifiable persons are now interested in meeting in person or even just via electronic communication means to exchange thoughts about the less locally popular other topics.
The example of “Rare Adverse Drug Interactions between Pharmaceuticals in the Class 8 Compound Category” (a purely hypothetical example) is merely illustrative. The two or more doctors at the Diabetes conference may instead have the topic of “Best Baseball Players of the 1950's” as their common esoteric topic of current focus to be shared during dinner.
Yet another example of an esoteric-topic filtering inquiry mechanism supportable by the STAN_3 system 410 may involve shared topics that have high probability of being ridiculed within the wider population but are understood and cherished by the rarified few who indulge in that topic. Assume as a purely hypothetical further example that one of the secret current passions of the exemplary doctor attending the Diabetes conference is collecting mint condition SuperMan™ Comic Books of the 1950's. However, in the general population of other Diabetes focused doctors, this secret passion of his is likely to be greeted with ridicule. As dinner time approaches, and after spending a whole day of listening to lectures on the number one topic (“Treatment and Prevention of Diabetes”) the first user would welcome an introduction to a fellow doctor at the same conference who is currently focused-upon the esoteric topic of “Mint Condition SuperMan™ Comic Books of the 1950's”. In accordance with the present disclosure, the “My Top 5 Now DIVERSIFIED Topics” is again employed except that this time, it is automatically deployed in conjunction with a True Passion Confirmation mechanism (not shown). Before the system generates invitations or other introductory propositions as between the two or more STAN users who are currently focused-upon an esoteric and likely-to-meet-with-ridicule topic, the STAN_3 system 410 automatically performs a background check on each of the potential invitees to verify that they are indeed devotees to the same topic, for example because they each participated to an extent beyond a predetermined threshold in chat room discussions on the topic and/or they each cast an above-threshold amount of “heat” at nodes within topic space (TS) directed to that esoteric topic. Then before they are identified to each other by the system, the system sends them some form of verification or proof that the other person is also a devotee to the same esoteric but likely-to-meet-with-ridicule by the general populace topic. Once again, the example of “Mint Condition SuperMan™ Comic Books of the 1950's” is merely an illustrative example. The likely-to-meet-with-ridicule by the general populace topic can be something else such as for example, People Who Believe in Abduction By UFO's, People Who Believe in one conspiracy theory or another or all of them, etc. In accordance with one embodiment, the STAN_3 system 410 provides all users with a protected-nodes marking tool (not shown) which allows each user to mark one or more nodes or subregions in topic space and/or in another space as being “protected” nodes or subregions for which the user is not to be identified to other users unless some form of evidence is first submitted indicating that the other user is trustable in obtaining the identification information, for example where the pre-offered evidence demonstrates that the other user is a true devotee to the same topic based on past above-threshold casting of heat on the topic for greater than a predetermined time duration. The “protected” nodes or subregions category is to be contrasted against the “blocked” nodes or subregions category, where for the latter, no other member of the user community can gain access to the identification of the first user and his or her ‘touchings’ with those “blocked” nodes or subregions unless explicit permission of a predefined kind is given by the first user. In one embodiment, a nascent meet up (online or in real life) that involves potentially sensitive (e.g., embarrassing) subject matter is presaged by a series of progressively more revealing communication. For example, the at first, strangers-to-each-other users might first receive an invite that is text only as a prelude to a next communication where the hesitant invitees (if they indicate acceptance to the text only suggestion or request) are shown avatar-only images of one another. If they indicate acceptance to that next more revealing mode of communication, the system can step up the revelation by displaying partially masked (e.g., upper face covered) versions of their real body images. If the hesitant to meet invitees accept each successive level of increased unmasking, eventually they may agree to meet in person or to start a live video chat where they show themselves and perhaps reveal their real life (ReL) identities to each other.
Referring again to
Referring to
Process 470 is initiated at step 471 (Begin). The initiation might be in automated response to the STAN_3 system determining that user 432 is not heavily focusing upon any on-screen content of his CPU (e.g., 432a) at this time and therefore this would likely be a good time to push an unsolicited survey or favor request on user 432 for accessing his external user-to-user associations (U2U) information.
The unsolicited usage survey push begins at step 472. Dashed logical connection 472a points to a possible survey dialog box 482 that might then be displayed to user 432 as part of step 472. The illustrated content of dialog box 482 may provide one or more conventional control buttons such as a virtual pushbutton 482b for allowing the user 432 to quickly respond affirmatively to the pushed (e.g., popped up) survey proposal 482. Reference numbers like 482b do not appear in the popped-up survey dialog box 482. Embracing hyphens like the ones around reference number 482b (e.g., “—482b-”) indicate that it is a nondisplayed reference number. A same use of embracing hyphens is used in other illustrations herein of display content to indicate nondisplay thereof.
More specifically, introduction information 482a of dialog box 482 informs the user of what he is being asked to do. Pushbutton 482b allows the user to respond affirmatively in a general way. However, if the STAN_3 has detected that the user is currently using a particular external content site (e.g., FaceBook™, MySpace™, LinkedIn™, etc.) more heavily than others, the popped-up dialog box 482 may provide a suggestive and more specific answer option 482e for the user whereby the user can push one rather than a sequence of numerous answer buttons to navigate to his desired conclusion. If the user hits the close window button (the upper right X) that is taken as a no, don't bother me about this. On the other hand, if the user does not want to be now bothered, he can click or tap on (or otherwise activate) the Not-Now button 482c. In response to this, the STAN_3 system will understand that it guessed wrong on user 432 being in a solicitation welcoming mode and thus ready to participate in such a survey. The STAN_3 system will adaptively alter its survey option algorithms for user 432 so as to better guess when in the future (through a series of trials and errors) it is better to bother user 432 with such pushed (unsolicited) surveys about his external user-to-user associations (U2U). Pressing of the Not-Now button 482c does not mean user 432 never wants to be queried about such information, just not now. The task is rescheduled for a later time. User 432 may alternatively press the Remind-me-via-email button 482d. In the latter case, the STAN_3 system will automatically send an email to a pre-selected email account of user 432 for again inviting him to engage in the same survey (482, 483) at a time of his choosing. The sent email will include a hyperlink for returning the user to the state of step 472 of
If in step 472 the user has agreed to now being questioned, then step 473 is next executed. Otherwise, process 470 is exited in accordance with an exit option chosen by the user in step 472. As seen in the next popped-up and corresponding dialog box 483, after agreeing to the survey, the user is again given some introductory information 483a about what is happening in this proposed dialog box 483. Data entry box 483b asks the user for his user-name as used in the identified outside account. A default answer may be displayed such as the user-name (e.g., “Tom”) that user 432 uses when logging into the STAN_3 system. Data entry box 483c asks the user for his user-password as used in the identified outside account. The default answer may indicate that filling in this information is optional. In one embodiment, one or both of entry boxes 483b, 483c may be automatically pre-filled by identification data automatically obtained from the encodings acquisition mechanism of the user's local data processing device. For example a built-in webcam automatically recognizes the user's face and thus user identity, or a built-in audio pick-up automatically recognizes his/her voice and/or a built-in wireless key detector automatically recognizes presence of a user possessed key device whereby manual entry of the user's name and/or password is not necessary and instead an encrypted container having such information is unlocked by the biometric recognition and its plaintext data sent to entry boxes 483b, 483c; thus step 473 can be performed automatically without the user's manual participation. Pressing button 483e provides the user with additional information and/or optional actions. Pressing button 483d returns the user to the previous dialog box (482). In one embodiment, if the user provides the STAN_3 system with his external account password (483c), an additional pop-up window asks the user to give STAN_3 some time (e.g., 24 hours) before changing his password and then advices him to change his password thereafter for his protection. In one embodiment, the user is given an option of simultaneously importing user account information from multiple external platforms and for plural ones of possibly differently named personas of the user all at once.
In one embodiment, after having obtained the user's username and password for an external platform, the STAN_3 system asks the user for permission to continue using the user's login name and password of the external platform for purpose of sending lurker BOT's under his login for thereby automatically collecting data that the user is entitled to access; which data may input chat or other forum participation sessions within the external platform that appear to be on-topic with respect to a listed top N now topics of the user and thus worthy of alerting to user about, especially if he is currently logged into the STAN_3 system but not into the external platform.
In one embodiment, after having obtained the user's username and password for an external platform, the STAN_3 system asks the user for permission to log in at a later time and refresh its database regarding the user's friendship circles without bothering the user again.
Although the interfacing between the user and the STAN_3 system is shown illustratively as a series of dialog boxes like 482 and 483 it is within the contemplation of this disclosure that various other kinds of control interfacing may be used to query the user and that the selected control interfacing may depend on user context at the time. For example, if the user (e.g., 432) is currently focusing upon a SecondLife™ environment in which he is represented by an animated avatar (e.g., MW_2nd_life in
If in step 473 the user has provided one or more of the requested items of information (e.g., 483b, 483c), then in subsequent step 474 the obtained information is automatically stored into an aliases tracking portion (e.g., record(s)) of the system database (DB 419). Part of an exemplary DB record structure is shown at 484 and a more detailed version is shown as database section 484.1 in
In next step 475 of
Then in step 478 the STAN_3 system converts the imported external contacts data into formats that conform to data structures used within the External STAN Profile records (431p2, 432p2) for that user. In one embodiment, the conform format is in accordance with the user-to-user (U2U) relationships defining sections, 484.1, 484.2, . . . , etc. shown in
This kind of additional information (e.g., displayed in columns 101 and 101r of
Still referring to
At next to last step 479a of
Referring again to
More to the point, while a given user (e.g., 432) is individually, and in relative isolation, casting individualized cognitive “heat” on one or more points, nodes or subregions in a given Cognitive Attention Receiving Space (e.g., topic space, keyword space, URL space, meta-tag space and so on); other STAN_3 system users (including the first user's friends for example) may be similarly individually casting individualized cognitive “heats” (by “touching”) on same or closely related points, nodes or subregions of same or interrelated Cognitive Attention Receiving Spaces during roughly same time periods. The STAN_3 system can detect such cross-correlated and chronologically adjacent (and optionally geographically adjacent) but individualized castings of heat by monitored individuals on the respective same or similar points, nodes or subregions of Cognitive Attention Receiving Spaces (e.g., topic space) maintained by the STAN_3 system. The STAN_3 system can then indicate, at minimum, to the various isolated users that they are not alone in their heat casting activities. However, what is yet more beneficial to those of the users who are willing to accept is that the STAN_3 system can bring the isolated users into a collective chat or other forum participation activities wherein they begin to collaboratively work together (due, for example to their predetermined co-compatibilities to collaboratively work together) and they can thereby refine or add to the work product that they had individually developed thus far. As a result, individualized work efforts directed to a given topic node or topic subregion (TSR) are merged into a collaborative effort that can be beneficial to all involved. The individualized work efforts or cognition efforts of the joined individuals need not be directed to an established point, node or subregion in topic space and instead can be directed to one or more of different points, nodes or subregions in other Cognitive Attention Receiving Spaces such as, but not limited to, keyword space, URL space, ERL space, meta-tag space and so on (where here, ERL represents an Exclusive Resource Locater as distinguished from a Universal Resource Locater (URL)). The concept of starting with individualized user-selected keywords, URL's, ERL's, etc. and converting these into collectively favored (e.g., popular or expert-approved) keywords, URL's, ERL's, etc. and corresponding collaborative specification of what is being discussed (e.g., what is the topic or topics around which the current exchanges circle about?) will be revisited below in yet greater detail in conjunction with
For now it is sufficient to understand that a computer-facilitated and automated method is being here disclosed for: (1) identifying closely related cognitions and identifications thereof such as but not limited to, closely related topic points, nodes or subregions to which one or more users is/are apparently casting attentive heat during a specified time period; (2) for identifying people (or groups of people) who, during a specified time period, are apparently casting attentive heat at substantially same or similar points, nodes or subregions of a Cognitive Attention Receiving Space such as for example a topic space (but it could be a different shared cognition/shared experience space, such as for example, a “music space”, an “emotional states” space and so on); (3) for identifying people (or groups of people) who, during a specified time period, will satisfy a prespecified recipe of mixed personality types for then forming an “interesting” chat room session or other “interesting” forum participation session; (4) for inviting available ones of such identified personas (real or virtual) into nascent chat or other forum participation opportunities in hopes that the desired mixture of “interesting” personas will accept and an “interesting” forum session will then take place; and (5) for timely exposing the identified personas to one or more promotional offerings that the personas are likely to perceive as being “welcomed” promotional offerings. These various concepts will be described below in conjunction with various figures including
In addition to bringing individualized users together for co-beneficial collaboration regarding points, nodes or subregions of Cognitive Attention Receiving Spaces (e.g., topic space) that they are probably directing their attentions to, each user's experience (e.g., 432's of
Referring next to
More specifically, the illustrated perspective view in
Yet more specifically, the two platforms, 410′ and 420 are respectively represented in the multiplatform space 400′ of
The STAN_3 topic space mapping mechanism (413′ of
Tn22(Topic=mammals),Tn11(Topic=mammals/subclass=omnivore),Tn01(Topic=mammals/subclass=omnivore/super-subclass=fruit-eating),Tn02(Topic=mammals/subclass=omnivore/super-subclass=grass-eating) and so on.
The term clustering (or clustered) was mentioned above with reference to spatial and/or temporal and/or hierarchical clustering but without yet providing clarifying explanations. It is still too soon in the present disclosure to fully define these terms. However, for now it is sufficient to think of hierarchically clustered nodes as including sibling nodes of a hierarchical tree structure where the hierarchically clustered sibling nodes share a same parent node (see also siblings 30R.9a-30R.9c of parent 30R.30 in
Referring back to
In the same (140) or another exemplary embodiment where the user is deemed to have directly ‘touched’ topic node Tn01 and to have indirectly ‘touched’ topic node Tn11, the user is further automatically deemed to have indirectly touched grandparent node Tn22 in the yet next higher plane TSp2 due to an attributed halo of a greater hierarchical extent (e.g., two jumps upward along the hierarchical tree rather than one) or due to an attributed greater spatial radius in spatial topic space for his halo if it is a spatial halo (e.g., bigger halo 132h′ in
In one embodiment, topic space auditing servers (not shown) of the STAN_3 system 410 keep track of the percent time spent and/or degree of energetic engagement with which each monitored STAN user engages directly and/or indirectly in touching different topic nodes within respective time slots. (Alternatively or additionally the same concept applies to ‘touchings’ made in other Cognitions-representing Spaces.) The time spent and/or the emotional or other energy intensity per unit time (power density) that are deemed to have been cast by indirect touchings may be attenuated based on a predetermined halo diminution function (e.g., decays with hierarchical step distance of spatial radial distance—not necessarily at same decay rate in all directions) assigned to the user's halo 132h. More specifically, during a first time slot represented by left and right borders of box 146b of
Before continuing with explanation of
Also for sake of simplicity of the current example (140), it will be assumed that during journey subparts 132a3, 132a4 and 132a5 of respective traveler 132, that traveler 132 is merely skimming through web content at his client device end of the system and not activating any hyperlinks or entering on-topic chat rooms—which latter activities would be examples of more energetic attention giving activities and thus direct ‘touchings’ in URL space and in chat room space respectively. Although traveler 132 is not yet clicking or tapping or otherwise activating hyperlinks and is not entering chat rooms or accepting invitations to chat or other forum participation opportunities, the domain-lookup servers (DLUX's) of the system 410 may nonetheless be responding to his less energetic, but still attention giving activities (e.g., skimmings; as reported by respectively uploaded CFi signals) through web content and the system will be concurrently determining most likely topic nodes to attribute to this energetic (even if low level energetic) activity of the user 132. Each topic node that is deemed to be a currently more likely than not, now focused-upon node (now attention receiving node) in the system's topic space can be simultaneously deemed by the system 410 to be a directly ‘touched’ upon topic node. Each such direct ‘touching’ can contribute to a score that is being totaled in the background by the system 410 for each node, where the total will indicate how much time and/or attention giving energy per unit time (power) at least the first user 132 just expended in directly touching' various ones of the topic nodes.
The first and third journey subparts 132a3 and 132a5 of traveler 132 are shown in
Accordingly, by using a process such as that of
Just as lists of top N topic nodes or topic space regions (TSRs) now being focused-upon now (e.g., 149a, 149b) can be automatically created for each STAN user based on the monitored and tracked journeys of the user (e.g., 131) through system topic space, and based on time spent focusing-upon those areas of topic space and/or based on emotional energies (or other energies per unit time) detected to have been expended by the user when focusing-upon those areas of topic space (nodes and/or topic space regions (TSRs) and/or topic space clustering-of-nodes regions (TScRs)), similar lists of top N′ nodes or regions (where N′ can be same or different from N) within other types of system “spaces” can be automatically generated where the lists indicate for example, top N″ URL's (where N″ can be same or different from N) or combinations or sequences of URL's being focused-upon now by the user based on his direct or indirect ‘touchings’ in URL space (see briefly 390 of
With the introductory concepts of
In
The domain of the exemplary, out-of-STAN platform 420 is illustrated as having a messaging support (and organizing) space 425 and as having a membership support (and organizing) space 421. Let it be assumed that initially, the messaging support space 425 of external platform 420 is completely empty. In other words, it has no discussion rings (e.g., blog threads) like that of illustrated ring 426′ yet formed in that space 425. Next, a single (an individualized) ring-creating user 403′ of space 421 (membership support space) starts things going by launching (for example in a figurative one-man boat 405′) a nascent discussion proposal 406′. This launching of a proposed discussion can be pictured as starting in the membership space 421 and creating a corresponding data object 426′ in the group discussion support space 425. In the LinkedIn™ environment this action is known as simply starting a proposed discussion by attaching a headline message (example: “What do you think about what the President said today?”) to a created discussion object and pushing that proposal (406′ in its outward bound boat 405′) out into the then empty discussions space 425. Once launched into discussions space 425 the launched (and substantially empty) ring 426′ can be seen by other members (e.g., 422) of a predefined Membership Group 424. The launched discussion proposal 406′ is thereby transformed into a fixedly attached child ring 426′ of parent node 426p (attached to 426′ by way of linking branch 427′), where point 426p is merely an identified starting point (root) for the Membership Group 424 but does not have message exchange rings like 426′ inside of it. Typically, child rings like 426′ attach to an ever growing (increasing in illustrated length) branch 427′ according to date of attachment. In other words, it is a mere chronologically growing, one dimensional branch with dated nodes attached to it, with the newly attached ring 426′ being one such dated node. As time progresses, a discussions proposal platform like the LinkedIn™ platform may have a long list of proposed discussions posted thereon according to date and ID of its launcher (e.g., posted 5 days ago by discussion leader Jones). Many of the proposals may remain empty and stagnate into oblivion if not responded to by other members of a same membership group within a reasonable span of time.
More specifically, in the initial launching stage of the newly attached-to-branch-427′ discussion proposal 426′, the latter discussion ring 426′ has only one member of group 424 associated with it, namely, its single launcher 403′. If no one else (e.g., a friend, a discussion group co-member) joins into that solo-launched discussion proposal 426′, it remains as a substantially empty boat and just sits there bobbing in the water so to speak, aging at its attached and fixed position along the ever growing history branch 427′ of group parent node 426p. On the other hand, if another member 422 of the same membership group 424 jumps into the ring (by way of by way of illustrated leap 428′) and responds to the affixed discussion proposal 426′ (e.g., “What do you think about what the President said today?”) by posting a responsive comment inside that ring 426′, for example, “Oh, I think what the President said today was good.”, then the discussion has begun. The discussion launcher/leader 403′ may then post a counter comment or other members of the discussion membership group 424 may also jump in and add their comments. In one embodiment, those members of an outside group 423 who are not also members of group 424 do not get to see the discussions of group 424 if the latter is a members-only-group. Irrespective of how many further members of the membership group 424 jump into the launched ring 426′ or later cease further participation within that ring 426′, that ring 426′ stays affixed to the parent node 426p and in the original historical position where it originally attached to historically-growing branch 427′. Some discussion rings in LinkedIn™ can grow to have hundreds of comments and a like number of members commenting therein. Other launched discussion rings of LinkedIn™ (used merely as an example here) may remain forever empty while still remaining affixed to the parent node in their historical position and having only the one discussion launcher 403′ logically linked to that otherwise empty discussion ring 426′. In some instances, two launched discussions can propose a same discussion question; one draws many responses, the other hardly any, and the two never merge. There is essentially no adaptive recategorization and/or adaptive migration in a topic space for the launched discussion ring 426′. This will be contrasted below against a concept of chat rooms or other forum participation sessions that drift (see drifting Notes Exchange session 416d) in an adaptive topic space 413′ supported by the STAN_3 system 410′ of
Still referring to the external platform 420, it is to be understood that not all discussion group rings like 426′ need to be carried out in a single common language such as a lay-person's English. It is quite possible that some discussion groups (membership groups) may conduct their internal exchanges in respective other languages such as, but not limited to, German, French, Italian, Swedish, Japanese, Chinese or Korean. It is also possible that some discussion groups have memberships that are multilingual and thus conduct internal exchanges within certain discussion rings using several languages at once, for example, throwing in French or German loan phrases (e.g., Schadenfreude) into a mostly English discourse where no English word quite suffices. It is also possible that some discussion groups use keywords of a mixed or alternate language type to describe what they are talking about. It is also possible that some discussion groups have members who are experts in certain esoteric arts (e.g., patent law, computer science, medicine, economics, etc.) and use art-based jargon that lay persons not skilled in such arts would not normally understand or use. The picture that emerges from the upper portion (non-STAN platform) of
By contrast, the birthing (instantiation) of a messaging ring (a TCONE) in the lower platform space 410′ (corresponding to the STAN_3 system 410 of
As mentioned above, the STAN_3 system 410 can also generate proposals for real life (ReL) gatherings (e.g., Let's meet for lunch this afternoon because we are both physically proximate to each other). In one embodiment, the STAN_3 system 410 automatically alerts co-compatible STAN users as to when they are in relatively close physical proximity to each other and/or the system 410 automatically spawns chat or other forum participation opportunities to which there are invited only those co-compatible and/or same-topic focused-upon STAN users who are in relatively close physical proximity to each other. This can encourage people to have more real life (ReL) gatherings in addition to having more online gatherings with co-compatible others. In one embodiment, if the if one person accepts an invite to a real life gathering (e.g., lunch date) but then no one else joins or the other person drops out at the last minute, or the planned venue (e.g., lunch restaurant) becomes unfeasible, then as soon as it is clear that the planned gathering cannot take place or will be of a diminished size, the STAN_3 system automatically posts a meeting update message that may display for example as stating, “Sorry no lunch rooms were available, meeting canceled”, or “Sorry none of other lunch mates could make it, meeting canceled”. In this way a user who signs up for a real life (ReL) gathering will not have to wait and be disappointed when no one else shows up. In some instance, even online chats may be automatically canceled, for example when the planned chat requires a certain key/essential person (e.g., expert 429 of
Another possibility is that too many users accept an invitation (above the holding capacity of the real life venue or above the maximum room size for an online chat) and a proposed gathering has to canceled or changed on account of this. More specifically, some proposed gatherings can be extremely popular (e.g., a well-known celebrity is promised to be present) and thus a large number of potential participants will be invited and a large number will accept (as is predictable from their respective PHAFUEL or other profiles). In such cases, the STAN_3 system automatically runs a random pick lottery (or alternatively performs an automated auction) for nonessential invitees where the number of predicted acceptances exceeds the maximum number of participants who can be accommodated. In one embodiment, however, the STAN_3 system automatically presents each user with plural invitations to plural ones of expected-to-be-over-sold and expected-to-be-under-sold chat or other forum participation opportunities. The plural invitations are color coded and/or otherwise marked to indicate the degree to which they are respectively expected-to-be-oversold or expected-to-be-undersold and then the invitees are asked to choose only one for acceptance. Since the invitees are pre-warned about their chances of getting into expected-to-be-oversold versus expected-to-be-undersold gatherings, they are “psychologically prepared” for a the corresponding low or high chance that he or she might be successful in getting into the chat or other gathering if they select that invite.
With regard to the layout of a topic space (TS), it was disclosed in the here incorporated STAN_2 application, how topic space can be both hierarchical and spatial and can have fixed points in a multidimensional reference frame (e.g., 413xyz of present
With regard to a specified common language and/or a common set of terms of art or jargon being assigned to each node of a given Cognitive Attention Receiving Space (e.g., topic space), it was disclosed in the here incorporated STAN_2 application, how cross language and cross-jargon dictionaries may be used to locate persons and/or groups that likely share a common topic of interest. More will be said herein, but later below, about how commonly-used keywords and the like may come to be spatially clustered in a semantic (Thesaurus-wise) sense in respective primitive storing memories. (See layer 371 of
With regard to user context, it was disclosed in the here incorporated STAN_2 application, how same people can have different personas within a same or different social networking (SN) platforms. Additionally, an example given in
The use of radar column 101r of
Referring to
Accordingly, in one embodiment, the distance-wise decaying, ‘touching’ halos of node touching persons (e.g., 131 in
Thus far, topic space (see for example 413′ of
In accordance with one embodiment, so-called Wiki-like collaboration project control software modules (418b, see
When the term, “Wiki-like” is used herein, for example in regards to the Wiki-like collaboration project control software modules (418b), that term does not imply or inherit all attributes of the Wikipedia™ project or the like. More specifically, although Wikipedia™ may strive for disambiguous and singular definitions of unique keywords or phraseologies (e.g., What is a “Topic” from a linguistic point of view, and more specifically, within the context of sentence/clause-level categorization versus discourse-level categorization?), the present application contemplates in the opposite direction, namely, that any two or more cognitive states (or sets of states), whether expressible as words, or pictures, or smells or sounds (e.g., of music), etc.; can have a same name (e.g., the topic is “Needles”) and yet different groups of collaborators (e.g., people) can reach respective and different consensuses to define that cognition in their own peculiar, group-approved way. So for example, the STAN_3 system can have many topic nodes each named “Needles” where two or more such topic nodes are hierarchical children of a first Parent node named “Knitting” (thus implying that the first pair of needles are Knitting Needles) and at the same time two or more other nodes each named “Needles” are hierarchical children of a second Parent node named “Safety” and yet other same named child nodes have a third Parent node named “Evergreen Tree” and yet a fourth Parent node for others is named “Medical” and so on. No one group has a monopoly on giving a definition to its version of “Needles” and insisting that users of the STAN_3 system accept that one definition as being exclusive and correct.
Additionally, it is to be appreciated that the cloud computing system used by the STAN_3 system has “chunky granularity”, this meaning that the local data centers of a first geographic area are usually not fully identical to those of a spaced apart second geographic area in that each may store locality-specific detailed data that is not fully stored by all the other data centers of the same cloud. What this implies is that “topic space” is not universally the same in all data centers of the cloud. One or a handful of first locality data centers may store topic node definitions for topics of purely local interest, say, a topic called “Proposed Improvements to our Local Library” where this topic node is hierarchical disposed under the domain of Local Politics for example and the same exact topic node will not appear in the “topic space” of a far away other locality because almost no one in the far away other locality will desire to join in on an online chat directed to “Proposed Improvements to our Local Library” of the first locality (and vise versa). Therefore the memory banks of the distant, other data centers are not cluttered up with the storing therein of topic node definitions for purely local topics of an insular first locality. And therefore, the distributed data centers of the cloud computing system are not all homogenously interchangeable with one another. Hence the system has a cloud structure characterized as having “chunky granularity” as opposed to smooth and homogenous granularity. However, with that said, it is within the contemplation of the present disclosure to store backup data for a first data center in the storage banks of one or more (but just a handful) of far away other localities so that; if the first data center does crash and its storage cannot be recreated based on local resources, the backup data stored in the far away other localities may be used to recreate the stored data of the crashed first data center.
With the above now said, it will be shown in conjunction with
Stated otherwise, if there was subject matter defined as “knitting needles” within system topic space, then each and all of the following would be perfectly acceptable under the substantially all-inclusive banner of the STAN_3 system: (1) Arts & Crafts/Knitting/Supplies/[knitting needles11], [knitting needles12], . . . [knitting needles1K]; (2) Engineering/plastics/manufacturing/[knitting needles21], [knitting needles22], . . . [knitting needles2K′]; (3) Education/Potentially Dangerous Supplies In Hands of Teenagers/Home Economics/[knitting needles31], [knitting needles32], . . . [knitting needles3K″]; and so on where here each of K, K′ and K″ is a natural number and each nodes [knitting needles11] through [knitting needles3K″] could be governed by and controlled by a different group of users having its own unique point of view as to how that topic node should be structured and updated either on a cloud-homogenous basis or for a locally granulated part of the cloud (e.g., if there is a sub-topic node called for example, “Meeting Schedules and Task Assignments for our Local Rural Knitting Club”). It may be appreciated from the given “knitting needles” example that user context (including for example, geographic locality and specificity) is often an important factor in determining what angle a given user is approaching the subject of “knitting needles”. For example, if a system user is an engineering professional residing in a big city college area and when in that role he wants to investigate what materials might be best from a manufacturing perspective for producing knitting needles, then for that person, the hierarchical pathway of://TopicSpace/Root/ . . . /Engineering/plastics/manufacturing/[knitting needles27] might be the optimal one for that person in that context. As will be detailed below, the present disclosure contemplates so-called, hybrid nodes including topic/context hybrid nodes which can have shortcut links pointing to context appropriate nodes within topic space. In one embodiment, when the system automatically invites the user to an on-topic chat room (see 102i of
In addition to “hierarchical” types of trees that link to all (universal for the STAN_3 system) or only a subset (semi-universal) of the topic nodes in the STAN_3 topic space, there can also be “non-hierarchical” trees (e.g., tree C of
The Wiki-like collaboration project governance bodies that use corresponding ones of the Wiki-like collaboration project control software modules (418b,
More specifically, and still referring to
In addition to the above, a full-privileges member of a respective Wiki-like collaboration project may also modify others of the Cognitive Attention Receiving Space data-objects within the STAN_3 system 410 for trees or space regions owned by the Wiki-like collaboration project. More specifically, aside from being able to modify and/or create topic-to-topic associations (T2T) for project-owned subregions of the topic-to-topic associations mapping mechanism 413 and topic-to-content associations (T2C) 414, the same user (e.g., 431) may be able to modify and/or create location-to-topic associations (L2T) 416 for project-owned ones of such lists or knowledge base rules; and/or modify and/or create topic-to-user associations (T2U) 412 for project-owned ones of such lists or knowledge base rules that affect project owned topic nodes and/or project owned community boards; and/or the fully-privileged user (431) may be able to modify and/or create user-to-user associations (U2U) 411 for project-owned ones of such lists or knowledge base rules that affect project owned definitions of user-to-user associations (e.g., how users within the project relate to one another).
In one embodiment, although not all STAN users may have such full or lesser privileged control of non-open Wiki-like collaboration projects, they can nonetheless visit the project-controlled nodes (if allowed to by the project owners) and at least observe what occurs in the chat or other forum participation sessions of those nodes if not also participate in those collaboration project controlled forums. For some Wiki-like collaboration projects, the other STAN users can view the credentials of the owners of the project and thus determine for themselves how to value or not the contributions that the collaborators in the respective Wiki-like collaboration projects make. In one embodiment, outside-of-the-project users can voice their opinions about the project even though they cannot directly control the project. They can voice their opinions for example by way of surveys and/or chat rooms that are not owned by the Wiki-like collaboration projects but instead have the corresponding Wiki-like collaboration projects as one of the topics of the not-owned chat room (or other such forum). Thus a feedback system is provided for whereby the project governance body can see how outsiders view the project's contributions and progress.
Additionally, in one embodiment, the workproduct of non-open Wiki-like collaboration projects may be made available for observation by paid subscribers. The STAN_3 system may automatically allocate subscription proceeds in part to contributors to the non-open Wiki-like collaboration projects and in part to system administrators based on for example, the amount of traffic that the points, nodes or subregions of the non-open Wiki-like collaboration projects draw. In one embodiment, the paid subscribers may use automated BOTs to automatically scan through the content of the non-open Wiki-like collaboration projects and to collect material based on search algorithms (e.g., knowledge base rules (KBR's)) devised by the paid subscribers.
Returning now to description of general usage members of the STAN_3 community and their attentive energies providing ‘touchings’ with system resources such as points, nodes or subregions of system topic space (413) or other system-maintained Cognitive Attention Receiving Spaces or system-maintained data organizing mechanisms (e.g., 411, 412, 414, 416), it is to be appreciated that when a general STAN user such as “Stanley” 431 focuses-upon his local data processing device (e.g., 431a) and STAN_3 activities-monitoring is turned on for that device (e.g., 431a of
The basis for automatically detecting one or more of these various ‘touchings’ (and optionally determining their corresponding “heats”) and automatically mapping the same into corresponding data-objects organizing spaces (e.g., topics space, keywords space, etc.) is that CFi, CVi or other alike reporting signals are being repeatedly collected by and from user-surrounding devices (e.g., 100) and these signals are being repeatedly in- or up-loaded into report analyzing resources (e.g., servers) of the STAN_3 system 410 where the report analyzing resources then logically link the collected reports with most-likely-to-be correlated points, nodes or subregions of one or more Cognitive Attention Receiving Spaces. More specifically and as an example, when CFi, CVi or other alike reporting signals are being repeatedly fed to domain-lookup servers (DLUX's, see 151 of
The machine-implemented determinations of where a given user is casting his/her attention giving energies (and/or attention giving powers over time and for how long and with what intensity) can be carried out by a machine-means in a manner similar to how such would be determined by fellow human beings when trying to deduce whether their observable friends are paying attention, and if so, to what and with how much intensity. If possible, the eyes are looked at by the machine means as primary indicators of visual attention giving activities. Are the user's eyelids open or closed, and if open, for how long? Is the user's face close to, or far away from the visual content? what does the determined distance imply, given system-known attributes about the user's visual capabilities (e.g., does he/she need to wear eyeglasses)? Is the user rolling his/her eyes to express boredom? Are the user's pupil dilated or not and where primarily is the user's gaze darting to or about?
Tone of voice and detectable vocal stress aberrations can be indicators used by the machine means of attention giving energies as well. Is the user repeatedly yawning or making gasping sounds? Other machine-detectable indicators might include determining if the user stretching his/her body in an attempt to wake up. Is the user fidgeting in his/her chair? What is the user's breathing rate?Based on the user's currently activated PEEP profile and/or activated PHAFUEL record or other such expression and routine categorizing records, the STAN_3 system can automatically determine degrees of likelihood or unlikelihood (probability scores) that the user is paying attention, and if so, more likely to what visual and/or auditory inputs and/or other inputs (e.g., smells, vibrations, etc.) and to what degree.
The content sub-portions that the user probably is casting his/her attention giving energies toward, or the identity of those content sub-portions, be they visual and/or auditory and/or other types of content (e.g., tactile inputs or outputs, smells, odors, fluid flows, temperature gradients, mechanical attributes such as force, acceleration, gravity, etc.) also can be indicative of which sub-portions of which system-maintained Cognitive Representing Spaces the user is aiming his/her attentions to. For example, is it a unique pattern of URL's looked at in a particular sequence over time? Is it a unique pattern of keywords searched on in a particular sequence over time? The context and/or emotional states under which the user probably is casting his/her attention giving energies also can be indicative of which points, nodes or subregions in various system-maintained Cognitive Attention Receiving Spaces the user is aiming his/her attentions to. In accordance with one aspect of the present disclosure, so-called, hybrid or cross-space nodes are maintained by the STAN_3 system for representing combinatorial and/or sequence-based circumstances that involve for example, location as a context-defining variable and time of day as another context-defining variable. More specifically, is the user at his normal work place and is it a time of week and hour of day in which the user routinely and/or by virtue of his/her calendared work schedule probably focusing upon corresponding points, nodes or subregions in Cognitive Attention Receiving Spaces that are determinable by means of a lookup table (LUT) or the like?
When respective significant ‘journeys’ (e.g., 431a″, 432a″) of plural social entities (e.g., 431′, 432″) cross within a relatively same region of hierarchical and/or spatial topic space (413′, or more generally of any relevant Cognitive Attention Receiving Space), then the heats produced by their respective halos will usually add up to thereby define cumulatively increased heats for the so-‘touched’ nodes do to group activities. This can give a global indication of how ‘hot’ each of the topic nodes is from the perspective of a collective community of users or specific groups of users. Unlike individualized heats, the detection that certain social entities (e.g., 431′, 432″) are both crossing through a same topic node during a predetermined same time period may be an event that warrants adding even more heat (a higher heat score) to the shared topic node, particularly if one or more of the those social entities whose paths (e.g., 431a″, 432a″) cross through a same node (e.g., 416c) is predetermined to be influential or Tipping Point Persons (TPP's, e.g., 429) by the system. When a given topic node experiences plural crossings through it by ‘significant journeys’ (e.g., 431a″, 432a″) of plural social entities (e.g., 431′, 432″, 429) within a predetermined time duration (e.g., same week), then it may be of value to track the preceding steps that brought those respective social entities to a same hot node (e.g., 416c) and it may be of value to track the subsequent journey steps of the influential persons soon after they have touched on the shared hot node (e.g., 416c). This can provide other users with insights as to the thinking of the influential or early trailblazing persons as it relates to the topic of the shared hot node (e.g., 416c). In other words, what next topic node(s) do the influential or otherwise trail-blazing social entities (e.g., 431′, 432″) associate with the topic(s) of the shared hot node (e.g., 416c)?
Sometimes influential social entities (e.g., 431′, 432″, 429) follow parallel, but not crossing ones of ‘significant journeys’ through adjacent subregions of topic space. This kind of event is exemplified by parallel ‘significant journeys’ 489a and 489b in
In one embodiment, the automated, journeys pattern detector 489 is further configured to automatically detect when the not-yet-finished ‘significant journeys’ of new, later-in-time users are tracking in substantially same sequences and/or closeness of paths with paths (e.g., 489a, 489b) previously taken by earlier and influential (e.g., pioneering) social entities (e.g., Tipping Point Persons). In such a case, the journeys pattern detector 489 sends alerts to subscribed promoters (or their automated BOT agents) of the presence of the new users whose more recent but not-yet-finished ‘significant journeys’ are taking them along paths similar to those earlier taken by the trail-blazing pioneers (e.g., Tipping Point Persons 429). The alerted promoters may then wish to make promotional offerings to the in-transit new travelers based on machine-made predictions that the new travelers will substantially follow in the footsteps (e.g., 489a, 489b) of the earlier and influential (e.g., pioneering) social entities. In one embodiment, the alerts generated by the journeys pattern detector 489 are offered up as leads that are to be bid upon (auctioned off to) persons who are looking for prospective new customers who are following behind in the footsteps of the trail-blazing pioneers. The journeys pattern detector 489 is also used for detecting path crossings such as of journeys 431a″ and 432a″ through common node 416c. In that case, the closeness of the tracked paths reduces to zero as the paths cross through a same node (e.g., 416c) in topic space 413′.
It is within the contemplation of the present disclosure to use automated, journeys pattern detectors like 489 for locating close or crossing ‘touching’ paths in other data-objects organizing spaces (other Cognitive Attention Receiving Spaces) besides just topic space. For example, influential trailblazers (e.g., Tipping Point Persons) may lead hoards of so-called, “followers” on sequential journeys through a music space (see
In one embodiment, heats are counted as absolute value numbers or scores. However, there are several drawbacks to using such a raw absolute numbers when computing global summation of heats. (But with that said, the present disclosure nonetheless contemplates the use of such a global summation of absolute heats or heat scores as a viable approach.) One drawback is that some topic nodes (or other ‘touched’ nodes of other spaces) may have thousands of visitors implicitly or actually ‘touching’ upon them every minute while other nodes—not because they are not worthy—have only a few visitors per week. The smaller visitations number does not necessarily mean that a next visitation by one person to the rarely visited node within a given space (e.g., topic space. keyword space, etc.) should not be considered “hot” or otherwise significant. By way of example, what if a very influential person (a Tipping Point Person 429) ‘touches’ upon the rarely visited node? That might be considered a significant event even though it was just one user who touched the node. A second drawback to a global summation of absolute heat scores approach is that most users do not care if random strangers ‘touched’ upon random ones of topic nodes (or nodes of other spaces). They are usually more interested in the cases where relevant social entities (relevant to them; e.g., friends and family) ‘touched’ upon points, nodes or subregions of topic space where the ‘touched’ points, nodes or subregions are relevant to them (e.g., My Top 5 Now Topics). This concept will be explored again below when filters of mechanisms that can generate spatial clustering mappings (
Given the above as introductory background, details of a ‘relevant’ heats measuring system 150 in accordance with
An example of a hybrid cross-talk identifier may include a system-maintained lookup table (LUT) that receives as its inputs, context signals (e.g., physical location, day of week, time of day, identities of nearby and attention giving other social entities as well as current roles probably adopted currently by those entities) and URL navigation sequence indicating signals (e.g., what sequence of URL's did the user recently traverse through?) and keyword sequence indicating signals (e.g., what sequence of keywords did the user recently focus-upon and/or submit to a search engine). The hybrid cross-talk identifier will then generate, in response, a sorted list of more probable to less probable points, nodes or subregions of topic space and/or other Cognitive Attention Receiving Spaces maintained by the system and that the user's context-based activities point to as more likely points or subregions of cast attention. The user's emotional states (as reported by biological telemetry signals for example) can also be used for narrowing the range of likely points, nodes or subregions in topic space and/or other Cognitive Attention Receiving Spaces that the user's context-based activities point to. Although emotions in general tend to be fuzzy constructs, and people can have more than one emotion at the same time, it is not the current emotions alone that are being used by the STAN_3 system to narrow the range of likely points, nodes or subregions in topic space and/or other Cognitive Attention Receiving Spaces that the user is likely casting his/her attention giving energies to, but rather the cross-talking combination of two or more of these various different factors (context, keywords, URL's, meta-tags, background music/noises, background odors, emotions etc.). Since the human brain tends to operate through association of simultaneously activated cognition centers (e.g., is the amygdala being fired up at the same time that the visual cortex is recognizing a snake in the grass?), the STAN_3 system tries to model this cross-associative process (but on a respective consensus-wise defined, communal recognitions basis) by detecting the likely and more intense attention giving energies being expended by the monitored user and to run these through a hybrid cross-talk identifier such as a lookup table (LUT) for thereby more narrowly pointing to corresponding, consensus-wise defined, representations (e.g., topic nodes) of corresponding communal cognitions.
When the time/location-parsed, and converted (normalized) and recombined (after normalization) data is forwarded to one or more domain-lookup servers (DLUX's) or other hybrid cross-talk identifiers whose jobs it is to automatically determine the most likely topic(s) in topic space (whether universal topic space or a locality augmented combination of universal topic space plus locality-supported only further topic nodes) and/or most likely other points, nodes or subregions in other Cognitive Attention Receiving Spaces that the respective user is likely to be casting his/her attention giving energies upon, the corresponding points, nodes or subregions are identified. Thereafter the initial set of such points, nodes or subregions may be further refined (narrowed in scope) by also using for example, the user's currently active, topic-predicting profiles (e.g., CpCCp's, DsCCp's, PHAFUEL, etc.). Once the more likely to be currently focused-upon points, nodes or subregions are identified, those items are referenced to determine what next resources they point to, including but not limited to, best chat or other forum participation opportunities to invite the user to (e.g., based on chat co-compatibilities), best additional, on-topic resources to point the user to, most likely to be welcomed promotional offerings to expose the user to, and so on.
It is to be noted in summarization here that the in-cloud processings of the received signal streamlets, 151i1 and 151i2, of corresponding users are not limited to the purpose of pinpointing in topic space (see 313″ of
Part of the signals 1510 output from the first set 151 of software modules and/or programmed servers illustrated in
Computed “heat” scores can come in many types, where type depends on mixtures of weights, baselines and optional normalizations picked when generating the respective “heat” scores. As the STAN_3 system 1F processes incoming CFi and like streamlets in pipelined fashion, the heats scoring subsystem 150 (
In addition to retaining the origin associations (TA1( ), TA2( ), etc.) as between determined topics and original source signals, the heats scoring system 150 of
‘Touching’ halos can be fixed or variable. If variable, their extent (e.g., how many hierarchical levels upward they extend), their fade factors (e.g., how rapidly their virtual torches diminish in energy intensity as a function of distance from a core ‘touching’ point) and their core energy intensities may vary as functions of the node touching user's reputation, and/or his current level and type of emotion and/or speed of travel through the corresponding topic region. In other words, if a given user is merely skimming very rapidly through content and thus implicitly skimming very rapidly through its associated topic region, then this rapid pace of focusing through content can diminish the intensity and/or extent of the user's variable halo (e.g., 132h) because it is assumed that the user is casting very little in the way of attention giving power versus time on the Cognitive Attention Receiving Spaces associated with that content. On the other hand, if a given user is determined to be spending a relatively large amount of time stepping very slowly and intently through content and thus implicitly stepping very slowly and with high focus through its associated topic region, then this comparatively slow pace of concentrated focusing can automatically translate into increased intensity and/or increased extent of the user's variable halo (e.g., 132h′) because it is assumed that the user is casting more in the way of attention giving power versus time on the Cognitive Attention Receiving Spaces associated with that more intently focused-upon content. In one embodiment, the halo of each user is also made an automated function of the specific region of topic space he or she is determined to be skimming through. If that person has very good reputation in that specific region of topic space (as determined for example by votes of others and/or by other credibility determinations), then his/her halo may automatically grow in intensity and/or extent and direction of reach (e.g., per larger halo 132h′ of
In one embodiment, the halo (and/or other enhance-able weighting attribute) of a Tipping Point Person (TPP) is automatically reduced in effectiveness when the TPP enters into, or otherwise touches a chat or other forum participation session where the demographics of that forum are determined to be substantially outside of an ideal audience demographics profile of that Tipping Point Person (TPP, which ideal demographics profile is predetermined and stored in system memory for that TPP). More specifically, a given TPP may be most influential with an older generation of people (audience) and/or within a certain geographic region but not regarded as so much of an influencer with a younger generation audience and/or with an audience located outside the certain geographic region. Accordingly, when the particular, age-mismatched and/or location-mismatched TPP enters into a chat room (or other forum) populated mostly by younger people and/or people who reside outside the certain geographic region, that particular TPP is not likely to be recognized by the other forum occupants as an influential person who deserves to be awarded with more heavily weighted attributes (e.g., a wider halo). The system 410 automatically senses such conditions in one embodiment and automatically shrinks the TPP's weighted attributes to more normally sized ones (e.g., more normally sized halos). This automated reduction of weighted attributes can be beneficial to the TPP as well as to the audience for whom the TPP is not considered influential. The reason is that TPP's, like other persons, typically have limited bandwidth for handling requests from other people. If the given TPP is bothered with responding to requests (e.g., for help in a topic region he is an expert in) by people who don't appreciate his influential credentials so much (e.g., due to age disparity or distance from the certain geographic regions in which the TPP is better appreciated) then the TPP will have less bandwidth for responding to requests from people who do appreciate to a greatly extent his help or attention. Hence the effectiveness of the TPP may be diminished by his being flagged as a TPP for forums or topic nodes where he will be less appreciated as a result of demographic miscorrelation. Therefore, in the one embodiment, the system automatically tones down the weighted attributes (e.g., halos) of the TPP when he journeys through or nearby forums or nodes that are substantially demographically miscorrelated relative to his ideal demographics profile.
The fixed or variable ‘touching’ halo (e.g., 132h) of each user (e.g., 132′) indirectly determines the extent of a touched “topic space region” of his, where this TSR (topic space region) includes a top topic of that user. Consider user 132′ in
The specified as ‘touched’, topic space region (TSR) not only identifies a compilation of directly or indirectly ‘touched’ topic nodes but also implicates, for example, a corresponding set of chat rooms or other forums of those ‘touched’ topic nodes, where relevant friends of the first user (e.g., 132′) may be currently participating in those chat rooms or other forums. (It is to be understood that a directly or indirectly touched topic node can also implicate nodes in other spaces besides forum space, where those other nodes (in respective Cognitive Attention Receiving Spaces) logically link to the touched topic node.) The first user (e.g., 132′) may therefore be interested in finding out how many or which ones of his relevant friends are ‘touching’ those relevant chat rooms or other forums and to what degree (to what extent of relative ‘heat’)? However, before moving on to explaining a next step where a given type of “heat” is calculated, let it be assumed alternatively that user 132′ is a reputable expert in this quadrant of topic space (the one including Tn01) and his halo 132h extends downwardly by two hierarchical levels as well as upwardly by three hierarchical levels. In such an alternate situation where the halo is larger and/or more intense, the associated topic space region (TSR) that is automatically determined based on the reputable user 132′ having touched node Tn01 will be larger and the number of encompassed chat rooms or other forums will be larger and/or the heat cast by the larger and more intense halo on each indirectly touched node will be greater. And this may be so arranged in order to allow the reputable expert to determine with aid of the enlarged halo which of his relevant friends (or other relevant social entities) are active both up and down in the hierarchy of nodes surrounding his one directly touched node. It is also so arranged in order to allow the relevant friends (those of importance in the user's given context) to see by way of indirect ‘touchings’ of the expert, what quadrant of topic space the expert is currently journeying through, and moreover, what intensity ‘heat’ the expert is casting onto the directly or indirectly ‘touched’ nodes of that quadrant of topic space. In one embodiment, a user can have two or more different halos (e.g., 132h and 132h′) where for example a first halo (132h) is used to define his topic space region (TSR) of interest and the second halo (132h′) is used to define the extent to which the first user's ‘touchings’ are of interest (relevance) to other social entities (e.g., to his friends). There can be multiple copies of second type halos (132h′, 132h″, etc., latter not shown) for indicating to different groups of friends or other social entities what the extent is of the first user's ‘touchings’ in one or both of hierarchical/spatial space and across chronological space.
Referring next to further modules beyond 151 of
The TSR signals 152o output from module 152 can flow to at least two places. A first destination is a heat parameters formulating module 160. A second destination is a U2U filter module 154. The user-to-user associations filtering module 154 automatically scans through the chat rooms or other forums of the corresponding TSR (e.g., forums of Tn01, Tn02 and Tn11 in this example) to thereby identify presence therein of friends or other relevant social entities belonging to a group (e.g., G2) being tracked by the first user's radar scopes (e.g., 101r of
Accordingly, two of a plurality of input signals received by the next-described, heat parameters formulating module 160 are the TSR identification signals 152o and the relevant active friends identifying signals 154o. Identifications of friends (or other relevant social entities) who are not yet currently active in the topic space region (TSR) of interest but who have been invited into that TSR may be obtained from partial output signals 153q of a matching forums determining module 153. The latter module 153 receives output signals 151o from module 151 and responsively outputs signal 1530, where the latter includes partial output signals 153q. Output signals 151o indicate which topic nodes are most likely to be of interest to a respective first user (e.g., 132′). The matching forums determining module 153 then finds chat rooms or other TCONE's (forums) having co-compatible chat mates. Some of those co-compatible chat mates can be pre-made friends of the first user (e.g., 132′) who are deemed to be currently focused-upon the same topics as the top N now topics of the first user; which is why those co-compatible chat mates are being invited into a same on-topic chat room. Accordingly, partial output signals 153q can include identifications of social entities (SPE's) in a target group (e.g., G2) of interest to the first user and thus their identifications plus the identifications of the topic nodes (e.g., Tnxy1, Tnxy2, etc.) to which they have been invited are optionally fed to the heat parameters formulating module 160 for possible use as a substitute for, or an augmentation of the 152o (TSR) and 154o (relevant SPE's) signals input into module 160.
For sake of completeness, description of the top row of modules in
Such adaptive changes in topic space, including creation of new topic nodes and ever changing population concentrations (clusterings, see
In other words, once a history of recent changes to topic space or other space population densities (e.g., clusterings), ebbs and flows is recorded (e.g., periodic snapshots of change reporting signals 155o are recorded), a next module 157 of the top row in
Once again, although
In a next step in the formation of a heat score in
Still referring to
Using its various inputs, the formulating module 160 will instruct a downstream engine (e.g., 170, 170A2, 170A3 etc.) how to next generate various kinds ‘heat’ measurement values (output by units 177, 178, 179 of engine 170 for example). The various kinds ‘heat’ measurement values are generated in correspondingly instantiated, heat formulating engines where engine 170 is representative of the others. The illustrated engine 170 cross-correlates received group parameters (G2 parameters) with attributes of the selected topic space region (e.g., TSR Tnxy, where node Tnxy here can be also named as node A1). For every tracked social entity group (e.g., G2) and every pre-identified topic space region (TSR) of each header entity (e.g., 101a equals Me and pre-identified TSR equals my number 2 of my top N now topics) there is instantiated, a corresponding heat formulating engine like 170. Blocks 170A2, 170A3, etc. represent other instantiated heat formulating engines like 170 directed to other topic space regions (e.g., where the pre-identified TSR equals my number 3, 4, 5, . . . of my top N now topics). Each instantiated heat formulating engine (e.g., 170, 170A2, 170A3, etc.) receives respectively pre-picked parameters 161, etc. from module 160, where as mentioned, the heat parameters formulating module 160 picks the parameters and their corresponding weights. The to-be-picked parameters (171, 172, etc.) and their respective weights (wt.0, wt.1, wt.2, wt.3, etc.) may be recorded in a generalized topic region lookup table (LUT, not shown) which module 160 automatically consults with when providing a corresponding, heat formulating engine (e.g., 170, 170A2, 170A3, etc.) with its respective parameters and weights.
It is to be understood at this juncture that “group” heat is different from individual heat. Because a group is a “social group”, it is subject to group dynamics rather than to just individual dynamics. Since each tracked group has its group dynamics (e.g., G2's dynamics) being cross-correlated against a selected TSR and its dynamics (e.g., the dynamics of the TSR identified as Tnxy), the social aspects of the group structure are important attributes in determining “group” heat. More specifically, often it is desirable to credit as a heat-increasing parameter, the fact that there are more relevant people (e.g., members of G2) participating within chat rooms etc. of this TSR then normally is the case for this TSR (e.g., the TSR identified as Tnxy). Accordingly, a first illustrated, but not limiting, computation that can be performed in engine 170 is that of determining a ratio of the current number of G2 members present (participating) in corresponding TSR Tnxy (e.g., Tn01, Tn01 and Tn11) in a recent duration versus the number of G2 members that are normally there as a baseline that has been pre-obtained over a predetermined and pro-rated baseline period (e.g., the last 30 minutes). This normalized first factor 171 can be fed as a first weighted signal 171o (fully weighted, or partially weighted) into summation unit 175 where the weighting factor wt.1 enters one input of multiplier 171x and first factor 171 enters the other. On the other hand, in some situations it may be desirable to not normalize relative to a baseline. In that case, a baseline weighting factor, wt.0 is set to zero for example in the denominator of the ratio shown for forming the first input parameter signal 171 of engine 170. In yet other situations it may be desirable to operate in a partially normalized and partially not normalized mode wherein the baseline weighting factor, wt.0 is set to a value that causes the product, (wt.0)*(Baseline) to be relatively close to a predetermined constant (e.g., 1) in the denominator. Thus the ratio that forms signal 171 is partially normalized by the baseline value but not completely so normalized. A variation on theme in forming input signal 171 (there can be many variations) is to first pre-weight the relevant friends count according to the reputation or other influence factor of each present (participating) member of the G2 group. In other words, rather than doing a simple body count, input factor 171 can be an optionally partially/fully normalized reputation mass count, where mass here means the relative influence attributed to each present member. A normal member may have a relative mass of 1.0 while a more influential or more respected or more highly credentialed member may have a weight of 1.25 or more (for example).
Yet another possibility (not shown due to space limitations in
As further seen in
Yet another factor that can be applied to summation unit 175 is the optionally normalized duration of focus by group G2 members on the topic nodes of the subject TSR (e.g., on subregion Tnxy1 for example) relative for example, to a baseline duration as summed with a predetermined constant (e.g., +1). In
The output signal 176 produced by summation unit 175 of engine 170 can therefore represent a relative amount of so-called ‘heat’ energy (attention giving energy) that has been recently cast over a predefined time duration by STAN users on the subject topic space region (e.g., TSR Tnxy1) by currently online members of the ‘insider’ G2 target group (as well as optionally by some outside strangers) and which heat energy has not yet faded away (e.g., in a black body radiating style similar to how black bodies of physics radiate their energies off into space) where this ‘heat’ energy value signal 176 is repeatedly recomputed for corresponding predetermined durations of time. The absolute lengths of these predetermined durations of time may vary depending on objective. In some cases it may be desirable to discount (filter out) what a group (e.g., G2) has been focusing-upon shortly after a major news event breaks out (e.g., an earthquake, a political upheaval) and causes the group (e.g., G2) to divert its focus momentarily to a new topic area (e.g., earthquake preparedness) whereas otherwise the group was focusing-upon a different subregion of topic space. In other words, it may be desirable to not or count or to discount what the group (e.g., G2) has been focusing-upon in the last say 5 minutes to two hours after a major news story unfolds and to count or more heavily weigh the heats cast on topic nodes in more normal time durations and/or longer durations (e.g., weeks, months) that are not tainted by a fad of the moment. On the other hand, in other situations it may be desirable to detect when the group (e.g., G2) has been diverted into focusing-upon a topic related to a fad of the moment and thereafter the group (e.g., G2) continues to remain fixated on the new topic rather than reverting back to the topic space subregion (TSR) that was earlier their region of prolonged focus. This may indicate a major shift in focus by the tracked group (e.g., G2).
Although ‘heated’ and maintained focus by a given group (e.g., G2) over a predetermined time duration and on a given subregion (TSR) of topic space is one kind of ‘heat’ that can be of interest to a given STAN user (e.g., user 131′), it is also within the contemplation of the present disclosure that the given STAN user (e.g., user 131′) may be interested in seeing (and having the system 410 automatically calculate for him) heats cast by his followed groups (e.g., G2) and/or his followed other social entities (e.g., influential individuals) on subregions or nodes of other kinds of Cognitive Attention Receiving Spaces such as keywords space, or URL space or music space or other such spaces as shall be more detailed when
It may be desirable to filter the parameters input into a given heat-calculating engine such as 170 of
In general, the reporting of negative emotional reactions by users to specific invitations, topics, sub-portions of content and so forth is taken as a negative vote by the user with regard to the corresponding data object. However, there is a special subclass where negative emotional reaction (e.g., CFi's or CVi's indicating disgust for example) cannot be automatically taken as indicative of the user rejecting the system-presented invitations or topics, or the user rejecting the sub-portions of content that he/she was focusing-upon. This occurs when the subject matter of the corresponding invitation or content is a revolting kind and the normal reaction of most people is disgust or another such negative emotional reaction. In accordance with one aspect of the present disclosure, invitations or content sub-portions that are expected to generate negative emotional reactions are automatically identified and tagged as such. And then when an expected, negative emotional reaction is reported back by the CFi's, CVi's of respective users, such negative emotional reactions are automatically discounted as not meaning that the user rejects the invitation and/or sub-portion of content, but rather that the user is nonetheless interested in the same even though demonstrating through telemetry detected emotion that the subject matter is repulsive to the respective user. With that said, it also within the contemplation of the present disclosure to allow sensitive users (e.g., those who are devout followers of religion X for example, as explained above) to self-designate themselves as users who are rejecting all invitations to which they exhibit negative emotional reaction and the system honors them as being exceptions to its general rule about the reverse emotional logic concerning normally revolting subject matter.
Still referring to
As mentioned above, heat measurement values may come in many different flavors or kinds including normalized, fully or partially not normalized, filtered or not according to above-threshold duration, above-threshold emotion levels, time, location, context, etc. Since the ‘heat’ energy value 176 produced by the weighted parameters summing unit 175 may fluctuate substantially over longer periods of time or smooth out over longer periods of time, it may be desirable to process the ‘heat’ energy value signals 176 with integrating and/or differentiating filter mechanisms. For example, it may be desirable to compute an averaged ‘heat’ energy value over a yet longer duration, T1 (longer than the relatively short time durations in which respective ‘heat’ energy value signals 176 are generated). The more averaged output signal is referred to here as Havg(T1). This Havg(T1) signal may be obtained by simply summing the user-cast “heat energies” during time T1 for each heat-casting member among all the members of group G2 who are ‘touching’ the subject topic node directly (or indirectly by means of a halo) and then dividing this sum by the duration length, T1. Alternatively, when such is possible, the Havg(T1) output signal may be obtained by regression fitting of sample points represented by the contributions of touching G2 members over time. The plot of over-time contributions is fitted to by a variably adjusting and thus conformably fitting but smooth and continuous over-time function. Then the area under the fitted smooth curve is determined by integrating over duration T1 to determine the total heat energy in period T1. In one embodiment the continuous fitting function is normalized into the form F(Hj(T1))/T1, where j spans the number of touching members of group Gk (where here k is a natural number such as 1, 2, etc.) and Hj(T1) (where here j is a natural number such as 1, 2, etc.) represents their respective heats cast over time window T1. F( ) may be a Fourier Transform.
In another embodiment, another appropriate smoothing function such as that of a running average filter unit 177 whose window duration T1 is predefined, is used and a representation of current average heat intensity may be had in this way. On the other hand, aside from computing average heat, it may be desirable to pinpoint topic space regions (TSR's) and/or social groups (e.g., G2) which are showing an unusual velocity of change in their heat, where the term velocity is used here to indicate either a significant increase or decrease in the heat energy function being considered relative to time. In the case of the continuous representation of this averaged heat energy this may be obtained by the first derivative with respect to time t, more specifically V=d {F(Hj(T1))/T1}/dt; and for the discrete representation it may be obtained by taking the difference of Havg(T1) at two different appropriate times and dividing by the time interval being considered.
Likewise, acceleration in corresponding ‘heat’ energy value 176 may be of interest. In one embodiment, production of an acceleration indicating signal may be carried out by double differentiating unit 178. (In this regard, unit 177 smooths the possibly discontinuous signal 176 and then unit 178 computes the acceleration of the smoothed and thus continuous output of unit 177.) In the continuous function fitting case, the acceleration may be made available by obtaining the second derivative of the smooth curve versus time that has been fitted to the sample points. If the discrete representation of sample points is instead used, the collective heat may be computed at two different time points and the difference of these heats divided by the time interval between them would indicate heat velocity for that time interval. Repeating for a next time interval would then give the heat velocity at that next adjacent time interval and production of a difference signal representing the difference between these two velocities divided by the sum of the time intervals would give an average acceleration value for the respective two time intervals.
It may also be desirable to keep an eye on the range of ‘heat’ energy values 176 over a predefined period of time and the MIN/MAX unit 179 may in this case use the same running time window T1 as used by unit 177 but instead output a bar graph or other indicator of the minimum to maximum ‘heat’ values seen over the relevant time window. The MIN/MAX unit 179 is periodically reset, for example at the start of each new running time window T1.
Although the description above has focused-upon “heat” as cast by a social group on one or more topic nodes, it is within the contemplation of the present disclosure to alternatively or additionally repeatedly compute with machine-implemented means, different kinds of “heat” as cast by a social group on one or more nodes or subregions of other kinds of data-objects organizing spaces, including but not limited to, keywords space, URL space and so on.
Block 180 of
In some instances, all this complex ‘heat’ tracking information may be more than what a given user of the STAN_3 system 410 wants. The user may instead wish to simply be informed when the tracked ‘heat’ information crosses above predefined threshold values; in which case the system 410 automatically throws up a HOT! flag like 115g in
Referring to
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Referring to
More specifically, and referring to the middle of
Inside the primary Community Topic Board Frame 185 there may be displayed one or more subsidiary boards (e.g., 186, 187, . . . ). Referring to the subsidiary board 186 which is shown displayed in the forefront, it has a corresponding subsidiary heading portion 186a indicating that the illustrated and ranked items are mostly people-picked and people-ranked ones (as opposed to being picked and ranked only or mostly by a computer program). The subsidiary heading portion 186a may have an information expansion tool (not shown, but like 185a+) attached to it. In the case of the back-positioned other exemplary board 187, the rankings and choosing of what items to post there were generated primarily by a computer system (410) rather than by real life people. In accordance with one aspect of an embodiment, users may look at the back subsidiary board 187 that was populated by mostly computer action and such people may then vote and/or comment on the items (187c) posted on the back subsidiary board 187 to a sufficient degree such that the item is automatically moved as a result of voting/commenting from the back subsidiary board 187 to column 186c of the forefront board 186. The knowledge base rules used for determining if and when to promote a on-backboard item (187c) to a forefront board 186 and where to place it (the on-board item) within the rankings of the forefront board may vary according to region of topic space, the kinds of users who are looking at the community board and so on. In one embodiment, for example, the automated determination deals with promotion of an on-backboard item (187c, e.g., an informational contribution made by a user of the STAN_3 system while engaged with, and to a chat or other forum participation session maintained by the system, where the chat or other forum participation session is pointed to by at least one of a point, node or subregion of a system-maintained Cognitive Attention Receiving Space such as topic space) where the promotion of the on-backboard item (187c) causes the item to instead become a forefront on-board item (e.g., 186c1) and the machine-implemented determination to promote is based at least on one or more factors selected from the factors group that includes: (1) number of net positive votes representing different people who voted to promote the on-board item; (2) reputations and/or credentials of people who voted to promote the on-board item versus that of those who voted against its promotion; (3) rapidity with which people voted to promote (or demote) the on-board item (e.g., number of net positive votes within a predetermined unit of time exceeds a threshold), (4) emotions relayed via CFi's or CVi's indicating how strongly the voters felt about the on-board item and whether the emotions were intensifying with time, etc.
Each subsidiary board 186, 187, etc. (only two shown) has a respective ranking column (e.g., 186b) for ranking the user contributions represented by arrayed items contained therein and a corresponding expansion tool (e.g., 186b+) for viewing and/or altering the method that has been pre-used by the system 410 for ranking the rank-wise shown items (e.g., comments, tweets or otherwise whole or abbreviated snippets of user-originated contributions of information). As in the case of promoting a posted item from backboard 187 to forefront board 186, the displayed rankings (186b) may be based on popularity of the on-board item (e.g., number of net positive votes exceeding a predetermined threshold crossing), on emotions running high and higher in a short time, and so on. When a user activates the ranking column expansion tool (e.g., 186b+), the user is automatically presented with an explanation of the currently displayed ranking system and with an option to ask for displaying of a differently sorted list based on a correspondingly different ranking system (e.g., show items ranked according to a ‘heat’ formula rather than according to raw number of net positive votes).
For the case of exemplary comment snippet 186c1 (the top or #1 ranked one in items containing column 186c), if the viewing user activates its respective expansion tool 186c1+, then the user is automatically presented with further information (not shown) such as, (1) who (which social entity) originated the comment or other user contribution 186c1; (2) a more complete copy of the originated comment/user contribution (where the snippet may be an abstracted/abbreviated version of the original full comment/contribution), (3) information about when the shown item (e.g., comment, tweet, abstracted comment, movie preview or other user contribution, etc.) in its whole was originated; (4) information about where the shown item (186c1) in its original whole form was originated and/or information about where this location of origination can be found, for example: (4a) an identification of an online region (e.g., ID of chat room or other TCONE, ID of its topic node, ID of discussion group and/or ID of external platform if it is an out-of-STAN playground) and/or this ‘more’ information can be (4b) an identification of a real life (ReL) location, in context appropriate form (e.g., GPS coordinates and/or name of meeting room, etc.) of where the shown item (186c1) was originated; (5) information about the reputation, credentials, etc. of the originator of the shown item (186c1) in its original whole form; (6) information about the reputation, credentials, etc. of the TCONE social entities whose votes indicated that the shown item (186c1) deserves promotion up to the forefront Community Topic Board (e.g., 186) either from a backboard 187 or from a TCONE (not shown); (7) information about the reputation, credentials, etc. of the TCONE social entities whose votes indicated that the shown item (186c1) deserves to be downgraded rather than up-ranked and/or promoted; and so on.
As shown in the voting/commenting options column 186d of
Although not shown in
In one embodiment, column 186d displays a user selected set of options. By clicking or tapping or otherwise activating an expansion tool (e.g., starburst+) associated with column 186d (shown in the magnified view under 186d), the user can modify the number of options displayed for each row and within column 186d to, for example, show how many My-2-cents comments or other My-2-cents user contributions have already been posted (where this displaying of number of comments may be in addition to or as an alternative to showing number of comments in each corresponding posted item (e.g., 186c1)). As alternatives or additions to text-based posts on the community board, posts (user contributions) can include embedded multimedia content, attached sound files, attached voice files, embedded or attached pictures, slide shows, database records, tables, movies, songs, whiteboards, simple interactive puzzles, maps, quizzes, etc.
The My-2-cents comments/contributions have already been posted can define one so-called, micro-blog directed at the correspondingly posted item (e.g., 186c1). However, there can be additional tweets, blogs, chats or other forum participation sessions directed at the correspondingly posted item (e.g., 186c1) and one of the further options (shown in the magnified view under 186d) causes a pop up window to automatically open up with links and/or data about those other or additional forum participation sessions (or further content providing resources) that are directed at the correspondingly posted item (e.g., 186c1). The STAN user can click or tap or otherwise activate any one or more of the links in the popped up window to thereby view (or otherwise perceive) the presentations made in those other streams or sessions if so interested. Alternatively or additionally the user may drag-and-drop the popped open links to a My-Cloud-Savings Bank tool 113c1h′″ (to be further described elsewhere) and investigate them at a later time. In one embodiment, the user may drag-and-drop any of the displayed objects on his tablet computer 100 that can be opened into the My-Cloud-Savings Bank tool 113c1h′″ for later review thereof. In one embodiment, the user may formulate automatic saving rules that cause the STAN_3 system to automatically save certain items without manual participation by the user. More specifically, one of the user-formulated (or user-activated among system provided templates) automatic saving rules may read as follows: “IF there are discussions/user contributions in a high ranked TSR of mine with heat values which are more than 20% higher than the normal ones AND I am not detected as paying attention to on-topic invitations or the like for the same (e.g., because I am away from my desk or have something else displayed), THEN automatically record the discussion/user-contribution for me to look at later”. In this way, if the user steps away from his data processing device, or turns it off, or is paying attention to something else or not paying attention to anything and a chat or other forum participation session comes up having user contributions that are probably of high-attention receiving value to the user, the STAN_3 system automatically records and saves the session in the user's My-Cloud-Savings Bank with an appropriate marker (e.g., tag, bookmark, etc.) indicating its importance (e.g., its extraordinary heat score and/or identifications of the most worthy of attention user contributions) so that the user can notice it/them later and have it/them presented to him/her at a later time if so desired.
Expansion tool 186b+ (e.g., a starburst+) in
It should be recalled that window 185 (e.g., community board for a given topic space subregion (TSR) favored by a given social entity, i.e. SE1) unfurled (where the unfurling was highlighted by translucent unfurling beam 115a7) in response to the user picking a ‘show community board’ option associated with topic invitation(s) item 102a2″. Although not shown, it is to be understood that the user may close or minimize that window 185 as desired and may pop open an associated other community board of another invitation (e.g., 102n′).
Additionally, in one embodiment, each displayed set of front and back community boards (e.g., 185) may include a ‘You are Here’ map 185b which indicates where the corresponding community board is rooted in STAN_3 topic space. (More generically, as will be explained below, a community board may be directed to a spatial or hierarchical subregion of any system-maintained Cognitive Attention Receiving Space (CARS) and the ‘You are Here’ map may show in spatial and/or hierarchical terms where the subregion is relative to surrounding subregions of the same CARS.) Referring briefly to
It is to be understood that topic space is merely a convenient and perhaps more easily grasped example of the general notion of similarly treated Cognitive Attention Receiving Spaces (CARS's) where each such CARS has respective points, nodes or subregions organized therein according to at least one of a hierarchical and spatial organization and where the respective points, nodes or subregions of that CARS (e.g., keyword space, URL space, social dynamics space and so on) may logically link to chat or other forum participation sessions and where respective users make user contributions in the forms of comments, tweets, emails, zip files and so on, and where user contributions in isolated ones of the sessions may be voted up (promoted, as “best of” examples) into a related community board for the respective node, or parent node, or space subregion so that a larger population of users who are tethered to the local subregion of the Cognitive Attention Receiving Space (CARS) by virtue of participation in an associated chat or other forum participation session or otherwise can see user contributions made in plural such participation sessions if the user contributions are promoted into the local community board or further up into a higher level community board. In other words, a given user of the STAN_3 system may be focusing-upon a clustered set of keywords (spatially clustered in a keywords expressions space) rather than on a specific topic node and there may be other system users also then focusing-upon the same clustered set of keywords or on keywords that are close by in a system-maintained keyword space (KwS—see 370 of
Returning again to
Referring to the process flow chart of
There are two process initiation threads in
Assuming that an instance of step 184.0 has been instantiated by the STAN_3 system 410 when bandwidth so allows, the process-implementing computer will jump to step 184.2 for a sampled TCONE to see if there are any items present there for possible promotion to a next higher level. However, before that happens, participants in the local TCONE (e.g., chat room, micro-blog, etc.) are chatting or otherwise exchanging informational notes with one another (which is why the online activity is referred to as a TCONE, or topic center-owned notes exchange session). One of the participants makes a remark (a comment, a local posting, a tweet, etc.) and/or provides a link (e.g., a URL) to topic relevant other content as that user's contribution to the local exchange. Other members of the same TCONE decide that the locally originated contribution is worthy of praise and promotion. So they give it a thumbs-up or other such positive vote (e.g., “Like”, “+1”, etc.). The voting may be explicit wherein the other members have to activate an “I Like This” button (not shown) or equivalent. In one embodiment, the voting may be implicit in that the STAN_3 system 410 collects CVi's from the TCONE members as they focus on the one item and the system 410 interprets the same as implicit positive or negative votes about that item (based on user PEEP files). In one embodiment, the implicit or explicit spectrum of voting and/or otherwise applying virtual object activating energies and/or applying attention giving energies includes various ones of combinations of facial contortions involving the tongue, the lips, the eyebrows, the nostrils for example where based on the individual's current PEEP record; pursing one's lips and raising one eyebrow may indicate one thing while doing the same with both eyebrows lifted means another and sticking ones tongue out through pursed lips means yet a different third thing. Making a kissing (puckered) lips contortion may mean the user “likes” something. Other examples of facial body language signals include: smiling, baring teeth, biting lips, puffing up ones cheeks; blushing; covering mouth with hand; and/or other facial body language cues. When votes are collected for evaluating an originator's remark for further promotion (or demotion), the originator's votes are not counted. It has to be the non-originating (non-contributing to that contribution) other members who decide so that there is less gaming of the system. Otherwise, there may be rampant self-promotion. In one embodiment, friends and family members of the contributing user are also blocked from voting. When the non-originating other members vote in step 184.1, their respective votes may be automatically enlarged in terms of score value or diminished based on the voter's reputation, current demeanor, credentials, possible bias (in favor of or against), etc. Different kinds of collective reactions to the originator's remark may be automatically generated, for example one representing just a raw popularity vote, one representing a credentials or reputations weighted vote, one representing just emotional ‘heat’ cast on the remark even if it is negative emotion just as long as it is strong emotion, and so on.
Then in step 184.2, the computer (or more specifically, an instantiated data collecting virtual agent) visits the TCONE, collects its more recent votes (older ones are typically decayed or faded with time so they get less weight and then disappear) and automatically evaluates it relative to one or more predetermined threshold crossing algorithms. One threshold crossing algorithm may look only at net, normalized popularity. More specifically, the number of negatively voting members (within a predetermined time window) is subtracted from the number of positively voting members (within same window) and that result is divided by a baseline net positive vote number. If the actual net positive vote exceeds the baseline value by a predetermined percentage, then the computer determines that a first threshold has been crossed. This alone may be sufficient for promotion of the item to a local community board. In one embodiment, other predetermined threshold crossing algorithms are also executed and a combined score is generated. The other threshold crossing algorithms may look at credentials weighted votes versus a normalizing baseline or the count versus time trending waveform of the net positive votes to see if there is an upward trend that indicates this item is becoming ‘hot’.
In one embodiment, in addition to user contributions that are submitted within the course of a chat or other forum participation session and are then explicitly or implicitly voted upon by in-session others for possible promotion into a local and/or promotion to a higher level community board, the STAN_3 system provides a tool (not shown, but can be an available expansion tool option wherever a map of a topic space subregion (TSR) is displayed or a map of another Cognitive Attention Receiving Space is displayed), that allows users who are not participants in an ongoing forum session to nonetheless submit a proposed user contribution for posting onto a community board (e.g., one disposed in topic space or one disposed in another space). In one variation, each community board has an associated one or more moderators who are automatically alerted as to the proposed user contribution (e.g., a movie file, a sound file, an associated editorial opinion, etc.) and who then vote explicitly or implicitly on posting it to their moderated community board. After that user contribution is posted onto the corresponding community board, it may be promoted to community boards higher up in the space hierarchy by reviewers of the respective community board. In an alternative or same embodiment, those users who have pre-established credentials, reputations, influence, etc. that exceed pre-specified corresponding thresholds as established for the respective community board can post their user contributions onto the board (e.g., topic board) without requiring approval from the board moderators. In this way, a recognized expert in a given field (e.g., on-topic field) can post a contribution onto the community board without having to engage in a forum session and without having to first get approval from the board moderators.
Still referring to
Still referring to step 184.4, sometimes the local TCONE votes that cause a posted item to become promoted to the local community board are cast by highly regarded Tipping Point Persons (e.g., ones having special influencing credentials). In that case, the computer may automatically decide to not only post the comment (e.g., revised snippet, abbreviated version, etc.) on the local community board but to also simultaneously post it or show a link to it on a next higher community board in the topic space hierarchy, the reason being that if such TPP persons voted so positively on the one item, it deserves accelerated (**wider**) promotion (so that it is thereby presented to a wider audience, e.g., the users associated with a parent or grandparent node, when they visit their local community board).
Several different things can happen once a comment is promoted up to one or more community boards. First, the originator of the promoted remark (or other user contribution) may optionally want to be automatically notified of the promotion (or demotion in the case where the latter happens). This is managed in step 189.5. The originator may have certain threshold crossing rules for determining when he or she will be so notified for example by email, sms, chat notify, tweet, or other such signaling techniques.
Second, the local TCONE members who voted the item up for posting on the local and/or other community board may optionally be automatically notified of the posting.
Third, there may be STAN users who have subscribed to an automated alert system of the community board that received the newly promoted item. Notification to such users is managed in step 189.4. The respective subscribers may have corresponding threshold crossing rules for determining if and when (or even where) they will be so notified. The corresponding alerts are sent out in step 189.3 based on the then active alerting rules. An example of such an alerting rules can be: “IF two or more of my influential followed others voted positively on the community board item THEN send me a notification alert pinpointing its place of posting and identifying the followed influencers who voted for promoting it ELSE IF four or more members of my custom-created Group5 social entity voted positively on the community board item THEN send me a notification alert pinpointing its time and place of posting and identifying the Group5 members who voted positively for promoting it as well as nay Group5 members who voted against the promotion/END IFs”.
Once a comment item (e.g., 186c1 of
Second, the local TCONE members who voted the item up for posting on the local community board may continue to think highly of that promoted comment (e.g., 186c1) and they too may alert their friends, family and familiars via email, tweeting, etc., as to the posting. Additionally, they may record their own custom tailored posting rules for if, when and where to post the news.
Third, now that the posting is on a community board shared by all TCONE's of the corresponding topic node (topic center), members in the various TCONE's besides the one where the comment originated may choose to look at the posting, vote on it (positively or negatively), or comment further on it (via My 2 Cents). The new round of voting is depicted as taking place in step 184.5. The members of the other TCONE's may not like it as much or may like the posting more and thus it can move up or down in ranking depending on the collective votes of all the voters who are allowed to vote on it. For some topic nodes, only admitted participants in the TCONE's of that topic center are allowed to vote on items (e.g., 186c1) posted on their local community board. Thus evaluation of the items is not contaminated by interloping outsiders (e.g., those who are not trusted, pre-qualified, etc., to cast such votes). For other topic nodes, the governing members of such nodes may have voted to open up voting to outsiders as well as topic node members (those who are members of TCONE's that are primarily “owned” by the topic center).
In step 184.6, the computer may detect that the on-board posting (e.g., 186c1) has been voted into a higher ranking or lower ranking within the local community board or promoted (or demoted) to the community board of a next higher or lower topic node in the topic space hierarchy. At this point, step 184.6 substantially melds with step 188.6. For both of steps 184.6 and 188.6, if a posted item is persistently voted down or ignored over a predetermined length of time, a garbage collector virtual agent 184.7 comes around to remove the no-longer relevant comment from the bottommost rankings of the board.
Referring briefly again to the topic space mapping mechanism 413′ in
In one embodiment, when a given topic node changes location in the hierarchy of topic space or relocates spatially in topic space, or merges with another topic node, or cleaves into plural nodes, the system automatically invites the users of that changed/new topic node to review and vote on cross-associating links between that changed/new topic node and points, nodes or subregions of other Cognitive Attention Receiving Spaces (e.g., keyword space, URL space, meta-tag space and so on). The reason is that with change of positioning in topic space, the node's cross-links to points in other spaces may no longer be optimal or may no longer be valid. More specifically, if a given topic node was originally stored in the system database as: (1)//Root/ . . . /Arts & Crafts/Knitting/Supplies/[knitting needles18] and its users voted to move it so it instead becomes: (2)//Root/ . . . /Engineering/plastics/manufacturing/[knitting needles28], then some of the keywords, URL's, etc. that related to the arts-and-crafts aspects of that topic node may no longer be valid under the new Engineering/plastics theme of the moved node. Accordingly, the current users of the new, changed or merged topic node may wish to review the sorted lists of most relevant keywords, URL's, etc. that are cross-associated with the changed/moved node and they may wish to vote on editing those lists. The automated invitation to review and modify helps to increase the likelihood that such a process takes place.
Although the above discussion is focused-upon movement and/or deletion of topic nodes in/out of topic space and the consequences that such has on the cross-associating links of the moved, merged or otherwise altered topic node to points, nodes or subregions of other Cognitive Attention Receiving Spaces (e.g., keyword space, URL space, etc.), it is also within the contemplation of the present disclosure to apply the same in a vice versa way. In other words and for example, if a URL(s) representing node moves, merges or is otherwise altered in the system-maintained keywords cross-associating space (see for example 390 of
People generally do not want to look at empty community boards because there is nothing there to study, vote on or further comment on (my 2 cents). With that in mind, even if no members of any TCONE's of a newly born topic node vote to promote one of their local comments per process flow 184.0, 184.1, 184.2 of
Some of the automated notifications that happen with people promoted comments as described above also happen with computer-promoted comments. For example, after step 188.4, the originator of the comment may be optionally and automatically notified in step 189.5 for example if the promotion of his/her user contribution to the subsidiary community board (187) meets custom alert rules recorded by that originator. Then in step 189.6, the originator is given the option to revise the computer generated snippet, abbreviation etc. and then to run the revision past the community board conformance rules. If the revised comment (or other, revised user contribution) passes, then in step 189.7 it is submitted to non-originating others for revote on the revision. In this way, the originator does not get to do his own self promotion (or demotion) and instead needs the sentiment of the crowd to get the comment (or other, revised user contribution) further promoted (or demoted if the others do not like it).
In one embodiment, items posted to a main and/or subsidiary community board are automatically supplemented with a system-generated, descriptive title, a posting time and a permanent hyperlink thereto so that others can conveniently reference the posted community board item (e.g., 186c1). Additionally, the on-board items of a given community board may be hyperlinked to each other and/or to on-board items of other community boards so as to thereby link threads of ideas (or user contributions) that users of the board may wish to step through. Moreover, in an embodiment, associated keywords from the originator's topic node are automatically included to help others better grasp what the on-board contribution item is about. Unlike the individualized keywords that a contribution originator might pick, the top rated keywords of the corresponding topic node are keywords that the collective community of node users picked as being perhaps best descriptive of what the node is about and therefore also descriptive of what a user contribution made through that node is about.
In one embodiment, when a user contribution is promoted into or up along one board or up through a hierarchical chain of such community boards, the originator's credential, reputation and/or such profile attributes are automatically incremented to a degree commensurate with the positive acclaim that his/her contribution receives from those rating that contribution. The degree of positive acclaim may be a function of the number others rating the contribution and/or the credentials and reputations of those rating the contribution. While positively received contributions can result in automatic increase of the originator's credential, reputation and/or such profile attributes (there could be a specific, community board acclaims rating), the converse is not implemented in one embodiment. In other words, if the user's submitted contributions to community boards are often poorly received (not given high acclaim), the originator's credential, reputation and/or such profile attributes are not automatically downgraded for such poor reception on community boards. One reason is that fear of negative consequences may dissuade innovative thinkers from submitting their contributions. Another reason is that poor reception on a given one or more community boards does not necessarily mean the contribution was a bad one. It could be that the originator of the contribution is ahead of his or her times and the other users of the board are not yet ready to receive what, to them, appears to be a radical and ridicule-worthy idea. By way of example, one need not look further than the story of Chester Carlson and his invention of Xerography to realize that good ideas are sometimes met with widespread skepticism.
Referring next to
Under the first column heading 113b1h in
Additionally, the topmost functional card of highest stack 113c1 (highest in column 113b1) may show one or more pictures (real or iconic) of faces 113c1f of other users who have been invited into, or are already participating in the offered chat or other forum participation opportunity. While the displaying of such pictures 113c1f may not be spelled out in every GUI example given herein, it is to be understood that such representation of each user or group of users may be routinely had by means of adjacent real or iconic pictures, as for example, with each user comment item (e.g., 186c1) shown in
Additionally, the topmost functional card of highest stack 113c1 includes an instant join tool 113c1g (e.g., “G” for Go or a circled triangle from VCR days indicating this is the activation means for causing the chat session to “Play”). If and when the user clicks or taps or otherwise activates this instant join tool 113c1g (e.g., by clicking or tapping on the circle enclosed forward play arrow), the screen real estate (111″) is substantially taken over by the corresponding chat room interface function (which can vary from chat room to chat room and/or from platform to platform) and the user is joined into the corresponding chat room as either an active member or at least as a lurking observer. A back arrow function tool (not shown) is generally included within the screen real estate (111″) for allowing the user to quit the picked chat or other forum participation opportunity and try something else. (In one embodiment, a relatively short time, e.g., less than 30 seconds; between joining and quitting is interpreted by the STAN_3 system 410 as constituting a negative vote (a.k.a. CVi) directed to what is inside the joined and quickly quit forum. In one embodiment, the cloud includes a repeated, client pinging function for automatically determining whether the client machine is still connected to the network or not. If a user disconnects from a chat or other forum participation session at the same time that his client machine disconnects from the network; say due to a communications problem, that disconnects from the chat (or other) is not counted as a negative vote.) Although the description above assumes that the user is seeking one good chat or other forum participation opportunity to join into, it is further within the contemplation of the present disclosure that user can seek participation in multiple chats or other forums of his/her liking all at the same time.
Although the description thus far has been focusing-upon a user casting his/her attention giving energies to points, nodes or subregions of the system-maintained topic space (e.g., My Top 5 Now Topics 113b1h), it is within the contemplation of the present disclosure to alternatively or additionally provide the user with chat or other forum participation opportunities that revolve about points, nodes or subregions of other Cognitive Attention Receiving Spaces that are maintained by the system such as for example the system's keywords cross-associating space, the system's URLs cross-associating space, the meta-tags cross-associating space, a music space, an emotional states space, and so on (this list including social dynamics space where nodes thereof may specify chat co-compatibility types). It is not always true that people have a specific “topic” in mind or are casting their attention giving energies on a specific “topic” or subregion of topic space. They could instead be focusing-upon some shared stream of music or some other form of shareable cognition (e.g., shared experiences including for example reading abstract poetry or looking at an abstract painting (Picasso, Matisse, etc.) and musing about what emotional states the readings/viewings give rise to for them. The STAN_3 system maintains different ones of Cognitive Attention Receiving Spaces and allows isolated users to gather around, relevant-to-them points, nodes or subregions of such spaces and to then join in online or real life meetings based on the online clustering of the users (of their attention giving energies) about the respective points, nodes or subregions of the system-maintained Cognitive Attention Receiving Spaces. Accordingly, heading 113b1h could have alternatively read as “My Top 5 Now Movies” or “ . . . 5 Books” or “ . . . 3 Musical Pieces” or “ . . . 7 Keywords of the Day” or “ . . . 8 URLs of the Week” and so on. As is true in many other instance herein, topic space is used as a convenient and perhaps more easily graspable example, but is use does not exclude the same concepts being applicable to the other system-maintained Cognitive Attention Receiving Spaces.
Along the bottom right corner of each card stack there is provided a shuffle-to-back tool (e.g., 113cn). If the user does not like what he sees at the top of the stack (e.g., 113c), he can click or tap or gesture for a scrolling-down into, or otherwise activate the “next” or shuffle-to-back tool 113cn and thus view what next functional card lies underneath in the same deck. (In one embodiment, a relatively short time, e.g., less than 30 seconds; between being originally shown the top stack of cards 113c and requesting a shuffle-to-back operation (113cn) is interpreted by the STAN_3 system 410 as constituting a negative vote (a.k.a. CVi) directed to what the system 410 chose to present as the topmost card 113c1. This information is used to retune how the system automatically decides what the user's current context and/or mood is, what his intended top 5 topics are and what his chat room preferences are under current surrounding conditions. Of course this is not necessarily accomplished by recording a single negative CVi and more often it is a long sequence of positive and negative CVi's that are used to train the system 410 into better predicting what the given user would like to see as the number one choice (first shown top card 113c1) on the highest shown stack 113c of the primary column 113b1.)
More succinctly, if the system 410 is well tuned to the user's current mood, etc. (because the system has access to the user's recent activities history, the user's calendaring tools, the user's PHAFUEL records (habits and routines) and the user's PEEP profiles), the user is often automatically taken by Layer-vator 113″ to the correct floor 113b″ merely by popping open his clam shell style smart phone (—as an example—or more generally by clicking or tapping or otherwise activating an awaken option button, not shown, of his mobile device 100″) and at that metaphorical building floor, the user sees a set of options such as shown in
The next lower functional card stack 113d in
The next lower block 113e provides the user with further options “(more . . . )” in case the user wants to engage in different other forum types (e.g., tweet streams, email exchanges (i.e. list serves) or other) as suites his mood and within the column heading domain, namely, Show chat or other forum participation opportunities for: My now top 5 topics (113b1h). In one embodiment, the different other forum types (More . . . 113e) may include voice-only exchanges for a case where the user is (or soon will be) driving a vehicle and cannot use visual-based forum formats. Other possibilities include, but not limited to, live video conferences, formation of near field telephony or other chat networks with geographically nearby and like-minded other STAN users and so on. (An instant-chat now option will be described below in conjunction with
In some cases the user does not intend to chat online or otherwise participate now in the presented opportunities (e.g., those in functional cards stack 113c of
In addition to, or as an alternative to the tool 113c1h option that provides the Copy-Opp-to-(fill in this with menu chosen option) function, other option may be provided for allowing that user to pick as the send-copy-to target(s), one or more other STAN users or on-topic groups (e.g., My A1 Topic Group, shown as a dashed other option). In this way, a first user who spots interesting chat or other forum participation opportunities (e.g., in his stack 113c) that are now of particular interest to him can share the same as a user-initiated invitation (see 102j (consolidated invites) in
If the user does not want to now focus-upon his usual top 5 topics (column 113b1), he may instead click or tap or gesture for a scroll-in of, or otherwise activate an adjacent next column of options such as 2 (My Next top 5 topics) or 113b3 (Charlie's top 5 topics) or 113b4 (The top 5 topics of a group that I or the system defined and named as social entities group number B4) and so on (the more. option 113b5). Of importance, in one embodiment, the user is not limited to automatically filled (automatically updated and automatically served up) dishes like My Current Top 5 Topics or Charlie's Current Top 5 Topics. These are automated conveniences for filling up the user's slide-out tray 102 with automatically updated plates or dishes (see again the automatically served-up plate stacks 102aNow, 102b, 102c of
In shuffling through the various stacks of functional cards 113c, 113d, etc. in
In terms of more specifics, in the illustrated example of
Where such a forum governance side bar 113ds option is provided, the forum governance side bar may include one or more automatically computed and displayed metrics regarding governance attributes of that forum as already mentioned. As with other graphical user interfaces described herein, corresponding expansion tools (e.g., starburst with a plus symbol (+) inside) may be included for allowing the user to learn more about the feature or access further options for the feature. The expansion tool need not be an always-displayed one, but rather can be one that pops up when the user clicks or taps or otherwise activates a hot key combination (e.g., control-right mouse type button, or hot keyed tilted facial expressions—i.e. where user tilts the tablet rather than his head while making a pre-specified facial expression such as tongue out to the left and tablet camera facing the user captures that so-hot-keyed user input, or hand gestures such as those involving tilting tablet to the left or right).
Yet more specifically, if the radio-button identified governance style for the card-represented forum is a free-for-all type, one of the displayed metrics may indicate a current flame score and another may indicate a flame scores range and an average flame score for the day or for another unit of time. As those skilled in the art of social media may appreciate, a group of people within an unmoderated forum may sometimes fall into a mudslinging frenzy where they just throw verbally abusive insults at each other. This often is referred to as flaming. Some users of the STAN system may not wish to enter into a forum (e.g., chat room or blog thread) that is currently experiencing a high level of flaming or that on average or for the current day has been experiencing a high level of flaming. The displayed flame score (e.g., on a scale of 0 to 10) quickly gives the user a feel for how much flaming may be occurring within a prospective forum before the user even presses or taps the Click To Chat Now or other such entry button, and if the user does not like the indicated flame score, the user may elect to click or tap or otherwise activate the shuffle down option on the stack and thus move to a next available card or perhaps to copy it to his cellphone (tool 113c1h) for later review.
In similar vein, if the room or other forum is indicated by the checked radio button to be a dictatorially moderated one, one of the displayed metrics may indicate a current overbearance score and another may indicate an overbearance scores range and the average overbearance score for the day or for another unit of time. As those skilled in the art of social media may appreciate, solo leaders of dictatorially moderated forums may sometimes let their power get to their heads and they become overly dictatorial, perhaps just for the hour or the day as opposed to normally. Other participants in the dictatorially moderated room may cast anonymous polling responses that indicate how overbearing or not the leader is for the day hour, day, etc. The displayed overbearance score (e.g., on a scale of 0 to 10) quickly gives the shuffling-through card user a feel for how overbearing the one man rule may be considered to be within a prospective forum before the user even presses the Click To Chat Now or other such entry button, and if the user does not like the indicated overbearance score, the user may elect to click or tap or otherwise activate the shuffle down option on the stack and thus move to a next available card. In one embodiment, the dictatorial leader of the corresponding chat or other forum automatically receives reports from the system 410 indicating what overbearance scores he has been receiving and indicating how many potential entrants shuffled down past his room, perhaps because they didn't like the overbearance score.
Sometimes it is not the room leader who is an overbearance problem but rather one of the other forum participants because the latter is behaving too much like a troll or group bully. As those skilled in the art of social media may appreciate, some participants tend to hog the room's discussion (to consume a large portion of its finite exchange bandwidth) where this hogging is above and beyond what is considered polite for social interactions. The tactics used by trolls and/or bullies may vary and may sometimes be referred to as trollish or bullying or other types of similar behavior for example. In accordance with one aspect of the disclosure, other participants within the social forum may cast semi-anonymous votes which, when these scores cross a first threshold, cause an automated warning (113d2B, not fully shown) to be privately communicated to the person who is considered by others to be overly trollish or overly bullying or otherwise violating acceptable room etiquette. The warning may appear in a form somewhat similar to the illustrated dashed bubble 113dw of
When it comes to fully or hybrid-wise automatically moderated chat rooms or other so-moderated forum participation sessions, the STAN_3 system 410 provides two unique tools. One is a digressive topics rating and radar mapping tool (e.g.,
Referring to
In one embodiment, the automated method used by the STAN_3 system for determining likelihood of digressive activity by a respective one or more participants of a given chat or other forum participation session is based on the continued monitoring by the STAN_3 system of all the participants (if they have monitoring turned on and enabled for the chat room screen area and/or enable for the corresponding CARS point, node or subregion) and the continued mapping by the STAN_3 system of where in topic space and/or other Cognitive Attention Receiving Spaces, the respective users are casting significant portions of their respective attention giving energies. If a given user starts casting significant attention giving energies to a topic node that is substantially distanced in topic space from the target node of the chat (or other session) then that focus on the substantially distanced away topic node may be deemed as digressive activity. More specifically, and as will be detailed immediately below, if a given user/forum-participant (e.g., “DB”) is detected in his individualized capacity as casting attention giving energies at cognition points, nodes or subregions that are substantially spaced apart (hierarchically and/or spatially) from the cognition points, nodes or subregions that the group as a whole is determined by the STAN_3 system (a.k.a. attention modeling system) to be casting their “heats” on (see again
Yet more specifically for the illustrated example (
Functional card 193.1a is understood to have been clicked or tapped or otherwise activated here by the user of computer 100″″. A corresponding chat room transcript was then displayed and periodically updated in a current transcript frame 193.1b. The user, if he chooses, may momentarily or permanently step out of the forum (e.g., the online chat) by clicking or tapping or otherwise activating the Pause button within card 193.1a. Alternatively or additionally, such a momentary or more permanent stepping out action by the user may be determined by detection of the user moving his smartphone/tablet device relatively far away from his normal viewing distance and/or by the local eyeball tracking mechanism(s) sensing that the user's eyes are no longer looking at what used to be the active screen. When stepping away, the user may employ the Copy-Opp-to-(fill in with menu chosen option) tool 113c1h′ to save the link to the paused or stepped-away from functional card 193.1a for future reference. In the illustrated case, the default option allows for a quick drag-and-drop of card 193.1a into the user's Cloud Bank (My Cloud Bank).
Adjacent to the repeatedly updated transcript frame 193.1b is an enlarged and displayed first Digressive Topics Radar Map 113xt which is also automatically repeatedly updated, albeit not necessarily as quickly as is the transcript frame 193.1b. A minimized second such map 114xt is also displayed. It can be enlarged with use of its associated expansion tool (e.g., starburst+) to thereby display its inner contents. The second map 114xt will be explained later below. Referring still to the first map 113xt and its associated chat room 193.1a, it may be seen within the exemplary and corresponding transcript frame 193.1b that a first group of participants have begun a discussion aimed toward a current main or central topic concerning which beer vending establishment is considered the best in their local town. However, a first digresser (DA) is seen to interject what seems to be a somewhat off-topic comment about sushi. A second digresser (DB) interjects what seems to be a somewhat off-topic comment about hockey. And a third digresser (DC) interjects what seems to be a somewhat off-topic comment about local history. Then a room participant named Joe calls them out for apparently trying to take the discussion off-topic and tries to steer the discussion back to the current main or central topic of the room.
At the center area of the correspondingly displayed radar map tool 113xt, there are displayed representations of the node or nodes in STAN_3 topic space corresponding to the central theme(s) of the exemplary chat room (193.1a). In the illustrated example these nodes are shown as being hierarchically interconnected nodes although they do not have to be so displayed. The internal heading of inner circle 113x0 identifies these nodes as the current forefront topic(s). The STAN_3 system can automatically determine that these are the current forefront topic(s) of the group by computing group heat calculations for different candidate nodes using for example an algorithm such as the one depicted in
With the inner or central focus circle 113x0 displayed, a user may click or tap or otherwise activate the displayed nodes (circles on the hierarchical tree) to cause a pop-up window (not shown) to automatically emerge showing more details about that region (TSR) of STAN_3 topic space (or of another CARS if that is instead displayed). As usual with the other GUI examples given herein, a corresponding expansion tool (e.g., starburst+) is provided in conjunction with the map center 113x0 and this gives the user the options of learning more about what the displayed map center 113x0 shows and what further functions the user may deploy in conjunction with the items displayed in the map center 113x0.
Still referring to the exemplary transcript frame 193.1b of
In correspondence with the dialogs taking place in frame 193.1b, the first Digressive Topics Radar Map 113xt is repeatedly updated to display prime driver icons driving towards the center or towards peripheral side topics. More specifically, a first driver(s) icon 113d0 is displayed showing a central group or clique of participants (Joe, John and Bob) metaphorically driving the discussion towards the central area 113x0. Clicking or tapping or otherwise activating the associated expansion tool (e.g., starburst+) of driver(s) icon 113d0 provides the user with more detailed information (not shown) about the identifications of the inwardly driving participants, what their full persona names are, what “heats” they are each applying towards keeping the discussion focused on the central topic space region (indicated within map center area 113x0) and so on. (With regard to determining which participants are directing their attention giving energies to the central themes of the forum and which are focusing-upon digressive nodes or subregions, once the central focal point of the forum is determined by the STAN_3 system, the system automatically and repeatedly computes the deviance between that group focal point and the individualized focal points that it is also repeatedly determines in the background. Deviance may be quantified as number of hierarchical branches separating two nodes taken alone or as combined with a spatial distance either uni- or two dimensionally along a spatial plane or multi-dimensionally in a multi-dimensional space of higher order. Those users whose deviance values are smallest are deemed to be the ones applying their attention giving energies towards keeping the discussion focused on the central topic space region.)
Similar to the icon of first digressor 113d5, a second displayed driver icon 113d1 shows a respective one or more participants (in this case just digressor DB again) driving the discussion towards an offshoot topic, for example “hockey”. The associated topic space region (TSR) for this first offshoot topic is displayed in map area 113x1. Like the case for the central topic area 113x0, the user of the data processing device 100″″ can click, tap, or otherwise activate the nodes displayed within secondary map area 113x1 to explore more details about it (about the apparently digressive topic of “Hockey”). The user can utilize an associated expansion tool (e.g., starburst+) for help and more options. The user can click or otherwise activate an adjacent first exit door 113e1 (if it is being displayed, where such displaying does not always happen). Activating the first exit door 113e1 will take the user virtually into a first sidebar chat room 113r1. In such a case, another transcript like 193.1b automatically pops up and displays a current transcript of discussions ongoing in the first side room 113r1. In one embodiment, the first transcript 193.1b remains simultaneously displayed and repeatedly updated whenever new contributions are provided in the first chat room 193.1a. At the same time a repeatedly updated transcript (not shown) for the first side room 113r1 also appears. The user therefore feels as if he is in both rooms at the same time. He can use his mouse (and/or other user information input means, e.g., tapping/swiping on the touch sensitive screen, etc. to open a contribution submitting tool for entering text and/or other material for insertion as a contribution into either room. Accordingly, the first transcript 193.1b will not indicate that the user of data processing device 100″″ has left that room. In an alternate embodiment, when the user takes the side exit door 113e1, he is deemed to have left the first chat room (193.1a) and to have focused his attentions exclusively upon the Notes Exchange session within the side room 113r1. It should go without saying at this point that it is within the contemplation of the present disclosure to similarly apply this form of digressive topics mapping to live web conferences and other forum types (e.g., blogs, tweet stream, etc.). In the case of live web conferencing (be it combined video and audio or audio alone), an automated closed-captions feature (the uses speech to text conversion software) is employed so that vocal contributions of participants are automatically converted into a near real time wise, repeatedly and automatically updated transcript inserts generated by a closed-captions supporting module. Participants may edit the output of the closed-captions supporting module if they find it has made a mistake. In one embodiment, it takes approval by a predetermined plurality (e.g., two or more) of the conference participants before a proposed edit to the output of the closed-captions supporting module takes place and optionally, the original is also shown.
Similar to the way that the apparently digressive actions of the so-called, second digresser DB are displayed in the enlarged mapping circle 113xt as showing him driving (icon 113d1) towards a first set of off-topic nodes 113x1 and optionally towards an optionally displayed, exit door 113e1 (which optionally connects to optional side chat room 113r1), another driver(s) identifying icon 113d2 shows the first digresser DA driving towards off-topic nodes 113x2 (Sushi) and optionally towards an optionally displayed, other exit door 113e2 (which optionally connects to an optional and respective side chat room—not referenced). Yet a further driver(s) identifying icon 113d3 shows the third digresser, DC driving towards a corresponding set of off-topic nodes (history nodes—not shown) and optionally towards an optionally displayed, third exit door 113e3 (which optionally connects to an optional side chat room—denoted as Beer History) and so on. In one embodiment, the combinations of two or more of the driver(s) identifying icon 113dN (N=1, 2, 3, etc. here), the associated off-topic nodes 113xN, the associated exit door 113eN and the associated side chat room 113rN are displayed as a consolidated single icon (e.g., a car beginning to drive through partially open exit doors). It is to be understood that the examples given here of metaphorical icons such as room participants riding in a car (e.g., 113d0) towards a set of topic nodes (e.g., 113x0) and/or towards an exit door (e.g., 113e1) and/or a room beyond (e.g., 113r1) may be replaced with other suitable representations of the underlying concepts. In one embodiment, the user can employ the format picker tool 113xto to switch to other metaphorical representations more suitable to his or her tastes. The format picker tool 113xto may also provide the user with various options such as: (1) show-or-hide the central and/or peripheral destination topic nodes (e.g., 113x1); (2) show-or-hide the central and/or peripheral driver(s) identifying icons (e.g., 113d1); (3) show-or-hide the central and/or peripheral exit doors (e.g., 113e1); (4) show-or-hide the peripheral side room icons (e.g., 113r1); (5) show-or-hide the displaying of yet more peripheral main or side room icons (e.g., 114xt, 114r2); (6) show-or-hide the displaying of main and digression metric meters such as Heats meter 113H; and so on. The meaning of the yet more peripheral main or side room icons (e.g., 114xt, 114r2) will be explained shortly.
Referring next to the digression metrics Heats meter 113H of
Among the digressive topics which can be brought up by various ones of the in-room participants, is a class of topics directed towards how the room is to be governed and/or what social dynamics take place between groups of two or more of the participants. For example, recall that DB challenged Joe's apparent leadership role within transcript 193.1b. Also recall that Bob tried to smooth the social friction by using a humbling phraseology: IMHO (which, when looked up in Bob's PEEP file, is found to mean: In My Humble Opinion and is found to be indicative of Bob trying to calm down a possibly contentious social situation). These governance and dynamics types of in-room interactions may fall under a subset of topic nodes 113x5 within STAN_3 topic space that are directed to group dynamics and/or group governance issues. This aspect will be yet further explored in conjunction with
Before moving on, the question comes up regarding how the machine system 410 automatically determines who is driving towards what side topics or towards the central set of room topics. In this regard, recall that at least a significant number of the room participants are STAN users. Their CFi's and/or CVi's are being monitored (112″″) by the STAN_3 system 410 even while they are participating in the chat room or other forum. These CFi's and/or CVi's are being converted into best guess topic determinations as well as best guess emotional heat determinations and so on. More generally, the STAN_3 system is repeatedly and automatically determining for each respective member of a specified group of members (e.g., the forum participants), which if any of system-maintained points, nodes or subregions of system-maintained Cognitive Attention Receiving Spaces (CARSs) are receiving attention giving energies from the respective member, and if so to what extent (and/or to what comparative extent relative to other cast energies); and the system is using the determination of which points, nodes or subregions are receiving respective and significant individualized attention giving energies to determine which if any of the system-maintained points, nodes or subregions of the same system-maintained Cognitive Attention Receiving Spaces (CARSs) are receiving at least a majority of the group's attention giving energies and if so to what absolute and/or relative extent. The latter can be deemed to be the central area of energetic focus by the group. In one embodiment, those group members who are actively (energetically) typing, copy-and-pasting, or otherwise providing user contributions to the group exchange are weighted as contributing more heat power for defining the group's central points of focus versus users who are just reading for example (just focusing with lesser attention giving energies) on what is going on within the group exchange.
Recall also that the monitored STAN users have respective user profile records stored in the machine system 410 which are indicative of various attributes of the users such as their respective chat co-compatibility preferences, their respective domain and/or topic specific preferences, their respective personal expression propensities, their respective personal habit and routine propensities, and so on (e.g., their mood/context-based CpCCp's, DsCCp's, PEEP's, PHAFUEL's or other such profile records). Participation in a chat room is a form of context in and of itself. There are at least two kinds of participation: active listening or other such attention giving to informational inputs and active speaking or typing or texting or other such attentive informational outputs (user contributions). This aspect will be covered in more detail in conjunction with
Referring again to the example of second digresser DB and his drive towards the peripheral Hockey exit door 113e1 in
Although not shown in the transcript 193.1b of
At around the same time that DB was gathering together his group of beer and hockey fans, there was another ongoing Instan-Chat™ room (114xt) within the STAN_3 system 410 whose central theme was the local hockey team. However in that second chat room, one or more participants indicated a present desire to talk about not only hockey, but also where is the best tavern to go to in town to a have a good glass of beer after the game. If the digressive topics map 114xt of
Moreover, the other illustrated exit doors of the enlarged radar map 113xt can lead to yet other combine topic rooms. Digresser DA for example, may be a food guru who likes Japanese foods, including good quality Japanese beers and good quality sushi. When he posed his question in transcript 193.1b, he may have been trying to reach out to like minded other participants. If there are such participants, the system 410 can automatically spawn exit door 113e2 and its associated side chat room. The third digresser DC may have wanted to explain why a certain tavern near the hockey stadium has the best beer in town because they use casks made of an aged wood that has historical roots to the town. If he gather some adherents to his insights about an old forest near the town and how that interrelates to a given tavern now having the best beer, the system 410 may responsively and automatically spawn exit door 113e3 and its associated side chat room for him and his followers. Similarly, yet another automatically spawned exit door 113e4 may deal with do-it-yourself (DIY) beer techniques and so on. Spawned exit door 113e5 may deal with off topic issues such as how the first room (113xt) should be governed and/or how to manage social dynamics within the first room (113xt). Participants of the first room (113xt) who are interested in those kinds of topics may step out in to side room 113r5 to discuss the same there. In one embodiment, the system automatically displays to those users who have shown digressive focus in the direction of a respective side room (e.g., 113r5) that someone else has entered that side room or is already in that side room (e.g., 113r5). In this way, users who are interested in the digressive topic(s) of the side room can know if the side chat rooms have people in them and thus are worth entering into.
In one embodiment, the mapping system also displays topic space tethering links such as 113tst5 which show how each side room tethers as a driftable TCONE to one or more nodes in a corresponding one or more subregions (TSR's) (e.g., 113x5) of the system's topic space mecahnism (see 413′ of
Therefore it may be seen, in summing up
Referring next to
Before explaining mapping tool 113Zt however, a further GUI feature of STAN_3 chat or other forum participation sessions is described for the illustrated screen shot of
In one embodiment, clicking or tapping or otherwise activating the expansion tool (e.g., starburst+) of the Mad/sad face(s) (right side of sub-panel 193.1a′) automatically causes a multi-colored pie chart (like 113PC) to pop open where the displayed pie chart then breaks the 10% value down into more specific subtotals (e.g., 10%=6%+3%+1%). Hovering over each segment of the pie chart (like that at 113PC) causes a corresponding role icon (e.g., 113z6=troll, 113z2=primary leadership challenger) in below described tool 113Zt to light up. This tells the user more specifically, how other participants are viewing him/her and voting negatively (or positively) because of that view. Due to space constraints in
When users who are interested in the social dynamics aspects of the chat or other forum participation session pop open the social dynamics modeling tool 113Zt, they are presented with a current set of archetypes and a respective participant (or group) being cast into each of the archetype roles. They may agree or disagree with the role casting and that could become a sideroom chat of its own for those who are so inclined to discuss that subtext topic. When the social dynamics modeling tool 113Zt is used, then, even before a user (such as that of tablet computer 100.M) receives a warning like the one (113d2B) of
Additionally or alternatively, the user may elect to activate a Show-My-Face tool 193.1a3 (Your Face). A selected picture or icon dragged from a menu of faces can be representative of the user's current mood or emotional state (e.g., happy, sad, mad, etc.). In an embodiment, the STAN_3 system relies on the recently in-loaded CVi's for the given user (e.g., “Me”) and automatically makes a My Face choice (193.1a3) for the given user (e.g., “Me”). In one embodiment, if the system detects the given user focusing-upon the picked Show-My-Face picture or icon and smiling, the system interprets that facial language as indicating agreement. On the other hand, if the user frowns (and/or sticks tongue out while shaking head to indicate “No”), the system automatically tries a different pick. Interpretation of what mood or emotional state the selected picture or icon represents can be based on the currently active PEEP profile of the user. More specifically, the active PEEP profile (not shown) may include knowledge base rules such as, IF Selected_Face=Happy1 AND Context=At_Home THEN Mood=Calm, Emotion=Content ELSE IF Selected_Face=Happy2 AND Time=Lunch THEN Mood=Glad, Emotion=Happy ELSE . . . The currently active PEEP profile may interact with others of currently active user profiles (see 301p of
Just as individuals may each select a representative face icon and fore/backdrop for themselves, groups of social entities may vote on how to represent themselves with an iconic group portrait or the like. This may appear on the user's computer 100.M as a Your Group's Face image (not shown) similar to the way the Your Face image 193.1a3 is displayed. Additionally, groups may express positive and/or negative votes as against each other. More specifically, if the Your Face image 193.1a3 was replaced by a Your Group's Face image (not shown), the positive and/or negative percentages in subpanel 193.1a2 may be directed to the persona of the Your Group's Face rather than to the persona of the Your Face image 193.1a3. In one embodiment, the system generates a rotatable 3D amalgamation of all the currently-chosen facial expressions of each of the active persons in the group and this amalgamation is rotated as if it were one head that represents all the more significant emotional states within the group.
Tool 113Zt includes a theory picking sub-tool 113zto. In regard to the picked theory, there is no complete consensus as to what theories and types of room governance schemes and/or explanations of social dynamics are best. The illustrated embodiment allows the governing entities of each room to have a voice in choosing a form of governance (e.g., in a spectrum from one man dictatorial control to free-for-all anarchy, with differing degrees of democracy somewhere along that spectrum). In one embodiment, the system topic space mechanism (see 413′ of
The illustrated second automated mapping tool 113Zt provides an access window 113zTS into a corresponding topic space region (TSR) from where the picked theory and template (e.g., room-archetypes template) was obtained. If the user wishes to do so, the user can double click, double tap, or otherwise activate any one of the displayed topic nodes within access window 113zTS in order to explore that subregion of topic space in greater detail. Also the user can utilize an associated expansion tool (e.g., starburst+) for help and more options. In exploring that portion of the governance/social dynamics area of the system topic space mechanism (see 413′ of
When determining who specifically is to be displayed by tool as the current room discussion leader (archetype 113z1), any of a variety of user selectable methods can be used ranging from the user manually identifying each based on his own subjective opinion to having the STAN_3 system 410 provide automated suggestions as to which participant or group of room participants fits into each role and allowing authorized room members to vote implicitly or explicitly on those choices.
The entity holding the room leadership role may be automatically determined by testing the transcript and/or other CFi's collected from potential candidates for traits such as current assertiveness. Each person's assertiveness may be accessed on an automated basis by picking up inferencing clues from their current tone of voice if the forum includes live audio or from the tone of speaking present in their text output, where the person's PEEP file may reveal certain phrases or tonality that indicate an assertive or leadership role being undertaken by the person. A person's current assertiveness attribute may be automatically determined based on any one or more of objectively measured factors including for example: (a) Assertiveness based on total amount of chat text entered by the person, where a comparatively high number indicates a very vocal person; (b) Assertiveness based on total amount of chat text entered compared to the amount of text entered by others in the same chat room, where a comparatively low number may indicate a less vocal person or even one who is merely a lurker/silent watcher in the room; (c) Assertiveness based on total amount of chat text entered compared to the amount of time spent otherwise surfing online, where a comparatively high number (e.g., ratio) may indicate the person talks more than they research while a low number may indicate the person is well informed and accurate when they talk; (d) Assertiveness based on the percentage of all capital letter words used by the person (understood to denote shouting in online text stream) where the counted words should be ones identified in a computer readable dictionary or other lists as being ones not likely to be capitalized acronyms used in specific fields; (e) Assertiveness or leadership role based on the percentage of times that this user (versus a baseline for the group) is the initial one in the chat room or is the first one in the chat room to suggest a topic change which is agreed to with little debate from others (indicating a group recognized leader); (f) Lower assertiveness or sub-leadership role based on the percentage of times this user is the one in the chat room agreeing to and echoing a topic change (a yes-man) after some other user (the prime leader) suggested it; (g) Assertiveness or leadership role based on the percentage of times this user's suggested topic change was followed by a majority of other users in the room; (h) Assertiveness or leadership role based on the percentage of times this user is the one in the chat room first urging against a topic change and the majority group sides with him instead of with the want-to-be room drifter; (i) Assertiveness or leadership role based on the percentage of times this user votes in line with the governing majority on any issue including for example to keep or change a topic or expel another from the room or to chastise a person for being an apparent troll, bully or other despised social archetype (where inline voting may indicate a follower rather than a leader and thus leadership role determination may require more factors than just this one); (j) Assertiveness or leadership role based on automated detection of key words or phrases that, in accordance with the user's PEEP or PHAFUAL profile files indicate social posturing within a group (e.g., phrases such as “please don't interrupt me”, “if I may be so bold as to suggest”, “no way”, “everyone else here sees you are wrong”, etc.).
The labels or Archetype Names (113zAN) used for each archetype role may vary depending on the archetype template chosen. Aside from “troll” (113z6) or “bully” (113z7) many other kinds of role definitions may be used such as but not limited to, lurker, choir-member, soft-influencer, strong-influencer, gang or clique leader, gang or clique member, topic drifter, rebel, digresser, head of the loyal opposition, etc. Aside from the exemplary knowledge base rules provided immediately above for automatically determining degree of assertiveness or leadership/followship, many alternate knowledge base rules may be used for automatically determining degree of fit in one type of social dynamics role or another. As already mentioned, it is left up to room members to pick the social dynamics defining templates they believe in and the corresponding knowledge base rules to be used therewith and to directly or indirectly identify both to the social dynamics theory picking tool 113zto, whereafter the social dynamics mapping tool 113Zt generates corresponding graphics for display on the user's screen 111″″. The chosen social dynamics defining templates and corresponding knowledge base rules may be obtained from template/rules holding content nodes that link to corresponding topic nodes in the social-dynamics topic space subregions (e.g., You are here 113zTS) maintained by the system topic space mechanism (see 413′ of
The example given in
Still referring to
The role-fitting heat score (see meter 113zH) given to each room member may be one that is formulated entirely automatically by using knowledge base rules and an automated knowledge base rules, data processing engine or it may be one that is subjectively generated by a room dictator or it may be one that is produced on the basis of automatically generated first scores being refined (slightly modulated) by votes cast implicitly or explicitly by authorized room members. For example, an automated knowledge base rules using, data processing engine (not shown) within system 410 may determine that “Bill” is the number one room bully. However a room oversight committee might downgrade Bill's bully score by an amount within an allowed and predetermined range and the oversight committee might upgrade Brent's bully score by an amount so that after the adjustment by the human overseers, Brent rather than Bill is displayed as being the current number one room bully.
Referring momentarily to
Examples of other words/phrases that may relate to room dynamics may include: “Let's get back to”, “Let's stick with”, etc and when these are found by the system 410 to be near words/phrases related to the then primary topic(s) of the room, the system 410 can determine with good likelihood that the corresponding user is acting in the role of a topic anchor who does not want to change the topic. At minimum, it can be one more factor included in knowledge base determination of the heat attributed to that user for the role of room anchor or room leader or otherwise. Words/phrases that relate to room dynamics may be specially clustered in room dynamics subregions of a system-maintained, semantic-wise clustering, textual-content organizing space. As will be detailed later below, degree of sameness or similarity as between expressions representing such words/phrases may be determined based on hierarchical and/or spatial distancing within the corresponding content organizing space of the representative expressions and special rules of exception for determining such degrees of sameness or similarity may be stored in the system and used as such.
With regard to room dynamics, other roles that may be of value for determining where the room dynamics are heading (and/or how fast) may include those social entities who are identified as fitting into the role of primary trend setters, where votes by the latter are given greater weight than votes by in-room personas who are not deemed to be as influential in terms of trend setting as are the primary trend setters. In one embodiment, the votes of the primary trend setters are further weighted by their topic-specific credentials and reputations (DsCCp profiles). In one embodiment, if the votes of the primary trend setters do not establish a supermajority (e.g., at least 60% of the weighted vote), the system either automatically bifurcates the room into two or more corresponding rooms each with its own clustered coalition of trend setters or at least it proposes such a split to the in-room participants and then they vote on the automatically provided proposition. In this way the system can keep social harmony within its rooms rather than letting debates over the next direction of the room discussion overtake the primary substantive topic(s) of discussion. In one embodiment, the demographic and other preferences identified in each user's active CpCCp (Current personhood-based Chat Compatibility Profile) are used to determine most likely social dynamics for the room. For example, if the room is mostly populated by Generation X people, then common attributes assigned to such Generation X people may be thrown in as a factor for automatically determining most likely social dynamics of the room. Of course, there can be exceptions; for example if the in-room Generation X people are rebels relative to their own generation, and so on.
One important aspect of trying to maintain social harmony in the STAN-system maintained forums is to try and keep a good balance of active listeners and active talkers. (Room fill recipes will also be discussed in conjunction with
Although in one embodiment, the social mixing engine (described elsewhere herein—see 555-557 of
The above method of automatically filtering out an excessively deviant response from a group of collected responses of STAN_3 system users is not limited to just emotional or other responses to test promotional offerings. The process may be applied to other telemetry based determinations such as for example, implicit or explicit votings by STAN_3 system users. In one embodiment, if for example, the CFi's or CVi's of one out of 5 sampled users within a non-customized group deviates from the rest by a percentage exceeding a predetermined threshold, that deviant feedback result is automatically tossed out or given a reduced weight when the result report is generated and/or transmitted for use in an appropriate way (e.g., displaying results to an end user). The response of the group as a whole may be based on an average of the individualized responses of the members or based on another collectivized method of representing a group response such as, but not limited to, a weighted average where some members receive more weight than others due to credentials, social dynamic role within the group, etc., or a mean response or a median response.
Notwithstanding the above, in one embodiment, pro-promotion chat or other forum participation sessions are preformulated by first automatically identifying one or more to-be-invited system users who are predetermined, based on their past online histories and based on their predetermined social dynamics profiles, to be likely group leaders who will also likely favor a to-be-promoted cognition (e.g., the idea of buying into a pre-specified good and/or service) and inviting those personas first into a nascent chat or other forum participation opportunity. If a sufficient number exceeding a predetermined threshold accept that invitation, then more users who are predetermined, based on their past online histories and based on their predetermined social dynamics profiles, to be likely to follow the accepting first invitees, are also invited into the forum. Thereafter, the to-be-promoted cognition is interjected into the forum discourse. In one embodiment, one or more of the first invited and likely to become group leaders is someone who has previously tried a to-be-promoted product or service (or one relatively similar to the be-promoted product/service) and has reacted positively to it (e.g., by posting a positive reaction on Yelp.com™ or another such product/service rating site.)
Referring next to
One or more pointer bubbles, 190p.1, 190p.2, etc. are displayed on or adjacent to the displayed map 190a. The pointer bubbles, 190p1., 190p.2, etc. point places on the map (e.g., 190a.1, 190a.3) where on-topic events are already occurring (e.g., on-topic conference 190p.4) and/or where on-topic events may soon be caused to occur (e.g., good meeting place for topic(s) of bubble 190p.1) and/or where resources are or can be made available (e.g., at a resource-rich university campus 190p.6). The displayed bubbles, 190p.1, 190p.2, etc. are all, or for the most part, ones directed to topics that satisfy the filtering criteria indicated by the selection tool 190b (e.g., a displayed filtering criteria box). In the illustrated example, My Top 5 Now Topics implies that these are the top 5 topics the user is currently deemed to be focusing-upon by the STAN_3 system 410. The user may click, tap or otherwise activate a more-menus options arrow (down arrow in box 190b) to see and select other more popular options available through his system-supported data processing device 100′″. Alternatively, if the user wants more flexible and complex selection tool options, the user may use the associated expansion tool 190b+. Examples of other “filter by” menu options that can be accessed by way of the menus options arrow may include: My next 5 top topics, My best friends' 5 top topics, My favorite group's 3 top topics, and so on. Activation of the expansion tool (e.g., 190b+) also reveals to the user more specifics about what the names and further attributes are of the selected filter category (My Top 5 Topics, My best friends' 5 top topics, etc.). When the user activates one of the other “filter by” choices, the pointer bubbles and the places on the map they point to automatically change to satisfy the new criteria. The map 190a may also change in terms of zoom factor, central location and/or format so as to correspond with the newly chosen criteria and perhaps also in response to an intervening change of context for the user of computer 100′″.
Referring to the specifics of the top left pointer bubble, 190p.1 as an example, this one is pointing out a possible meeting place where a not-yet-fully-arranged, real life (ReL) meeting may soon take place between like-minded STAN users. First, the system 410 has automatically located for the user of tablet computer 100′″, neighboring other users 190a.12, 190a.13, etc. who happen to be situated in a timely reachable radius relative to the possible meeting place 190a.1. Needless to say, the user of computer 100′″ is also situated within the timely reachable radius 190a.11. By timely reachable, what is meant here is that the respective users have various modes of transportation available to them (e.g., taxi, bus, train, walking, etc.) for reaching the planned destination 190a.1 within a reasonable amount of time such that the meeting and its intended outcome can take place and such that the invited participants can thereafter make any subsequent deadlines indicated on their respective computer calendars/schedules. In addition to presenting one or more first transport mechanisms (e.g., taxi, bus, etc.) by way of which one or more of the potential participants in the being-planned (or pre-planned) meeting can timely get to the proposed or planned meeting place, the STAN_3 system may optionally present indications (e.g., icons) of one or more second transport mechanisms (e.g., taxi, bus, etc.) by way of which one or more of the potential participants can, at the conclusion of the meeting; timely get to a next desired destination (e.g., back to the office, to a hotel having vacancies, to a convention center, to a customer site, etc.). The first and/or second transport mechanisms may serve as meeting enabling and/or facilitating means in that, without them, some or all of the invited (or to be invited) participants would not be able to attend or would be inconvenienced in attempting to attend. By providing representations of the first and/or second transport mechanisms, the STAN_3 system can encourage potential participants who otherwise may not have attended (e.g., due to worry over how to timely get back to the convention center) to attend because one or more impediments to their attending the proposed or planned meeting is removed.
In one embodiment, the user of computer 100′″can click, tap or otherwise activate an expansion tool (e.g., a plus sign starburst like 190b+) adjacent to a displayed icon of each invited other user to get additional information about their exact location or other situation, to optionally locate their current mobile telephone number or other communication access means (e.g., a start private chat now option) and to thereby call/contact the corresponding user so as to better coordinate the meeting, including its timing, venue and planned topic(s) of discussion. (It is to be understood that when the locations and/or other situations of the other potential invitees is ascertained, typically their exact identities, locations, age or other demographics are not revealed or the users are in pre-existing privity with one another and have agreed ahead of time to share such information whose revelation may, in some circumstances otherwise compromise the safety or privacy of those involved. The meeting generating process may, in one embodiment, occur only over a secured communication channel to which only users who trusted one another have access.)
Once an acceptable quorum number of invitees have agreed to the venue, as to the timing and/or the topics; one of them may volunteer to act as coordinator (social leader) and to make a reservation at the chosen location (e.g., restaurant) and to confirm with the other STAN users that they will be there (e.g., how many will likely show up and is the facility sized to suite that number?). In one embodiment, the system 410 automatically facilitates one or more of the meeting arranging steps by, for example automatically suggesting who should act as the meeting coordinator/leader (e.g., because that person can get to the venue before all others and he or she is a relatively assertive person), automatically contacting the chosen location (e.g., restaurant) via an online reservation making system or otherwise to begin or expedite the reservation making process and automatically confirming with all that they are committed to attending the meeting and agreeable to the planned topic(s) of discussion. In short; if by happenstance the user of computer 100′″ is located within timely radius (e.g., 190a.11) of a likely to be agreeable to all venue 190a.1 and other socially co-compatible other STAN users also happen to be located within timely radius of the same location and they are all likely agreeable to lunching together, or having coffee together, etc. and possibly otherwise meeting with regard to one or more currently focused-upon topics of commonality (e.g., they all share in common three topics which topics are members of their personal top 5 current topics of focus), then the STAN_3 system 410 automatically starts to bring the group of previously separated persons together for a mutually beneficial get together. Instead of each eating alone (as an example) they eat together and engage socially with one another and perhaps enrich one another with news, insights or other contributions regarding a topic of common and currently shared focus. In one embodiment, various ones of the social cocktail mixing attributes discussed above in conjunction with
Still referring to proposed meeting location 190a.1 of
Still referring to
A second nascent meeting group bubble 190p.2 is shown in
Contents within the respective pointer bubbles (e.g., 190p.3, 190p.4, etc.) of each event may vary depending on the nature of the event. For example, if the event is already a definite one (e.g., scheduled baseball game in the location identified by 190p.3) then of course, some of the query data provided in bubble 190p.1 (e.g., who is likely to be nearby and likely to agree to attend?) may not be applicable. On the other hand, the alternate event may have its own, event-specific query data (e.g., who has RSVP'ed in bubble 190.p5) for the user to look at. In one embodiment, clicking, tapping or otherwise activating venue representing icons like 190a.3 automatically provides the user with a street level photograph of the venue and it surrounding neighborhood (e.g., nearby landmarks) so as to help the user get to the meeting place. In one embodiment, the STAN_3 system automatically causes the user's data processing device (100′″) to launch the Google Maps™ web site (or equivalent, e.g., MapQuest™) with the location address preloaded where the automatically launched web page shows the user automatically what public transit routes to take and what are the next arrival/departure times for buses, trams, etc. in the next hour as based on the user's desired estimated ETA (estimated time of arrival) for the planned meeting. More specifically, the STAN_3 system may preload into the web-map providing service link (e.g., Google Maps™ or MapQuest™) the origin and destination locations as well as the type of map information desired (e.g., public transit connections and times, street view, etc.) thereby easing the user's access to such web-map providing services based on information known the STAN_3 system about the planned meeting.
Referring to example 190p.6 in
Although
Additionally, while the above description of
Referring to
It can be a common occurrence for some users of the STAN_3 system 410 to find themselves alone and bored or curious or needing a 5-minute or like short-duration break while they wait for a next, in-real life (ReL) event to take place; such as meeting with habitually-late friend at a coffee shop. In such a situation, the user will often have only his or her small-sized PDA or smart cellphone with them. The latter device may have a relatively small display screen 111″″. As such, the device compatible user interface (GUI 100.4 of
Additionally, it is to be understood that, although
When the STAN_3 system presents the user with a proposed one or more Instan-Chat™ or Instan™-other online forum participation opportunities, such proposal routinely comes in an abbreviated format (e.g., card stack 193.1). However, if the user wants to see in more detail what the proposed 5 topics are, the user can click, tap or otherwise activate the proposal-stack's expansion tool 193.h+ for more information and for the option of quickly switching to a previous one of a set of system recalled lists of other top 5 topics that the user may previously have focused-upon at earlier times or for indicating to the system that a different context is active and thereby implicitly (or explicitly) requesting that the system present a different set of, more context appropriate, Instan-Chat™ proposals. The user can then quickly click, tap or otherwise activate on one of those alternate options and thus switch to a different set of top 5 topics (or top N points, nodes or subregions of other CARSs). Alternatively, if the user has time, the user may manually define a new collection of current top 5 topics that the user feels he/she is currently focused-upon.
In an alternate embodiment, the system 410 uses the current detected context of the user (e.g., sitting at favorite coffee shop waiting for politically oriented friend to show up as indicated in online calendar) in combination with a randomizer to automatically pick likely current points, nodes or subregions of context appropriate CARSs for the user to consider. Examples include: picking a top 5 topics that the user and the to-be-met friend(s) have in common recently or over the past week or month; picking a top 5 recent keywords that the user and the to-be-met friend(s) have in common; picking a top 5 recent URL's that the user and the to-be-met friend(s) have in common; picking a top 5 trending keywords of recent broadcast news, of recent on-Internet news and/or of a more narrowly defined information-sharing network; and randomly picking from a list of favorite topics or favorite other points, nodes or subregions of other CARSs of the user.
However, if the STAN_3 system has yet more specific context-hinting data at its disposal, it can propose yet more context relevant chat or other forum participation opportunities. More specifically, if the GPS subsystem indicates the user is stuck on metered on ramp to a backed up Los Angeles highway and current news sources indicate that traffic is heavy in that location, the system 410 may automatically determine that the user's current top 5 topics include one regarding the over-crowded roadways and how mad he is about the situation. On the other hand, if the GPS subsystem indicates the user is in the bookstore (and optionally more specifically, in the science fiction aisle of the store), the system 410 may automatically determine that the user's current top 5 topics include one regarding new books (e.g., science fiction books) that his book club friends might recommend to him. Of course, it is within the contemplation of the present disclosure that the number of top N topics to be used for the given user can be a value other than N=5, for example 1, 2, 3 or 10 as example alternatives.
Accordingly, if the user has approximately 5 to 15 minutes or more of spare time and the user wishes to instantly join into an interesting online chat or other forum participation opportunity, the one Instan-Chat™ participation opportunities stack 193.1 automatically provides the user with a simple interface for entering such a group participation forum with a single click, tap or other such activation. The time based chat proposal may also include an associated maximum number of co-chatters value. More specifically, if the user has only 5 free minutes, it is unlikely that a meaningful chat can take place for him/her if ten other people are in the same chat room because each will likely want at least about a minute of time to talk. So the better approach is to automatically pre-limit the room size based on the user's expected length of free time. If the user has 30 minutes of expected free time for example, the maximum number of participants may be increased from 3 to 5 (as shown in block 192.2).
In one embodiment, a context determining module of the system 410 automatically determines based on context that the user wants to be presented with an Instan-Chat™ participation interface on power-up and also what card the user will most likely want to be first presented within this Instan-Chat™ participation interface when opening his/her smart cellphone (e.g., because the system 410 has detected that the user is in a car and stuck on the zero speed on-ramp to a backed-up Los Angles freeway for example). Alternatively, the user may utilize the Layer-Vator tool 113″″ after power-up to virtually take himself to a metaphorical virtual floor that contains the Instan-Chat™ participation interface of
Still referring to
Column 192 shows examples of default and other settings that the user may have established for controlling what quick chat or other quick forum participation opportunities will be presented for example visually in column 193. (In an alternate embodiment, the opportunities can be presented by way of a voice and/or music driven automated announcement system that responds to voice commands and/or haptic/muscle based and/or gesture-based commands of the user.) More specifically, menu box 192.2 allows the user to select the approximate duration of his intended participation within the chat or other forum participation opportunities and the desired maximum number of participants in that forum. The expected duration can alter the nature of which topics are offered as possibilities, how many and which other users are co-invited into or are already present in the forum and what the nature of the forum will be (e.g., short micro-tweets as opposed to lengthy blog entries). In one embodiment, the STAN_3 system uses recently acquired data (e.g., CFi's) that hints at the user's current context to automatically pick the expected chat duration length and number of others who are co-invited to participate. In some situations, it may be detrimental to room harmony and/or social dynamics if some users need to exit in less than 5 minutes and plan on contributing only superficial comments while others had hopes for a 30 minute in depth exchange of non-superficial ideas. Therefore, and in accordance with one aspect of the present disclosure, the STAN_3 system 410 automatically spawns empty chat rooms that have certain room attributes pre-attached to the room; for example, an attribute indicating that this room is dedicated to STAN users who plan to be in and out in 5 minutes or less as opposed to a second attribute indicating that this room is dedicated to STAN users who plan to participate for substantially longer than 5 minutes and who desire to have alike other users join in for a more in depth discussion (or other Notes Exchange session) directed to one or more out of the current top N topics of the those users.
Another menu box 192.3 in the usually hidden settings column 192 shows a method by which the user may signal a certain current mood of his (or hers). For example, if a first user currently feels happy (joyous) and wants to share his/her current feelings with empathetic others among the currently online population of STAN users, the first user may click, tap or otherwise activate a radio button indicating the user is happy and wants to share. It may be detrimental to room harmony and/or social dynamics if some users are not in a co-sympathetic mood, don't want to hear happy talk at the moment from another (because perhaps the joy of another may make them more miserable) and therefore will exit the room immediately upon detecting the then-unwelcomed mood of a fellow online roommate. Therefore, and in accordance with one aspect of the present disclosure, the STAN_3 system 410 automatically spawns empty chat rooms that have certain room attributes pre-attached to the room; for example, an attribute indicating that this room is dedicated to STAN users who plan to share happy or joyous thoughts with one another (e.g., I just fell in love with the most wonderful person in the world and I want to share the feeling with others). By contrast, another empty room that is automatically spawned by the system 410 for purpose of being populated by short term (quick chat) users can have an opposed attribute indicating that this room is dedicated to STAN users who plan to commiserate with one another (e.g., I just broke up with my significant other, or I just lost my job, or both, etc.). Such, attribute-pretagged empty chat or other forum participation spaces are then matched with current quick chat candidates who have correspondingly identified themselves as being currently happy, miserable, etc.; as having 2, 5, 10, 15 minutes, etc. of spare time to engage in a quick online chat or other Notes Exchange session of like situated STAN users where the other STAN users share one or more topics of currently focused-upon interest with each other. In one embodiment, rather than having the user manually indicate current mood, the STAN_3 system determines mood automatically by for example using the user's online calendaring information and the user's PHAFUEL record. If the PHAFUEL record (habits and routines,—see
As yet another example, the third menu box 192.4 in the usually hidden settings column 192 shows a method by which the user may signal a certain other attribute that he or she desires of the chat or other forum participation opportunities presented to him/her. In this merely illustrative case, the user indicates a preference for being matched into a room with other co-compatibles who are situated within a 5 mile radius of where that user is located. One possible reason for desiring this is that the subsequently joined together chatterers may want to discuss a recent local event (e.g., a current traffic jam, a fire, a felt earthquake, etc.). Another possible reason for desiring this is that the subsequently joined together chatterers may want to entertain the possibility of physically getting together in real life (ReL) if the initial discussions go well. This kind of quick-discussion group creating mechanism allows people who would otherwise be bored for the next N minutes (where N=1, 2, 3, etc. here), or unable to immediately vent their current emotions and so on; to join up when possible with other like-situated STAN users for a possibly, mutually beneficial discussion or other Notes Exchange session. In one embodiment, as each such quick chat or other forum space is spawned and peopled with STAN users who substantially match the pre-tagged room attributes, the so-peopled participation spaces are made accessible to a limited number (e.g., 1-3) promotion offering entities (e.g., vendors of goods and/or services) for placing their corresponding promotional offerings in corresponding first, second and so on promotion spots on tray 104″″ of the screen presentation produced for participants of the corresponding chat or other forum participation opportunity. In one embodiment, the promotion offering entities are required to competitively bid for the corresponding first, second and so on promotion spots on tray 104″″ as will be explained in more detail in conjunction with
Referring to
In accordance with one aspect of the present disclosure, the camera-captured imagery (it could include IR band imagery as well as visible light band imagery, and the data may include collected direction and/or distance and/or related sound information as well) is transmitted to an in-cloud object recognizing module (not shown) of the STAN_3 system. The object recognizing module then automatically produces descriptive keywords and the like (e.g., meta-tags, cross-associated URL's, etc.) for logical association with the camera captured imagery (e.g., 198). Then the produced descriptive keywords and/or other descriptive data is/are automatically forwarded to topic lookup modules (e.g., 151 of
In the illustrated embodiment 200, the device screen 211 of handheld device 199 can operate as a 3D image projecting screen. The bifocular positionings of the user's eyes can be detected by means of one or more back facing cameras 206, 209 (or alternatively using the IR beam reflecting method of
In the illustrated example 200, the user sees a 3D bent version of the graphical user interface (GUI) that was shown in
In the illustrated example 200, the user is shown wearing a biometrics detecting and/or reporting head band 201b. The head band 201b may include an earclip 201d that electrically and/or optically (in IR band) couples to the user's ear for detecting pulse rate, muscles twitches (e.g., via EMG signals) and the like where these are indicative of the user's likely biometric states. These signals are then wirelessly relayed from the head band 201b to the handheld device 199 (or another nearby relaying device 197) and then uploaded to the cloud as CFi data used for processing therein and automatically determining the user's biometric states and the corresponding user emotional or other states that are likely associated with the reported biometric states. The head band 201b may be battery powered (or powered by photovoltaic means) and may include an IR light source (not shown) that points at the IR sensitive screen 211 and thus indicates what direction the user is tilting his head towards and/or how the user is otherwise moving his/her head, where the latter is determined based on what part of the IR sensitive screen 211 the headband produced (or reflected) IR beam strikes. The head band 201b may include voice and sound pickup and exhalation/inhalation gas pickup sensors 201c for detecting what the user 201A is saying and/or what music or other background noises the user may be listening to and/or for detecting exhalation/inhalation gases and flow rates thereof and chemical contents thereof for reporting as CFi data to the remote STAN_3 system. In one embodiment, detected background music and/or other background noises are used as possibly focused-upon CFi reporting signals (see 298′ of
Although not explicitly shown in
More generally, various means such as the illustrated user-worn head band 201b (but these various means can include other user-worn or held other devices or devices that are not worn or held by the user) can discern, sense and/or measure one or more of: (1) physical body states of the user's and/or (2) states of physical things surrounding or near to the user. More specifically, the sensed physical body states of the user may include: (1a) geographic and/or chronological location of the user in terms of one or more of on-map location, local clock settings, current altitude above sea level; (1b) body orientation and/or speed and direction and/or acceleration of the user and/or of any of his/her body parts relative to a defined frame; (1c) measurable physiological states of the user such as but not limited to, body temperature, heart rate, body weight, breathing rate, breathe components and ratios/flowrates thereof, metabolism rates (e.g., blood glucose levels), body fluid chemistries and so on. The states of physical things surrounding or near to the user may include: (2a) ambient climactic states surrounding the user such as but not limited to, current air temperature, air flow speed and direction, humidity, barometric pressure, air carried particulates including microscopic ones and those visible to the eye such as fog, snow and rain and bugs and so on; (2b) lighting conditions surrounding the user such as but not limited to, bright or glaring lights, shadows, visibility-obscuring conditions and so on; (2c) foods, chemicals, odors and the like which the user can perceive or be affected by even if unconsciously; and (2d) types of structures and/or vehicles in which the user is situated or otherwise surrounded by such as but not limited to, airplanes, trains, cars, buses, bicycles, buildings, arenas, no buildings at all but rather trees, wilderness, and so on. The various sensor may alternatively or additionally sense changes in (rates of) the various physical parameters rather than directly sensing the physical parameters.
In one embodiment, the handheld device 199 of
Given presence of the various sensors described for example immediately above, in one embodiment, the STAN_3 system 410 automatically compares the more usual physiological parameters of the user (as recorded in corresponding profile records of the user) versus his/her currently sensed physiological parameters and the system automatically alerts the user and/or other entities the user has given permission for (e.g., the user's primary health provider) with regard to likely deterioration of health of the user and/or with regard to out-of-matching biometric ranges of the user. In the latter case, detection of out-of-matching biometric range physiological attributes for the holder of the interface device being used to network with the STAN_3 system 410 may be indicative of the device having been stolen by a stranger (whose voice patterns for example do not match the normal ones of the legitimate user) or indicative of a stranger trying to spoof as if he/she were the registered STAN user when in fact they are not, whereby proper authorities might be alerted to the possibility that unauthorized entities appear to be trying to access user information and/or alter user profiles. In the case of the former (e.g., changed health or other alike conditions, even if the user is not aware of the same), in one embodiment, the STAN_3 system 410 automatically activates user profiles associated with the changed health or other alike conditions, even if the user is not aware of the same, so that corresponding subregions of topic space and the like can be appropriately activated in response to user inputs under the changed health or other alike conditions.
Although in the exemplary cases of
Referring next to
Also in the shown first environment 300A, the user 301A is at times having a local data processing device 299 automatically sensing second signals 298 indicative of input types energetic attention giving activities ei(t, x, f, {TS, XS, . . . }) of the user (another form of attention giving energies), where here, ei denotes input type energetic attention giving activities of the user 301A which activities ei have at least a time t parameter associated therewith and optionally have other parameters associated therewith such as but not limited to, x: physical location at which or to which attention is being given (and optionally v: for velocity and a: for acceleration); f: distribution in frequency domain of the attention giving activities; Ts: associated nodes or regions in topic space that more likely correlate with the attention giving activities; Xs: associated nodes or regions in a system maintained context space that more likely correlate with the attention giving activities (where context can include a perceived physical or virtual presence of on-looking other users if such presence is perceived by the first user); Cs: associated points or regions in an available-to-user content space; EmoS: associated points or regions in an available-to-user emotions and/or behavioral states space; Ss: associated points or regions in an available-to-user social dynamics space; and so on. (See also and briefly again the lower half of
Also represented for the first environment 300A and the user 301A is symbol 301xp representing the surrounding physical contexts of the user and signals (also denoted as 301xp) indicative of what some of those surrounding physical contexts are (e.g., time on the local clock, location, velocity, etc.). Included within the concept of the user 301A having a current (and perhaps predictable next) surrounding physical context 301xp is the concept of the user being knowingly engaged (known or believed by the user 301A) with other social entities where those other social entities (not explicitly shown) are knowingly/believed to be there because the first user 301A knows or believes they are attentively there, and such knowledge/belief can affect how the first user behaves, what his/her current moods, social dynamic states, etc. are. The attentively present, other social entities may connect with the first user 301A by way of a near-field communications network 301c such as one that uses short range wireless communication means to interconnect persons who are physically close by to each other (e.g., within a mile) or they may be physically in the presence of the first user 301A or engaged with him/her by means of televideo conferencing or the like.
Referring in yet more detail to possible elements of the output type first signals 302 that are indicative of energetic output expressions Eo(t, x, f, {TS, XS, . . . }) of the user, these may include user identification signals actively produced by the user (e.g., password) or passively obtained from the user (e.g., biometric identification). These may include energetic clicking, tapping and/or typing and/or copying-and-pasting and/or other touching/gesturing signal streams produced by the user 301A in corresponding time periods (t) and within corresponding physical space (x) domains where the latter click/tap/etc. streams or the like are input into at least one local data receiving and/or processing device 299 (there could be more), and where the device(s) 299 has/have appropriate graphical and/or other user interfaces (G+UI) for receiving the user's energetic, and attention giving-indicative streams 302. The first signals 302 which are indicative of energetic output expressions Eo(t, x, f, {TS, XS, . . . }) of the user may yet further include facial configurations (e.g., intentional eyebrow raises, lip pursings, puckerings, tongue projections and/or movements) and/or head gestures and/or other body gesture streams produced by the user and detected and converted into corresponding data signals. They may include voice and/or other sound streams produced by the user, biometric streams produced by or obtained from the user, GPS and/or other location or physical context steams obtained that are indicative of the physical context-giving surrounds (301xp) of the user, data streams that include imagery or other representations of nearby objects and/or persons where the data streams can be processed by object/person recognizing automated modules and thus augmented with informational data about the recognized object/person (see
Referring to possible elements of the input type second signals 298 that are indicative of energetic but not outputting, attention giving activities ei (t, x, f, {TS, XS, . . . }) of the user, these can include eye tracking signals that are automatically obtained by one of the local data processing devices (299) near the user 301A, where the eye tracking signals (e.g., as tracked over time and statistically processed to identify the predominant points, lines or curves of focus) may indicate how attentive the user is and/or they may identify one or more objects, images or other visualizations that the user is currently giving predominant energetic attention to by virtue of his/her eye activities (which activities can include eyelid blinks, pupil dilations, changes in rates of same, etc. as alternatives to or as additions to eye focusing and eye darting actions of the user). The energetic attention giving activities ei (t, x, f, {TS, XS, . . . }) of the user may alternatively or additionally include not fully intentional head tilts, nods, wobbles, shakes, etc. where some may indicate the user is listening to or for certain sounds, nostril flares that may indicate the user is smelling or trying to detect certain odors, eyebrow raises and/or other facial muscle tensionings or relaxations that may indicate the user is particularly amused or otherwise emotionally moved by something he/she perceives, and so on but is not intentionally trying to communicate something to someone or to his/her machine by means of such not fully intentional body language factors. Categorization of body language factors into being intended versus not fully intentional may be based on the currently activated PEEP record (Personal Emotions Expression Profile) of the user where the PEEP record includes a lookup table (LUT) and/or knowledge base rules (KBR's) differentiating between the two kinds of body language factors.
In the illustrated first environment 300A, at least one of the user's local data processing devices (299) is operatively coupled to or includes as a part thereof of web content displaying and/or otherwise presenting means (e.g., a flat panel display and/or sound reproducing components). The at least one of the user's local data processing devices (299) is further operatively coupled to and/or has executing within it, a corresponding one or more network browsing modules 303 where at least one of the browsing modules 303 is causing a presenting (e.g., displaying) of browser generated content to the user, where the browser-provided content 299xt can have one or more of positioning (x), timing (t) and spatial and/or temporal frequency (f) attributes associated therewith. As those skilled in the art may appreciate, the browser generated content may include, but is not limited to, HTML, XML or otherwise pre-coded content that is converted by the browsing module(s) 303 into user perception-friendly content. The browser generated content may alternatively or additionally include video flash streams or the like. In one embodiment, the network browsing modules 303 are cognizant of where on a corresponding display screen or through another medium various sub-portions of their content is being presented, when it is being presented, and thus when the user is detected by machine means to be then casting input and/or output energies of the attentive kind to the sources (e.g., display screen area) of the browser generated sub-portions of content (299xt, see also for example sub-portions 117a of window 117 of
When the STAN_3 system portion 310 receives the combination (322) of the content-sub-portion identifying signals (e.g., time, place and/or data of browser-generated content 299xt) and the signals representing user-expended attention-giving energies (Eo(x,t, . . . ), ei(x,t, . . . )) cast on those sub-portions and/or user-aware-of context indicators Cx(x,t, . . . ), etc., the STAN_3 system portion 310 can treat the same in a manner generally similar to how it treats directly uploaded CFi's (current focus indicator records) of the user 301A. The STAN_3 system portion 310 can therefore produce responsive result signals 324 for use by the web/net server 305 or a further downstream unit, where the responsive result signals 324 may include, but not limited to, identifications of the most likely topic nodes or topic space regions (TSR's) within the system topic space (413′; or another such space if applicable) that correspond with the received combination 322 of content, focus and/or context representing signals. In one embodiment, the number of returned as likely, topic node (or other node) identifications is limited to a predetermined number such as N=1, 2, 3, . . . and therefore the returned topic/other node or subregion identifications may be referred to as the top N topic node/region ID's in
Although topic space is mentioned as a convenient example, it is fully within the contemplation of the present disclosure for the responsive result signals 324 (produced by the STAN_3 system 310) to represent points, nodes or subregions of other system-maintained Cognitive Attention Receiving Spaces such as, but not limited to, keyword space, URL space, social dynamics space and so on. The responsive result signals 324 may be seen as results of having tapped into the collection of collective Cognitive Attention Receiving Spaces maintained by the system 310 and having selectively extracted from that “collective brain” (in a manner of speaking) the informational resources maintained by that “collective brain”, including, but not limited to, most currently popular chat or other forum participation sessions directed to the corresponding points, nodes or subregions of system-maintained Cognitive Attention Receiving Spaces (e.g., topic space) where the corresponding points, nodes or subregions may be selected on a context-sensitive basis. Context-based selection is possible because the context representing signals Cx(x,t, . . . ) of the first user 301A are input into the STAN_3 system 310 and because (as shall be better detailed below), the STAN_3 system 310 maintains hybrid spaces whose nodes can point to context-specific nodes of other spaces and/or chat or other forum participation opportunities or other informational resources that cross-correlate with the hybrid space nodes. Just as the purebred or non-hybrid Cognitions-representing Spaces (e.g., topic space, keyword space, URL space, etc.) have consensus-wise created PNOS-type points, or nodes or subregions respectively representing consensus-wise defined, communal cognitions associated with the purebred types of cognitions, the hybrid Cognitions-representing Spaces (e.g., topic-plus-context space) have stored therein, consensus-wise created PNOS-type points, or nodes or subregions respectively representing consensus-wise defined, communal cognitions associated with the hybrid types of cognitions. For example, when the topic of “football” is taken within the context of being at Ken's house (see again the introductory hypothetical) and it being SuperBowl Sunday™ that day and the first user's calendaring database indicating that he has clean-up crew duty that hour, the system can identify a corresponding and context-based PNOS-type point, node or subregion in a corresponding topic-plus-context space subregion that points to co-associated chat or other forum participation opportunities that other users in similar contextual situations would likely want to participate in. Yet more specifically, one such online chat room might be directed to the topic of “How to finish your clean-up assignments without missing high points of today's game”. In other words, rather than the user having to fish through many possible chat rooms looking for one specifically directed to his unique situation, other users whose current attention giving energies are focused-upon the same or a substantially similar node in the same subregion of topic-plus-context space are brought together and invited to simultaneously or in close temporal proximity, join in on a chat or other forum participation session linked to that combination of context plus topic.
As explained in the here-incorporated STAN_1 and STAN_2 applications, each topic node within the system-maintained topic space may include pointers or other links to corresponding on-topic chat rooms and/or other such forum participation opportunities. The linked-to forums may be sorted, for example according to which ones are most popular among different demographic segments (e.g., age groups) of the node-using population. In one embodiment, the number returned as likely, most popular chat rooms (or other so associated forums) is limited to a predetermined number such as M=1, 2, 3, . . . and therefore the returned forum identifying signals may be referred to as the top M online forums in
As also explained in the here-incorporated STAN_1 and STAN_2 applications, each topic node may include pointers or other links to corresponding on-topic topic content that could be suggested as further research areas (non-forum types of informational resources) to STAN users who are currently focused-upon the topic of the corresponding node. The linked-to suggestible content sources may be sorted, for example according to which ones are most popular among different demographic segments (e.g., age groups) of the node-using population. In one embodiment, the number returned as likely, most popular research sources (or other so associated suppliers of on-topic material) is limited to a predetermined number such as P=1, 2, 3, . . . and therefore the returned resource identifying signals may be referred to as the top P on-topic other contents in
As yet further explained in the here-incorporated STAN_1 and STAN_2 applications, each topic node may include pointers or other links to corresponding people (e.g., Tipping Point Persons or other social entities) who are uniquely associated with the corresponding topic node for any of a variety of reasons including, but not limited to, the fact that they are deemed by the system 410 to be experts on that topic, they are deemed by the system to be able to act as human links (connectors) to other people or resources that can be very helpful with regard to the corresponding topic of the topic node; they are deemed by the system to be trustworthy with regard to what they say about the corresponding topic, they are deemed by the system to be very influential with regard to what they say about the corresponding topic, and so on. In one embodiment, the number returned as likely to be best human resources with regard to topic of the topic node (or topic space region: TSR) is limited to a predetermined number such as Q=1, 2, 3, . . . and therefore the returned resource identifying signals may be referred to as the top Q on-topic people in
The list of topic-node-to-associated informational items can go on and on. Further examples may include, most relevant on-topic tweet streams, most relevant on-topic blogs or micro-blogs, most relevant on-topic URLs, most relevant on-topic online or real life (ReL) conferences, most relevant on-topic social groups (of online and/or real life gathering kinds), and so on. And also, of course, it is within the contemplation of the present disclosure for the produced responsive result signals 324 of the STAN_3 system portion 310 to be representative of informational resources extracted from, or by way of other Cognitive Attention Receiving Spaces maintained by the system besides or in addition to topic space.
The produced responsive result signals 324 of the STAN_3 system portion 310 can then be processed by the web or net server 305 and converted into appropriate, downloadable content signals 314 (e.g., HTML, XML, flash or otherwise encoded signals) that are then supplied to the one or more browsing module(s) 303 then being used by the user 301A where the browsing module(s) 303 thereafter provide the same as presented content (299xt, e.g., through the user's computer or TV screen, audio unit and/or other media presentation device).
More specifically, the initially present content (299xt) on the user's local data processing device 299, before that initial content (299xt) is enhanced (supplemented, augmented) by use of the STAN_3 system 310; may have been a news compilation web page that was originated from the net/web server 305, converted into appropriate, downloadable content signals 314 by the browser module(s) 303 and thus initially presented to the user 301A. Then the context-indicating and/or focus-indicating signals 301xp, 302, 298 obtained or generated by the local data processing devices (e.g., 299) then surrounding the user are automatically relayed upstream to the STAN_3 system portion 310. In response to these, unit 310 automatically returns response signals 324. The latter flow downstream and in the process they are converted into on-topic, new (post-initial) displayable information (or otherwise presentable information; e.g., audible information) that the user may first need to approve/accept before a final presentation is provided (e.g., after the user accepts a corresponding invitation to enter an online chat room) or that the user is automatically treated to without need for invitation acceptance. This new, post-initial and displayable and/or otherwise presentable information (e.g., encoded by downstream heading signals 314) can enhance the initial web-using experience of the respective user 310A by for example automatically including or suggesting for inclusion, currently hot and on topic chat or other forum participation opportunities that are or will be populated by co-compatible other users.
Yet more specifically, in the case of the initial news compilation web page (e.g., displayed in area 299xt at first time t1), once the system automatically determines what topics and/or specific sub-portions of the initially available content the user 301A is currently more focused-upon (e.g., energetically paying attention more to and/or more energetically responding to), the initially presented news compilation transforms automatically and shortly thereafter (e.g., within a minute or less) into a “living” news compilation that seems to magically know what the user 301A has currently been focusing-upon (casting significant attention giving energies upon) and which then serves up correlated additional content (e.g., invitations to immediately join in on related chat rooms and/or suggestions of additional resources the user might want to investigate) which the user 301A likely will welcome as being beneficially useful to the user rather than as being unwelcomed and annoying. Yet more specifically, if the user 301A was reading a short news clip about a well known entertainment celebrity (movie star) or politician named X, or sports figure (e.g., Joe-the-Throw Nebraska (fictitious)), the system 299-310 may shortly thereafter automatically pop open a live chat room (or invitation thereto) where like-minded other STAN users are starting to discuss a particular aspect regarding celebrity X that happens to now be predominantly on the first user's (301A) mind. The way that the system 299-310 came to infer what was most likely receiving the more significant attention giving energies within the first user's (301A) mind is by utilizing a trial and error technique in combination with the system-maintained Cognitive Attention Receiving Spaces (CARSs) where the trial and error technique makes a first guess at likely points, nodes or subregions in the CARSs that the user might agree he/she is focusing his/her attention giving energies upon, then presenting corresponding content (e.g., invitations) to the user, then collecting implicit or explicit vote indicators (CVi's) respecting the newly presented content and repeating so as to thereby home in on the most likely topics on the user's mind as well as homing in on the most likely context that the user is apparently operating under with aid of pre-developed profiles (301p in
Referring to the flow chart of
In next step 352, the system automatically obtains or generates focus-indicating signals 298 that indicate certain inwardly directed (inputting types of) attention giving activities of the user such as, but not limited to, staring (e.g., having eye dart pattern predominantly hovering there) for a time duration in excess of a predetermined threshold amount at a specific on-screen area (e.g., 117a of
In next step 353, the system automatically obtains or generates context-indicating signals 301xp. Here, such context-indicating signals 301xp may indicate one or more most likely contextual attributes of the user such as, but not limited to: his/her geographic location, his/her economic activities disposition (e.g., working, on vacation, has large cash amount in checking account, has been recently spending more than usual and thus is in shopping spree mode, etc.), his/her biometric disposition (e.g., sleepy, drowsy, alert, jittery, calm and sedate, etc.), his/her disposition relative to known habits and routines (see briefly
In next step 354 (optional) of
In one embodiment the CFi's (or HyCFi's) received by the upstream unit 310 are time and/or place stamped. As a result of presence of such chronological and spatial identifications, the system 299-310 (
In next carried out step 355 of
In a next carried out step 356 of
In next carried out step 357 of
If and when the user reacts emotionally in step 357 to the updated/upgraded content presented to the user by step 356, steps 358a and 358b may be executed. In step 358a, the system automatically obtains reaction indicating signals (CVi's) from sensors surrounding the user (or even embedded on or in the user—e.g., intra-oral cavity instrumentation, intra-nasal cavity instrumentation, etc.) and the system determines whether or not to treat such emotion-indicating signals as implicit or explicit votes of confidence or no confidence regarding the newly updated/upgraded content based on the user's currently activated PEEP record. If for example, the user quickly re-focuses his/her attention upon the newly updated/upgraded content and reacts positively (e.g., smiles), then the STAN_3 system can treat this positive reaction as a reinforcement in step 358b for neural networking-wise learning or like learned models (e.g., KBR's) the system has/is developed/developing for the user, for his/her current context, and for determining what the user apparently wants to then have presented (e.g., displayed) to him/her. On the other hand, if the user ignores the newly updated/upgraded content (generated by step 356) or reacts in a manner which indicates disapproval of how the STAN_3 system behaved (as opposed to disapproval directed to the newly updated/upgraded content itself), the system automatically alters its behavior (the system adaptively “learns”) in step 358b so that hopefully the system will do better in the next go-around through steps 351-356. In other words, the learning loop that includes steps 358a, 358b and repetition pathway 359x operates on a trial and error basis that is designed to urge the STAN_3 system into better servicing the user by taking note of his/her positive or negative reactions (if any, and in step 357) to service provided thus far and/or by also taking note of changing circumstances (changed context determined in step 353). As should be apparent from
Before moving on to the details of
Referring now to
Accordingly, a user's current context can be viewed as an amalgamation of concurrent context primitives and/or temporal sequences of such primitives (e.g., if the user is multitasking and thus jumping back and forth between different contexts). More specifically, a user can be assuming multiple roles at one time where each role has a corresponding one or more activities or performances expected of it and the expressive outputs Eo(x, t, f, . . . ) produced by the user while in each respective contextual state are colored by the respective contextual state. The context primitives aspect of this disclosure will be explained in more detail in conjunction with
Because various semantic spins and/or other cognitive senses can be inferred from the “context” or “contextual state” of the user and can then be attributed for example to each output word 301w of
Determination of the semantic/other-sense spin that is to be attributed to various individual and user focused-upon expressions (e.g., “Lincoln”) is not limited to the processing of individualized user actions per se (e.g., clicking tapping or otherwise activating user interface means such as hyperlinks, menus, etc.), it may also be used in the clustering together and processing of sequences of user actions. For example, if the user context is determined to be that of the Fifth Grade student doing his/her History homework and the user is detected to also concurrently focus-upon the expression, “war”, then the system can logically combine the two and determine the combination to be likely pointing to Abraham Lincoln's involvement with the U.S. Civil War. Once again, this aspect of automatically determining most likely combinations of individual expressions may rely on a pointing to different clustering areas in primitive expression layers (see for example layer 371 of
Stated more simply here, the machine determined ones of likely context(s) of the user (as represented by a signal 316o output from the context determining mechanism 316″ of
More generally, and just to summarize the above (and perhaps overly long winded) passages: the user is part of his/her own context. The user's current memories (e.g., recent history) and current state of awareness can be part of his/her context. The user's current physical identity and current physical surroundings and/or the user's current biological states and/or the user's current chronological positioning within time as well as spatial positioning can be part of his/her context and the user's current context. Sensor detectable ones of context-indicating states (which sensor signals are collectively denoted as XP in
Context-appropriate selection of the user's currently activated profile records (e.g., PEEP, PHAFUEL, etc.) is an important step. If such selection is repeatedly done incorrectly, it can drive the system into a state of repeatedly picking wrong topic nodes and repeatedly suggesting wrong chat or other forum participation opportunities. In one embodiment, a fail-safe default or checkpoint switching system 301s (controlled by module 301pvp in
Additionally, the default profile selections 301d may be pre-recorded to select relatively universal or general chat co-compatibility, PHAFUEL's (personal habits and routines logs, see
In one embodiment, in addition to the physical context detecting unit(s) 304, the system includes a proximate resources identifying unit 306 (shown next to 314″ in
In one embodiment, the physical context determining devices (e.g., 304, 306) that are proximate to the user 301A′ may include means for automatically recognizing non-instrumented objects, such as for example, conventional pots, pans, plates, cups, silverware, etc. and for recognizing movement of such non-instrumented objects and sequence of movement of such objects, where the physical context determining devices are configured for reporting to the system core (e.g., the cloud) the presence and/or movement and/or order of movement of such non-instrumented objects as defining part of the physical surroundings context of, and/or activities of the user 301A′. Therefore, and as an example, the user is seated in front of his smartphone camera and the camera captures automatically recognizable images of plates, spoons, forks, cups moving in the background behind the user, the system core (e.g., cloud) may use these background captured image portions to automatically determine that perhaps the user is in a restaurant (or cafeteria, meeting hall, etc.) and is surrounded by other people who are consuming meal courses in a discernable sequence based on the order of use of their utensils. It may then be inferred by the system that the user is doing the same (mirroring the behavior of the others) at substantially the same times. Such information may be used for automatically determining a behavioral context in which the user is surrounded and/or engaged in.
Assuming that, when the user's local machine systems are initially activated, there is no specific and refined context yet established by the STAN_3 system for the respective user, and assuming further that the default profiles state 301d for the user 301A′ have been instead used for establishing during system initialization or during a user PoV state reset operation, then after this initialization process completes, switch 301s is automatically flipped into its normal mode wherein the current context indicating signals 316o, produced and output from the context space mapping mechanism (Xs) 316″ are used for determining which next user profiles 301p (beyond the relatively vague default ones) will become the new, currently active profiles of the user 301A′. It should be recalled that profiles can have knowledge base rules (KBR's) embedded in them (e.g., 599 of
It is to be also noted here that interactions between the knowledge base rules (KBR's) subsystem and the current context defining output signals 316o of the context mapping mechanism 316″ can synergistically complement each other rather than conflicting with one another. The Conflicts and Errors Resolver module 301pvp is there for the rare occasions where conflict does arise and a fall back is made to relying on current physical context (XP) and associated safe profiles. However, a more common situation can be that where the current context defining output, 316o of context mapping mechanism 316″ is used by the knowledge base rules (KBR's) subsystem to determine a next-to-be active, and more context-appropriate profile. For example, one of the knowledge base rules (KBR's) within a currently active profile may read as follows: “IF The Current Most Probable Context(s) Determining signals 316o include an active pointer to context space subregion XSR2 (a subregion determined by the system to be likely for the user) THEN Switch to PEEP profile number PEEP5.7 as being the currently active PEEP profile, and also Switch to CpCCp profile number PHood5.9 as being the currently active personhood profile, ELSE . . . ”. In such a case therefore, the output 316o of the context mapping mechanism 316″ is supplying the knowledge base rules (KBR's) subsystem with input signals that the latter calls for as its input parameters and the two systems synergistically complement each other rather than conflicting with one another. The dependency may flow the other way incidentally, wherein the context mapping mechanism 316″ uses an output signal produced by a context resolving KBR algorithm embedded within a currently activated profile, where for example such a KBR algorithm may read as follows: “IF Current PHAFUEL profile is number PHA6.8 THEN exclude context subregion XSR3 as being likely, ELSE . . . ” Accordingly, such a profile-dependent KBR algorithm portion thereby controls how other, next activated profiles will be selected or not. In-profile knowledge base rules (KBR's) and/or other knowledge base rules used by the context mapping mechanism 316″ may rely on the current physical context signal (XP) as an alternative to, or in addition to relying on the current user context defining output signal, 316o of the context mapping mechanism 316″. More specifically, one of the knowledge base rules (KBR's) within a currently active profile may read as follows: “IF Current Physical Context signal XP indicates that the user (301A′) is at his workplace site and indicates that time is normal work hours and today is Wednesday, THEN Switch to PEEP profile number PEEP5.8 as being the currently active PEEP profile, ELSE . . . ”.
From the above, it can be seen that, in accordance with one aspect of the present disclosure, context guessing signals 316o (which signals often represent the apparent mental or perceived context(s) of greatest likelihood(s) for the user 301A′ rather than merely physical context 301x) are produced and output from a context space mapping mechanism (Xs) 316″ which mechanism (Xs) is schematically shown in
A more specific example of how a user's current mental or perceived context (as represented by result signal 316o) may be developed is as follows. Suppose that the physical context detecting unit 304 reports to mapping mechanism 316″ (by way of the XP signal) that user 310A′ is physically located at address 21771 Stanley Creek Blvd., Cupertino Calif. (a hypothetical example) and the day of week for that user is Wednesday and the time of day is 10:00 AM and the biological states of the user include being awake (e.g., not asleep) and alert (e.g., not groggy). Assume that, at that instant, the system is basically using a generic (e.g., like 301d) rather than context-based set of profiles for the user. However, in response to the GPS data and the biological state data, one or more of numerous software modules in mapping mechanism 316″ fetches more up to date and currently activated and personalized and pre-specified profile records (e.g., PHAFUEL and CpCCp (the personhood demographic profile) of the specific user and from these, the software module(s) automatically determine that, in all likelihood, the user is at his/her workplace (e.g., based on habits and routines for location and time) and that the user is likely to be perceiving him/herself as being in a normal employee role (e.g., Senior Software Design Engineer—again, a hypothetical example). Additionally, suppose the one or more of numerous software modules in mapping mechanism 316″ next responsively fetch data from a currently activated workplace calendaring tool (e.g., Microsoft Office™) of the user where the automatically fetched calendaring data indicates that the user (301A′) is scheduled to work on a so-called, STAN-Development-Project-3D (a hypothetical example) at this time of the current work day and week within the current month. In response to this fetched information and as yet a next step in the context-refining process, the one or more software modules in mapping mechanism 316″ send instructions, by way of current output signals 316o which connect to and drive unit 301p, to thereby cause unit 301p to activate a specific and more context-appropriate PEEP profile for the user and specific topic domain specifying profiles (DsCCP) that relate more closely to the scheduled STAN-Development-Project-3D. As a consequence, the profiles-produced, decision-guiding input vector signal 301p′ (which feeds from unit 301p into the formation of input vector signal 316v) points to a more specific subregion within context space 316″ and the current context representing signal 316o is updated to reflects this for the corresponding user 301A′. As part of the feedback loop, the produced context representing signal 316o is next used by unit 301p to perhaps pick yet another combination of user profiles.
In one embodiment, after new context defining signals 316o are produced (signals representing the one or top n best guesses as to current user context(s)) the system next causes automatic loading of context-appropriate web content (e.g., 117 of
The above example illustrated a case where one or more current contexts of the user (301A′), as represented by context(s) indicating signal 316o, are refined and resolved by starting with a relatively coarse determination or guess of context (e.g., alive, awake, alert and at this location) and then narrowing the machine-generated result to a finer determination of more likely context(s) (e.g., in work mode and working on specific project). It is to be appreciated that, just like the having of a large number of less “fuzzy” and more informative pointer vectors 316v (vector signals 316v) generally helps the system to metaphorically home in or resolve down to more narrow and well bounded context states or context space subregions of smaller hierarchical scope near the base (upper surface) of the inverted pyramid; conversely, as the number of context-differentiating, input vector signals (e.g., 316v) and the information in them decreases, the tendency is for the resolving power of the metaphorical “fuzzy” pointer vectors to decrease whereby, in hindsight, it appears as if the comparatively more “fuzzy” pointer vectors 316v were pointing to and resolving around only coarser (less hierarchically refined) nodes and/or coarser subregions of the respective mapping mechanism space (CARS, e.g., 316″), where those coarser nodes and/or subregions are conceptually located near the more “coarsely-resolved” apex portion of the inverted hierarchical pyramids (which represent the respective CARS) rather than near the more “finely-resolved” base layers of the corresponding inverted hierarchical pyramids depicted in
The above example was a simple one based on a GPS reporting of a single location (e.g., 21771 Stanley Creek Blvd., Cupertino Calif.—a hypothetical example) for the user and on a single point in time (e.g., Wednesday, 10:00 AM) for the user. However, it is within the contemplation of the present disclosure to determine the top n most likely user context(s) (where n=1, 2, 3, . . . here) based on a sequence of significant events (optionally interrupted by a sequence of none or insignificant events) such as for example, the user's GPS and/or other locater device reporting the user as hopping from one spatial location to another (in real and/or virtual world) with this occurring at respective times of day, week, month etc. (in real or virtual world time). The user's activated PHAFUEL record (habits and routines—see
As explained above, the input vector signals (e.g., 316v being input into context mapping mechanism 316″) are not actually “fuzzy” pointer vectors that of themselves point to a specific point, node or subregion in the mapped Cognitive Attention Receiving Space (e.g., context space 316″) because the results (e.g., context(s) representing output signal 316o) arising from their being inputted into the corresponding mapping mechanism (e.g., 316″) are usually not known until after the mapping mechanism (e.g., 316″) has processed the supplied input vector signals (e.g., 316v) in combination with other available information (e.g., currently activated profiles) and has responsively generated newer or updated state signals (e.g., new top n most likely contexts as represented by context representing signal 316o) which then in turn may help to identify the more appropriate user profiles and the better fitting or more appropriate points, nodes or subregions in other, cross-associated Cognitive Attention Receiving Spaces such as topic space for example to which yet newer CFi's (next received CFi's) may apply. In one embodiment, the output signals (e.g., 316o) of each, “user-is-likely-here” mapping mechanism (e.g., context mapping mechanism 316″) are output as a sorted list that provides ranked identifications of the best fitted-to and more hierarchically refined internal points, nodes and/or subregions in that space (e.g., at the top of the list and with regard to context space for example) and that also provides ranked identifications of the more poorly fitted-to and less hierarchically refined internal points, nodes and/or subregions as last (e.g., at the bottom of the list and again with regard to context space for example). The outputted resolving signals (e.g., 316o) may also include indications of how well or poorly the internal resolution process executed (e.g., with what level of confidence). If the resolution process is indicated to have executed more poorly than a predetermined acceptable level, and as a result confidence in the results is poor; the STAN_3 system 410 may elect to not generate any invitations (and/or promotional offerings) on the basis of the subpar resolution of, or confidence in the current context determination and/or in the current other focused-upon points, nodes and/or subregions within the corresponding other spaces (e.g., topic space (Ts, 313″), keyword space, URL space, social dynamics space and so on).
The input vector signals (e.g., 316v) that are supplied to the various nodes-mapping and space maintaining mechanisms (e.g., to context space 316″, to topic space 313″, etc.) as briefly noted above can include various context resolving signals obtained from one or more of a plurality of context indicating signals, such as but not limited to: (1) “pre-clustered” or “pre-categorized” or “pre-cross-associated” first CFi signals 3020 produced by, and stored in, a first CFi clustering/categorizing-mechanism 302″ (shown in
While not shown in the drawings for all the various and possible mapping mechanisms, it is to be observed that in general, each mapping mechanism 312″-316″ produces a respective mapped results output signal (e.g., 312o) which represents mapping results (also denoted as 312o for example) generated internally within that respective mapping mechanism (inside the pyramid). The respective mapped results output signal (e.g., 312o, 313o, 316o, etc.) can define a sorted list of ranked identifications of internal points, nodes and/or subregions within the represented space of the respective mapping mechanism (e.g., 312″, 313″, 316″, etc.) where those identified internal parts which are deemed most likely for a given time period (e.g., “Now”) are ranked highest to thereby indicate which focused upon cognitions of the respective social entity (e.g., STAN user 301A′) with regard to attributes (e.g., topics, context, keywords, etc.) that are categorized within that mapped space are comparatively more or less likely. More specifically, one of the energy-consuming cognitions that a STAN user may consciously or subconsciously have (or not) can be those revolving around the question of what “topic” or “topics” best describe content being currently focused-upon by the user and being thought about by the user under a user-assumed (picked) context. More to the point, if the currently focused-upon content contains the text, “Joe-the-Throw Nebraska” (using the hypothetical Superbowl™ Sunday Party example of above), that alone may not indicate a specific topic being cross-associated in the user's mind with the hypothetical celebrity's name. The topic could be, what book does Joe recommend to his Twitter™ followers? The topic could be, what food does Joe like to eat; or it could pertain to the current state of Joe's health. And so on. A recent heat map history of where the specific STAN user (e.g., 301A′) has been recently casting a predominant amounts of his/her attention giving energies may give hints, clues and best guess answers as to which topic node(s) in system-maintained topic space is/are the more likely one(s). More specifically, if the user has been inputting health-related keywords into his utilized search engine, that may help to narrow the likely topic(s) to that or those dealing with the combination of “Joe-the-Throw's” identity and Joe's health.
It is to be understood that sometimes there is no specific “topic” yet emerged in the user's conscious or subconscious mind and instead the user is casting attention giving energies on merely a keyword or keyphrase (where herein and in the context of the disclosure of invention, the term “keyword” is to be understood as encompassing the concept of phrases or other combinations or sequences of text and/or sounds rather than merely one word taken at a time) that a user would input into a respective search engine for the purpose of retrieve corresponding search results. The user could instead be casting attention giving energies on merely a scent or a feeling. As explained above, in accordance with one aspect of the present disclosure, users of the STAN_3 system may be brought into an online and/or a real life (ReL) joinder with other users on the basis of shared cognitions or experiences including on the basis of non-topical and/or non-textual shared cognitions where the mapped cognitions of the respective users are deemed by the system to be substantially same or similar based on relative hierarchical and/or spatial distances within corresponding Cognitions-representing Spaces.
The “triangulation” wise identified points, nodes or subregions of a CFi and XP driven mapping mechanism (e.g., 302″, 312″, 313″, 316″ of
As a more specific example, user 301A′ may be interested in locating other system users who were located in a particular geographic region (e.g., California, USA) and who focused their attention giving activities upon a specific one or more subregions of topic space (313″) while also operating in a specific context (e.g., “at work”) where this occurred in a specified time zone (e.g., last month). The various Cognitive Attention Receiving Spaces maintained by the STAN_3 system (not all shown in
One of the system-maintained location “spaces” is a real life (ReL) geography mapping mechanism whose points, nodes and/or subregions cross-correlate with real life locations on the basis of a variety of designations including but not limited to, GPS coordinates; latitude, longitude, altitude coordinates; street map coordinates (e.g., postal address and street name) and so on. A user's personhood profile (e.g., CpCCp) may include logical links pointing into the system-maintained ReL geography mapping mechanism (not shown) and identifying parts thereof as being the user's “normal work place”, “normal place of residence” (a.k.a. “home”) and so on. The combination of the user's currently activated personhood profile (e.g., CpCCp) and the system-maintained ReL geography mapping mechanism (not shown) then provides a ReL location-to-context mapping. Such mapping may include use of knowledge base rules (KBR's). For example: IF Month=June-August THEN Home=GPScoords(x1,y1,z1) ELSE Home=GPScoords(x2,y2,z2). The system's context space mapping mechanism 316″ does not contain specific information about most users' home address, workplace address, etc.; but instead refers abstractly to such context-oriented items as, for example, Primary Home, Secondary Home, etc. The reason is because the system's context space mapping mechanism 316″ is used as a collectively shared resource among many users and not as an individualized resource. This will become clearer when
While real life (ReL) location is one type of spatial location that can be mapped and tracked by the STAN_3 system, it is within also within the contemplation of the present disclosure to similarly map virtual life (e.g., SecondLife™) locations, except with a separate mapping mechanism dedicated to a respective virtual life support platform.
Real life (ReL) time durations (e.g., this week, this day, this hour; last month, etc.) are similarly mapped in a system-maintained ReL time mapping mechanism (not shown). Each user's personhood profile (e.g., CpCCp) may include logical links pointing into the system-maintained ReL time mapping mechanism (not shown) and identifying parts thereof as being the user's “normal work week”, “normal time at home” and so on. The combination of the user's currently activated personhood profile (e.g., CpCCp, in its user Demographics section) and the system-maintained ReL time mapping mechanism (not shown) then provides a ReL time-to-context mapping. Such mapping may include use of knowledge base rules (KBR's). For example: IF Month=June-August THEN “Normal Work Week”=None ELSE “Normal Work Week”=Monday/9:00 AM to Friday/5:00 PM. The system's context space mapping mechanism 316″ does not contain specific information about most users' normal work hours, normal vacation time, etc.; but instead refers abstractly to such context-oriented items as, for example, “Normal Work Week”, “Normal Vacation Time”, etc. Once again, the reason for this is because the system's context space mapping mechanism 316″ is used as a collectively shared resource among many users and not as an individualized resource. This aspect will become clearer when
While real life (ReL) time periods is one type of chronological location that can be mapped and tracked by the STAN_3 system, it is within also within the contemplation of the present disclosure to similarly map virtual life (e.g., SecondLife™) chronological locations, except with a separate mapping mechanism dedicated to each respective virtual life support platform. Accordingly interactions between virtual personas or between real and virtual personas can be specified for purpose of creating chat or other forum participation opportunities just as interactions just between real life (ReL) persons can be tracked.
When an individual user's CFi signals (and/or other signals like CVi's and HyCFi's) upload into the STAN system cloud (and/or other support platform), they generally have “normalizing” data added to them or substituted for them so that they can better match with consensus-wise defined, communal cognitions and/or communal expressions. More specifically, if the uploading CFi's of user 301A′ (
At this point in the discussion, an important observation that was made above is again repeated with slightly different wording. The user (e.g., 301A′) is part of his/her own context(s) from under which his or her various attention giving actions emanate and that/those individualized context(s) may be mapped to corresponding, communally understandable (e.g., more generalized) contexts that populate a communally created and communally updated context space (XS). More specifically, the user's currently “perceived” and/or “virtual” (PoV) set of contextual states (what is activated in his or her mind) is part of the individualized context from under which that user's actions emanate. So if the user is thinking to him/herself, “I am currently taking on the role of Senior Software Design Engineer” that is part of that user's overall and individually-adopted context. Often, the user's current physical surroundings (location, furniture, operational data processing devices, etc.) and/or body states (collectively denoted as 301x) are part of the perceived context from under which the individual user's actions emanate. The user's current physical surroundings and/or current body states (301x) can be sensed by various sensors, including but not limited to, sensors that sense, discern and/or measure: (1) current location and time (in real life (ReL) and/or in a virtual world that the user is participating within; (2) surrounding images and their locations relative to the user, (3) surrounding sounds and their locations relative to the user, (4) surrounding physical odors or chemicals, (5) presence of nearby other persons (not shown in
The feedback loop is not an entirely closed and isolated one because the real physical surroundings and state indicating signals 301x′ (which include the XP signal) of the user are included in the input vector signals (e.g., 316v) that are supplied to the context mapping mechanism 316″. Thus context is usually not determined purely due to guessing about the currently activated (e.g., lit up in an fMRI sense) internal mind states (PoV's, a.k.a. “perceived” and/or “virtual” set of contextual states) of the individual user 301A′ based on previously guessed-at mind states but rather also on the basis of surrounding reality. The real physical surrounding context signals 301x′ (a.k.a. the XP signals) of the user are grounded in physical reality (e.g., What are the current GPS coordinates of the user? What non-mobile devices is he proximate to? What other persons is he proximate to? What is their currently determined context? What biometric data is currently being collected from the user? and so on) and thus the output signals 316o of the context mapping mechanism 316″ are generally prevented from running amuck into purely fantasy-based determinations of the likely current mind set of the user. Moreover, fresh and newly received CFi signals (302e′ and 298e′) are repeatedly being admixed into the input vector signals 316v. Thus the profiles-to-context space feedback loop is not free to operate in a completely unbounded and fantasy-based manner but instead keeps being re-grounded with surrounding physical realities.
With that said, it may still be possible for the context mapping mechanism 316″ to nonetheless output context representing signals 316o that make no sense (because they point to or imply untenable nodes or subregions in other spaces as shall be explained below). In accordance with one aspect of the present disclosure and in an embodiment, the conflicts and errors resolving module 301pvp automatically detects such untenable conditions and in response to the same, automatically forces a reversion to use of the default set of safe profiles 301d. In that case, the context mapping mechanism 316″ “learns” that its previous context-determining steps were erroneous ones and adaptively alters its neural net and/or other trainable modeling parts and then restarts from a safe broad definition of current user profile states and then tries to narrow the definition of current user context to one or more, smaller, finer subregions (e.g., XSR1 and/or XSR2) in the communally created and communally updated context space (XS) as new CFi signals 302e′, 298e′ are received and processed by CFi categorizing-mechanisms 302″ and 298″ and then processed by the context mapping mechanism 316″ as well as other such mapping mechanisms (e.g., 313″, 314″ etc.) included within the STAN_3 system.
It will now be explained in yet more detail how input vector signals (like 316v) for the mapping mechanisms (e.g., 316″, 313″, etc.) are generated from raw CFi signals and the like. There are at least two different kinds of energetic activities the user (301A′ of
The CFi's processing portion of system 300D of
Incidentally, just as each user may have one or more unique (e.g., idiosyncratic) facial expressions or the like for expressing internal emotional states (e.g., happy, sad, angry, etc.), each user may also have one or more unique other kinds of expressions or codings (e.g., unique keywords, unique topic names, etc.) that they personally use to represent things that the more general populace (the relevant community) expresses with use of other, more-universally accepted expressions (e.g., popular keywords, popular topic names, etc.). More specifically, and using the hypothetical example of the Superbowl™ Sunday Party up top, one system user may have an idiosyncratic pet name he uses in place of a more commonly, communally used name for a well known celebrity. The nonconforming user might routinely refer to “Joe-the-Throw Nebraska” as “Yo Ho Joe”. This kind of information is stored in a currently activated personhood profile of the user, under a section entitled for example, Favorite Idiosyncratic Keywords, where a translation to the more commonly used terminology (e.g., “Joe-the-Throw Nebraska”) is included and where the STAN_3 system automatically performs the translation when normalizing the raw CFi's received from that individual user. More generally and in accordance with one aspect of the disclosure, one or more of the user profiles 301p include expression-translating lookup tables (LUT's) and/or knowledge base rules (KBR's) that provide translation from relatively idiosyncratic CFi expressions often produced by the respective individual user into more universally understood (communally understandable), normal CFi expressions. This expression normalizing process is represented in
In addition to replacing and/or supplementing ‘abnormal’ (user-idiosyncratic) CFi-transmitted expressions with more universally-accepted and/or spell-corrected counterparts, the system includes a new permutations generating module 302qe3′ which automatically tests CFi-carried material for intentional uniqueness by, for example, detecting whether plural reputable users (e.g., influential persons) have started to use a unique and previously not commonly seen pattern of CFi-carried data at about the same time. This may signal that perhaps a newly observed pattern or permutation is not an idiosyncratic aberration of one or a few non-influential users but rather that it is likely being adopted by the user community (e.g., firstly by influential early-adopter or Tipping Point Persons within that community, and later by following others) and thus it is not a misspelling or an individually unique pattern (e.g., a pet idiosyncratic name) that is used only by one or a small handful of users in place of a more universally accepted pattern. If the new-permutations generating module 302qe3′ determines that the new pattern or permutation is being adopted by the user community, the new-permutations generating module 302qe3′ automatically inserts a corresponding new node into the system-maintained keyword expressions space (e.g., in expressions layer 371 of
In addition to replacing and/or supplementing ‘abnormal’ (user-idiosyncratic) CFi-transmitted expressions with more universally-accepted and/or spell-corrected counterparts, the system includes an expressions expanding or supplementing/augmenting module (not separately shown, but part of the 302qe′ complex) which optionally adds to the normalized expressions already provided by the individual user, supplemental expressions that are of similar meaning (e.g., synonyms) and/or are of opposite meaning (e.g., antonyms) and/or are of similar sound (e.g., homonyms). This may be done by referencing online Thesauruses and/or dictionaries and/or system-maintained lists that provide such augmenting information. In this way, if the user picked a non-idiosyncratic, but nonetheless not popularly used term, the system can automatically add a more popularly used term to the mix and, as a result, the context and/or other mapping mechanisms (e.g., 316″, 313″ of
Sometimes, a same one system user can have multiple sensing machines (e.g., 298a′, 302b′, 304) reading out similar and basically duplicative CFi reporting records for uploading into the system cloud. Such redundant generating of duplicative CFi's may make it appear as if the respective user is more intensely focused-upon something than is really the case. However, each locally generated CFi signal usually has attached to it at least a time stamp if not also a location stamp and/or machine ID stamp and/or user ID stamp and/or data-type indicating stamp (e.g., image data, text data, coded data, biometric data, etc.). When a string or streamlet of CFi signals are received at the head end (e.g., cloud end) of the STAN_3 system, in one embodiment they are preprocessed by a data deduplicating module (not shown) which is configured to detect likely data duplication conditions and remove data that is likely to be duplicative from the data stream sent further upstream for yet further processing. In this way, the upstream resources are not unduly swamped with duplicative CFi data so that, for example, one person's duplicative CFi's do not unfairly swamp out (e.g., out-vote) another person's CFi's just because the latter user has a fewer number of local CFi generators than does the first user. In one embodiment, the number of CFi generating instruments that can simultaneously supply CFi reporting records on behalf of a respective individual user (e.g., 301a′) is limited to a predefined number and hierarchical rankings are attributed to different ones of such duplicative reporting instruments whereby, if the predetermined CFi inputs per person per unit of time threshold is exceeded, the lower ranked ones among the duplicative reporting instruments are disabled or ignored first so that the higher quality, better reporting ones are the ones who contribute to the limited reporting bandwidth granted to each STAN_3 system user. (Of course, in one embodiment, users who pay for premium subscriptions are granted a higher maximum CFi's/unit-time value than are those with no or lesser subscriptions.)
After deduplication, the received CFi signals are sorted according to data type. As indicated above, CFi signals are typically delivered to the head end of the system core (e.g., cloud 410) with time, location and data type stamps attached to the payload data. One payload may represent simple text content (e.g., ASCII encoded) while another payload may represent simple sound content (e.g., .wav encoded) and yet another payload may represent bit-mapped encoded imagery (e.g., .bmp encoded). These different data types are sorted according to their data types so that sounds get stored adjacent to other sounds of the same general time-stamped period and/or of the same general location-stamped place and so that odor (smell) indicating signals get stored adjacent to other odor (smell) indicating signals of same place/time and so on. This is a first step in categorizing and parsing the possibly multi-typed ones of the received CFi signals. The goal is to form clusters of reasonably combinable CFi primitives that pass so-called, sanity checks before being used to build more complex combinations or clusterings of CFi signals. More specifically, if a musical-tone detecting sensor (not shown) at the user end (301A′) sends a first CFi packet holding 3 notes and then sends a second CFi packet holding 5 more notes, it is possible and likely that the total of 8 notes belong together as part of one melody; or perhaps they don't. Perhaps the latter 5 notes need to instead be clustered with the payload of yet a third, not yet, but to-be-sent CFi packet containing 7 further notes. In other words, there are a number of possible first level “permutations” here for clustering together received sequences of CFi signals, namely: (1) CFiPacket#1 (first 3 notes) belongs or does not belong as a prefix to CFiPacket#2 (next 5 notes); (2) CFiPacket#2 (the 5 notes) belongs or does not belong as a prefix to CFiPacket#3 (next 7 notes); (3) all of CFiPacket#1, #2 and #3 belong together as a continuous melody; (4) none of CFiPacket#1, #2 and #3 belong together as a continuous melody. The concept of forming likely “permutations” or clusters of alike CFi data signals; and then clusters of clusters will be explored in more detail later below.
First, and getting back to basics, it is to be understood that each of the CFi generating units 302b′ and 298a′ of
One of the basic processings that the data packet receiving servers (or automated services) perform at a front or downstream receiving part of the head end is to group (e.g., cluster and/or cross-associate with logical links) the separately received packets of respective users and/or of data-originating devices according to user-ID (and/or according to local originating device-ID and/or data-type ID) and to also group received packets belonging to different times of origination and/or different times of transmission into respective chronologically ordered groups of alike types of data. In other words, musical note signals get grouped with other musical note signals, image defining signals get grouped with other and alike (e.g., .bmp, .jpg, .mp3) image defining signals and so on. The so pre-processed CFi signals are then normalized by normalizing modules like 302qe′-302qe2′ if the signals had not been yet normalized (e.g., de-idiosyncratized) earlier downstream. Then the normalized CFi and/or context indicating signals are fed into CFi clustering, cross-associating and categorizing-mechanisms 302″ and 298″ provided further upstream for yet further processing. (This further processing will be explained shortly but later below). At this stage it is understood that the muddled streams of data from different users and different ones of their local sensors have been untangled and purified, so to speak, such that the CFi data payloads of a first user, UsrA have been sorted out and stored in a storage area associated with user UsrA while the CFi data payloads of a second user, UsrB have been sorted out and stored in a storage area associated with that second user, UsrB. Moreover, for each user (for each persona of each user), the received CFi data payloads have further been chronologically and type wise and location wise been untangled and purified, so to speak, such that musical notes data picked up by a respective first musical-notes sensor are grouped together with one another in a correct time ordered manner and such that musical notes data picked up by a respective second musical-notes sensor (at a different location) are grouped together with one another in a correct time ordered manner, and the so-ordered data sets are further organized relative to one another in chronologically and type wise and location wise manner, and so on. More specifically, for the given example, the first and second musical-notes sensors may be differently placed microphones within an orchestra and the picked up notes may be from different musical instruments (e.g., piano, violin, clarinet) where the orchestra is playing harmonized stanzas which respectively are intended to be cognitively perceived in organized combinations or clusterings. Therefore one of the intended functions of a CFi's storing and organizing space such as 302″ is to store in context appropriate organizations, CFi signals whose represented physical counterparts were intended by the user (301A′) or another to be cognitively perceived in relative unison.
The first set of sensors 298a′ have already been substantially described above (as eyeball movement trackers, head direction trackers, etc.). A second set of sensors 302b′ (referred to here as attentive-outputting tracking sensors) are also provided and appropriately disposed for tracking various expression outputting (code outputting) actions of the user, such as the user uttering in-context words (301w), consciously nodding or shaking or wobbling his head, typing on a keyboard, making apparently-intentional hand gestures, clicking, tapping or otherwise activating different activateable data objects displayed on his screen and so on. As in the case of facial expressions that show attentive inputting of user accessible content (e.g., what is then displayed on the user's computer screen and/or played through his/her earphones even though the user may not watch it or listen to it), unique and abnormal output expressions (e.g., pet names for things, pre-coded combinations of tongue projections and other actions, a.k.a. hot-keying gestures) are run through expression-translating lookup tables (LUT's) and/or knowledge base rules (KBR's) of then active PEEP, CpCCp and/or other profiles for translating such raw expressions into more normalized (less idiosyncratic), Active Attention Evidencing Energy (AAEE) indicator signals of the outputting kind. In one embodiment, the in-context uttered words of the user are supplied to an automated speech recognition module (not shown) that automatically uses context (e.g., signal 316o) in combination with speech pattern matching to then generate semantic codings representing the user uttered words in a textual and/or other more readily processible manner. The so-generates, semantic codings of the user's raw outputs form part of the “normalized” output signals of the user. The normalized AAEE indicator signals 298e′ of the inputting kind have already been described above. One example, by the way, of the normalization of abnormal output expressions may occur when the respective user is a multilingual user and is using an uncommon foreign language whereas keyword expressions then being received by the head end are pre-characterized as needing to belong to one agreed-upon standard language (e.g., English). In that case, words that the respective user may inadvertently output in a non-standard language are automatically translated into the agreed-upon standard language (e.g., English).
The normalized Active Attention Evidencing Energy (AAEE) signals, 302e′ and 298e′ are next inputted into corresponding first and second CFi clustering/categorizing mechanisms 302″ and 298″ as already mentioned. These clustering/categorizing mechanisms organizingly store the separately received CFi signals (302e′ and 298e′) into yet more finely categorized and usable groupings (clusterings and/or categories) than just having them grouped according to user-ID and/or time or telemetry origination and/or location of telemetry origination. The further organizing of the received CFi signals (302e′ and 298e′) is carried out with aid of so-called, CFi categorizing, clustering and inferencing engines 310′ that connect in a feedback loop manner to the CFi clustering/categorizing spaces (mapping mechanisms) 302″ and 298″ and also in a feedback loop manner to other system-maintained mapping mechanisms (e.g., to content source space 314″ (css), to context space (xs), to emotions space (es), and so on). One form of such finer categorizing of the received CFi signals (302e′ and 298e′) is to parse them as being limbically-directed CFi's (example: “Please can't we just all get along without engaging in ad hominem attacks?”), as being neo-cortically directed CFi's (example: “Those numbers do not add up.”) or as being more primitive cognitions (example: “You have me blowing coffee out of my nostrils and laughing out loud (LOL)”). Another form of such finer categorizing of the received CFi signals (302e′ and 298e′) is to parse them as being loosely directed to one broad topic domain or another (example: the liberal arts versus the math and science arts). Additionally, the finer categorizing of the received CFi signals (302e′ and 298e′) includes parsing them according to more likely groupings (clusterings) and less likely combinatorial assemblages.
This latter part of the improved grouping/clustering process provided by the CFi categorizing, clustering and inferencing engines 310′ is best explained with a few yet more specific examples. Assume that within the 302e′ signals (AAEE outputting signals) of the corresponding user 301A′ there are found three keyword expressions: KWE1, KWE2 and KWE3 that have been input into a search engine input box, one at a time over the course of, say, 9 minutes. (The latter timings can be automatically determined from the time stamps of the corresponding CFi data packet signals that carry the keyword payloads.) One problem for the CFi categorizing mechanism 302″ (and its clustering/organizing engines 310′) is how to resolve whether each of the three received and stored keyword expressions: KWE1, KWE2 and KWE3 is directed to a respective separate topic or whether all three are directed to a same topic such that they should be processed as the full combination of all three keywords or whether some other permutation holds true (e.g., KWE1 and KWE3 are directed to one topic but the time-wise interposed KWE2 is directed to an unrelated second topic—or is just a nonsense word inadvertently thrown in to the sequence of events). This is referred to here as the CFi grouping and parsing problem. Which CFi's belong with each of the others and which belong to another group or stand by themselves or do not belong at all (and thus deserve to be ignored)? By way of a more specific example, assume that KWE1=“Lincoln” and KWE3=“address” while KWE2=“Goldwater” although perhaps the user (a Fifth Grade student) intended a different second keyword such as “Gettysburg”. (Note: At the time of authoring of this example, a Google™ online search for the string, “lincoln goldwater address” produced zero matches while “lincoln gettysburg address” produced over 500,000 results. An educated human being can quickly see that the example of KWE2=“Goldwater” does not belong. It makes no sense. But for a computer, the problem may not be easily spotted and resolved.
A second problem for the CFi clustering/categorizing mechanism 302″/310′ is how to resolve what kinds of CFi signals is it receiving in the first place?How did it know that expressions: KWE1, KWE2 and KWE3 were in the “keyword” category, as opposed to, for example, in the URL's category? In the case of keyword expressions, that question can be resolved fairly easily because the exemplary KWE1, KWE2 and KWE3 expressions are detected as having been submitted to a search engine through a search engine dialog box or a search engine input procedure. But other text-based CFi's and more to the point, non-textual CFi's, can be more difficult to categorize. Consider for example, a nod of the user's head up and down by the user and/or a simultaneous grunting noise made by the user. What kind of intentional expression, if at all, is that? The answer depends at least partly on context, culture and/or user mood. If the most recent context state of the user is determined by the STAN_3 system 410 (by output signal 316o in
In general, the CFi receiving and clustering/categorizing mechanisms 302″/298″ and the interconnected engines 310′ first cooperatively assign incoming CFi signals (e.g., normalized/augmented CFi signals) to one or the other or both of two mapping mechanism parts, the first being dedicated to handling information outputting activities (302′) of the user 301A′ and the second being dedicated to handling more passive information inputting activities (298′) of the user 301A′. If the CFi receiving and categorizing mechanisms 302″/298″/310′ cannot parse as between the two, they copy the same received CFi signals to both sides. Next, the CFi receiving and categorizing mechanisms/engines 302″/298″/310′ try to categorize the received CFi signals into predetermined subcategories unique to that side of the combined categorizing mapping mechanism 302″/298″. Keywords versus URL expressions would be one example of such categorizing operations. In this case, both of keywords and URL's belong to a broader class of sequential textual content (which could include sequentially supplied codes or symbols as well as traditional alphanumeric characters). Musical notes versus random background noise may be another example of CFi's of different categories. (Ultimately, musical background notes might be mapped as corresponding to communally-created and communally-accepted music primitives having data structures such as shown in
URL string expressions can be automatically categorizing as such (as being Universal Resource Locator type expressions) by their prefix and/or suffix and/or in-fix strings (e.g., by detection of having a “dot.com” character string embedded therein or having the “at mark” symbol infixed therein if it is an email address for example). Other such categorization parsings include but are not limited to: distinguishing as between meta-tag type CFi's, image types, sounds, emphasized text runs (e.g., those that are italicized, bolded, underlined, etc.), body part gestures, topic names, context names (i.e. role undertaken by the user), physical location identifications, platform identifications, social entity identifications, social group identifications, neo-cortically directed expressions (e.g., “Let X be a first algebraic variable . . . ”), limbically-directed expressions (e.g., “Please, can't we all just get along?”), and so on. More specifically, in a social dynamics subregion of a hybrid topic and context space, there will typically be a node disposed hierarchically under limbic-type expression strings and it will define a string having the word “Please” in it as well as a group-inclusive expression such as “we all” as being very probably directed to a social harmony proposition. In one embodiment, expressions output by a user (consciously or subconsciously are automatically categorized as belonging to none, or at least one of the following layers of a triune brain model: (1) neo-cortically directed expressions (i.e., those appealing to the intellect), (2) limbically-directed expressions (i.e., those appealing to social interrelation attributes) and (3) reptilian core-directed expressions (i.e., those pertaining to raw animal urges such as hunger, fight/flight, etc.). In one embodiment, the neo-cortically directed expressions are automatically allocated for processing at least by the topic space mapping mechanism 313″ because expressions appealing to the intellect are generally categorizable under different specific topic nodes. In one embodiment, the limbically-directed expressions are automatically allocated for processing at least by the emotional/behavioral states mapping mechanism 315″ because expressions appealing to social interrelation attributes are generally categorizable under different specific emotion and/or social behavioral state nodes. In one embodiment, the reptilian core-directed expressions are automatically allocated for processing by at least a biological/medical/emotional state(s) mapping mechanism (315″, see also exemplary primitive data object of
As mentioned, the automated and augmenting categorization of incoming CFi's is performed with the aid of one or more CFi clustering/categorizing and inferencing engines 310′ where the inferencing engines 310′ have access to categorizing nodes and/or subregions within, for example, to parts within topic and/or context space and/or within biological states space (e.g., in the case of the social harmony invoking example given immediately above: “Please, can't we all just get along?”) or more generally, access to categorizing nodes and/or subregions within the various system-maintained Cognitive Attention Receiving Spaces (CARSs). The inferencing engines 310′ receive as their inputs, last known state signals (e.g., 316o) from various ones of the state mapping mechanisms (CARSs) as representing rough indications of associated CARSs points cross-correlating to current CFi clusters and indirectly, the respective user's state of mind. More specifically, the last determined to be most-likely context states are represented by “xs” signals received by the inferencing engines 310′ from the output 316o of the context mapping mechanism 316″; the last determined to be most-likely focused-upon sub-portions of content materials are represented by “css” signals received from the output 3140 of the content source space mapping mechanism 314″ (where 314″ stores pointers to (e.g., URL's to), or abbreviated representations of content that is likely available to be currently focused-upon by the user 301A′); the previously determined to be most-likely CFi clusterings/categorizations are received as currently stored “HyCFis” signals from the CFi categorizing mechanism 302″/298″; the last determined as probable emotional/behavioral states of the user 301A′ are received as “es” signals (emo signals) from an output 3150 of an emotional/behavioral state mapping mechanism 315″, and so on.
In one embodiment, the inferencing engines 310′ operate on a weighted assumption that the past is a good predictor of the present and of the near future. In other words, the most recently determined states “xs”, “es”, “HyCFi's of the respective CFi's from the one user (or of another social entity that is being processed) are first used for categorizing the more likely categories for next incoming new CFi signals 302e′ and 298e′. The “css” signals tell the inferencing engines 310′ what content was logically available (e.g., on a nearby TV screen—by looking up TV show scheduling databases, on a nearby computer screen, via nearby loudspeakers or earphones, etc.) to the user 310A′ at the time one of the CFi's was generated (time and place stamped CFi signals—see 30U.10 of
Yet more specifically and by way of example, it will be seen below that the present disclosure contemplates a music-objects organizing space (or more simply a music space, see
Aside from categorizing individual ones of the incoming CFi's as being one type or another (e.g., textual versus melodic), the CFi clustering/categorizing and inferencing engines 310′ parse and group (cluster) the incoming CFi's as either probably belonging together with each other or probably not belonging together. It is desirable to correctly group together emotion-indicating CFi's with their cross-associated non-emotional CFi's (e.g., keywords, URL's) because that is later often used by the system to determine how much “heat” a user is casting on one node or another in topic space (TS) and/or in other such spaces (e.g., keyword space, URL space, and so on). More specifically, if biological state telemetry indicates the user's heart rate has suddenly increased, his/her respiration level has increased, and the user's current PEEP record indicates that this user tends to experience such increase of heart rate (e.g., beats per minute) approximately 10 seconds after having visually perceived emotionally-inciting content, the system can then logically cross-associate the later-in-time, fight-or-flight reaction (e.g., increased heart rate/increased respiration rate) with content that was presented to the same user 10 seconds ago. Consequently, that content, and/or the URL of the site from which it was presented, are given enhanced “heat” signatures.
In terms of a yet more specific example, consider again the sequentially received set of keyword expressions: KWE1, KWE2 and KWE3; where as one example, KWE1=“Lincoln”, KWE3=“address” while KWE2 is something else and its specific content may color what comes next. More specifically, consider how topic and context may be very different in a first case where KWE2=“Gettysburg” versus an alternate case where KWE2=“car dealership”. Those familiar with contemporary automobile manufacture would realize that “Lincoln car dealership” probably corresponds to a sales office of a car distributor who sells on behalf of the Mercury/Lincoln™ brand division of the Ford Motor Company. “Gettysburg Address” on the other hand, corresponds to a famous political event in American history. These are usually considered to be two entirely different topics and normally would have two separate nodes or subregions in topic space, although a topic node covering both at the same time is possible.
Assume also that about 90 seconds after KWE3 was entered into a search engine and results were revealed to the user, the user 301A′ became “anxious” (as is evidenced by subsequently received physiological CFi's; perhaps because the user is in Fifth Grade and just realized his/her history teacher expects the student to memorize the entire “Gettysburg Address”). A question for the machine system to resolve in this example is which of the possible permutations of KWE1, KWE2 and KWE3 plus the emotion-indicating CFi that followed form a cross-associated cluster indicating there is a specific keyword expressions clustering (where the latter clustering in keyword space points to a corresponding topic in topic space—see keyword to topic link 370.6 of
It should be recalled at this juncture that the inferencing engines 310′ of
In one embodiment, the inferencing engines 310′ alternatively or additionally have access to one or more online search engines (e.g., Google™, Bing™) and/or Wiki-sites (e.g., Wikipedia™) and the inferencing engines 310′ are configured to submit some of their entertained keyword permutations to the one or more online search engines and/or wiki engines (and in one embodiment, in a spread spectrum fashion so as to protect the user's privacy expectations by not dishing out all permutations of all CFi clusters to just one search/wiki engine) and to determine the quality (and/or quantity) of matches found so as to thereby perform a sanity check and automatically determine the likelihood that the entertained keyword permutation is a relatively valid one (e.g., one that can make semantic sense) as opposed to being a set of unrelated terms which combination is not worthy of prioritized consideration at the moment. However, in discovering that one permutation of, say plural keywords has more search engine hits than another, the inferencing engines automatically discount the popularity of shorter keyword permutations versus longer ones (ones with more terms to match) because, of course; the shorter ones are more likely to have a larger number of hits. For example, the one keyword, “Lincoln” will typically draw a much larger number of hits (matches) than the more defined, two word permutation of “Lincoln AND Address”. In one embodiment, the system is configured to prefer medium sized clusters of roughly three words each (or more specifically, in the range of two words minimum and five words maximum as an example); e.g., “Lincoln AND Gettysburg AND Address” over one word clusters and over say, 7 word clusters. The reason is because it has been found that the human brain works best in building up concepts as singlets, doublets and triads of linguistic cognitions (e.g., “the”/“quick brown fox”/“jumped over”).
More generally speaking, the inferencing engines 310′ function as trial permutation generating engines which generate different trial permutations of clustered or otherwise grouped together CFi's or HyCFi's and then test the generated permutations for cross-correlation strengths relative to search engine results for the same trial permutations and/or for cross-correlation strengths relative to best-matched points, nodes or subregions of system-maintained/stored Cognitive Attention Receiving Spaces (CARSs), where respective cross-correlation strength scores are then assigned to the tested CFi and/or HyCFi permutations (and discounted for the unfair advantage that short permutations have over longer ones). The scored permutations are then sorted and stored as a sorted list. A subset of the scored permutations that have comparatively highest scores (after discounting for length and number of words) are then used to identify corresponding ones of the CARSs and points, nodes or subregions within them as being most likely ones of such portions of the system-maintained CARSs to which the received and test-wise clustered CFi's belong (see briefly, cluster definer 30U.12 in
In terms of a more specific example, if the permutation of “Lincoln's Address” (“KWE1 AND KWE3” of the above example where KWE2 is ignored) receives the highest, post-discount cross-correlation scores, that permutation is combined with demographic context information indicating, for example, that the respective user is a Fifth Grade student now trying to do his/her history homework. The context-augmented search permutation is then applied for example, as an input vector into the topic space mapping mechanism 313″ with instructions to find the best matching nodes or subregions for that context-augmented search permutation (e.g., a Fifth Grade Student doing homework re a so-called, “Lincoln's Address”). Those will likely lead to topic nodes that are relevant to the specific user and his/her current areas of focus. It is within the contemplation of the present disclosure to repeat the above for creating sorted lists of hybrid-wise clusters of clusters (e.g., “KWE1 AND KWE3” AND “URL5 AND URL7”); and then clusters of clusters of clusters and so on.
Stated in other words, eventually, the inferencing engines 310′ will have automatically built up and entertained a more complex keyword permutation represented for example by “KWE1 AND KWE3 AND Context=user's current context” (e.g., “Lincoln's Address for purposes of a Fifth Grade Student”) of the above given example. Then, according to this example, the inferencing engines 310′ determine the probable sanity of this more complex keyword permutation by trying to find one or more corresponding nodes and/or subregions in keyword and context hybrid space (e.g., cross-correlating strongly with “Lincoln's Address”) and/or many search hits from the utilized online search engines (e.g., Google™, Bing™) where some nodes and/or hits are identified as being more likely than others to be applicable, given the demographic context of the user 301A′ who is being then tracked (e.g., a Fifth Grade student). This tells the inferencing engines 310′ that the “KWE1 AND KWE3” permutation is a reasonable one that should be further processed (ahead of other less likely, more lowly scored permutations) by the topic and/or other mapping mechanisms (313″ or others) so as to produce a current state output signal (e.g., 313o) corresponding to that reasonable-to-the-machine keyword permutation (e.g., “KWE1 AND KWE3”) and corresponding to the then applicable user context (e.g., a Fifth Grade student who just came home from school and normally does his/her homework at this time of day). One of the outcomes of determining that “KWE1 AND KWE3” is a more likely to be valid permutation while “KWE2 AND KWE3” is not or is an unlikely to be sensible one (because KWE2 is accidentally interjected noise) is that the timing of emotion development (e.g., user 301A′ becoming “anxious”) can be cross-associated as likely to have begun either with the results obtained from user-supplied keyword, KWE1 or the results obtained from KWE3 but not from the time of interjection of the accidentally interjected KWE2. That outcome may then influence the degree of “heat” and the timing of “heat” cast on topic space nodes and/or subregions that are next logically linked to the keyword permutation of “KWE1 AND KWE3”. Thus it is seen how the CFi-permutations testing and inferencing engines 310′ can help form reasonable groupings or clusterings of keywords and/or other CFi's that deserve prioritized further processing while filtering out unreasonable groupings that will likely waste processing bandwidth in the downstream mapping mechanisms (e.g., topic space 313″) without likely producing useful results (e.g., valid topic identifying signals 313o).
The grouped (e.g., clustered or cross-associated and thus parsed) and categorized CFi permutations are then selected and applied for further testing against nodes and/or subregions in what are referred to here as either “pure” data-objects organizing spaces (e.g., like topic space 313″) or “hybrid” data-objects organizing spaces (e.g., 397 of
Still referring to
Referring yet a bit longer to
Stated in alternative words, topic space is not the one and only means by way of which STAN users can be automatically joined together based on the CFi's up or in-loaded on their behalf into the STAN_3 system core from their local monitoring devices. The raw CFi's alone (298e′, 302e′) or normalized ones may provide a sufficient basis by themselves for automatically generating invitations and/or suggesting additional content for the users to look at. It will be seen shortly in
The types of raw CFi's (298e′, 302e′) or normalized/categorized CFi's (298o, 302o) that two or more STAN users have substantially in common are not limited to text-based information (textual CFi's). It could instead or additionally be musical or other sound-based information that has been normalized into a primitive that represents that non-textual information (see briefly the musical primitive object 30F.0 of
Referring now to
Yet another of the other interlinked mapping mechanisms shown in
Before describing yet further details of the illustrated keyword expressions space 370, a quick return tour is provided here through the hierarchical, and plural tree branches-containing, structure (e.g., having the “A” tree, the “B” tree and the “C” tree intertwined with one another) of the topic space mechanism 313′. In the enlarged portion 313.51′ of the space 313′ as shown in
Additionally, in
Parent node Tn51 of the magnified portion 313.51′ of the topic space mapping mechanism 313′ has a number of chat or other forum participation sessions (forum sessions) 30E.50 currently tethered to it either on a relatively strongly anchored basis (whereby a breaking off from, and drifting away from that mooring is relatively difficult) or on a relatively weak anchored basis (whereby a stretching away from, and/or a breaking off of the corresponding forum (e.g., chat room) and a drifting away from that mooring point Tn51 is relatively easier). Recall that members of chat rooms and/or other forums can vote to drift apart from one topic center (TC) and to more strongly attach one of their anchors (figuratively speaking) to a different topic centers as forum membership and circumstances change. In general, topic space 313′ can be a constantly and robustly changing combination of interlinked topic nodes and/or topic subregions whose hierarchical organizations, names of nodes, governance bodies controlling the nodes, and so on can change over time to correspond with changing circumstances in the virtual and/or non-virtual world and the chat or other forum participation sessions attached to those plastic-wise re-configurable topic nodes or subregions can also change robustly.
The illustrated plurality of forum sessions, 30E.50 are servicing a first group of STAN users 30E.49, where those users are currently dropping their figurative anchors onto those forum sessions 30E.50 and thereby ‘touching’ topic node Tn51 to one extent of cast “heat” energy or another (e.g., casting attention giving energies on that node) depending on various “heat” generating attributes (e.g., duration of participation, degree of participation, emotions and levels thereof detected as being associated with the chat room participation and so on). Depending on the sizes and directional orientations of their halos, some of the first users 30E.49 may apply a halo-extended ‘touching’ heat to child node Tn61 or even to grandchildren of Tn51, such as topic node Tn71. Other STAN users 30E.48 may be simultaneously ‘touching’ other parts of topic space 313′ and/or simultaneously ‘touching’ parts of one or more other spaces, where those touched other spaces are represented in
Referring to now to the specifics of the keyword expressions space 370 of the embodiment represented by
In one embodiment, the “regular” keyword expressions of the near-apex layer 371 come to be spatially clustered around keystone expressions and/or are clustered according to Thesaurus-like senses of the words that are to be covered by the clustered keyword primitives. By way of example, assume again that a first node 371.1 in primitives layer 371 defines its keyword expression (Kw1) as “*lincoln*” where this would cover “Abe Lincoln”, “President Abraham Lincoln” and so on, but where this first node 371.1 is not intended to cover other contextual senses of the “*lincoln*” expression such as those that deal with the Lincoln™ brand of automobiles or the city of Lincoln, Nebr. Instead, the “*lincoln*” expression according to one of those other senses would be covered by another primitive node 371.5 that is clustered elsewhere (371.50) in addressable memory space near nodes (371.6) for yet other keyword expressions (e.g., Kw6?*) related to that alternate sense of “Lincoln”.
The clustering center point (a COGS) 371.50 of the alternate sense, “*lincoln*” expression 371.5 is a point or small subregion in the space of the primitive cognitions layer 371 of keyword space 370 to which that alternate sense expression, “*lincoln*” (371.5) is anchored. Unlike the keyword node 371.5 (Kw1′=“*lincoln*”, but in another cognitive sense), the clustering center point (COGS) 371.50 is not given a specific name or other articulable attributes by system users. Instead, this data object (the COGS 371.50) operates like a shadowy entity that represents a cognitive sense, where the represented COGnitive Sense (where the capitalized letters explain where the acronym COGS comes from) is inferred from the keyword nodes closest to it and where the distances (hierarchically and/or spatially speaking) of the clustered-about nodes relative to the given, cognitive-sense-representing clustering center point (e.g., COGS 371.50) indicates how close in a cognitive sense way, the cognitive senses of the respective nodes are to that of the center point (e.g., COGS 371.50). In other words, the first mentioned Kw1 of the given example, “*lincoln*” (371.1) represents “*lincoln*” taken according to a respective, first cognitive sense (e.g., the 16th President of the United States or 16th POTUS) while the second mentioned Kw1′ of the given example, “*lincoln*” (371.5) represents “*lincoln*” taken according to a respective, second and different cognitive sense (e.g., the Lincoln™ brand of automobiles or the city of Lincoln, Nebr.) and the shadowy, cognitive-sense-representing clustering center points (e.g., 371.0, 371.50) which are most closely disposed (hierarchically and/or spatially) to the respective keyword nodes that have a same keyword expression (e.g., “*lincoln*”) but different cognitive senses for the same, respectively represent the cognitive sense but without providing an “expression” (e.g., Lincoln, the 16th President; or Lincoln, the automobile brand) for that cognitive sense. Instead, each of cognitive-sense-representing clustering center points 371.0 and 371.50 respectively draws its represented cognitive sense from the keyword expressing nodes (e.g., Kw1, Kw2, Kw1′ Kw6) closest to it. It is essentially a symbiotic relationship. The one or more closest COGS (e.g., 371.50) adjacent to a given keyword node gives a cognitive sense form of spin to the keyword expression (e.g., “*lincoln*”) of that node while the one or more closest keyword nodes (e.g., Kw1′ Kw6) to a given COGS (e.g., 371.50) inferentially give cognitive sense to that COGS (e.g., 371.50). If system users vote to add-to or delete or move the keyword nodes (e.g., Kw1′ Kw6) that are closest to a given COGS (e.g., 371.50), such a user-driven change can alter the inferred cognitive sense of the corresponding COGS. On the other hand, if system users vote to add or delete or move the closest COGS's that surround a given keyword node (e.g., Kw2), such a user-driven change can alter the cognitive sense spin that is projected onto the keyword expression (e.g., “*lincoln*”) of that node by the nearest cognitive-sense-representing clustering center points (COGS's).
The hierarchical and/or spatial space of the primitive cognitions layer 371 shown in
In terms of a more concrete example, assume that the cognitive sense of, as well as the expressional equivalent of the alternate sense expression, “*lincoln*” (371.5) is “Lincoln, Nebr.; the City of”. Assume that the cognitive sense of, as well as the expressional equivalent of the nearby Kw6 expression node 371.6 is “Nebraska; The Capital City of”. It turns out that Lincoln Nebr. is the Capital City of the State of Nebraska. Therefore, although the expressions “Lincoln, Nebr.; the City of” and “Nebraska; The Capital City of” are not the same expressions, under a cognitive sense analysis they refer to substantially the same cognitive concept. Hence the hierarchical and/or spatial distance between points, nodes or subregions 371.5 and 371.6 should be approximately zero. In one embodiment, a relative pointer 371.56 that logically links node 371.5 (Kw1′) to node 371.6 (Kw6) includes an indication of how far away, hierarchically and/or spatially, from starting position 371.50 (the clustering center point) is the nearby node 371.6 (Kw6). In this case, the first exemplary node 371.5 (Kw1′) is assumed to be positioned dead center on top of clustering position 371.50 (the clustering center point or COGS). The nearby other node 371.6 (Kw6) is deemed to be slightly spaced apart, and in a corresponding direction, from the clustering center position 371.50 (a relative origin). The data that represents relative pointer 371.56 may also include an indication of the location in system memory where the nearby expression Kw6 (371.5) is stored as well as hierarchical and/or spatial vector indicating how far away and in what direction the nearby expression Kw6 (371.5) is displaced relative to the center point expression Kw1′ (371.5).
In similar fashion, the first used example of keyword expression Kw1 (node 371.1), where its expression, “*lincoln*” is determined by communal consensus to refer to the Abraham Lincoln sense of that expression, is located dead center over different clustering center point 371.0. A relative distancing and direction pointer 370.12 (which like other pointers discussed herein is understood to be a stored physical signal pointing to a stored other physical signal, e.g., the one representing second keyword Kw2) is provided to indicate that the second keyword expression Kw2 has a substantially same or similar cognitive sense as does the first keyword expression Kw1 even if the second keyword expression Kw2 is substantially different from the first keyword expression (e.g., “16th USA President” versus “Ab* Lincoln”). (Because illustration space is relatively tight in
As indicated by the above, each respective, clustering center point (e.g., COGS's 371.0 and 317.50—each represented in
As mentioned, each consensus-wise created or communally-updated clustering center point (e.g., 371.0 or 317.50) has assigned to it a respective hierarchical and/or spatial position in the space of the corresponding Cognitions-representing Space or subregion thereof (e.g., keyword expressions primitives layer 370). Each clustering center point (e.g., 371.0 or 317.50) also has assigned to it a first creation date indicating time stamp and optionally, a list of later position and/or cognitive sense modification dates. Each clustering center point (e.g., 371.0 or 317.50) further has assigned to it a primary expression pointer (not shown) that points to the one keyword expression (e.g., Kw1 371.1) that is deemed by the controlling community to be the expression which is most closely linked with the respective clustering center point (e.g., 371.0). Each clustering center point (e.g., 371.0 or 317.50) may further have assigned to it, one or both of re-direction and expansion pointers 371.52 (both represented by the one arrowed line in
After a clustering center point (e.g., 371.0 or 317.50) is first created by a corresponding governance body and the hierarchical and/or spatial areas around it are populated by associated keyword expressions (e.g., Kw2, Kw3, Kw4, etc.) it may become desirable to add yet further keyword expressions in same close proximity with the cognitive sense represented by the first created center point (e.g., 371.0). However, it may become inconvenient or impractical or otherwise not proper to crowd all the new keyword expressions around the same center point (e.g., 371.0). Instead, it may become desirable to create a “twin” (e.g., 371.51) of the first created center point (e.g., 371.50) in another location of memory. This may be done with use of a so-called, center point “expansion” pointer 371.52 (see also 30W.7ERR of
Alternatively or additionally after a clustering center point (e.g., 371.0 or 317.50) is first created by a corresponding governance body and the hierarchical and/or spatial areas around it are populated by associated keyword expressions (e.g., Kw2, Kw3, Kw4, etc.) it may become desirable to drastically change the keyword expressions associated with that earlier-in-time center point (e.g., 371.50) and/or to drastically change the hierarchical and/or spatial distancings between the surrounding keyword expressions and the center point (e.g., 371.50). At the same time, it may be desirable to preserve legacy structures. Accordingly, rather than erasing an originally created structuring of clustering center points (e.g., 371.0 or 317.50) and surrounding expression nodes thereof (e.g., Kw1 and Kw1′), a re-directing pointer (represented by the same link 371.52 as used for the expansion pointer, see also 30W.7ERR of
In one embodiment, each primary keyword expression node (e.g., Kw1 371.1) of a respective first clustering center point includes a linked list pointer pointing to the next node having a same or substantially same keyword expression but located in a different clustering area. For example, linked list pointer 371.49 may link from the node (371.1) of expression Kw1 to the node (371.5) of identical expression Kw1′ (node 371.5 which is located over different clustering center point 371.50). The latter node would have a similar linked list pointer (not shown) pointing to the next node also having the same keyword expression (e.g., “*lincoln*”) but a different cognitive sense represented by a respective other clustering center point (not shown). In one embodiment, the linked list pointers (e.g., 371.49) also each include a pair of expression ranking values that rank the expressions at the terminal ends of the respective linked list pointer according to which is the most popular cognitive sense for that expression and which is the least. For example, the expression, “911” may have earlier had the cognitive sense of an emergency phone number as its number one ranked sense, However, after September 2001 the World Trade Center attack becomes the new number one ranked sense, System software can quickly scan through the linked list of pointers to find the current, top N cognitive senses for a given expression, where N can be 1, 2, 3, . . . here.
While clustering center points (e.g., 371.0, 371.50, 371.51) have been described thus far as providing, in one instance, a Thesaurus like or semantic type of flavoring to the keyword(s) overlaid directly on top of, or disposed hierarchically and/or spatially nearby to the respective clustering center point (e.g., keyword nodes 371.1, 371.5 and 371.6), more generally speaking, clustering center points (COGS's) can be used to imply other kinds of cognitive senses to respective PNOS-type points, nodes or subregions of other types of Cognitions-representing Spaces (e.g., music space, emotion space, historical events space) where there is no easy way (or any way) to articulate a communal “sense” that a relevant community cognitively attributes to the PNOS's that are disposed hierarchically and/or spatially in close proximity with each respective clustering center point (COGS). More specifically and for example, certain ones of advertising jingles or popular show tunes or movie scenes may evoke in a relevant community (e.g., a specific demographic group) a particular cognitive sensation that cannot be easily described with words and yet, when two or more of those advertising jingles or popular show tunes or movie clips are played to that demographic audience as representative examples of the cognitive sense, the audience knows it when it hears it (or knows it when they see it, this referring to the played video clips). Yet more specifically, and in the case of an American audience, the showing of a first image depicting the raising of the American flag at Iwo Jima, a second image depicting George Washington crossing the Delaware River and a third image depicting George W. Bush with a bullhorn at the attacked World Trade Center site soon after 9/11 may evoke certain emotions of patriotic pride and yet that cognitive sense cannot be easily put into words. In accordance with the present disclosure, nodes representing images such as these (and/or movie clips of this kind) may be closely clustered in a respective imagery space (see for example primitive data object 30M.0 of
An example use for such a clustering center point is as follows. Assume that a user of the STAN_3 system recalls the imagery of the raising of the American flag at Iwo Jima (World War II) as one example that evokes patriotic pride and the crossing of the Delaware as a second “of its kind”, but the user does not remember yet further examples and the user wants to identify such further examples as understood by a given sub-community among system users. To this end the user instructs the STAN_3 system to find for him (or her) a closely clustered group of images in a system-maintained Image-type Cognitions-representing Space where two of the closely clustered representations of images (imagery nodes) are the ones for the recalled cases of Iwo Jima and the crossing of the Delaware. In response, the system automatically searches the given Cognitions-representing Space (and/or other interrelated spaces) for one or more clustering center points (COGS's) that have two such images in close proximity thereto, or overlaid directly on that found one or more clustering center points. More specifically, such a found clustering center point may additionally have adjacent thereto, images of specific events taking place at the Arlington Cemetery, or in front of the Lincoln Memorial, or with the Statue of Liberty as a backdrop, and so on. In other words, the system can automatically find others “of its kind” (as defined by respective user sub-communities) once a cognitive sense is hinted at by two or more user-provided examples that fit under the vague specification of, find for me more “of its kind” like these two or more examples. Stated otherwise, a given user sub-community may communally cross-associate in its communal mind, certain imageries, songs, historical events, etc. that belong together because they satisfy a perhaps-unarticulable cognitive sense (e.g., a communal “common sense”). The here-disclosed clustering center points enable the clustering together of such communally cross-associated items about respective clustering center points (COGS's) even if there is no one clear topic or central keyword expression or other specifiable other node that can tie the loose ends together just as well.
In one embodiment, the postionings in system memory of clustering center points are defined by absolute (long form) address pointers (e.g., stored in a lookup table (LUT) that cross-associates the COGS unique ID with its memory storage address and its hierarchical and/or spatial positioning) while the postionings in system memory of keyword nodes (e.g., 371.1, 371.2) clustered around that center point are defined by relative (short form) address pointers that use the center point address as a base. As a result, the bit lengths of digital pointers (memory address references) that point to the keyword primitives can be made relatively short while just one long-form base address is used for pointing to the corresponding clustering center point (e.g., 371.0).
Alternatively or additionally after a clustering center point (e.g., 371.0 or 317.50) is first created by a corresponding governance body and the hierarchical and/or spatial areas around it are populated by associated keyword expressions (e.g., Kw2, Kw3, Kw4, etc.), thereby defining relative distances between the various keyword nodes, it may become desirable to alter those represented distances. However, locations in hierarchical and/or spatial space are already defined for the originally created and center point surrounding nodes. In one aspect of the present disclosure, rather than changing the defined locations in hierarchical and/or spatial space of the already formed nodes, an altered distance calculating file or record is added to the definition of the clustering center point. The altered distance calculating file or record is represented by symbol 371.56 (but see also 30W.7ERR of
Assume for sake of a more concrete example of how primitives may be combined by operator nodes that the illustrated second keyword node 371.2 is disposed in the primitives holding layer 371 fairly close, in terms of spatial and/or hierarchical clustering (and optionally also in terms of memory address number) to the location assigned to the first keyword expression-holding node 371.1. Assume moreover, that the keyword expression (Kw2) of the second node 371.2 covers the expression, “*Abe” and by so doing (with asterisk in front) it covers the permutations of “Honest Abe”, “President Abe” and perhaps many other such variations. As a result, the Boolean combination calling for Kw1 AND Kw2 may be found in many of so-called, “operator nodes” for representing cognitions such as those related to “Honest President Abe Lincoln” and the like. An operator node, as the term is used herein, is provided and functions somewhat similarly to an ordinary expression-containing node in a hierarchical tree structure (and it inherits some attributes of its base or parent node(s)—see
While
It is stated above that, often, keyword expressions (e.g., Kw1 371.1 and Kw2 371.2) come to be clustered together spatially and/or hierarchically next to one another and near a clustering center point (e.g., 371.0). But the mechanisms that can cause this close clustering together of nodes to happen have not been fully explained above yet. One option is that the spatial (e.g., in keyword space) and/or hierarchical (e.g., within a keyword ‘A’-tree) clustering together of semantically belonging-together keyword expressions is initially established on a permanent or modifiable basis by manual intervention by system operators and/or by trusted system users who have been granted privileges to manually assign spatial and/or hierarchical locations to all or a pre-specified subset of initial points, nodes or subregions of one or more Cognitive Attention Receiving Spaces (e.g., keyword expressions space). In that case, the so-privileged system operators/trusted users may organize the spatial and/or hierarchical placements of cognition-representing primitive and some higher level data-objects (e.g., keyword expressions) such that those that sensibly belong together are clustered together. More specifically, system operators and/or trusted system users may initially populate a primitives layer of a textual cognition space (e.g., keyword space, URL space, etc.) with multiple and spaced apart copies of textual expression clustering center points (e.g., 370.1, 371.50, etc.) paired directly with respective textual expression nodes (see
Alternatively or additionally, the spatial and/or hierarchical placements of cognition-representing data-objects such as the keyword expression representing ones (e.g., 371.1, 371.2, 373.1), URL expression representing ones (e.g., 391.2, 394.1), meta-tag expression representing ones (not explicitly shown—see 395) are voted on by one or more direct or indirect voting mechanisms, where the vote is for continued approval of a current placement or for moving to a newly proposed placement, and/or continued approval of the current way the expression is expressed or for changing to a newly proposed way of expressing it (with characters or other symbols or codes). In response to such voting, the STAN_3 system automatically and responsively modifies the spatial and/or hierarchical placements of cognition-representing data-objects and/or of their contained expressions according to results of such voting mechanisms. One example of indirect (implicit) voting is when, as a result of a chat or other forum participation session, a subset of keyword expressions (e.g., 371.1, 371.2, 373.1) are determined to be the top N keywords now most popular with participants of the forum; in which case the popularity-wise clustered set of keyword expressions may be given corresponding nudges towards becoming clustered closer together (not necessarily over a clustering center point such as 371.0) in terms of their spatial and/or hierarchical placements within the corresponding Cognitive Attention Receiving Space (e.g., keyword expressions space). If enough chat or other forum participation sessions give cumulative nudges in a same direction to one or more such keyword expression holding nodes (e.g., 371.1, 371.2), the system responds by moving them closer together in the spatial and/or hierarchical placement sense. In accordance with one aspect of the present disclosure, some keyword expression holding nodes (e.g., 371.1) may be assigned a greater anchoring strength at their current position than others. As a result, when certain keywords are determined to have increased commonality with each other such that they merit being nudged closer together, the one with the greatest anchoring strength moves the least and the others therefore move toward its original location in hierarchical and/or spatial space. (The concept of anchoring will be discussed at greater length below in conjunction with 30R.9d of
While co-popularity among all users (or among a pre-specified subset of users; e.g., expert users) is one basis for nudging together into closer co-clustering with one another and in a corresponding hierarchical and/or spatial space of certain keyword expressing nodes (e.g., 371.1, 371.2, 373.1 as one example, but could be other nudged together points, nodes or subregions in other Cognitive Attention Receiving Spaces as a more general example), it is within the contemplation of the present disclosure to have oppositely acting mechanisms that nudge apart (and thus de-cluster in a spatial or hierarchical sense) certain groups of cognition representing data objects one from another. A more specific example will be given by way of section 30T.12e8 of
Automatic clustering and/or de-clustering of the cognition representing data objects (e.g., topic nodes, keyword expression nodes, etc.) within the spatial and/or hierarchical space of a corresponding Cognitive Attention Receiving Space (CARS, e.g., topic space, keyword expressions space, etc.) need not be limited to or based only on the above described indirect voting where; as a result of a chat or other forum participation session, a subset of cognition representing data objects (e.g., keyword expressions 371.1, 371.2, 373.1 in
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In one embodiment, the virtual size/shape/location field 30Q.1 may additionally provide real world information about the memory space consumed by data structure 30Q.0 (e.g., in terms of number of bits or words) and/or information about how the remaining fields of data structure 30Q.0 are organized.
A second field 30Q.2 of data structure 30Q.0 lists pointer types (e.g., long, short, operator or operand, etc.) and numbers and/or orders in the represented expression of each. A third field 30Q.3 contains a pointer to an expression structure definition that defines the structure of the subsequent combination of operator pointers and operand pointers. The operator pointers logically link to corresponding operator definitions. The operand pointers logically link to corresponding operand definitions. An example of an operand definition can be one of the keyword expressions (e.g., 371.6) of
Aside from including operand and operator indicators (e.g., 30Q.5, 30Q.4), the data structure 30Q.0 of the operator node will typically include one or more, so-called, inheritance fields 30Q.H by way of which the data structure 30Q.0 inherits data structure parts of base level primitives and/or of its parent nodes of the one or more Cognitive Attention Receiving Spaces (CARSs) the operator belongs to. More specifically, most primitives will include a field containing pointers to points, nodes or subregions in the same and/or other Cognitive Attention Receiving Spaces (e.g., nodes or subregions of topic space) and/or a field containing pointers to chat or other forum participation sessions or other informational resources. The operator node (30Q.0) will similarly, and by means of inheritance (30Q.H) contain such pointers as well so that the operator node (30Q.0) can function as cross-linking data object just as can the base level primitives of the CARSs to which its operand pointers (e.g., 30Q.5) point to and/or so that the operator node (30Q.0) can function as a cross-referencing data object to informational resources just as can the base level primitives of the CARSs to which it belongs.
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In view of the above, it may be seen that the cross-spaces (inter-space) bi-directional link 370.6 of
The scores contributed by the cross-spaces (inter-space) logical links (e.g., 370.6) need not indicate or merely indicate what candidate topic nodes/subregions the STAN user (e.g., user 301A′) appears to be now focusing-upon based on received raw or categorized CFi's (which received signals can be clustered per
Moreover, linkage strength scores to competing ones of topic nodes (e.g., Tn71 versus Tn74 in the case of
Cross-spaces logical linkages such as 370.6 (a.k.a. IntEr-Space cross-associating links or “IoS-CAX's”) are referred to herein as “reflective” when they link to a node (e.g., to topic node Tn71) that has additional links back to the same space (e.g., keyword space) from which the first link (e.g., 370.6) came from. Although not shown in
More will be said about
More generally and in accordance with the present disclosure, a context data-objects organizing space (a.k.a. context space or context mapping mechanism, e.g., 316″ of
Another of the fields in each context primitive defining object 30J.0 (
Assigned roles (as defined by field 30J.1) will often have one or more normally expected activities or performances that correspond to the named formal role. For example, a normally expected activity of someone in the context of being a “manager” might be “managing subordinates”. Therefore, when a user is determined (by signal 316o) as likely to currently be in the context of being an acting manager (as defined by field 30J.1, if primitive 30Q.0 is being referenced based on the current version of output signal 316o), corresponding third field 30J.3 may include a pointer pointing to an operator node object in context space or in an activities space (not directly shown) that combines the activity “managing” with the object of the activity, “subordinates”. Each of those primitives (“managing” and “subordinates”) may logically link to nodes in topic space and/or to nodes in other spaces. (Another example of “expected performances” 30J.3 might be “does homework immediately after school” for the case of the Fifth Grade Student working on his/her Abe-Lincoln assignment.) Although each user who operates under an assumed role (context) is “expected” to perform one or more of the expected activities of that role, it may be the case that the individual user has habits or routines wherein the individual user avoids certain of those “expected” performances. Such exceptions to the general rule are defined (in one embodiment) within the individual user's currently active PHAFUEL profile (e.g.,
A fourth field 30J.4 (
A fifth field 30J.5 of each context primitive may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding subregions of a demographics space (not shown). The logical links between context space (e.g., 316″) and demographics space (not shown) should be bi-directional ones such that the providing of specific demographic attributes (e.g., age, gender, height, weight, income group, etc.) will link with different linkage strength values (positive or negative) to nodes and/or subregions in context space (e.g., 316″) and such that the providing of specific context attributes (e.g., role name equals normal or average “Fifth Grade Student”) link with different linkage strength values (positive or negative) to nodes and/or subregions in demographics space (e.g., age is probably less than 15 years old, height is probably less than 6 feet and so on).
A sixth field 30J.6 of each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of a forums space (not shown, in other words, a space defining different kinds of chat or other forum participation opportunities which the in-context average or normal user is likely to be excluded from and/or included within).
A seventh field 30J.7 of each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of a related-users space (not shown, but whose nodes would indicate other users to whom the first user is likely to be currently relating to (or vise versa) because of the currently undertaken role of the first user). More specifically, a primitive 30J.0 whose formal role is “Fifth Grade Student” may have pointers and/or KBR's in seventh field 30J.7 pointing to “Fifth Grade Teachers” and/or “Fifth Grade Tutors” and/or “Other Fifth Grade Students”. In one embodiment, the seventh field 30J.7 specifies other social entities that are likely to be currently giving attention to the person who holds the role of primitive 30J.0 (or vise versa). More specifically, a social entity with the role of “Fifth Grade Teacher” may be specified as a role of another person who is likely giving current attention to the inhabitant who holds the role of primitive 30J.0 (e.g., “Fifth Grade Student”) or vise versa where the average or normal “Fifth Grade Student” is likely giving partial focusing attention to the “Fifth Grade Teacher”. The context of a STAN user can often include a current expectation that other users (e.g., his online “Fifth Grade Teacher” and/or his “Mother” who just reminded him to do his homework) are currently casting attention on that first user. People may act differently when alone as opposed to when they believe others are watching them, auditing them, or otherwise currently paying attention to what the first user (e.g., “Fifth Grade Student”) is currently doing.
Each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of yet other spaces (other data-objects organizing spaces) and/or other informational resources as is indicated by eighth area 30J.8 of data structure 30J.0. The pointed to other informational resources may include chat or other forum participation sessions cross-associated with the context primitive 30J.0. They may alternatively or additionally include non-forum research sources. The pointed to other informational resources may include personas or groups (expert groups, influential persons, etc.) cross-associated with the context primitive 30J.0; where once again, the results apply to the average or normal user within the relevant community but not necessarily to the given specific user. Chat rooms full of, and/or individualized users do not necessarily have to tether to, or only to a topic center (topic node). They may alternatively or additionally tether to a context node within the system's context space such as one represented by context primitive 30J.0 or one represented by an operator node that is a progeny of context node 30J.0. More specifically, and by way of example, one context node in context space may be that of pretending (e.g., as part of an online game) to take on the role of “President of the United States” (POTUS, i.e. in field 30J.1) and one of the expected performance or activities may be that of acting as Commander in Chief (e.g., in field 30J.3). There can be online chat or other forum participation sessions devoted to this contextual role-playing aspect, where for example, eighth area 30J.8 may include pointers to online forum participation sessions devoted to a corresponding online game. At the same time, there may be one or more topic nodes or subregions in topic space dedicated to the topic of pretending to be POTUS. Unlike a conventional Wikipedia™ structure, the Cognitive Attention Receiving Spaces of the STAN_3 system may each have many points, nodes or subregions that each, on the surface, appears to be directed to a same or similar cognition. More specifically, just as was true for the above exemplary case of “*lincoln*” being plurally expressed in a corresponding plurality of different hierarchical and/or spatial locations within keyword space, context space (XS)—as another example—may be filled with many copies of data structure 30J.0 each having a same formal role name and a same informal role name and yet the on-the-surface apparently same context specifications respectively overlie different cognitive senses of the specified role (e.g., pretending to be POTUS as part of a serious strategic game, pretending to be POTUS as part of a comic or mocking game, pretending to be POTUS as part of an educational Fifth Grade level exercise and so on). In one embodiment, just as keyword space may be populated by clustering center points each representing a respective cognitive sense for nearby keyword expressions, context space may be similarly populated by clustering center points each representing a respective cognitive sense for nearby context-specifying expressions (e.g., substantially same or similar copies of primitive object 30J.0). Moreover, topic space and yet others of the system-maintained Cognitions-representing Spaces may be similarly populated by clustering center points each representing a respective cognitive sense for nearby cognition-representing topic or other respective types of nodes. By providing such clustering center points in each respective space, distinctions can be made as between apparently (on the surface) same Cognitive Attention Receiving Nodes or Subregions (CARNS) where the underlying cognitive senses are actually different. Ranking and sorting according to different cognitive senses may be based on a complex set of currently activate user states that indicate likely user mood, likely user context, the user's currently chosen persona name, recent user activity history, and so on.
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In terms of further specifics, it should be recalled that often, the received CFi's of a given user (e.g., 301A′ of
The newer found, hybrid node 30Y.9 has a cross-spaces logical link 380.9″ that points to a topic space subregion 370.7″ containing topic nodes Tn74″ and Tn75″. In one embodiment, cross-spaces logical link 380.9″ points to the center of an elliptical region 370.7″ by specifying a nearby, cognitive sense representing, clustering center point 370.9″, by specifying an offset distance and offset direction from that center point 370.9″ and then by specifying the two focal points of elliptical region 370.7″ relative to the offset vector (the vector defined by the offset distance and offset direction). The referenced cognitive sense representing, clustering center point 370.9″ defines, among other things, spatial distances such as 370.10″ and 370.11″ between itself and nearby topic nodes such as Tn61′, Tn74′, etc. The defined spatial distances indicate relative closeness of cognitive sense as between a central cognitive sense of the clustering center point 370.9″ and respective cognitive senses of the nearby topic nodes (e.g., Tn61′ and Tn74′). The linked-to elliptical region 370.7″ encompasses subregions Tn74″ and Tn75″ within its interior and thereby references them. These, traced-to and corresponding topic nodes and/or topic subregions (e.g., Tn74″ and Tn75″) in topic space may then point to a context-appropriate set of chat or other forum participation sessions (not shown) which the user will be invited to join in on where the forum participation sessions are closely related to “Lincoln's Gettysburg Address” and how historians currently view it and where the participation sessions are co-compatibility appropriate for an average or normal Fifth Grade Student. Contrastingly, the so traced-to corresponding nodes and/or subregions (e.g., Tn74″ and Tn75″) will not be ones directed to a local Ford/Lincoln™ automobile dealership or to a topic directed to the city of Lincoln, Nebr. Thus the corresponding invitation(s) and/or suggestions which the Fifth Grade Student receives from the STAN_3 system will be demographics-wise appropriate and topic-wise appropriate and context-wise appropriate.
By way of contrast, had the system user been an older person who recently was searching for a new car, the keywords “Lincoln's Address” would have instead led to the system pointing to a topic or other kind of node (e.g., geography space node) directed to the local Ford/Lincoln™ automobile dealership. This would be so because under that alternate context (older user and different user history), the possibility of the user being a Fifth Grade student would have been excluded, or at least much reduced in score in terms of context and a corresponding topic likely to be then be on the user's mind. At the same time logical connections to nodes or subregions pointing to automobile dealerships would have received substantially greater scores.
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Some of the recently received CFi's, 30Y.1 will be those indicating current physical context (e.g., geographic location and temporal positioning within a user associated calendar) where these current physical context CFi's 30Y.1 operate to identify a more likely, current PHAFUEL log (habits and routines) 30Y.10 for the user and to identify a more likely, current PEEP record (personhood and emotional expressions profile) 30Y.20 for the user. Aside from the emotional expressions profile (30Y.20), the user may have a corresponding, currently exposable other personhood profile 30Y.30 which the user has indicated as being currently exposable over the network, except that the exposed data from the personhood profile 30Y.30 may be less detailed or specific than that of the current PEEP record (30Y.20). For example, the exposed data from the personhood profile 30Y.30 may only show a rough yearly income range (e.g., “above $30K per year”) rather than the user's actual income numbers. The logged-in persona 30Y.3 of the user may point to a specific personhood profile 30Y.30 as well as to a specific (but not exposed) PEEP 30Y.20. A last determined, mental context of the user (e.g., recent user history) may also point to specific ones of the user's PHAFUEL records, PEEP profiles and personhood profile (e.g., 30Y.10, 30Y.20, 30Y.30) as being the currently most likely to use. These currently activated profile records may then match with or strongly cross-correlate with a specific subregion, XSR5 in context space 316′″ (e.g., by pointing to the parent node of that subregion). The cross-correlation is represented by respective pointers 30Y.15, 30Y.25 and 30Y.35. Although not shown in
Sometimes, a user is momentarily interrupted out of one context and asked to temporarily switch into a second context with the expectation that the user will soon return to the first context. By way of example, while the Fifth Grade Student is doing his/her homework, the mother comes into the room and asks, “Sorry to interrupt, but my computer is down; can you do me a favor and print out some driving directions to my friend's house?” In this exemplary case, the student is momentarily taken out of his/her first context (e.g., researching the question about how modern historians view Abe-Lincoln's Address) and put into a different context (e.g., temporarily helping his/her mother to get driving directions). The STAN_3 system can automatically detect this sudden switch of context by, for example, detecting that the new search keywords being inputted into respective search engines (e.g., “What is the shortest driving directions to Montgomery Street?”) are incongruent with the context (30Y.36) last determined for that user (Fifth Grade Student).
In response to this determination, and in accordance with one aspect of the present disclosure, the system automatically saves the previously determined context (represented by signals 30Y.36 and 30Y.36′) into a first context swap stack (or other such history memory) 30Y.59 that is associated with recent activities of the first user. The system also automatically saves the previously determined set of activated profiles (of active profiles layer 30Y.63) into a second user's context swap stack (or other such memory) 30Y.58. Additionally, the system automatically saves the previously determined set of pointers (the pointers of active pointers layer 30Y.65) into a corresponding hybrid space pointers saving stack (or other such memory) 30Y.55 that belongs to the interrupted user. In one embodiment, a synchronizing signal is also stored that indicates which levels of the various context swap stacks belong to one another.
Once the interrupted context-development process is stored away in the swap stacks, the STAN_3 system can then begin to develop a new determination of the newly inserted and current context (e.g., helping mother get driving directions) for the same user and it can then begin making context-appropriate suggestions for that new context. When the interrupting second context completes (as evidenced by changed CFi's from the user), the system temporarily saves the parameters of that second context into the context swap stacks, 30Y.59, 30Y.58, 30Y.55, and retrieves the earlier saved parameters of the first, and temporarily interrupted context (e.g., researching the question about how modern historians view Abe-Lincoln's Address). In this way, the work done by the system in refining its understandings of the user's context for the first, temporarily interrupted task (Fifth Grade homework task) is not lost and the interrupted user can pick up where he/she last left off. It is within the contemplation of the disclosure that the context swap stacks, 30Y.59, 30Y.58, 30Y.55 may be sized and organized for swapping as between three or more interleaving tasks. In one embodiment, the user identifies to the system, one or more tasks as being long-term continuing ones and the system then understands that other intervening tasks are shorter-term ones for which the parameters do not have to be saved for a long time.
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Stated otherwise, a machine-implemented and automated process (e.g., 30Y.60-67) is provided which empowers a first user (e.g., 30R.0A) whose attention giving activities are being automatically monitored by one or more local devices (e.g., mobile wireless device 30R.00 in
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The music primitive object 30F.0 of
Of importance, it is to be understood that the illustrated data structures of the different cognition representing data objects being introduced here-at; where the music primitive object 30F.0 of
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In one embodiment, the STAN_3 system 410 further includes a differences/equivalences locating module that automatically crawls through the respective node-versus-node comparison matrix of each space (e.g., topic space, context space, keyword expressions space, URL expressions space, etc.) looking for nodes (or points or subregions) that are substantially the same and/or very different from one another and generating further records that identify the substantially same and/or substantially different nodes (e.g., substantially different sibling nodes of a same tree branch, or ditto for respective points or respective subregions). The generated and stored records that are automatically produced by the differences/equivalences locating module are subsequently automatically crawled through by other modules and used for generating various reports and/or for identifying unusual situations (e.g., possible error conditions that warrant further investigation). One of the other modules that crawl through the differences/equivalences records can be the local space consolidating module (e.g., 370.8′ of
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In
One machine-implemented and automated process followed here starts with the exemplary first user 30R.0A shown near the bottom left corner of
For sake of completeness,
While the user (e.g., 30R.0A) is operating under his/her current individualized context 30R.1, the user will generally have user-internal cognitions. There are at least two different kinds of such possible mental cognitions, conscious and subconscious cognitions. These conscious and subconscious cognitions are respectively denoted as 30R.2b and 30R.2a in
Moving radially inward in the depiction of
One subset of the user's personal codings 30R.3a is referred to here as the user's authored-coded expressions 30R.4a. The latter may include user-selected keywords (30R.4b, which selections are understood to include user-typed out keywords), user-selected URL's (30R.4c, which selections are understood to include user-typed out hyperlink specifications), user-selected ERL's (30R.4d, which Exclusive Resource Locaters are ditto-wise understood to include user-typed out hyperlink specifications), and so on.
The respective user-authored coded expressions 30R.4a are transmitted by way of CFi carrying data packets (see 30U.10 of
The respective, and optionally normalized/optionally augmented CFi's of the respective individual users are collectively represented by packet 30R.8 in
The core-received and optionally normalized (30R.23) packets 30R.8 (generally, or 30R.8b, 8c, 8d, etc. more specifically) are next parsed, categorized and re-grouped (clustered as likes together with alikes of a same categorization) within the system core as already explained above with respect to
Stated more simply, the individual user's current and specific context (30R.1) is cross-matched with a system-maintained and more generic context; the individual user's current and specific outward expressions (e.g., user-selected keywords 30R.4b) are cross-matched with system-maintained and more generic expressions of same type (e.g., more popular keywords, URL's, ERL's etc.), a hybrid expressions-and-context Cognitive Attention Receiving Space is pointed to (e.g., by hybrid space scanner 30Y.50 of
In the discussion regarding
In the illustrated embodiment 30R, almost any migrating topic node (see 30S.53 of
More specifically, child nodes (e.g., 30R.9a-9c) who receive net-positive promotion votes from governance bodies of parent node (PaTN, 30R.30) get to move upwardly towards closer spatial clustering with the parent node. Child nodes who receive net-negative promotion votes from parent node governance bodies get repelled away from the parent node (PaTN) and thus migrate towards the basement level 30R.19 of the illustrated cylindrical branch space 30R.10. Thus the parent node governance bodies exert vertically promoting (up) or demoting (down) pressures on the spatial dispositions of child nodes found within the corresponding children-containing branch space 30R.10 of that parent node 30R.30. It should be recalled that the nature of a given topic node can change over time as new chat rooms or other forum participation sessions tether onto that given topic node or de-tether and move away to preferably hover about other topic nodes. Thus the votes given by parent node governance bodies to underlying child nodes can vary over time. (It is to be understood that it is within the contemplation of the present disclosure to have chat rooms or other forum participation sessions that are simultaneously shared by plural topic nodes, in which case the sessions may be perceived as if they loop in and out of orbit with each of the planet-wise represented topic nodes. It is also within the contemplation of the present disclosure to have chat rooms or other forum participation sessions that orbit about cognitive-sense-representing clustering center points rather than about any specific topic node or other such node in another comparable space. In the later case, the chat or other forum participation opportunities presented to users may be based on hierarchical and/or spatial distance of a matched node to corresponding nearby clustering center points rather than based on the identity of the matched node itself and the chat or other forum participation sessions deemed to be tethered to that node.)
In a similar manner, same Z-direction depth level co-siblings (e.g., CSiTN1-N3) within a branch space can cast positive and thus R-direction attracting pressures on nearby other co-siblings or negative and thus R-direction repelling pressures on nearby co-siblings. Eventually, as these various votes are cast (implicit or explicitly), co-siblings of a branch space whose governance bodies like each other, come to be spatially disposed as clusters near each other while co-siblings whose respective governance bodies dislike each other (vote to repel the other away), come to be spatially spaced apart in the corresponding cylindrical branch space (e.g., 30R.10). In this way a rogue topic node that drifts itself into branch space 30R.10, but is disliked by substantially all other occupants of that branch space (e.g., 30R.10) and is disliked by substantially all governance bodies of the parent node 30R.30 will be shifted into, or kept in the periphery of the basement level 30R.19 of the siblings-containing space. On the other hand, an in-harmony topic node that drifts itself into branch space 30R.10, and is well liked by substantially all other, central core occupants of that branch space (e.g., 30R.10) and is well liked by substantially all governance bodies of the parent node 30R.30 will be shifted into, or kept at the upper level of that branch space and clustered near the center of the space (close to the vertical Z-direction axis, ZTsBr, of the topic space branch). All child nodes within branch space 30R.10 are considered to be hierarchically tethered on the “A”-tree (AT) as a child of the corresponding parent node (PaTN). However, in terms of spatial disposition, some of the child nodes are deemed to be more favored by the parent and co-siblings while others are deemed to be less favored by the parent and the major mass of co-siblings.
When it comes to determining which sibling node will push (repel) another away without being itself displaced from its current mooring, the notion of strong anchors and weak anchors may be used. Each child node is assigned a respective, anchor strength score. For example, anchor tether 30R.9d of co-sibling node 30R.9c is assigned a local strength value based on a number of factors such as, but not limited to, the number of system users who regularly use that topic node directly or indirectly (e.g., through an attached chat room like 30R.60), the reputations and/or topic-relevant credentials of the system users who regularly use that topic node 30R.9c, and so on. However, the locally assigned tethering strength 30R.9d is not the effective tethering strength when push comes to shove. Instead, if a challenged node (e.g., 30R.9c) is repelled by a challenging node (e.g., a new comer node (not shown) in layer 30R.19), the challenged node (and the challenging node) each get to inherit addition positive or negative tethering strength scores respectively from spatially nearby other nodes which respectively “like” or “dislike” the corresponding challenged and challenging node (e.g., a new comer node in layer 30R.19). More specifically, since a new comer node will often have no adjacent friend nodes that “like” that new comer node, the new comer node will have a relatively low tethering strength score. On the other hand, an already well established node (e.g., 30R.9c) that has many strongly tethered “friend” nodes (e.g., 30R.9b, 30R.9a) lending a positive tethering support value on top of the first node's native tethering strength 30R.9d will have a significantly greater, effective tethering strength. Hence, when push comes to shove, it will be the repulsive new-comer who gets spatially pushed away (by a distance proportional to repulsing votes and inversely proportional to effective tethering strength) while the counter-repulsing, established node (e.g., 30R.9c) will mostly stand its ground. Through a series of repulsing and attracting, pushes and shoves of this nature, the various nodes in each horizontal level of the cylindrical branch space 30R.10 will sort it out amongst themselves as to which nodes get to spatially cluster near the central or mainstream core section and which nodes are marginalized toward the periphery (pushed out in the direction of outward bound arrow 30R.71).
Vertical positioning of nodes within the cylindrical branch space 30R.10 can be driven primary by votes cast by the more influential (more highly regarded) governance bodies of the parent node 30R.30. In one embodiment, “like” and “dislike” votes from sibling nodes in the horizontal layers directly above (and optionally also directly below) are factored into the vote. Thus, if both the parent node governance bodies and the higher up sibling nodes vote to “like” an up-and-coming node currently disposed beneath them, that node gets promoted upwardly in the Z-direction so as to be closer to the top level of the cylindrical branch space 30R.10. On the other hand, if the parent node governance bodies and the higher up sibling nodes vote to “dislike” a despised node beneath them, that node gets demoted downwardly in the Z-direction so as to be closer to the basement level 30R.19. Through a series of repulsing and attracting, pushes and shoves of this nature, the parent node 30R.30 as well as the various nodes in each horizontal level of the cylindrical branch space 30R.10 will sort it out amongst themselves as to which nodes (see 30S.77 of
When a group of system users (e.g., those who are members of a chat room like 30R.60 and who) are seeking a child node within the specific cylindrical branch space 30R.10 to link up with (via tether 30R.63 of respective tethering strength), one of the factors that may be considered during the selection process is where in the cylindrical interior of branch space 30R.10 do each of the candidate child nodes (e.g., 30R.9a,b,c) place, why, and who are the other child nodes that “like” the spatially placed candidate node or “dislike” it. In accordance with one aspect of the present disclosure, the effective tethering strength scores (e.g., 30R.9d) of respective candidate nodes are made available to system users or chat room governance entities for consideration when those entities are making a decision as to which one or more child nodes to link up with. A relatively high, effective tethering strength score means the candidate node (e.g., 30R.9c) is well “liked” by the other nodes in its immediate neighborhood while a relatively low (or even negative), effective tethering strength score means the candidate node is “disliked”. It is up to the internal politics of each in-drifting chat room (e.g., 30R.60) to decide if they want to associate with the underdogs in that cylindrical branch space 30R.10 or with the overlords and why.
Child nodes (e.g., 30R.9a) inside cylindrical branch space 30R.10 may cross-link to other branch spaces, such as the illustrated 30R.5 to the left of 30R.9a. The inter-branch space linking linkage, 30R.7a/b (which has sub parts 30R.7a and 30R.7b) may be one that points to a relatively wide subregion of the other space as does, the wide horned, first linking symbol 30R.7a; or the inter-branch space linking may be one that points to a relatively narrower subregion of the other space as does, the narrower horned, second linking symbol 30R.7b. For example, the narrower horned, second linking symbol 30R.7b may point to child node 30R.6 within other branch space 30R.5. That other branch space 30R.5 may be disposed inside of topic space; or it may be disposed inside of a different Cognitive Attention Receiving Space; for example inside a URL's expressions clustering space. If the latter case is true, then a system user who is directed to topic node 30R.9a (a.k.a. CSiTn1) is concomitantly also indirectly directed to identified URL expressions which are spatially clustered within the ambit of narrow horn 30R.7b or wider horn 30R.7a.
Just as keyword expressions may be spatially clustered in a semantic/Thesaurus sense near to each other in layer 371 of
Incidentally, in one embodiment, when a user requests to view on his/her screen a map of a specified subregion of topic space (or of any similarly structured other system-maintained space), one of the options is to view the space in a 3-dimensional fashion similar to that shown in
The chat rooms or other forums that tether to the respective topic nodes (or other space nodes) may be depicted as orbiting cubes (or as other shaped 3D geometric objects, e.g., orbiting space satellites). A display control tool may be provided for hiding one or more different types of such objects or changing their relative sizes, etc. In one version, the relative sizes of sibling objects (e.g., nodes and/or chat rooms) indicate in a relative sense how many system users are or have recently utilized those resources. Hence a topic node that has a relatively large population of users engaged with its informational resources will appear as a large planet (and/or as a more fully colored rather than ghostly planet) while a chat room with a relatively small number of active participants will appear as a comparatively small orbiting satellite (and/or as a more translucent and less colored globe) disposed next to another, more populated forum orbiting the same node (e.g., depicted as a spherical planet).
Color codings may additionally be provided for indicating additional attributes, such as for example if a mapping is of dimensionality greater than 3 and the colors represent placement in a fourth or higher dimension. The display control tool (not shown) may be used to alter the default assignment of color codes. Some colors (e.g., red, pink and blue) may be reserved for showing which 3-dimensional objects or subregions are receiving above threshold heat, unusually large values of heat and/or which are abnormally cold. The user is given the ability to zoom in or out on a magnification gradient so that patterns of unusual heat or abnormal coldness can be visually spotted. In one embodiment, the provided color codings include ones for indicating strength of repulsing or attracting forces (pushes and pulls) as between clustering or outcast sibling nodes and/or as between the parent node and upward or downward moved sibling nodes. Lines of attraction or repulsion can be automatically drawn between selected ones of displayed nodes where the color codes (and/or line thickness) indicate attraction versus repulsion and the strength of each. In one embodiment, the chat room or other forums that are optionally displayed as orbiting their tethered-to topic nodes may also be displayed as clustering with one or more if their governance bodies vote for spatially attracting those sibling forums or as distanced from one another if their governance bodies vote for spatially repelling those sibling forums. Respective lines of attraction or repulsion and their strengths may be similarly displayed for the forums as they are for clustered together or repulsed apart nodes.
In one embodiment, as an alternative to, or as a supplementing addition to displaying points, nodes or subregions of Cognitive Attention Receiving Spaces and/or their associated chat or other forum participation sessions with aid of color coding and/or line thickness/pattern coding for representing various attributes of the displayed objects, the system may provide sound effects for audibly indicating various node or forum attributes. One of the audibly indicated attributes can be that representing the volume (number of) and/or intensity (e.g., hotness) of discourses taking place for respective nodes, subregions or forum. This can be in the form of different kinds of musical pieces representing collective mood or even a montage of text-converted-to-voice transcripts from selected rooms. In one embodiment, the user hears the audibly indicated attributes when hovering a cursor representing virtual object (or the user's finger) over a displayed representation of the node, forum or over a collection of such graphically represented objects.
In one embodiment and as a supplementing addition to displaying points, nodes or subregions of Cognitive Attention Receiving Spaces and/or their associated chat or other forum participation sessions, the presentation also depicts the cognitive-sense-representing clustering center points and their respective hierarchical and/or spatial positionings in the respective space.
Referring next to
More specifically for
In view of the above, a machine-implemented method may be provided for automatically bringing the first and second users (A′ and B′) into a same chat room 30S.60 (shown disposed in
As is in the case of
As an aside, it is to be understood that the CARSs maintained by the system can constantly change in numbers and types and neuroplastic like cross-connections as between one another's points, nodes or subregions (and/or cognitive-sense-representing clustering center points) so as to adapt to changing cognitions and cognitive sentiments of the user population. More specifically, cognitions that did not exist before can come into being while others fade into disuse and the system's users can start populating the system's topic space with new topic nodes and/or topic space regions (TSR's) that represent the newer cognitions as well as creating new Cognitive Attention Receiving Spaces that have new points, nodes or subregions that innervate with (logically link with) and thus cross-correlate with corresponding new topic nodes or topic subregions or nodes in other pre-created and system-maintained spaces. As an example, imagine with reference to
Assume next, that while aimlessly chatting within the exemplary, no-specific-topic chat room (e.g., 30S.62), users A and B conjure up a new topic that has not existed before (has not been predefined before. at least in the terms used by users A and B) within the system-maintained topic space (whose root is node 30S.59). Assume that users A and B start throwing out proposed keywords or URL's to each other respecting the new but un-named, not-yet-specified topic. (They don't have to know that this is what they are doing, that they are negotiating the question of, What are we talking about or What one or more topics is our discussion circling around?. They merely do it. An example may be as follows: UserA writes to userB: “What do you think about what is said at www.URLa.com/PXA?”.) At first they don't have a good grasp of what those proposed keywords, URL's fully mean or how the dots may interconnect because they don't have a good grasp of what the new topic is, what cross-correlates strongly to it and what does not. However, while they are transmitting trial keywords, URL's and the like to each other, the STAN_3 system automatically responds to whatever single keywords or clusters of keywords (or URL's or other codings) they toss out at each other by having the system core (the SS3 core) automatically send invitations to each of the users regarding possible chat or other forum participation opportunities that relate to the keyword clusters and/or URL clusters the respective two users (A and B) have tried thus far. Those invitations may include ones for merging their private two-user online chat (and null-topic thus far chat) with non-private ones of other users where the cross-matched other chat or other forum participation sessions may already have topic nodes cross-associated with them. Eventually in this example, let it be assumed that the two users, A and B privately converge on the keyword combination of: “neuroplasticity of the STAN_3 system” while electing to not yet merge with other chats proposed by the SS3 core. The two users, A and B may have converged on this exemplary keyword combination (“neuroplasticity of the STAN_3 system”) because they eventually realized, after much research that such best describes the new concept they had been circling around and reaching for but could not earlier clearly articulate it with words. However, at this point their private two-user online chat is still tethered to (cross-associated with) the top catch-all node 30S.55 (a.k.a. null-topic node) in the system-maintained topic space. This is so because users A and B are the exclusive controlling governance body of their nascent chat room (30S.61) and they have not yet voted (implicitly or explicitly) to move their chat room from its initial attachment (tethering, anchoring) to another node within topic space. Movement is at their discretion. If they do decide by implicit or explicit voting to move their chat room's tethering (anchoring) to a different node in topic space, they may eventually also decide by implicit or explicit voting to create a node in topic space that did not exist before and further move their chat room's tethering to that newly-created topic node.
In one embodiment, the system automatically and repeatedly transmits suggestions to the room governance body (in this case users A and B) to move their null-topic forum to a different location within the system-maintained topic space and/or to merge it with another forum that the system has determined is one whose topic is substantially similar or same to theirs. In this example however, the users, A and B, have not accepted such automatically presented suggestions because they believe that they have not yet settled on an acceptable definition of what their private topic is. Ultimately in this hypothetical example they decide on the topic definition being: “Nonbiological Neuroplasticity of Social-Topical Adaptive Networks”, but there is no such topic node pre-existing (in this hypothetical example) inside the STAN_3 system at that time. While the system keeps automatically suggesting to them where to move their chat, they decide to instead create their own unique topic node (not shown) and to first to tether it (the newly created topic node) to a Zone-3 child (a hypothetical subregion) of a ubiquitous zones node 30S.57. Later they decide to also or instead to tether their chat room to parent node 30S.30 of
In this example, the parent node 30S.30 to which users A and B decided to partially tether their co-governed chat room, is a hierarchical child of grandparent node 30S.50. Therefore in this example, the governance bodies who control grandparent node 30S.50 decide during the interim to move it (and all its child nodes contained in its branch space 30S.40) to a new location within the system-maintained topic space. While it and its progeny are thus in transit, the grandparent node 30S.50 and all the progeny nodes in its hierarchically subsumed branch space 30S.40 are denoted in
Later in the exemplary drift-of-topic process, the co-governing users A and B of the drifting chat room 30S.61 decide to more fixedly (but not necessarily permanently) anchor their chat room (now denoted as CR 30S.60) to the specific topic node denoted as 30S.9a in
At this stage, users A′ and B′ also form the governance body for their previously brand new and then drifted and now re-planted topic node 30S.9a. This topic node has drifted together with their flying chat room 30S.61 as they voted to keep drifting both of their controlled chat room and co-controlled topic node out of a first branch space (not explicitly shown, see 30S.49′) and ultimately into the illustrated branch space 30S.10 of parent node 30S.30. Also at this stage, users A′ and B′ may vote for designating URL expressions 30S.75a and 30S.75b as being the most representative URL's for the new topic they conjured up and are now discussing online. As the still exclusive governance body of their newly-located topic node and chat room, they may also vote to approve the topic specification of “Nonbiological Neuroplasticity of Social-Topical Adaptive Networks” as being the short-form textual descriptor of their created and re-parked node 30S.9a and they may at the same time vote to approve the following keywords as being the most representative or top keywords for their node: “Neuroplastic Social-Topical Adaptive Network” and “Nonbiological Neuroplasticity”. They may open up their so modified and previously private chat room for entry by other system users who are interested in joining based on any of possible bases for co-compatibility with topic node 30S.9a, including but not limited to, use of same or similar keywords, use of same or similar URL's, having same or similar normalized contexts, and/or having same or similar other normalized cognitions.
As new system users learn of its existence and join in on the earlier created and now implanted topic node 30S.9a by way of (for example) accepting system generated invitations to a one or more chat rooms (e.g., 30S.60) or other forums that tether to topic node 30S.9a, governance of this topic node 30S.9a and/or governance of the forums tethered to it may change for any of a number of reasons including the possibility that the original birthers (founding fathers, A′ and B′) of that topic node 30S.9a and/or of its first chat room 30S.60 have dropped away and have let others take control. The new governance bodies of the earlier-implanted topic node 30S.9a and/or the forums (e.g., 30S.60) tethered to it may vote to change its attributes yet further (e.g., top URL's, top keywords, top other cross-associated cognitions, etc.) and perhaps even to move it to a yet different location in topic space. Thus, a topic that may have not earlier existed in topic space (e.g., the “Nonbiological Neuroplasticity of Social-Topical Adaptive Networks” node) is created in the form of a new topic node (e.g., 30S.9a) implanted into a first branch space (e.g., 30S.10) and is provided with changeable IntEr-Space cross-associating links (e.g., IoS-CAX's) from it to other Cognitive Attention Receiving Spaces (e.g., URL's space 30S.72; hybrid space 30S.5; other hybrid space 30S.1). The location of the created topic node (e.g., 30S.9a) in topic space and the innervations or cross-associations between that node and nodes of other spaces may change over time due to user actions (e.g., implicit or explicit vote castings). In other words, a machine-implemented and neuroplastic wise adaptable combination of cognition representing nodes (representing communally-agreed upon expressions of respective cognitive senses) and nerve-connection representing logical links (loS-CAX's and InS-CAX's) is formed and modified over time in response to implicit or explicit votes cast by node and forum governing bodies where the governance bodies are typically constituted by plural system users and thus re-adaptation decisions are typically reached on a communal consensus basis or majority rule basis rather than on the basis of the idiosyncratic whims of a single user.
Additionally,
Referring to
As an aside and with regard to the exemplary TPO data structure (30T.0), it is to be understood that the here detailed topic primitive object (TPO) is an example of a more generic concept of a machine stored and pre-categorized, cognition-representing primitive object (CRPO). There are a number of choices that system designers can make when implementing a topic space and/or another system-maintained Cognitions-representing Space. The points, nodes or subregions (PNOS's) of the designed space may be free-ranging; meaning that such PNOS's can be freely moved to any desired part of hierarchical and/or spatial space at the users' whims; or in another extreme the PNOS's may be restricted to specific parts of hierarchical and/or spatial space as dictated by system administrators. Between these extremes are various sub-combinations, including the possibility of having cognitive-sense-representing clustering center points that are fixed within hierarchical and/or spatial space or are free-floating or are restricted to specific parts of hierarchical and/or spatial space as dictated by system administrators. In the example given by
A first field 30T.1a of the exemplary TPO data structure (30T.0) of
In view of the above explanation, it may be seen that the first field 30T.1a of
A second field 30T.1b of the exemplary TPO data structure (30T.0) contains the primitive's uniqueness-guarantying stamp. The uniqueness-guarantying stamp 30T.1b can be an extension of the primary node identification provided by the first field 30T.1a. More specifically, if the first field 30T.1a uniquely identifies a corresponding parent node, the unique-making stamp 30T.1b may simply be a unique serial number (or other code sequence) that uniquely identifies the represented node relative to other children of the parent node. However, since in one embodiment, every topic node (except the root 30S.59 of course, and also the top catch-all (the null-topic topic node) 30S.55 and the top, notnull-topic topic zones node 30S.57) is free to drift to a new parent node and/or to a new spatial location, it is preferable that each created TPO have its own unique serial number/identifier as well as a corresponding one or more version dates stamps.
When a topic primitive object (TPO, e.g., a topic node) breaks away from (or is otherwise removed from) a previous location on the universal “A”-Tree and/or from a previous location within the universal spatial space of the corresponding topics mapping mechanism (a.k.a. topic space) so as to, for example, move to a new location, the breaking away TPO (e.g., 30S.53 of
In addition to the “I was here” tag, there also and optionally can be a “this-TPO-is-dead” versus “this-TPO-is-alive” flag area 30T.1c which indicates whether or not the corresponding topic primitive object (TPO) is no longer attached to the pointed-to hierarchical parent node and/or to the pointed-to spatial parent location. If the tagged location is one where the TPO no longer resides, the “this-TPO-is-dead/alive” flag area 30T.1c of that tag will include an explanation of why the TPO is no longer there and perhaps where it next moved to. In one embodiment, system administrators can kill a node if its attached chat rooms are persistently engaging in inappropriate conduct. In that case, the “this-TPO-tag-is-dead” flag 30T.1c will include an explanation of why the system administrators killed it so that offending users know the reason. The “this-TPO-tag-is-dead” flag area 30T.1c may further include a link to an appeal site whereat system users might appeal the administrators' decision to kill the now-dead node. In one embodiment, one or more spaces-crawling automated bots crawl through all nodes of topic space (and/or other system-maintained CARSs) and all the chat or other forum participation sessions tethered to them, searching for evidence of inappropriate conduct. If a below threshold amount of inappropriate conduct (e.g., use of language that is predetermined to be inappropriate for that zone) is discovered by the bot, the node and its forums are marked for more frequent return visits and an accumulating score is kept (stored) for each. If the accumulating score crosses a first predetermined threshold, warnings are automatically sent to members of the governance body. If the accumulating score next crosses beyond a second predetermined threshold, the node and/or its cross-associated forums are automatically killed by the bot. Progeny nodes and their associated forums are also killed by this operation. Explanations are emailed or otherwise transmitted automatically to the governance bodies of the killed nodes/forums explaining why the automated kill took place and explaining the procedure for appealing.
In one embodiment, the dead-or-alive flag area 30T.1c may include a map of (or a pointer to such a map which maps) the remainder of the data structure 30T.0. This included or point to map (not shown) indicates the respective locations in machine memory space of the other fields of the TPO representing data structure 30T.0 and their respective sizes. For example, if the represented TPO does not appear on alternate trees besides the “A”-Tree and/or does not appear in alternate spatial coordinates (B, C, etc.) besides that of the “A”-universal space, then respective fields 30T.2 and 30T.3 may be empty and of minimized size or not there at all. The data structure map (not shown) of flag area 30T.1c will indicate this and may further indicate the same for others of the fields of data structure 30T.0 and may further indicate how many such fields (e.g., beyond 30T.14, 30T.15 or 30T.16 of
In one embodiment, the dead-or-alive flag area 30T.1c may further provide an over-time fade out function. The over-time fade out function operates as follows. If certain fields (e.g., 30T.13 of
A further field 30T.1d of the exemplary TPO data structure (30T.0) contains so-called, anchor factors. These indicate how strongly the represented node anchors by itself to its respective location in topic space (see also 30R.61/63 of
If the represented node is dead or moved away, then fields 30T.1a through 30T.1c are all that remain of it. The rest of the data structure (30T.0) is not needed and thus, in one embodiment, is not stored or in other embodiment compressed and stored as compressed data. On the other hand, if the represented node is alive and well at its present location, then in addition to the anchor factors field 30T.1d, it may include a next section 30T.2 filled with sorted pointers (or a pointer to such sorted pointers) pointing to optional other parent nodes on optional other tree structures (beyond the “A”-Tree). In other words, the represented node may have a different parent node or linked-to peer on the “B”-Tree, on the “C”-Tree, and so on. Typically, system users will want to know which of these alternate parent nodes (on the “B”-Tree, etc.) is/are the most recently referenced ones, the hottest ones, the most popular alternate parent node among users of the represented node, which is the one that is most well regarded by only users having high reputation and/or high credentials, etc. There can be many such lists having different ranking categories (with optional sorting per the rankings) and different effective dates or durations. (See also section 30T.12 of
Just as section 30T.2 provides sorted lists of pointers to alternate parent nodes of alternate hierarchical trees, next section 30T.3 provides sorted lists of pointers to alternate locations in the branch spaces of the alternate parent nodes. The sorted lists (e.g., most popular, most reputable) of section 30T.3 correspond on essentially a one-for-one basis with those of previous section 30T.2 and thus further explanation is not needed. If a respective alternate parent node does not have a spatial branch space, the corresponding pointer of section 30T.3 is coded as a null or invalid pointer.
In the hypothetical story above of how node 30S.9a (of
As indicated at 30T.4a, the respective bibliographic information about each founding father (which founder may be a virtual persona instead of a real life person) may include a system-provided unique identification number for that persona, public biography information about the identified persona if such information is available, information about publicly available (and optionally certified) credentials of the identified persona (e.g., college degrees, etc.), information about publicly available reputation scores (optionally certified) for that identified persona in different subject matter areas, information about publicly known affiliations (e.g., business groups, scholastic groups, etc.) of the identified persona, and so on. One of the functions that the founding fathers section 30T.4 can serve is to provide attribution to those personas who decided to launch a new topic node when moving their chat or other forum participation session from under the topic catch-all node (a.k.a. null topic node 30S.55) to a new position within topic space, which new position includes the new topic node they create (by naming it, positioning it, etc.). In one embodiment, the STAN_3 system automatically suggests to participants of forums taking place under null topic node 30S.55 the possibility of creating their own new topic node if they can't find a pre-existing one to which their Notes Exchange session belongs and/or the possibility of moving their Notes Exchange session (e.g., online chat) for attachment to (tethering to) one or more pre-existing nodes or subregions in topic space to which their Notes Exchange session appears to belong. The suggestion to create a new topic node may also include mention that the founders will receive attribution for being the founding fathers of the new node. Hence there is incentive for creating new nodes. The suggestion to create a new topic node may also include a pointer to instructions of how to create a new topic node. The Help menu for STAN-spawned forums may also include a pointer to instructions of how to create a new topic node so that participants who are dissatisfied with a current topic node and want to form a new, different node can easily do so.
Some of the information provided in data structure section 30T.4a may be in the form of a pointer to a system-maintained user-to-user associations (U2U) database 30T.6b of the STAN_3 system. More specifically, if a first tracked founding father is indicated to be affiliated with a system-tracked group of other system users, the logical link from data structure section 30T.4a into database 30T.6b may be further traced back through to identify the other system users (e.g., 30T.6a) with whom the first founding father is affiliated and to discover the nature of that affiliation. The traced-back-to other persona may also be a founding father or a member of a governance body or of another group (30T.6) associated with the node and hence an otherwise hidden network of connections between the various personas who founded or ruled or currently rule the given node may be uncovered by tracing back through the system-maintained user-to-user associations (U2U) database 30T.6b. Such a discovery tool for determining who is affiliated with whom cab be particularly valuable in business oriented research where it is desirable to know which hidden other personas are cross affiliated with the node's founding fathers, with the node's current governance body and how.
In one embodiment, a further tool is provided (not shown) for uncovering the currently shared areas of topical or other focus as between two or more of the founding fathers and/or of other personas (e.g., governance body members) cross-associated with them. Therefore, once a system user finds a topic node he/she admires and decides that he/she might want to follow one or more influential persons or groups (e.g., founding fathers, governance body members) and/or follow up on topics of current hot focus by those persons or groups, the tool allows the user to do so.
As indicated at 30T.5a and 30T.5b, the exemplary data structure for the represented topic primitive object (TPO) may include pointers to one or more histories regarding the node's migration histories (plural intended). Reasons for why a given node can have plural migration histories are many. First, the node can simultaneously reside on the “A”-Tree, a “B”-Tree, a “C”-Tree, etc. and the node's positionings on each such hierarchical or non-hierarchical tree can vary. Second, a current node can be the result of merger of two or more separate and earlier existing nodes or a splitting of an earlier existing node into plural nodes. Each pre-existing node may have its own pre-merger history of migrations (and the parent node of a split may also have one or more histories). The pointed to histories may include narrative of what happened and when (e.g., what votes were cast by what members of a controlling governance body) to invoke each migratory move and/or bifurcation and/or merger. The various histories may be used to automatically depict a trajectory and optionally also automatically generate a prediction of further migration based on past history. By contrast, section 30T.5b contains sequential pointers to the locations in hierarchical and/or spatial frames between which migratory moves took place where the sequential pointers are ordered according to the time lines of the migratory moves. This too can be used for mapping the migrations and predicting future moves. It is within the contemplation of the present disclosure to provide an automated tool that can display to a user the migration histories (through a same hierarchical and/or spatial frame) of two or more identified nodes so that the user can see where (and when) the identified nodes were clustered close together and where/when they were spaced relatively far apart. A user may optionally also follow the plural migratory moves of a single node as it drifts within respective ones of the “A”-Tree, “B”-Tree, “C”-Tree, etc. This may help shed light on how and why a particular topic evolved to what it is at present. An automated trending tool may be included within the system's informational resources for predicting where to and when certain topic nodes are expected to next migrate. Such information can be useful to marketing groups who wish to proactively anticipate where certain demographic groups of people are heading in terms of clustering of previously spaced apart topical concepts. (By way of example, assume that the keyword, “neuorplasticity” was previously restricted to the biological sciences quadrant of topic space, but more recently—as a hypothetical—growing clusters of people are drifting respectively controlled nodes with this as one of their top keywords into the cloud computing quadrant of topic space. Such a hypothetical might lead to an evidence supported conclusion that there is growing and snowballing group cognition out there that a cloud computing environment can have a neuorplastic type of innervation structure embedded within it (where the innervation is composed of machine-implemented logical links and the links strengthen or weaken, grow in one direction or recede from another based on how many users fire up those innervations by means of direct or indirect ‘touchings’ on the nodes—i.e. synaptic ends—of those machine-implemented logical links).
Referring to section 30T.6 of
Another type of node-associated set of groups or personas that are identified by section 30T.6 are the so-called, stable forums and groups. These are distinguished from node-associated fly-by-night forums/groups. An example of a fly-by-night forum would be a two-person online chat room that temporarily tethers to the represented topic node (TPO) for just a few minutes or hours and then breaks away and then drifts away to tether to a different node. By contrast, other forums; such as node-dedicated blogs and tweets whose communications are generally dedicated to that specific one and represented node would be tethered basically for their lives to the represented node (married to that node) and thus such would be the most stably attached to that node. In the spectrum between fly-by-night forums or groups and married-to-the-node forums/group there can be all variations of attachment to the represented node including for example forums or groups that are tethered on a 50/50% basis to the represented node and also to another such node. As may be apparent at this stage, node-associated forums can include chat rooms, blogs, live video conferences and the like. Node-associated other “groups” however, are not necessarily engaged in communicative discourse with one another, but rather they remain cross-associated to the one represented node nonetheless. An example would be a group of so-called, “experts” (30T.6e) who basically leave their virtual calling or virtual business cards attached to the given node so that people who want to contact them with regard to the specific topic or another attribute of the represented node can do so. The pointers of “experts” subsection 30T.6e may point to corresponding records in the user-to-user associations (U2U) database 30T.6b.
In one embodiment, the pre-sorted pointers of section 30T.6 each point to a corresponding record 30T.6a in the system's user-to-user associations (U2U) database 30T.6b. Accordingly, just as a trace back may be carried out from a given founding father's record 30T.4a and by way of his/her public affiliations fields to other users or groups identified within the U2U database 30T.6b, the public record 30T.6a of almost any forum, persona or other type of group listed in the tethered persons/groups/forums section 30T.6 can be consulted by system users to trace forward through its public affiliations fields to yet other users or groups identified within the U2U database 30T.6b.
Although in theory an almost unlimited number of node-associated groups, personas and forums could be point to by section 30T.6, such is not practical. Instead the provided lists are limited to a pre-specified top Nk such entities where Nk may vary as a function of the kth set of groups, personas or forums being considered. More specifically, Nk for the k value associated with most stable node-associated forums might be set to 100 while Nk for the k value associated with least stable of recent fly-by-night entities that most recently tethered to the represented node might be set to 5 (as an example). In terms of visualization, the represented node may be likened to a planet having different orbital shells as well as a terra firma surface. Entities that marry/dedicate themselves essentially for life to that planet (e.g., the node's primary governance body) can be visualized as being rooted to the planet's surface. On the other hand, fly-by-night chat rooms that temporarily pop into orbit around that node and then move on a short time later can be visualized as being temporarily parked in the outermost orbit. Other entities in the spectrum between those extremes can be visualized as parking themselves in lower planetary orbits. Section 30T.6 can be visualized as a sort of census bureau that keeps track of the more prominent citizens and visitors but not necessarily of everyone.
When a system user, or even an automated bot that is crawling through a given sector of topic space, comes upon a node (e.g., the represented TPO) that he/it is not yet familiar with, he/it may wish to know; even before exploring deeper, what kind of node is being encountered based on evaluations provided by earlier visitors and/or by inhabitants of neighboring nodes. Therefore and in accordance with one aspect of the present disclosure, a ratings and warnings section 30T.7 is provided as part of the TPO data structure 30T.0 where this section 30T.7 may contain sorted lists of (or pointers to such lists of) ratings given by rating providing organizations or services to the node and/or warnings posted by such organizations or services or previous visitors regarding the nature of the node. More specifically and by way of example, an included warnings subsection 30T.7a may provide warnings that indicate the node and its children (if any) are intended for mature audiences only (no minors) and/or that the forums associated with the node or the informational other resources provided by the node might be viewed as offensive to some persons where the potentially offensive material pertains to politics and/or religion and/or ethnicity and so on. Therefore, and as an example, an automated search bot (see 30T.11b) that is crawling through that area of topic space on behalf of a minor user (e.g., Fifth Grade Student), stops crawling down that branch and subbranches of topic space when it encounters warning signs (30T.7a) indicating the material is inappropriate. Accordingly time is saved and persons for whom the material is deemed inappropriate may be blocked from seeing it.
With regard to the illustrated ratings subsection 30T.7b, one of the stored ratings may be based on where in a parent node's branch space (e.g., 30R.10) the represented node resides and what attractive or repulsive clustering scores are given to that node from the parent node and/or from neighboring sibling nodes. As may be recalled from the discussion of
Referring to section 30T.8 of
Referring to section 30T.9 of
Referring to section 30T.10 of
In some cases, it may be of value to list the more popular or otherwise classified child nodes of the represented TPO and/or their locations within the specified branch space. Such sorted lists of (or pointers to such lists of) classified child nodes and their locations may further be provided in section 30T.10. An example use of this prestored and presorted information would be for an automated search bot that is looking to find the most popular top 5 child nodes of the given parent or the 7 most well credentialed or highest reputed child nodes of the given parent. A background service of the STAN_3 system repeatedly tests the branch space of each parent node to determine which children are currently the most popular, the most reputable, etc. and then it updates the information stored in section 30T.10. Therefore when a user's private search bot later comes through looking for such information, it is already there.
Referring to section 30T.11 of
Referring to section 30T.12 of
Examples of possible column titles are shown by blocks 30T.12e1 through 30T.12e8. The corresponding columns may include a first one (30T.12e1) listing a most recent subset of new URL's (or URL expressions) that were not listed elsewhere in section 30T.12 and are thus currently new within section 30T.12, where the period for recentness may be a predetermined value N1, for example, in the last 5 minutes (and the column update time 30T.12f indicates when the 5 minute period ended). A different spreadsheet tab may store similar information for an earlier 5 minutes and so on. This allows for quick calculations of trending changes or persistences (for example indicating that a given new URL has been persistently mentioned for the last hour in each 5 minute subsection of that hour).
A second exemplary column (30T.12e2) may provide a listing of pointers pointing to most recent external space PNOS's (e.g., in URL's space) that are new to section 30T.12 over the last N2b minutes (where N2b is a pre-specified number) and that were referenced within one of the top N2a “expert” forums (or by TPO-associated expert groups (30T.6e) even though those are not currently engaged in an online notes exchange), where these top N2a “expert” forums are currently strongly tethered to the represented TPO or are otherwise cross-associated to the represented TPO (topic primitive object), and where N2a is a pre-specified number.
A third exemplary column (30T.12e3) may provide a listing of pointers that are pointing to most recent external space PNOS's that are new to section 30T.12 over the last N3b minutes and that were referenced within one of the top N3a “most reputable” forums (or TPO-associated reputable groups) that are currently strongly tethered to the represented TPO or otherwise cross-associated to the represented TPO. A fourth exemplary column (30T.12e4) may do the same for the top N4a “hottest” forums or groups (where the definition of hotness can vary and will be given in the detailed specification pointed to by pointer 30T.12g).
A fifth exemplary column (30T.12e5) may provide a listing of pointers that are pointing to the “hottest” N5a external space PNOS's that were referenced within one of the top N5b “hottest” forums (or hottest TPO-associated reputable groups) that are currently tethered to the represented TPO or otherwise cross-associated to the represented TPO, where there is not necessarily a time limit or effective time span associated to this category.
A sixth exemplary column (30T.12e6) is shown generically to provide a listing of pointers that are pointing to the top N6a “other” external space PNOS's that were referenced within one of the top N6b “otherwise categorized” forums (or “otherwise categorized” TPO-associated groups) that are currently tethered to the represented TPO or are otherwise cross-associated to the represented TPO where there is not necessarily a time limit or time span associated to this category, but if there is it is denoted generically as “when” in the generic example of block 30T.12e6.
Block 30T.12e7 shows an example that was already shown for earlier sections of the TPO data structure, namely, providing a listing of pointers that are pointing to the top N7a most popular URL's (or URL expressions) as referenced by any of the forums currently tethered to the represented TPO or are otherwise cross-associated to the represented TPO where N7a is a predetermined number. Similar additional blocks may provide pointers to a top N7c URL's ever recommended by the most reputable N7d users in any of the forums currently tethered to the represented TPO or are otherwise cross-associated to the represented TPO, and so on.
In general, and if not otherwise specifically stated herein, heat or other attention giving energies cast onto respective points, nodes or subregions of corresponding Cognitive Attention Receiving Spaces (CARS's) can be assumed to be of a positive or “I like this” kind. However, it is within the contemplation of the present disclosure to also indicate when attention giving energies cast onto respective points, nodes or subregions are of a negative or “I especially do not like/despise this” kind. In other words, just as certain URL expressions (or other ranked/rated cognition representing codes) can be rated by users as being the top N7a most popular (most liked, most used) such cognition representing codes and the ranked codes can be optionally pre-sorted according to their comparative rankings; other certain URL expressions (or other ranked/rated cognition representing codes) can be rated by users as being the top N8a most hated, most despised or otherwise negatively thought about representations of corresponding cognitions, where the pointers to the respectively despised cognition representations may be pre-sorted according to their comparative rankings so that the most despised one is listed first for example. Block 30T.12e8 shows an example (a non-limiting example), namely, one providing a listing of pointers that are pointing to the top N8a most hated or despised by users of this topic primitive object (TPO 30T.0) among URL's (or URL expressions) as referenced by any of the forums currently tethered to the represented TPO or are otherwise cross-associated to the represented TPO where N8a is a predetermined number and degree of hatred (or despising) is based on number of users voting negatively by implicit or explicit means with regard to connecting the hated URL expression with the represented TPO. Similar additional blocks may provide pointers to a top N8b URL's most despised ever by the most reputable N8c users in any of the forums currently tethered to the represented TPO or are otherwise cross-associated to the represented TPO, and so on.
Although not shown in expanded form, next section 30T.13 may do the same thing for ERL's (Exclusive Resource Locators, i.e. private subscription databases) of a system-maintained ERL space where those identified ERL's are cross-correlated with the represented TPO of
Similarly, next sections 30T.14 and 30T.15 may respectively do the same thing in positive affirmation sense or negative despising sense for points, nodes or subregions in a system-maintained context space (e.g., 316″ of
Referring next to
Current focus indicators (CFi's) may come in many different “types”, and when received as packet-packaged data (see packet 30U.10) at the SS3 core portion of the system, the payload CFi data (see field 30U.10g of packet 30U.10) may have to be reformatted and then matched up with other reformatted (e.g., normalized) CFi data received at other times and/or from different CFi sourcing machines so that CFi's which should be clustered together can be identified (because the clustering thereof makes a system-recognized “cognitive sense” of one kind or another) and clustered together. A simple example of three CFi's that have been cross-correlated to one another and then formed into a CFi's cluster is seen under a first illustrated cluster holder data object 30U.12, where the three CFi's are denoted as CFi#1, CFi#2 and CFi#3. The fact that the one cluster holder data object 30U.12 points to them means that they are clustered together at least temporarily on a trial basis. As explained above, trial clusters of CFi's are formed and trial clusters of clusters (see 30U.14) are formed and these trial basis clusters are subjected to so-called, sanity checks to thereby determine on an artificial intelligence basis if they make sense in view of surrounding contexts.
One method for automatically clustering CFi's includes clustering likes with likes. In other words, a first received CFi that represents a particular smell or chemical vapor is logically linked with a second received CFi that represents a particular smell or chemical vapor if the two were transmitted at roughly the same time (per their time stamps 30U.10b) and from roughly the same place (per their respective place of origin stamps 30U.10c as provided by the transmitting packet). Normally, a received CFi that represents a particular smell would not be paired up with a received CFi that represents a particular sound, for example because for most normal cognitions, smells belong with other smells and sounds belong with other sounds of same place of origin and roughly same time of origination. In view of this, when primitive level clustering is being undertaken with aid of a CFi's Sorting and Reorganizing Object (CFiSRO) 30U.0, a CFi's typing specification is provided inside a first section 30U.1 of the CFiSRO data object to specify the type or limited types of CFi's that are to be clustered together under the umbrella of the given CFiSRO.
More specifically, the CFi's collecting node 30U.0 may specify in its first section 30U.1 that it is collecting only smell type CFi's or only emotion representing CFi's or only textual types of CFi's (e.g., only keywords). With that said, it is within the contemplation of the present disclosure that non-primitive or higher cognition level collecting nodes (e.g., those that cluster together clusters of clusters of primitive CFi's collecting nodes like 30U.0) might mix and match cognition representations of different types, for example, a musical sequence and a set of emotions that go together (for whatever reason) with that musical sequence. An example could be marching music mixed with a heart pounding biological state that often comes with that music (i.e. a national anthem) and emotional states that follow as a consequence. Among the different types of CFi's that first section 30U.1 might specify, there could be (but this is not limited to just these), CFi's representing sights, sounds, smells, tastes, different kinds of touch sensations, different kinds of kinesthetic sensations, different kinds of emotional or biological state sensations, textual cognitions (e.g., including keywords, URL's, meta-tags etc.), physical context representations (e.g., specification of surrounding environment, i.e. at work, at home, etc.) and hybrid cognitions including those that mix sensed physical context (XP) with one of the other types of CFi's (e.g., keywords, URL's, etc.).
Aside from trying to cluster likes with likes in terms of type when creating trial clusters of individually received CFi's, the first section may also specify that similarly sized ones of same types of CFi's should be clustered together. More specifically, short textual sequences of some types may be more likely to belong together with other short textual sequences rather than with proportionally much larger/longer sequences. For example a first CFi representing a single word or short phrase is unlikely to belong together with a second CFi representing a full chapter out of a book although a third CFi also representing a full chapter might. So first section 30U.1 may specify size limitations or ranges for the highest level of clusters of clusters that it will hold. (More detailed cluster size ranges are provided in a later described section 30U.3b.) The size specification in first section 30U.1 tells the system memory management software what rough size of data objects it is dealing with.
When the types and generalized broad sizes of the to-be collected CFi data objects are specified in first section 30U.1, it is often the case that a corresponding inferencing engine (see 310′ of
Some subtypes will receive relatively high scores for sanity check (when so checked) while others will receive relatively lower scores. Due to section limitations in the drawing, only one sanity-score storing area 30U.2e corresponding to subtype 30U.2d is shown. However, it is to be understood that each subtype (30U.2a, 30U.2b etc.) will have a respective sanity-score storing area like 30U.2e logically linked with it. The trial-wise tested subtypes that score highest (and are ranked as such) will be pursued more so by the corresponding inferencing engine (see 310′ of
Referring to section 30U.3a of
Referring to section 30U.3b, it is often the case that raw CFi data packets (e.g., 30U.10) keep streaming in on a non-stop basis from a monitored system user (identified in portion 30U.10a of each received packet) as the user moves to different locations over different spans of time. The clusters building process cannot build clusters of infinite size and then make sense of them. A limit has to be set as to how many payloads of a given type (and/or subtype) will be collected under the auspices of a single collecting node 30U.0 for a respective time span and/or for a respective geographic area. Section 30U.3b stores data for placing a limit on the number of payloads to be processed for each type (and optionally each subtype) of cognition and for respective time spans of origination, locations of origination and so on. A more specific example is shown at 30U.3c′ (extending from magnifier of 30U.14). In the example, the desired span of origination time spans for level one CFi's is between 10 and 30 seconds. In other words, a continuous stream of CFi's that covers an origination span of less than about 10 seconds is rejected and a continuous stream of CFi's that covers an origination span greater than about 30 seconds is rejected for forming a level one cluster under this collecting node 30U.0. (The rejected continuous stream may nonetheless collect under another collecting node 30U.0 having a different setting in its section 30U.3c′.) The geographic distance between data collecting locations (30U.10c) may also be delimited in settings section 30U.3c′, for example having to be in the range 5 to 50 feet. The size of each payload may also be delimited in settings section 30U.3c′, for example having to be in the range 7 to 80 bytes. For a level 2 clusters of clusters (see 30U.14) the time span of origination can be different than that of the level one clusters, for example 18 to 180 seconds. This can happen because one level one cluster (30U.12) can belong to a first half while a second level one cluster (30U.13) can belong to a second half of the longer span length.
More specifically under this example, a first trial cluster holder 30U.12 may be limited to collecting no more than three CFi's (#1, #2, #3) but no less than two under its auspices. A second trial cluster holder 30U.13 may be limited to collecting no more than five CFi's (#4, #5, #6) but no less than three under its auspices. At the same time, the corresponding level two trial cluster holder 30U.14 (which forms a clusters of clusters) may be limited to collecting no more than 32 CFi's under its auspices but no less than six CFi's (namely, the illustrated CFi's #1, #2, #3, #4, #5, #6). In
Referring to section 30U.4 of
Referring to section 30U.5, after raw ones of received CFi payloads have been reformatted (and/or re-coded) to conform with the normative codes and formats section 30U.3a, the raw keywords, URL's, etc. defined by the reformatted (and/or re-coded) data may still be idiosyncratic (not normal) relative to a predetermine set of “normalized” keywords, keyword expressions, URL's, URL expressions and so on associated with the current collecting node 30U.0. Section 30U.5 contains pointers pointing to such CFi normalizing and/or augmenting sets for respective CFi's clustering holders 30U.12, 30U.13, 30U.14, etc. Because the clustered CFi's of holders 30U.12, 30U.13, 30U.14, etc. are so-clustered initially on only a trial and error basis, the per-cluster pointers to CFi normalizing and/or augmenting sets are also taken as being on a trial and error basis. The inferencing engines (310′) may use the normalizing/augmenting pointers of section 30U.5 for aiding in performing sanity checks. The tested against PNOS's in system-maintained Cognitive Attention Receiving Spaces will be already normalized and/or augmented. Therefore it may be necessary to normalize and/or augment the raw CFi data of the currently clustered CFi's (e.g., #1, #2, . . . , etc.).
Referring next to section 30U.6, it again should be remembered that the clustered CFi's of holders 30U.12, 30U.13, 30U.14, etc. are so-clustered initially on only a trial and error basis. Nonetheless, initial matchings can be made for each level one cluster, each level two cluster (e.g., 30U.14), etc., for matching chat rooms. Section 30U.6 may contain respective pointers to such trial and error basis matched chat rooms. The data stored in section 30U.6 may be used to invite two or more system users to a same chat room based on trial and error basis clustered CFi's alone. Section 30U.7 provides substantially the same function for other forum participation sessions. Section 30U.8 provides substantially the same function for other informational resources that currently cross-correlate on a trial and error basis with the currently clustered CFi's of the given collecting node 30U.0.
Referring to
For the case of the exemplary, level one clustering of CFi-delivered keywords: “How Historians See-it” (30V.13′),
The data structure shown in
Referring to
The illustrated TexPO data object 30W.0 is deemed to reside at a respective anchored location in a textual primitives layer 30W.71 (see also 371 of
TexPO data objects may have respective directional distances associated with their intra-space cross-linkages (e.g., d.0.14 and d.14.0) for purpose of visually displaying a corresponding 2D or 3D map of how the TexPO's cluster closely together or more far apart and/or how they anchor (30W.2a) strongly or weakly to their respective spots in the textual cognition primitive or other layer (see again 371). Distance values may be computed as combined functions of map room needs for squeezing in other TexPO's and on attractive or repulsive clustering strengths. However, before discussing these co-clustering factors, first some additional discussion for tertiary sections 30W.3a, 3b and 3c of the detailed data structure 30W.0 is provided here. The textual expression code stored in second section 30W.2 can have various control codes associated with it, including but not limited to, various predefined wildcard codes (30W.3a), various predefined delimiter codes (30W.3b), and various predefined expression matching rules (30W.3c). The expression matching rules (30W.3c) may include specialized knowledge base rules (KBR's) indicating which symbols in the expression specification (30W.2) may require an exact match in terms of specialized formatting (e.g., font, bold, underline, italicized, capitalized-only, lower-case only, etc.). The expression matching rules (30W.3c) may define special case exceptions to more general rules for match scoring. The expression matching rules (30W.3c) may include rules that allow for less than perfect matching; for example a 75% cross-correlation factor being enough in place of a 100% cross-correlation factor. The expression matching rules (30W.3c) may further include more sophisticated matching rule specifications directed to anchoring strength requirements (see 30W.2a), effective distances (see d.0.14) from other TexPO's and so on. When the STAN_3 system tries to match (or otherwise cross-correlate) a user-supplied CFi (e.g., 30V.10g of
Referring next to section 30W.4 of the illustrated data structure 30W.0 (the first TexPO), each such textual primitive object may logically link to other TexPO's in its respective region 30W.71 of its respective textual expression space (e.g., in keyword space—see also link 370.12 of
In one embodiment, the relative and/or absolute logical links stored in section 30W.4 are ranked and sorted according to effective anchoring strength (e.g., 30W.12a) and/or relative clustering strength (e.g., s0.12). For example, the most central and foundational other TexPO for the current TexPO (e.g., 30W.0, Abe-Lincoln) might be 30W.12 (=Civil War) and the pointer to it would then be listed first in the pre-ranked and sorted list of section 30W.4; and then the next most important one (e.g., 30W.14=Gettysburg Address) would have the pointer to it listed and so on. Accordingly, when a user-launched automated search bot 30W.11b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in intra-space links section 30W.4 will already have an indication of relative importance of other TexPO's (e.g., 30W.12, 30W.14) to the given TexPO (e.g., 30W.0) based on relative anchoring strengths and/or relative clustering strengths. If the automated search bot 30W.11b has respective search instructions 30W.11si containing search criteria directed to relative importance of other TexPO's relative to the being-considered TexPO (e.g., 30W.0) serving as a base, then the computational work of determining the strength and/or distance and/or rankings of the other TexPO's relative to the being-considered TexPO will already have been done by section 30W.4. Thus the data processing workload of the automated search bot 30W.11b is reduced. More specifically, the pre-specified search instructions 30W.11si of the bot may include an instruction to find a TexPO whose top N most important other TexPO's relate to: (1) the Civil War, (2) Gettysburg and (3) Washington D.C. (last TexPO not shown); N being a predefined number here. In such a case, an automated testing of a sorted list provided in pre-ranked and pre-sorted section 30W.4 will indicate to the search bot 30W.11b how well the given TexPO under consideration (e.g., 30W.0) satisfies that part of the bot's search criteria (30W.11si).
Before moving on to description of next section 30W.5, first a word about launched user search bot's like 30W.11b is in order here. Like topic space, the textual cognition spaces of the STAN_3 system (e.g., keyword space, focused-upon content sub-portions space, etc.) can be constantly changing in response to the fluctuating attention giving activities of the user population. New catch phrases may come into vogue while others fade away. So the anchoring and/or clustering strengths of respective TexPO's may change over time in response to changing preferences of the user population pool. (In one embodiment, the re-direction aspect of the cognitive-sense-representing clustering center points is used to create more up to date, replacement subregions of the given textual expression space to replace the older and gone stale subregions while retaining a legacy history of the older versions.) Sophisticated users; and in particular market research specialists might want to keep track of trending changes among general population pools and their uses of various subregions of various textual expression spaces where those changes are reflected in how the organizing of TexPO's in a corresponding textual cognition space changes or in another expressed cognition space. Eventually, many such changes show up as corresponding changes in topic space. However, they may first appear as a new catch phrase (e.g., “If you love me, pass my bill”—President Obama Sep. 14, 2011) in a corresponding textual cognition space or as a catchy new other expression (e.g., a visual cartoon) in another type of expressed cognition space. Sophisticated users may wish to launch space-crawling, automated bots like 30W.11b which virtually crawl through respective areas of specified expression cognition spaces in search of tell tale signs of changing user mood and changing usages of language or other forms of expression. Such may be signaled by the appearance of a new catch expression and/or by changes of relative rankings as between pre-established catch phrases or as between other such expressed cognitions. Search instructions (30W.1si) that the sophisticated user formulates on his/her own or with the aid of search templates provided by the STAN_3 system are inserted into scripted code that search bot obeys. An example of a scripted code might say, “Alert me if Gettysburg Address (30W.14) becomes more highly ranked than Civil War (30W.12) in section 30W.4 of TexPO 30W.0, otherwise keep crawling”. In other words, if no important changes occur, the user does not want to be bothered by his/her in-the-background crawling around search bot 30W.11b. The user is not focusing his/her current attention giving energies on the possible change of organization within the crawled through textual or other expressed cognition space. The user launched crawl bot 30W.11b keeps doing this as system bandwidth allows and as long as the respective user does not cancel a subscribed to crawl service (if such subscribing is needed). Specifics regarding how to create an in-the-background crawling bot (e.g., 30W.11b), how to program it the first time and/or how to recall it for change of search and alert instructions (e.g., 30W.11si) may be provided by tutorial web pages or the like provided by the STAN_3 system.
Referring to section 30W.5 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to normalization, augmentation and/or translation dictionaries. This concept has been discussed above. Briefly, the textual expression in section 30W.2 (assume for this explanation it says “Yo Ho Joe” rather than Abe-Lincoln) may be a relatively nonconforming one that only a small subset of system users use while the majority of users routinely refer to the referenced target as “Joe-the-Throw Nebraska” rather than as “Yo Ho Joe”. In this case, a first pointer in section 30W.5 may point to the more normal naming of the targeted cognition (e.g., Joe-the-Throw Nebraska”). The normalization pointers in section 30W.5 may be pre-ranked and/or pre-sorted according to most popular to least popular normalized alternatives. Accordingly, when a user's automated search bot 30W.11b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the normalized alternatives part of section 30W.5 will already have an indication of alternative other ways that the targeted textual cognition can be expressed. In one embodiment, the normalized alternative pointers may point to expressions in respective sections 30W.2 of respective other textual primitive objects (other TexPO's, for example TexPO2, TexPO3, etc.).
Another subsection of part 30W.5 may contain a pre-ranked and pre-sorted listing of pointers pointing to other TexPO's whose expressions are not substitutes for the textual cognition of the current TexPO (e.g., 30W.0) but rather are expansions, extensions of the given textual cognition (e.g., Abe-Lincoln). Such expansion/extension lists may be used when the system user does not have at the tip of his/her tongue the exact expression he/she is trying to grasp. For example, the user may say to themselves (or others), “It's got something to do with Abe-Lincoln (or with “Yo Ho Joe” as another example), but I can't pull the exact naming of it out of mind at the moment”. The expansion/extension lists may be pre-ranked and/or sorted according to current popularity scores or according to other, additional criteria (e.g., expert user's preferences). More specifically, as an example, if there a popular joke circulating among system users relating to the Abe-Lincoln example (e.g., “Other than that Mrs. Lincoln, how did you enjoy the show?”), one of the expansion/extension pointers may point to an intra-space node or subregion related to that currently popular joke. Often, if the textual cognition represented by section 30W.2 is a living celebrity, the number 1 popular expansion/extension pointer will point to an intra-space node or subregion related to a current events textual cognition that is currently “hot” or most popular.
Another subsection of part 30W.5 may contain a pre-ranked and pre-sorted listing of pointers pointing to other TexPO's whose expressions are substitutes for the textual cognition underlying the textual expression of the current TexPO (e.g., 30W.0) but are expressed in a different language (e.g., Spanish, French, Chinese) or with use of very different words. For example, the expression, “sixteenth president of the USA” may be a way of expressing the concept of Abe-Lincoln but with very different words. In one embodiment, a language conversion that is most often called for (most popular) at the time is automatically listed first. Two uses may be derived from such a configuration. First, because most users who need a translation will be asking for that number 1 most popular translation, it will be most readily available at the top of the pre-sorted list. Secondly, for people doing market or other research regarding the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2 and which language based demographic groups are accessing it most, such information will be readily given by the pre-sorted list in the translations part of section 30W.5.
Referring to section 30W.6 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to points, nodes or subregions (and/or cognitive-sense-representing clustering center points) in topic space that strongly cross-correlate with the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2. This concept has been discussed above. Briefly, one or more pre-ranked and pre-sorted listings of pointers pointing to topic space may be provided. These may be ranked according to current “hotness”, according to long-term popularity, according to co-related topics that experts users currently consider to be most related, and so on. Accordingly, when a user's automated search bot 30W.11b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the topic space pointers section 30W.6 will already have indications of which topic nodes and/or cognitive-sense-representing clustering center points are most currently “hot” in relation to the textual expression and corresponding cognition of section 30W.2, which are most popular over a long term duration (e.g., last 2 years), which are most currently popular among expert users, among users having pre-specified demographic attributes, and so on.
Referring to section 30W.7a of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to chat or other forum participation sessions that strongly cross-correlate with the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2. This concept has been discussed above. Briefly, these sessions may be ranked according to current “hotness”, according to long-term popularity, according to participation by known expert and/or influential users currently consider to be most related to the textual cognition represented by section 30W.2, and so on. Accordingly, when a user's automated search bot 30W.11b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the cross-associated forum pointers section 30W.7a will already have indications of which forums are most currently “hot” in relation to the textual cognition of section 30W.2, which are most popular over a long term duration (e.g., last 6 months), which are the most currently popular among expert users who are cross-associated with the textual cognition of section 30W.2, which are currently focusing-upon the textual cognition of section 30W.2 while at the same time being most currently popular or hottest among users having pre-specified demographic attributes, and so on.
Referring to section 30W.7b of the illustrated data structure 30W.0 (the first TexPO under consideration), this functionality has also been briefly mentioned above. A same one textual expression (e.g., “Best USA President ever”) may have very different meanings or cognitive senses to different groups of users. More specifically, one group of users may consider Abe-Lincoln to be the “Best USA President ever” and thus they routinely equate the textual expression, “Best USA President ever” with Abe-Lincoln as well as that sense of Abe-Lincoln that deals with the Civil War and the Gettysburg Address for example. On the other hand, another group of users may consider Ronald Reagan or FDR to be the “Best USA President ever” for their respective various reasons. Of course the present disclosure is not picking one over the other but rather providing a means by way of which these different interpretations of the exemplary textual expression, “Best USA President ever” may be logically linked one to the next. That is what the linked list pointers of section 30W.7b do. In one embodiment, each pointer also includes a relative ranking indication such as this next cognitive sense of the same textual expression is ranked number 3 out of the top 100. A search bot can use this linked list to locate, for example, the top 3 current understandings of what the exemplary textual expression, “Best USA President ever” means to system users.
Referring to section 30W.7c of the illustrated data structure 30W.0 (the first TexPO under consideration), this functionality has also been briefly mentioned above. Textual primitive objects (TexPO's) such as 30W.0 may be deemed to lay directly over a specific cognitive-sense-representing clustering center point (e.g., 30W.7p) or to be clustered near to that clustering center point (e.g., 30W.7p) where such distance (in a hierarchical and/or spatial sense) may be calculated based on the literal locations (e.g., 30W.1b) given respectively for the TexPO 30W.0 and its nearby clustering center point (e.g., 30W.7p) or where such distance may be calculated based on one or more distance recalculation rules provided for the corresponding clustering center point (one of the three pointers represented by pointers trio, 30W.ERR). Although due to drawing space limitations,
Referring next to section 30W.8 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to points, nodes or subregions in other system-maintained Cognitive Attention Receiving Spaces (CARSs) besides topic space, forum space or the textual space (e.g., keyword space) of the first TexPO 30W.0. These other logical links (e.g., pointers) may be pre-ranked and pre-sorted according to appropriate ranking and sorting algorithms that serve popular desires of the user population. The other system-maintained CARSs that are referenced by section 30W.8 of the data structure may include representations of non-textual cognitions such as, for example those directed to sights, sounds, tastes, smells, emotions and so on. A more specific example of non-textual cognitions may be a plurality of image sequences relating to Abe-Lincoln giving his famous Gettysburg Address at Gettysburg. The image sequences may not have any text immediately linked to them but rather they may be simply raw image sequences as stated. However, even though there is no textual expression immediately linked to them, each of the plural image sequences may share a consensus-wise agreed to cognitive sense with the others of the plural image sequences. These plural image sequences may be clustered about a cognitive-sense-representing clustering center point in a respective, images-only space. A cross-spaces pointer such as one in field 30W.8 can point to the clustering center point in the respective, images-only space and thus logically link textual primitive object (TexPO) 30W.0 to the images-only center point in the other Cognitions-representing Space.
Referring to section 30W.9, the textual space (e.g., keyword space) of the first TexPO 30W.0 will typically have operator nodes such as 374.1′ pointing back to (e.g., via pointer 370.4′) textual primitive objects such as TexPO 30W.0, where the to-primitive pointers (e.g., 370.4′) function to define a more complex, less primitive textual cognition of the respective operator node 374.1′. In its turn, the pointed-to TexPO 30W.0 can have pre-ranked and pre-sorted pointers stored in section 30W.9 that point to the back referencing operator nodes (e.g., 370.4′). Stated otherwise, section 30W.9 points to the hierarchical child nodes of node 30W.0. The pointers of section 30W.9 may have respective distance and/or strength values (e.g., d.0.74, s.0.74) logically attributed to them for indicating, in similar manner to the primitive layer links (section 30W.4) how strongly and/or closely clustered or not the more complex textual cognitions of the operator nodes are to the primitive textual cognition 30W.2 of data structure 30W.0. In one embodiment, the pointers of section 30W.9 may comprise a pointer to a specific cognitive-sense-representing clustering center point plus a relative offset from that center point to the intended operator node. In this way, each pointer of section 30W.9 may simultaneously identify the co-related center point as well as the child node (e.g., operator node) which is ultimately being pointed to.
In one embodiment, system users have the option of seeing the clustering distance and/or strength values between primitive nodes (e.g., TexPO 30W.0) and/or between selected ones of more complex nodes (e.g., 370.4′) and/or between selected ones of cognitive-sense-representing clustering center points (if used in the respective space) visually displayed to them on a screen in similar manner to the way that topic or other space nodes of
The pointers of section 30W.9 may be pre-ranked and pre-sorted according to appropriate ranking and sorting algorithms, including for example, according to which operator nodes are most frequently in recent times (e.g., last day, week or month) referenced by all system users, which are most frequently in recent times referenced by system recognized experts or influential persons, which are most frequently in recent times referenced by chat or other forum participation sessions that have hotness scores exceeding predetermined threshold values, and so on. Accordingly, when a user's automated search bot 30W.11b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the cross-associated operator nodes section 30W.9 will already have indications for exploitation by the bot including indications of which more complex (less primitive) textual cognitions (as represented by respective operator nodes like 370.4′) are most currently “hot”, which are most popular over a long term duration (e.g., last 3 months), which are most currently popular among expert users who are cross-associated with the primitive textual cognition of section 30W.2, which users are currently focusing-upon a textual cognition having that of section 30W.2 as its primitive, and so on. The automated search bot 30W.11b may use the results for purposes of market research or other purposes.
Referring to section 30W.10 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links pointing into user-to-user associations (U2U) space (see for example 30T.6b of
Referring to section 30W.11, in addition to strongly cross-associated users (of section 30W.10), listings of pre-ranked and pre-sorted pointers may be provided in section 30W.11 for logically linking to other informational resources which are cross-associated with the textual cognition of section 30W.2. These other informational resources may include cross-correlated conference events, research facilities, non-public database resources and so on. The list sortings may indicate which are most preferred by lay or expert users, which are currently the most “hotly” referenced ones and so on.
The automated space updating engine 30W.37 may also from time to time, update the cognitive-sense-representing clustering center points (e.g., 30W.7p) by for example creating a substitute (replacement) subregion that contains a copy of the first center point but at a different location and pointing to different nearby PNOS's in its respective subregion. In one embodiment, when such a replacement copy of a cognitive-sense-representing clustering center point is created, it is done by use of a redirect pointer (“Redirect” in
The automated space updating engine 30W.37 may additionally from time to time, update the distance recalculation algorithms (“ReCalc” in
When each new subregion in a textual space or in another cognition space is created and initially populated, it may be manually or automatically pre-seeded with information obtained from one or more listings of expert or influential users who are strongly cross-associated with that new space or new subregion of the space. In one embodiment, various hierarchical and/or spatial dimension ranges of each Cognitions-representing Space are designated as “reserved for future expansion needs” and these are released for populating with new points, nodes or sub-subregions as the need arises. When a new subregion is opened up for homesteading by new nodes or other such data objects, a rough city plan for the new area may be defined by sparse seeding with expert-created and placed nodes and/or with expert-created and placed cognitive-sense-representing clustering center points. Consider by way of an example the creation of a new textual cognition subregion directed to the concepts of “Abe-Lincoln” (30W.0) and “The Civil War” (30W.12). At the time of creation of the new textual cognition subregion, there already may exist various bibliographic databases or the like which contain listings of renowned scholars or experts who wrote books, treatises or the like that are logically cross-associated with the given primitive textual cognitions taken alone or as more complex combinations (e.g., 374.1′). More specifically, the title of a newly released paper written by a renowned scholar might be, “Abe-Lincoln, the Civil War years” (a hypothetical example). The release of the newly published paper may alone be sufficient reason for seeding an empty and correspondingly newly released or created area of a textual cognition region (e.g., 30W.71) devoted to that paper. The releasing or creation of the new (sub)area may be automatically accompanied by a sparse seeding thereof with TexPO's like the illustrated 30W.0, 30W.12 and 30W.15. When system monitored ones of such expert or influential users directly or indirectly induce the introduction a new textual subregion or of a new textual expression (or a different expression) in a pre-existing subregion because they released a new treatise, a new talk/lecture or other form of communication, the STAN_3 system automatically searches for and seeds within the newly introduced subregion or around the newly introduced expression, additional cognition-representing nodes or clustering center points that are obvious variations of first seeds implanted into the new subregion. In other words, the STAN_3 system (or more specifically an automated space populating engine 30W.30 thereof) automatically creates one or more respective new nodes (e.g., 30W.0, 30W.12 and 30W.15 for “Abe-Lincoln, the Civil War years”) in that newly spawned subregion; where the new TexPO nodes are weakly cross linked (e.g., with a pointer such as one in 30W.4 or 30W.9) to/from a corresponding, less complex node (which could be a root node of keyword space for example—not shown or a pre-existing other node like 30W.13 (“How Historians See It”) for example). In other words, the automated space populating engine 30W.30 keeps track of system monitored ones of expert or influential users (30W.31), and it automatically tests for novelty of expressions or other works they generate regarding a corresponding subregion of an expressible Cognitive Attention Receiving Space (e.g., keyword space), and it automatically inserts a new one or more nodes (and/or cross clustering connectors, e.g., s.0.12; d.14.15) when the generated expression or other work is determined to be novel and optionally a hot or catchy one.
The automated space populating engine 30W.30 keeps track of system monitored chat or other forum participation sessions that are strongly cross-associated with respective subregions assigned to the space populating engine 30W.30, testing for newly trending usages 30W.32 in such forums of expressions not otherwise found in the assigned subregions. When usage in a tracked one or more forums exceeds a predetermined threshold in terms of “hotness” and/or popularity, the space populating engine 30W.30 automatically adds a corresponding new node into the assigned subregion where a textual or other cognition storing section (e.g., 30W.2) of the newly added node stores a respective digital representation of the new expression. Aside from system-spawned or supported forums (e.g., system generated online chat rooms), the STAN_3 system may monitor other informational resources such as Twitter™ feeds for trending new expressions (e.g., new or hot turns of phrase; for example an actor's novel line in a new movie (i.e. ‘make my day’—Clint Eastwood; ‘I'll be back’—Arnold Schwarzenegger, etc.) and the respective space populating engine 30W.30 may then insert the new textual or other cognition node (e.g., 374.1′) as trending developments warrant. The same can be done for trending catch phrases 30W.34 found on parts of the internet (e.g., micro-blogs, news headlines consolidating sites, movie reviews, which may not be directly driven by the STAN_3 system and in other (30W.35) such informational resources. New cognitions for which new nodes are generated and inserted into a respective subregion of a system-maintained Cognitive Attention Receiving Space need not be limited to digitally-represented-by-text cognitions (e.g., 30W.2). They can be new musical cognitions (see again
After the automated space populating engine 30W.30 has added a new textual or other cognition representing node or subregion into a respective, system-maintained Cognitive Attention Receiving Space (CARS), the new node or subregion is tracked by an automated space update engine 30W.37 assigned to that subregion of the given CARS. The assigned automated space update engine 30W.37 is assigned with various consolidation and update tasks. An example of a consolidation task may be as follows. One chat room shows excited trending (e.g., great hotness) for a first version of a celebrity's novel expression (example: ‘make my day’—Clint Eastwood) and a new node is created for that version. Then another forum (e.g., a web blog) shows excited trending (e.g., great popularity) for a second version of the same celebrity's novel expression (example: ‘Go ahead, make my day’—Clint Eastwood) and a separate new node is created for that version. After a while, the automated space update engine 30W.37 assigned to that subregion of expression space automatically realizes that the two versions are actually referring to a substantially a same cognitive expression. One basis for so realizing by automated machine means is that same users are found by the automated machine means to be interchangeably referring to both. In that case the update engine 30W.37 automatically consolidates the two nodes into one or makes one the hierarchical child of the other. When making one node the parent of the other node or consolidating two nodes into one, the update engine 30W.37 may generate a wild-cards filled version of the expression that covers both versions. For example, ‘Go ahead, make my day’—Clint Eastwood may be consolidated into the wild card padded expression: ‘*make my day*’—C* Eastwood*; where here the asterisk (*) denotes any additional or no symbol string. Thus the parent node expression covers the varied versions of the child node expressions.
Another of the assigned tasks of the automated space update engine 30W.37 is to update the rankings and optional sorted listings in the various pointer storing sections (e.g., 30W.4-30W.11) of the primitive of more complex nodes in its assigned subregion of the Cognitive Attention Receiving Space. For example, a usage that was most popular last week may suddenly drop into second or third place this week while a new usage takes over the number spot. Such change in rankings is handled by the automated space update engine 30W.37.
Referring to
Once the identifications (e.g., signals 55102) of the identified social entities are pooled together into respective pooling areas (e.g., 504) based on one or more specified commonalities, another module 553 fetches a copy of the identifications (as signals 55101) and uses the same to scan the currently active, preferences profiles (e.g., 501p) of those social entities where the fetched preferences profiles (501p) include indications of currently active preferences of the pooled persons (or other social entities) for being invited or not into different kinds of chat or other forum participation sessions. The indications may include, for example, indications of the maximum or minimum size of a chat room that they would be willing to participate in (in terms of how many other participants are invited into and join that chat room), of the level of expertise or credentials of other participants that they desire to be present or not within the forum, of the personality types of other participants whom they wish to avoid or whom they wish to join with, and so on. The fetched preferences profiles (501p) should include indications of social dynamic propensity attributes to be expected of the respective users if and when they are invited into and participate in a respective chat or other forum participation session directed to the topic and/or other PNOS's of a respective Cognitive Attention Receiving Space. In other words, the social dynamic propensity attributes indicate which users are likely to be room leaders or respected room participants or social-discourse facilitating members relative to the topic and/or other PNOS's of a respective CARS of the corresponding waiting pool 504. The preferences collecting module 553 forwards its results (of the aggregate desires and/or the social dynamic propensity attributes of the currently pooled (504) users) to a chat rooms spawning engine 552. The spawning engine 552 then uses the combination of the preferences collected by module 553 and the demographic data obtained for the identified social entities collected in the waiting pool 504 to predict what sizes and how many of each of now-empty, chat or other forum participation opportunities are probably needed to satisfy the wishes (preferences) of gathered identifications in the waiting pool 504.
Representations of the various types, sizes and numbers of the empty chat or other forum participation opportunities are automatically recorded into launching area 565. Each of the empty forum descriptions in launching area 565 is next to be populated with a socially “interesting” mix of co-compatible personalities (with identifications of those personas) so that a socially “interesting” interchange will likely develop when invitees (those waiting in pool 504) are accordingly invited to join into the, soon-to be launched forums (565) and when a statistically predictable subpopulation of them subsequently accept the invitations. To this end, an automated social dynamics, recipe assigning engine 555 is deployed. The recipe assigning engine 555 has access to predefined room-filling recipes 555i4 (a.k.a. social-mix recipes) which respectively define different mixes of personality types that usually (based on earlier collected statistical data and survey results) can be invited into a chat room or other forum participation session where that mixture of personality types will usually produce well-received results for the participants. In one embodiment, promoters (e.g., vendors) who plan to make promotional offerings later downstream in the process, get to supply some of their preferences as requested mixes or mix modification 555i2 into the recipe assigning/formulating engine 555. In one embodiment, a listing of the current top topics identified by module 551 (or other current top N points, nodes or subregions (PNOS's) in other Cognitive Attention Receiving Spaces) are fed into recipe assigning/formulating engine 555 as input 555i3 so that assigning/formulating engine 555 can pick out or formulate recipes based on those current top topics (or other PNOS's). As the recipe assigning/formulating engine 555 begins to generate corresponding room make-up recipes, it will start to detect that certain participant personality types are more desired (e.g., more in short supply) than others and it will feed this information as signal 555o2 to one or more bottleneck traits identifying engines 577.
The bottleneck traits identifying engines 577 compare what they have (551o3) in the waiting pool 504 versus what is called-for by the initially generated recipes. The bottleneck traits identifying engines 577 then responsively transmit bottleneck warning signals 557i2 to a next-in-the-assembly line, recipes modifying engine 557. As in the case, for example, of high production restaurant kitchen, the inventory of raw materials on hand (in 504) may not always perfectly match what an idealized recipe calls for; and the chef (or in this case, the automated recipes modifying engine 557) has to make adjustments to the recipes so that a good-enough result is produced from ingredients on hand as opposed to the ideally desired ingredients (pool of available users). In the instant case, the ingredients on hand are the entity identifications waiting in pool area 504. The automated recipes modifying engine 557 has been warned by signal 557i2 that certain types of social entities (e.g., potential room leaders or top influencers) are in short supply. So the recipes modifying engine 557 has to make adjustments accordingly.
The recipe assigning module 555 assigns an idealized recipe from its recipes compilation storage area 555i4 to the pre-sized and otherwise pre-designed empty chat rooms or empty other forums flowing out of staging area 565 to thereby produce corresponding forums 567 (rolling out on the assembly line) having idealized recipes logically attached to them. The automated recipes modifying engine 557 then looks into the ingredients pool 504 then on hand and makes adjustments to the recipes as necessary to compensate for expected bottlenecks or shortages in desired personality types. More specifically, a recipe may call for two leaders and two influencers, but these personas are in currently short supply in pool 504. So the recipes modifying engine 557 automatically trims the recipe to one of each for example. The on-assembly-line rooms 568 with correspondingly modified recipes attached to them are then output assembly line wise along a data flow storing path (delaying and buffering path) to await acceptances of corresponding invitations to these rooms by respective entities in pool 504. The invitations are sent to the pooled personas (504) by the automated recipes modifying and invitations sending engine 557.
In an alternate or supplemental embodiment, the output signal from bottleneck traits identifying engines 577 is also transmitted to the recipe assigning module 555. In response, the recipe assigning module 555 curtails its selections to those that do not overdraw on the identified scarce ingredients. In other words, even though the currently identified top N topics (555i3—or top N′ other PNOS's of another CARS) and/or the received vendor requests (555i2) point to a first subset of the stock recipes 555i4 as being ideal ones for the currently hot topics (or hot other ‘touchings’); if the bottleneck traits identifying engines 577 indicate that the called-for personas are not present in sufficient quantities (or at all) inside the current waiting pool 504, then the recipe assigning module 555 adjusts accordingly, making do with the available people, or better yet with the people who have actually accepted the chat invitations rather than picking recipes first and then trying to produce room participant populations in accordance to the pre-picked recipes.
Next in the assembly line, an RSVP receiving engine 559 automatically receives acceptances (or not) from the invited potential participants of pool 504. Some chat rooms or other forums will receive an insufficient number of the right kinds of acceptances (e.g., a critically needed and scarce room leader does not sign up). If that happens, the RSVP receiving engine 559 automatically trashes the room (removal flow 569) and sends apologies to the invitees indicating that the party had to be canceled due to unforeseen circumstances. On the other hand, with regard to rooms for which a sufficient number of the right kinds of acceptances (e.g., critically needed room leaders and/or rebels and/or social butterflies and/or Tipping Point Persons) are received so as to allow the intent of the room recipe to substantially work as intended, those rooms (or other forums) 570 continue flowing down the assembly buffer line (memory system that functions as if it were a conveyor belt) for processing next by engine 561. At the same time, a feedback signal, FB4 is output from the RSVP's receiving engine 559 and transmitted to a recipes perfecting engine (not shown) that is operatively coupled to the holding area of the social-mix recipes 555i4. The FB4 feedback signal (e.g., percentage of acceptances and/or types of acceptances) are used by the recipes perfecting engine (of holding module 555i4) to tweak the existing recipes so they better conform to actual results (what is observed in the field) as opposed to theoretical predictions of results (e.g., which room recipes are most successful in getting the right kinds and numbers of positive RSVP's). The recipes perfecting engine (which tweaks one or more recipes in holding module 555i4) receives yet other feedback signals (e.g., FB3, 575o3—described below) which it can use alone or in combination with FB4 for tweaking the existing recipes and thus improving them based on obtained in-field data (on FB4, etc.).
Engine 561 is referred to as the demographics reporting and new social dynamics predicting engine. It collects the demographics data of the social entities (e.g., people) who actually accepted the invitations and forwards the same to auctioning engine 562. It also predicts the new social dynamics that are expected to occur within the chat room (or other forum) based on who actually joined as opposed who was earlier expected to join (expected by upstream engine 557).
The auctioning engine 562 is referred to as a post-RSVP auctioning engine 562 because it tries to auction off (or sell off) populated rooms to potential promotion offerors (vendors) 560p based on who actually joined the room and on what social dynamics are predicted to occur within the room by predicting engine 561. By auctioning off (or selling off), it is meant here that the winning/buying promotion offeror(s) will correspondingly receive a chance to post a promotional offering (e.g., discounted pizza) to participants of the corresponding chat or other forum participation session. Naturally, chat or other forum participation sessions that have influential Tipping Point Persons or the like joined in to them and/or are predicted to have very entertaining or otherwise “interesting” social dynamics taking place in them, can be put up for auction or sale at minimum bid amounts that are higher than chat rooms or the like that are expected to be less “interesting”. The potential promotion offerors (vendors) 560p transmit their bids or sale acceptances to engine 562 after having received the demographics and/or social dynamics predicting reports from engine 562. Identifications of the auction winners or accepting buyers (from among buying/bidding population 560p) are transmitted to access awarding engine 563.
As an alternative to bidding or buying exclusive or non-exclusive access rights to post-RSVP forums that have already begun to have active participation therein, the potential promotion offerors (vendors) 560p may instead interact with a pre-RSVP's engine 560 that allows them to buy exclusive or non-exclusive access rights for making promotional offerings to spawned rooms even before the RSVP's are accepted. In one embodiment, the system 410 establishes fixed prices for such pre-RSVP purchases of rights. Since the potential promotion offerors (vendors) 560p take a bigger risk in the case where RSVP's are not yet received (e.g., because the room might get trashed 569), the pre-RSVP purchase prices are typically lower than the minimum bid prices established for post-RSVP rooms.
In one embodiment, influential Tipping Point Personas (e.g., 501a) present within the waiting pool 504 are identified before the auctioning off of promotional access takes place (in engine 562). Special preliminary invitations are sent to these identified TPP personas. The special preliminary invitations indicate to the targeted Tipping point people that, if they join, and the afterwards joining participants are happy with the chat (as indicated by feedback CVi data), then the early-wise committing TPP will be rewarded, for example with discount coupons offered by a corresponding promotion offeror (vendor) 560p. This mechanism can encourage certain people to establish themselves as happy-room-makers or as other forms of system-recognized, influential people (e.g., Tipping Point Persons) since they typically know they have personalities for making other people happy (as will be objectively reported by automatically collected CVi signals) and thus they are likely to win the promised rewards if they perform as expected of them. The result is a win-win for all involved because the other chat or forum participants perceive a more enjoyable chat or other forum participation experience thanks to the extra energies exerted by the happy-room-makers (the system-recognized, influential people (e.g., Tipping Point Persons)) to make the sessions enjoyable ones. The enjoyment factor induces pleased participants to return again for more such sessions. The enjoyment factor also induces the pleased participants to associate the promotional offerings of the winning promotion offeror (vendor) 564 with goodwill feelings which can lead to increased sales. Over time, as positive influence casting results are collected via feedback CVi signals obtained from the other forum participants, the STAN_3 system can automatically rank and thus determine who among the happy-room-makers are best at performing their task (of making the in-room experience more enjoyable for the other participants) for different categories of topics or other such classes of chat rooms; and the rewards offered to these identified TPP personas may be increased accordingly.
In one embodiment, the auction winners 564 can first test-pitch their promotional offerings to one or a few in-room representatives (e.g., the room discussion leader) in private before attempting to pitch the same to the general population of the chat room or other forum. Feedback (FB1) from the test run of the pitch (564a) on the room representative (e.g., leader) is sent to the access-rights owning promoters (564). They can use the feedback signals (FB1) to determine whether or not to pitch the same promotional presentation to the room's general population (with risk of losing goodwill if the pitch is poorly received) and/or to determine when to pitch the same to the room's general population and/or to determine whether modifying tweaks are to be made to the pitch before it is broadcast (564b) to the room's general population. It is to be noted that as time progresses while the instantiated forum advances on the room assembly-and-conveying line, various room participants may drop out and/or new ones may join the room. Thus the makeup and social dynamics of the room at a time period represented by 574 (when the pitch is made or thereafter) may not be the same as at a time period represented by test run 573.
In one embodiment, a further engine 575 (referred to here as the ongoing social dynamics and demographics following and reporting engine) periodically checks in on the in-process chat rooms (or other forums) 571, 573, 574 and it generates various feedback signals that can be used elsewhere in the system for improving system reliability and performance. One such feedback signal (FB2, a.k.a. signal 57502) indicates the way that participants actually behave in the rooms as opposed to what was expected of them, for example based on their currently activated profiles. These actual behavior reports are transmitted to another engine (not shown) which compares the actual behavior reports 57502 against the traits and habits recorded in the respective user's currently activate profiles 501p. (See also PHAFUEL log 501′ of
The statistics developed by the ongoing social dynamics and demographics following and reporting engine 575 may be used to signal (564) the best timings for pitching promotional offerings to respective rooms. By properly timing when a promotional offering is made and to whom, the promotional offering can be caused to be more often welcomed than not by those who receive it (e.g., “Pizza: Big Neighborhood Discount Offer, While it lasts, First 10 Households, Press here for more”). In one embodiment, the ongoing social dynamics and demographics following and reporting engine 575 is operatively coupled to receive context state reports generated by the context space mapping mechanism (316″ of
The present disclosure is to be taken as illustrative rather than as limiting the scope, nature, or spirit of the subject matter claimed below. Numerous modifications and variations will become apparent to those skilled in the art after studying the disclosure, including use of equivalent functional and/or structural substitutes for elements described herein, use of equivalent functional couplings for couplings described herein, and/or use of equivalent functional steps for steps described herein. Such insubstantial variations are to be considered within the scope of what is contemplated here. Moreover, if plural examples are given for specific means, or steps, and extrapolation between and/or beyond such given examples is obvious in view of the present disclosure, then the disclosure is to be deemed as effectively disclosing and thus covering at least such extrapolations.
In terms of some of the novel concepts that are presented herein, the following recaps are provided:
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In one embodiment, each STAN user can designate a top 5 topics of that user as broadcast-able topic identifications. The identifications are broadcast on a peer to peer basis and/or by way of a central server. As a result, if a first user is in proximity of other people who have one or more of their broadcast-able topic identifications matching at least one of the first user's broadcast-able topic identifications, then the system automatically alerts the respective users of this condition. In one embodiment, the system allows the matched and proximate persons to identify themselves to the others by, for example, showing the others via wireless communication a recent picture of themselves and/or their relative locations to one another (which resolution of location can be tuned by the respective users). This feature allows users who are in a crowded room to find other users who currently have same focus in topic space and/or other spaces supported by the STAN_3 system 410. Current focus is to be distinguished from reported “general interest” in a given topic. Just because someone has general interest, that does not mean they are currently focused-upon that topics and/or on specific nodes and/or subregions in other spaces maintained by the STAN_3 system 410. More specifically, just because a first user is a fisherman by profession, and thus it's a key general interest of his when considered over long periods of time, in a given moment and given context, it might not be one of his Top 5 Now Topics of focus and therefore the fisherman may not then be in a mood or disposition to want to engage in online or in person exchanges regarding the fishing profession at that moment and/or in that context. It is to be understood that the present disclosure arbitrarily calls it the top 5 now, but in reality it could instead be the top 3 or the top 7. The number N in the designation of top N Now (or then) topics may be a flexible one that varies based on context and most recent CFi's having substantial heat attached to them. In one embodiment, the broadcastable top 5 topic focuses can be put in a status message transmitted via the user's instant messenger program, and/or it can be posted on the user's Facebook™ or other alike platform profile.
In one embodiment, the system 410 supports automated scanning of NearFiledCodes and/or 2D barcodes as part of up or in-loaded CFi's where the automatically scanned codes demonstrate that the user is in range of corresponding merchandise or the like and thus “can” scan the 2d barcode, or any other object-identifying code (2d optical or not) that will show he or she is proximate to and thus probably focused on an object or environment in which the barcode or other scannable information is available.
In one embodiment, the system 410 automatically provides offers and notifications of events occurring now or soon which are triggered by socio-topical acts and/or proximity to corresponding locations.
In one embodiment, the system 410 automatically provides various Hot topic indicators, such as, but not limited to, showing each user's favorite groups of hot topics, showing personal group hot topics. In one embodiment, each user can give the system permission to automatically update the person's broadcastable or shareable hot topics whenever a new hot topic is detected as belonging to the user's current top 5. In one embodiment, the user needs to give permission to show, how long he will share this interest in the new hot topic (e.g., if more or less than the life of the CFi detections period), and/or the user needs to give permission with regard to who the broadcastable information will be broadcast or multi-cast or uni-cast to (e.g., individual person(s), group(s), or all persons or no persons (i.e. hide it)). If a given hot topic falls off the user's top 5 hot topic broadcastables list, it won't show in permitted broadcast. In one embodiment, an expansion tool (e.g., starburst+) is provided under each hot topic graphing bar and the user can click, tap or otherwise activate it to see the corresponding broadcast settings.
In one embodiment, the system 410 automatically provides for showing intersections of heat interests, and thus provides a quick way of finding out which groups have same CFi's, or which CFi's they have in common.
In one embodiment, the system 410 automatically provides for showing topic heat trending data, where the user can go back in time, and see how top hot topics heats trended or changed over given time frames.
In one embodiment, the system 410 automatically provides for use of a single thumb's up icon as an indicator of how the corresponding others in a chat or other forum participation session are looking at the user of the computer 100. If the perception of the others is neutral or good, the thumb icon points up, if its negative, the thumb icon points down and optionally it reciprocates up and down in that configuration show more negative valuation. Similarly, positive valuation by the group can be indicated with a reciprocating thumb's up configuration. So if a given user is not deemed to be rocking the boat (so to speak), then the system shows him a thumb's up icon. On the other hand, if the user is generating a negative raucous in the forum then the thumb points down. The thumb icon doesn't have to operate on a binary up or down basis. Instead, in one embodiment, it acts like a dial on a metered background scale, where if it's up 90 degrees it's good, down its bad, and in the middle it's a varying degree of good or bad or neutral.
In one embodiment, the system 410 automatically scans a local geographic area of predetermined scope surrounding a first user and automatically designates STAN users within that local geographic area as a relevant group of users for the first user. Then the system can display to the first user the top N now topics and/or the top N now other nodes and/or subregions of other spaces of the so designated group, thereby allowing the first user to see what is “hot” in his/her immediate surroundings. The system can also identify within that designated group, people in the immediate surroundings that have similar recent CFi's to the first user's top 5 CFi's and/or compatible personhood compatibility profiles. The geographic clusterings shown in
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In one embodiment, the map presenting system 400.E automatically indicates which persons or groups in the selected geographic/demographic specific subregion(s) (40E.6i—to be explained shortly—being one of them) of topic space whose clustered ‘touchings’ are displayed have shared a Top 5 Now Topics with the first user and moreover, if they have co-compatible personhood attributes. If such other users are present, the system may then automatically put up a suggestive invite (e.g., an invitation icon) for the first user to join with the others if the others have current “availability” for such suggested joinder. In other words, rather than starting with a predefined one user or group of users and asking what are these pre-identified social entities focusing-upon (as was disclosed for example by pyramid 101rb of
As mentioned, the number of displayed subregions can be more than one. Plane 40E.1 can be composed of a collage of selected subregions. Dividing line 40E.1X for example, may represent a collage or puzzle-pieces amalgamation line where a first cluster of significant ‘touchings’ 40E.1a from a first selected subregion (e.g., 40E.6i) is joined in displayed plane 40E.1 with a second cluster of significant ‘touchings’ 40E.1b taken from a different second selected subregion (not shown) of topic space mapping mechanism 413′. The number of stitched together subregions can be more than two. The user is given access to a subregions selecting tool with which the user can specify the one or more subregions of a selected space that are to be displayed in plane 40E.1 and how they should be organized in that displayed i40E.1.
As a more specific example, let's say the first user has a top-5-now topics set and a first selected topic subregion (e.g., 40E.6i) contains topic nodes corresponding to his top-5-now topics. Also say that the first user is publicly broadcasting a definition of this set as being his top 5. Let's say the co-compatible other users (whose currently significant ‘touchings’ are taking place in the same topic subregion, e.g., 40E.6i) cannot now meet physically (in real life (ReL) or meet as avatars in virtual life if the latter is in effect), but they can remotely chat with the first user; perhaps only by means of a short (e.g., 5 minute) chat. In that case, the availability score will indicate the limited way in which the other users are each available for the first user. In other words, there are different types of availabilities that can be indicated on a spectrum extending from real life (ReL) meeting availability for long chats to only virtual availability for short chats and perhaps only in a virtual life context. A significant ‘touchings’ clustering map such as 40E.1 can indicate all this. More specifically, if the first user used tool 40E.6 (explained shortly) to choose his selected topic subregions (e.g., 40E.6i) wisely, the displayed other users (or more specifically those whose significant ‘touchings’ are being displayed) will inherently be a in geographic area that the first user is also in and/or the other users will inherently belong to a demographic subgroup in which the first user is interested. As a result, even though the first user does not know the identities of these other users beforehand, the first user can find them (provided they are allowing themselves to be found) by virtue of the others having significant ‘touchings’ within the selected topic subregions (e.g., 40E.6i) that the first user has asked the system (400.E) to display to him.
The displayed clusterings map 40E.1 (which in this example displays clusterings of now-on-topic-touchings by other personas within at least topic subregion 40E.6i; but in other here-contemplated versions may display clusterings relative to a defined other subregions or more in a defined other spatial space, e.g., URL's space—see
The layout of the displayed first clusterings map 40E.1 can be varied to suit user preferences. More specifically, the system provides a user-operable, map format selector module 40E.3 that determines a format for a corresponding, virtual reference frame 40E.0 according to which the clusterings map 40E.1 will be displayed. As indicated in a non-limited way, user selectable input parameters for the map format selector module 40E.3 may designate a 3-dimensional format or a 2D format or a 4D format (e.g., animated or color coded) or even a higher dimensionality and also a reference frame geometry such as rectangular, cylindrical, spherical and so on. The quantitative parameters of the axes of the chosen reference frame 40E.0 may vary and may include one or more members of the set comprising: time, location, trending rate or trending acceleration, distance within a cognition space subregion from main-stream cognitions (see radius RTsBr of
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A further section 30K.2 of the EFDO data structure stores code identifying a type of focusing activity being defined by the respective EFDO data structure 30K.0. As illustrated in example block 30K.2a, the respective EFDO may be defining a set of top-N-now topics being focused-upon by the identified social entity (30K.1) where the latter is provided by a sorted list of N pointers (e.g., 30K.4a) in section 30K.4 that respectively point to respectively ranked topic nodes or topic subregions of the system's topic space. So if the code in the second section 30K.2 specifies the EFDO of the respective social entity (30K.1) as being directed to the top N topics now being focused-upon (or focused-upon in a previous time period), then section 30K.4 will include a sorted listing of pointers pointing to the corresponding nodes or subregions of topic space.
On the other hand, if the code in section 30K.2 specifies the EFDO of the respective social entity (30K.1) as being directed to a “diversified” top N now topics, the corresponding and pre-sorted pointers of the section 30K.4 will point to a ranked set of such “diversified” topic nodes or subregions. In one embodiment, it is permissive to have complex combinations of focus sets; indicating for example that the respective social entity is simultaneously focusing-upon a top K keywords AND a top N topics; or an undiversified Top 5 Now Topics plus a diversified next 3 topics, and so on. Accordingly, the illustrated EFDO data structure 30K.0 includes pointer storing sections like 30K.4-30K.7 for respectively each storing one or more sets of pre-sorted (and/or pre-ranked) pointers pointing to respectively pre-ranked ones of points, nodes or subregions in respective Cognitive Attention Receiving Spaces (CARS) that satisfy a corresponding subset definition (e.g., “diversified” topic nodes).
One of the sections, 30K.5 included in the EFDO data structure 30K.0 identifies the most probable current context of the respective social entity by pointing to (30K.5a) corresponding points, nodes or subregions (XSR) in the system-maintained context space. As with other examples provided herein, the system does not know for sure that the pointed to PNOS's are indeed the top ones currently receiving cognitive attention from the respective user of group of users and the exact order of attention giving energies directed to each. These are just best guess modelings of what probably is going on inside the users' minds based on collected CFi's telemetry and the clustering and categorizing of such telemetry in accordance with, for example, the process described herein for
Another of the sections, 30K.6 included in the EFDO data structure 30K.0 identifies (30K.6a) the most probable current hybrids of context-plus-topic nodes then determined by the system to be most likely receiving attention giving energies from the identified social entity.
Although the descriptions above focused-upon the “current” time period, yet another section 30K.3 of the illustrated EFDO data structure 30K.0 identifies the covered time period for the entity focus defining object (EFDO) and the corresponding physical context associated with the EFDO and/or other filtering attributes (e.g., real life (ReL) geographic location, temperature, humidity, wind velocity, biological status, etc.) associated with the EFDO. Accordingly, a plurality of different EFDO's (30K.0, 30K.0′) may be generated and stored by the system where the different EFDO's cover respective different time periods and/or different user contexts and/or different focus type (30K.2) and/or different user personas (30K.1) or different user groups, and so on. The generated and stored entity focus defining objects (EFDO's) may then be accessed by the map generating modules (e.g., 40E.7, 40E.6) of
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Aside from filtering on the basis of user types (e.g., 40E.7i) and/or subregions (e.g., 40E.6i) of the CARS (e.g., topic space) under consideration, the clusterings mapping subsystem 400.E of
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For sake of convenience, a first of the tear-drop tools (40F.2Ta) is shown in enlarged form 40F.Ta′ on the exterior side of the symbolic magnifier. The largest of the color coded (and/or texture coded) areas 40F.2Ta1 represents the cognition subregion (in this a topic node or topic subregion) of greatest popularity within the core ring (or within another area if the tip of the tear drop is moved there) while the next inward area, 40F.2Ta2 represents the cognition subregion (e.g., topic) of next greatest popularity and the third inward area, 40F.2Ta3 represents the cognition subregion (e.g., topic) of yet lesser concentration and popularity within the tipped-at ring area. A legend 40F.2TL may be automatically displayed adjacent to the lower clusters mapping 40F.2 for indicating which cognition subregion (e.g., topic) is represented by each respective color coded (and/or texture coded) area, 40F.2Ta1-a3 inside the displayed tear-drops (e.g., 40F.2Ta, 40F.2Tb, 40F.2Tc). In one embodiment, an expansion tool (e.g., starburst+) is provided adjacent to each named cognition subregion (e.g., topics TSR5.9, TSR5.917) for allowing the user to learn more about that represented cognition point, node or subregion if so desired.
Although not shown for all clustering ring sets (40F.1a, 40F.1b, 40F.1c) of the exemplary URL's map 40F.1, in one embodiment, translucent connection bands or tubes 40F.3Ta (optionally of different colors or textures) are made visible upon user request as between clusterings of URL expressions in a URL-expressions clustering space (e.g., mapped by 40F.1) and a geographic or other such space (e.g., mapped by 40F.2) having ‘touchings’ thereto cross-mapped to a third space (e.g., to topic space) by tear-drop display tools such as 40F.2Ta or the like. More specifically, primitive URL expressions (see 391.2 of
People of like propensities (e.g., of like demographic preferences) tend to congregate or cluster together geographically and/or in other ways (e.g., in terms of their top N topic, keyword and/or URL's ‘touchings’ in respective other spaces) and as a consequence, cross-space connection tubes (e.g., 40F.3Ta) may often be generated and drawn by the STAN_3 system to indicate machine-found cross-correlations between, say URL expression clusterings (of significant ‘touchings’) in a URL's space (mapped by 40F.1) and geographic space clusterings (of significant ‘touchings’) into a corresponding subregion (e.g., represented by 40F.2Ta1 of magnified tear drop) of say, topic space. These cross-correlations may run bi-directionally. By activating a respective expansion tool (e.g., starburst+) in the corresponding legend area, map area or connecting tube area (e.g., 40F.2TL+, 40F.2Ta1+, 40F.1a+, 40F.3Ta+), the user is empowered to being presented with additional information, including that indicating who the ‘touching’ users are, when did they touch and how intensely (e.g., emotionally) did they touch and so on. In some instances, the respective expansion tools (e.g., starbursts+) are not visible until the user zooms in with a viewing zoom-in/zoom-out tool (not shown) to see an enlarged view of the displayed object that contains its respective, information expansion tool. If the activated expansion tool (e.g., starburst+) is within an inter-space connecting tube (e.g., tool 40F.3Ta+), the user is automatically given an option of learning more information about users for the Boolean AND of the interconnected clusterings (e.g., 40F.1a AND 40F.2a) or learning more information resulting from the Boolean OR of the interconnected clustering or from the Boolean XOR (exclusive OR).
In accordance with one embodiment, the clusterings display subsystem (400.F) also automatically displays the locations of relevant promotion-enabling resources like 40F.2R1 adjacent to corresponding clusterings (e.g., 40F.2Tc) of on-topic ‘touchings’. More specifically, the illustrated promotion-enabling resource 40F.2R1 can include one or more of an electronically and remotely controlled billboard, a remotely controllable low powered wireless transmitter (including for example a low powered cellular communications transponder) and a remotely controllable loudspeaker or other information broadcasting means of limited geographic range. Then, when a marketing entity detects (automatically or otherwise) the presence of a clustering of users within the limited range of the promotion-enabling resource 40F.2R1 (e.g., billboard), where the clustered users are currently focusing-upon a topic node (or upon points, nodes or subregions of other spaces) that strongly cross-correlates with a predetermined, and to be promoted offering, the marketing entity may request or cause the predetermined offering to be then presented by way of the identified promotion-enabling resource 40F.2R1 (e.g., billboard). Therefore the respective promotion-enabling resource 40F.2R1 (e.g., billboard) is efficiently used to present to an adjacent clustering of target users a corresponding promotional offering (e.g., goods or services for sale) that directly relates to the subject matter they are currently focusing-upon. An expansion tool (e.g., starburst+) may be provided in or adjacent to the mapped representation of the promotion-enabling resource 40F.2R1 (e.g., billboard) for describing in more detail the capabilities, limitations or other attributes of that resource.
In accordance with one embodiment, the clusterings display subsystem (400.F) also automatically displays the locations of relevant other informational or facility/hardware resources like 40F.2R2 that are disposed adjacent to corresponding clusterings (e.g., 40F.2b) of on-topic ‘touchings’ that are directed to a corresponding and then focused-upon topic or other such node or subregion of a given Cognitive Attention Receiving Space (CARS). More specifically, if the clustered users of displayed clustering 40F.2b are currently focused-upon a topic whose appreciation may be enhanced or facilitated by making use of resources available at the nearby, resource-providing facility 40F.2R2 (e.g., a restaurant with audio-visual presentation resources, a university lecture hall, laboratory, etc.; including those large enough or small enough to efficiently accommodate the indicated number of users in the identified clustering), then in one embodiment one or more of the clustered users and the operator of the nearby resource-providing facility 40F.2R2 are automatically informed (e.g., via email and/or an on-screen advisement) of the proximity between the clustered group (e.g., 40F.2b) and the nearby resource-providing facility 40F.2R2 and the ability of the nearby, resource-providing facility to efficiently accommodate the indicated number and/or type of clustered together users (e.g., 40F.2b). An expansion tool (e.g., starburst+) may be provided in or adjacent to the mapped representation of the other resource 40F.2R2 (e.g., meeting hall) for describing in more detail the capabilities, limitations or other attributes of that other resource.
Given the above, it may be seen that in one embodiment, the STAN system 410 is provided with means for automatically determining if user-availabilities and/or resource-availabilities are such that users can have impromptu or pre-planned meetings based on local events, or on happenstance clusterings or groupings of alike focused people. These automated determinations may be optionally filtered to assure proper personhood co-compatibilities and/or dispositions in user-defined acceptable geographic vicinities. In an embodiment, the system provides the user with zoom in and out function (not shown) for the displayed clusterings map(s).
In one embodiment, the system 410 automatically determines if availability is such that users can have meetings based on one or more selection criteria such as: (1) Time available (e.g., for a 5, 10, 15 MINS chat) to communicate; (2) physical availability to travel X miles within available time so as to engage in a real life (ReL) meeting having a duration of at least Y minutes (where X and Y are predetermined numbers here); (3) level of attentions-giving capability of each user, and so on. For example, if a first user is multi-tasking, such as watching TV and trying to follow a pre-existing chat at same time and so not really going to be able to be very attentively involved in the planned next chat, but just to be a passive bystander vs. him totally looking at the planned next chat, then the attentions-giving capability of that user may be indicated as being low along a spectrum of possibilities extending from only casual and haphazard attention giving to full-blown attention giving. In one embodiment, the system asks the user what his/her current level of attentions-giving capability is. In the same or an alternate embodiment the system automatically determines the user's current level of attentions-giving capability based on environmental analysis (e.g., is the TV blasting loudly in the background, are people yelling in the background or is the background relatively quiet and at a calm emotional state per incoming CFi telemetry signals?). In one embodiment, the system 410 automatically determines if availability is such that users can have meetings based on user mood and/or based on user-to-user distances in real life (ReL) space and/or in various virtual spaces such as, but not limited to, topic space, context space, emotional/behavioral states space, etc.
Referring now to
The underlying logical link (or plural links) of each custom scoop (e.g., 102j′ on serving plate 102a″, which scoop is also shown in magnified form as having an exemplary donut shape at 102je′ with an expansion tool e.g., starburst+) in its center) may be a corresponding one or more links derived from the pre-ranked and/or pre-sorted pointers in
Some features of
In the embodiment 100.N of
The settings tool 114″ may be used to custom configure the then-displayed floor, by for example, changing the layout of where and how different main serving trays (e.g., 101″, 102″, 103″, 104″) are displayed, if displayed at all, where and how different subservient serving trays (e.g., 101r″) are displayed, if displayed at all, where and how different serving plates (e.g., 102a″, 102b″) are displayed, if displayed at all, and/or where and how different scoops (e.g., 102j′) or other such invitations or offerings are displayed, if displayed at all. The settings tool 114″ may also be used to custom configure when these various features are displayed, if displayed at all. For example, there may be certain times of the day (or certain other contextual conditions) for which the user does not wish to receive promotional offerings via lower tray 104″. In one embodiment, the user may disable the presentation of lower tray 104″ for those specified times of the day (or other contextual conditions, e.g., while in a meeting at work). The user may wish to have trays 101″, 102″, 103″ and/or 104″ displayed in different parts of the screen rather than in the default positions shown in
The settings tool 114″ may be used (e.g., via menu 114n1) to custom define which main screen windows will automatically open as default main content of that floor (e.g., the customized Help Grandma floor). For example, one of the default windows that the user/grandson may wish to have as always opening up at center screen is the month's activities calendar (not shown) for a local elders' community center that his grandmother belongs to. In this way, whenever the grandson visits the Help Grandma floor, he is immediately presented with a display (not shown) of the activities calendar for the local elders' community center and he can immediately see if there is an upcoming event that his grandmother may wish to attend. This of course is merely an example and depending on the title and/or function assigned by the user to the floor (e.g., the customized Help Grandma floor), the default central content may vary.
The default content settings option (menu 114n1) may also be used to custom define which serving plates (e.g., 102a″, 102b″) will appear as the top most serving plates on their respective serving tray (e.g., 102″) and/or which scoops (e.g., 102j′) will appear and in what order on the respective serving plates. The option is better illustrated by submenu 114n2. For example, the default topics subregion of serving plate 102a″ might be “Home Repairs” and the different chat-invitation or other informational resource offering scoops (e.g., 102j′) provided on that serving plate may all be directed to different aspects of helping Grandma with her home maintenance and repair problems. Other default topics that the helpful grandson/user may have pre-defined for this floor layout may include topic subregions directed to geriatric health care issues (see submenu 114n3) and/or local or more regional geriatric support groups, local or more regional card game and/or other entertainment options that specially cater to the elderly and so on.
Each of the setting menus (e.g., 114n1-114n3) may contain information expansion tools (e.g., starburst+) for enabling the user to navigate to additional informational resources. More specifically, one of the additional information providing resources of the Geriatric Health menu item in menus 114n3 opens up a spatial map 114n4 of a corresponding subregion of the system-maintained topic space. The user may then spot a new on-topic node within that region and elect to drag-and-drop (114n5a) a copy of that new node up serving plate 102b″ (where the continuation of the drag-and-drop operation is shown as 114n5b) whereby the chat or other forum participation sessions associated with that dragged and dropped copy of the node become a new scoop of automatically served up invitations made available to the user on serving plate 102b″. The user may elect to make all forums associated with that dragged-and-dropped node (operation 114n5a-114n5b) be ones to which he will by default receive invitations to, or; in accordance with a further settings option (not shown) for dragged-and-dropped objects (e.g., topic nodes or topic subregions), the user may attach pre-filtering criteria to the invitations providing new scoop (the dashed end circle of drag operation 114n5b) such as, but not limited to, invite to only the top 2 now chats if ongoing, or invite to only ongoing chats that have on-topic expert Ken54 as one of its participants and so on. In this way the user can custom configure the invitations he/she will receive by way of the dragged-and-dropped new node (or spatial subregion).
Referring again to menu 114n2, the add, delete or modify options made available to the user are not limited to topic nodes and/or to the top serving tray 102″. Another menu option empowers the user to alter the default personas and/or groups that will be displayed by the left sidebar tray 101″ and/or the types of what-are-they-focusing-upon icons (e.g., pyramids) displayed in radar sub-tray 101r″.
As mentioned for example in connection with mapped plane 40F.1 of
Demographic and/or geographic filtering, incidentally, does not have to be centered around Grandma's geographic neighborhood and/or Grandma's demographic brackets. The helpful grandson (a.k.a. first user) may select geriatric physicians as the central demographic bracket for example and/or more specifically, those who practice at a certain hospital and/or are affiliated with a certain university. By watching where the significant ‘touchings’ of those demographic and/or geographic user recently cluster and/or how they change relative to older clusterings of significant ‘touchings’, the helpful grandson (a.k.a. first user) may spot emerging trends even before there is a named topic and/or topic node given to the corresponding cognitions (those of the clustered significant ‘touchings’) and/or even before specific keywords are agreed to with regard to the newly emerging set of socially-mediated cognitions.
Referring to
Still referring to
In one embodiment, the system 410 allows for temporary assignment of pseudonames to its users. For example, a user might be producing CFi's directed to a usually embarrassing area of interest (embarrassing for him or her) such as comic book collector, beer bottle cap collector, etc. and that user does not want to expose his identity in an online chat or other such forum for fear of embarrassment. In such cases, the STAN user may request a temporary pseudoname to be used when joining the chat or other forum session directed to that potentially embarrassing area of interest. This allows the user to participate even though the other chat members cannot learn of his usual online or real life (ReL) identity. However, in one variation, his reputation profile(s) are still subject to the votes of the members of the group. So he still has something to lose if he or she doesn't act properly.
In one embodiment, the system 410 provides social icebreaker mechanism that smooths the ability of strangers who happen to have much in common to find each other and perhaps meet online and/or in real life (ReL). There are several ways of doing this: (1) a Double blind icebreaker mechanism-each person (initially identified only by his/her temporary pseudoname) invites one or more other persons (also each initially identified only by his/her temporary pseudoname) who appear to the first person to be topic-wise and/or otherwise co-compatible. If two or more of the pseudoname-identified persons invite one another, then and only then, do the non-pseudoname identifications (the more permanent identifications) of those people who invited each other get revealed simultaneously to the cross-inviters. In one embodiment, this temporary pseudoname-based Double blind invitations option remains active only for a predetermined time period and then shuts off. Cross-identification of Double blind invitations occurs only if the Double blind invitations mode is still active (typically 15 minutes or less).
Another way of the breaking the ice with aid of the STAN_3 system 410 is referred to here as the (2) Single Blind Method: A first user sends a message under his/her assigned temporary pseudoname to a target recipient while using the target's non-pseudoname identification (the more permanent identification). The system-forwarded message to the non-pseudoname-wise identified target may declare something such as: “I am open to talking online about potentially embarrassing topic X if you are also. Please say yes to start out online conversation”. If the recipient indicates acceptance, the system automatically invites both into a private chat room or other forum where they both can then chat about the suggested topic. If the targeted recipient says no or ignores the invite for more than a predetermined time duration (e.g., 15 minutes), the option lapses and an automated RSVP is sent to the Single Blind initiator indicating that the target is unable to accept at this time but thank you for suggesting it. In this way the Single Blind initiator is not hurt by a flat out rejection.
In one embodiment, the system 410 automatically broadcasts, or multi-casts to a select group, a first user's Top 5 Now Topics via Twitter™ or an alike short form messaging system so that all interested (e.g., Twitter following) people can see what the first user is currently focused-upon. In one variation, the system 410 also automatically broadcasts, or multi-casts the associated ‘heats” of the first user's Top 5 Now Topics via Twitter™ or an alike short form messaging system so that all interested (e.g., Twitter following) people can see the extent to which the first user is currently focused-upon the identified topics. In one variation, the Twitter™ or alike short form messaging of the first user's Top 5 Now Topics occurs only after a substantial change is automatically detected in the first user's ‘heat’ energies as cast upon one or more of their Top 5 Now Topics, and in one further variation of this method, the system first asks the first user for permission based on the new topic heat before broadcasting, or multi-casting the information via Twitter™ or an alike short form messaging system.
In one embodiment, the system 410 not only automatically broadcasts, or multi-casts to a select group, a first user's Top 5 Now Topics via Twitter™ or an alike short form messaging system, for example when the first user's heats substantially change, but also the system posts the information as a new status of the first user on a group readable status board (e.g., FaceBook™ wall). Accordingly, people who visit that group readable, online status board will note the change as it happens. In one embodiment, users are provided with a status board automated crawling tool that automatically crawls through online status boards of all or a preselected subset (e.g., geographically nearby) of STAN users looking for matches in top N Now topics of the tool user versus top N Now topics of the status board owner. This is one another way that STAN users can have the system automatically find for them other users who are now probably focused-upon same or similar nodes and/or subregions in topic space and/or in other system-maintained spaces. When a match is found, the system 410 may automatically send a match-found alert to the cellphone or other mobile device of the tool user. In other words, the tool user does not have to be then logged into the STAN_3 system 410. The system automatically hunts for matches even while the tool user is offline. This can be helpful particularly in the case of esoteric topics that are sporadically focused-upon by only a relatively small number (e.g., less than 1000, less than 100, etc.) of people per week or month or year.
In one embodiment, before posting changed information (e.g., re the first user's Top 5 Now Topics) to the first user's group readable, online status board, the system 410 first asks for permission to update the top 5, indicating to the first user for example that this one topic will drop off the list of top 5 and this new one will be added in. If the first user does not give permission (e.g., the first user ignores the permission request), then the no-longer hot old ones will drop off the posted list, but the new hot topics that have not yet gotten permission for being publicized via the first user's group readable, online status board will not show. On the other hand, currently hot topics (or alike hot nodes and/or subregions in other spaces) that have current permission for being publicized via the first user's group readable, online status board, will still show.
In one embodiment, the system 410 automatically collects CFi's on behalf of a user that specify real life (ReL) events that are happening in a local area where the user is situated and/or resides. These automatically collected CFi's are run for example through the domain-lookup servers (DLUX) of the system to determine if the events match up with any nodes and/or subregions in any system maintained space (e.g., topic space) that are recently being focused-upon by the user (e.g., within the last week, 2 weeks or month). If a substantial match is detected, the user is automatically notified of the match. The notification can come in the form of an on-screen invitation, an email, a tweet and so on. Such notification can allow the user to discover further information about the event (upcoming or in recent past) and to optionally enter a chat or other forum participation session directed to it and to discuss the event with people who are geographically proximate to the user. In one embodiment, the user can tune the notifications according to ‘heat’ energy cast by the user on the corresponding nodes and/or subregions of the system maintained space (e.g., topic space), so that if an event is occurring in a local area, and the event is related to a topic or other node that the user had recently cast a significantly high value of above-threshold “heat” on that node and/or subregion, then the user will be automatically notified of the event and the heat value(s) associated with it. The user can then determine based on heat value(s) whether he/she wants to chat with others about the event. In one embodiment, time windows are specified for pre-event activities, during-the-event activities and post-event activities and these predetermined windows are used for generating different kinds of notifications, for example, so that the user is notified one or more times prior to the event, one or more times during the event and one or more times after the event in accordance with the predetermined notification windows. In one embodiment, the user can use the pre-event window notifications for receiving promotional offerings for “tickets” to the event if applicable, for joining pre-event parties or other such pre-event social activities and/or for receiving promotional offerings directed to services and/or products related to the event.
In one embodiment, the system 410 automatically maintains an events data-objects organizing space. Primitives of such a data-objects organizing space may have a data structure that defines event-related attributes such as: “event name”, “event duration”, “event time”, “event cost”, “event location”, “event maximum capacity” (how many people can come to event) and current subscription fill percentage (how many seats and which are sold out), links to event-related nodes and/or subregions in various system maintained other spaces (e.g., topic space), and so on.
In one embodiment, the system 410 further automatically maintains an online registration service for one or more of the events recorded in its events data-objects organizing space. The online registration service is automated and allows STAN users to pre-register for the event (e.g., indicate to other STAN users that they plan to attend). The automated registration service may publicize various user status attributes relevant to the event such as “when registered” or when RSVP'd with regard to the event, or when the user has actually paid for the event, and so on. With the online registration service tracking the event-related status of each user and reporting the same to others, users can then responsively entering a chat room (e.g., when there is reported significant change of status, for example a Tipping Point Person agreed to attend) and the users can there discuss the event and aspects related to it.
In one embodiment, the system 410 automatically maintains trend analysis services for one or more of its system maintained spaces (e.g., topic space, events space) and the trend analysis services automatically provide trending reports by tracking how recently significant status changes occurred, frequency of significant status changes, velocity of such changes, demographic attributes of such changes (e.g., what kind of users are primarily behind the changes in terms of for example. age, gender, income levels and so on), and virality of such changes (how quickly news of the changes and/or discussions about the changes spread through forums of corresponding nodes and/or subregions of system maintained spaces (e.g., topic space) related to the changes.
References are made a number of times above to various persona characterizing profiles of respective system users. Referring to
Yet more specifically, and assuming a most common of contexts and moods 501a is determined to be in effect, the user 531 will habitually behave in a certain way during a normal work day as represented by Row-1 of table column 503 in
First however, it is to be observed that the predicted unfolding of the “normal work day” context row 503.1 has a context-confirming and unsuredness-resolving, starting point, such as for example, one that indicates what time the user normally awakens (e.g., 6:00 AM), where the user normally awakens (e.g., at home, and upstairs in that home), and perhaps (although not shown) what detailed set of biological or other conditions the user normally awakens under (e.g., hungry, groggy, etc.). The normal starting point predicting data (recorded in section 504.1) provides a form of self-confirming verification of the user context that was presumed by the record activating mood/context signal 5160. If, for example, the user 531 does not awaken at home and “upstairs” and at around 6:00 AM, that may indicate that the user is not operating under the assumed, “normal work day” context of row 503.1. In one embodiment, one or more confidence scores (516c) relating to the currently assumed user context are decremented if the current CFi's indicate user activities that deviate significantly from the expected normal. More specifically, if the deviation exceeds a predetermined first threshold (e.g., user awakens 1 hour later or more than 50 feet away from normal place), an error signal is automatically sent to the context determining engines (see 316o of
In addition to increasing or decreasing confidence scores (see 516c), the PHAFUEL record may provide a filler-in function. Sometimes there are lapses and/or communicative interruptions (502a) to the CFi's and/or CVi's signal streams 502 that are supposed to be received by the system core (e.g., cloud 510) from each respective user. In such a case, where the communication system drops some of the CFi's and/or CVi's in signals stream 502, or they are not able to be processed for some other reason; the then-activated PHAFUEL record (or the default PHAFUEL—see 301d of
Another occasion where the currently activated and unfolding-as-expected PHAFUEL record 501a can come to the rescue is when the received CFi signals 502 point ambiguously to two or more of equally probable outcomes in terms of likely current user state and the STAN_3 system is unsure how to resolve the ambiguity (e.g., an ambiguous clustering of CFi's) based on the CFi's alone. However, the normal habits and routines defined by the then activated PHAFUEL record 501a may act as tie breakers for re-scoring one of the two or more of equally probable outcomes as being more likely than the other(s). Accordingly, when such ambiguous situations arise, the STAN_3 system automatically looks to the then activated PHAFUEL record 501a (if there is one) for assistance in re-scoring tied-for-first-place results so as to better focus the finite bandwidth resources of the system hardware and/or software modules on the more likely of the beforehand tied determinations (e.g., as to what the user's current context is and therefore which nodes in hybrid context-topic space or the like are most likely the ones being currently focused-upon by the user).
Of importance, at the contextual starting point (e.g., 504.1) or shortly thereafter (e.g., 505), the user 531 will habitually have certain specific data processing devices proximate to them (which are normally turned on) for collecting CFi's (502) and the like for sending back to the STAN_3 system 510. The normal work day characterizing row (503.1) includes a respective habitual device sub-row 506 that indicates which of plural and normally used data processing devices are most likely to be turned on and operatively proximate to the user at the given time and/or in the given location. (In one embodiment, if the user forgot to turn on a vital device and automated turn-on capability is available, the STAN_3 system may automatically turn-on the device for the user.) For example, the user 531 may habitually keep her smartphone (see magnification 506a for list of possible devices) activated and at her bedside during the night such that it is operatively proximate to her as a first thing in the morning (e.g., 6:00 AM) when she gets up (starting point 504.1). In accordance with one aspect of the present disclosure, the STAN_3 system consults the user's PHAFUEL record to determine which data processing device (e.g., smartphone versus, or together with tablet computer) the STAN_3 system 510 should be expecting to receive CFi's (502) and like reporting signals regarding the user's current activities (e.g., attention giving activities) at the normal waking time. More specifically, during breakfast (normal event 507) the user might usually step away from her smartphone and instead resort to using her tablet computer and/or her at-home desktop computer. Each of these starts and stops in being operatively proximate to one reporting device (e.g., home desktop computer) or another (e.g., work desktop computer) acts as an additional context confirming/self-verifying condition as well as an explanation for stoppage of CFi signals stream from one device or another. If a deviation that exceeds a predetermined threshold (e.g., user is using next door neighbor's computer 1 hour earlier and more than 100 feet away from normal usage place), an error signal is automatically sent to the context determining engines (see 316o of
In one embodiment, the STAN_3 system (510) maintains and repeatedly updates a plurality of confidence scores 516c that it stores for each of its respectively monitored users (e.g., 531). A first of the confidence scores indicates a relative degree of confidence about the currently activated PHAFUEL record (e.g., 501a) based on recent activities and device usages of the user. If the recent activities and device usages (506) substantially conform with predictions made by the currently subscribed-to contextual time line (e.g., 503.1, “normal work day” flow) then the first confidence score is increased for the currently selected PHAFUEL record and a further second confidence score is increased for the currently subscribed-to contextual time line (e.g., 503.1). Conversely, if the recent activities and device usages (506) of the user deviate beyond predetermined threshold values, the confidence scores are correspondingly decremented and; if the deviation is very large, resort may be made to pre-specified default profiles 301d as was explained for
Continuing along the “normal work day” flow 503.1 of
The variance factors 507v for different ones of alternative attributes may be automatically updated (e.g., confirmed or changed) by the STAN_3 system on a statistics running average or other such basis as each user progresses through his/her normal day's habits and routines and the confirming or dis-affirming CFi's for the same are automatically collected by the system. More specifically, in the exemplary detailed case (507a) of user 531 normally eating cereal for breakfast 50% of the time; eggs 25% of the time and some other identified menu item another 25% of the time; the user's tastes may change over time and some other food stuff (e.g., pureed vegetable and fruit mix) may take the number one position in terms of preferences over cereal for example. The system will automatically change the relevant PHAFUEL record (e.g., 501a) over time as the user's actual habits and routines change.
Keeping track of mundane things such as what the user normally has for breakfast (e.g., 50% of the time cereal) can assist in generating so-called, likelihood-of-availability scores 516a for the user when the system considers (using automated machine means) whether to invite the user to a particular chat or other forum participation or real life (ReL) gathering event opportunity. More specifically, if the contemplated event involves consumption of specific food or drink stuffs (as an example of consumables) and the user's PHAFUEL record indicates the user has likely already consumed more than his/her normal weekly fill for that consumable, then a corresponding likelihood-of-availability score 516a for that user and with respect to that contemplated event and its corresponding consumables will be decreased. On the other hand, if the user's current week's consumption for that consumable item (or activity, e.g., involving a specific entertainment genre, i.e. seeing a movie, a sports event, etc.) is well below the normal amount, then the corresponding likelihood-of-availability score 516a for that user and with respect to that contemplated event will be increased. In this way the system can better predict which invitations the user is more likely to welcome and which the user is more likely to feel annoyed by. It should be recalled that in the introductory hypothetical (e.g., Superbowl™ Sunday Party) the system was automatically able to predict which promotional offerings certain users are more likely to welcome. The continuously updated PHAFUEL records of the respective users is one way the system is able to do this.
While the system is keeping track of mundane things such as what the user normally has for breakfast (e.g., 50% of the time cereal), and normally where (e.g., at a restaurant called McD—a hypothetical name) and with whom (e.g., with Bill 20% of the time), the system may also at times be in receipt of biological status telemetry (e.g., implicit voting CVi signals which are translated with aid of the user's currently activated PEEP profile). These signals (e.g., CVi's) may indicate whether the given user (e.g., 531) is implicitly liking or disliking a concurrent activity as reported by the then generated CFi's. Over time, a statistical database is developed for the implicit likes and dislikes of the user (where the statistical database is schematically represented by bar graph 507b that shows percentage of time something is liked versus percentage of time same thing is disliked). Likes and dislikes may alternatively or additionally be collected by means of explicit votes cast by the user. The likes and dislikes statistics may be used for automatically computing availability scores (516a) for the user based on associated attributes of a given event which attributes the user may be likely to like or dislike.
In addition to typical likes and dislikes (507b) of the user and typical consumption amounts per week for respective consumables, there may be certain patterns of behavior which the user exhibits in response to relevant variables. These may be recorded as typical “routines” of the user and encoded in the PHAFUEL embedded knowledge base rules (KBR's 599) for the user. For example, one KBR (516b) may contribute to determination of the user's availability scores (516a) and may define a routine such as: “IF current time is before normal lunch time AND contemplated activity includes lunch AND afternoon work load is low THEN increase Availability Score for contemplated activity by +20″. This is an example. Some KBR's may decrement the user's availability scores (516a) for a given event; for example: “IF expected duration of contemplated activity is greater than 1 hour AND afternoon work load is high THEN decrease Availability Score for contemplated activity by adding −30 to it.”
One of the example time lines, 503.4 is for an extended holiday weekend. Such a contextual time line may have an automatic KBR recorded for it to indicate that the user (531) is not available for any work-related activities when that contextual time line 503.4 is in effect. Accordingly, the currently activated PHAFUEL record (e.g., 501a) for the user and the currently activated contextual time line (e.g., 503.4) may influence which promotional offerings and/or invitations to online chat or other forum participation opportunities or real life (ReL) gatherings the user receives. And as further indicated above, the currently activated PHAFUEL record and its included contextual time line (e.g., 503.1) may work to increase or decrease a self-confirming confidence score (516c) or not based on how closely the user's actually observed activities conform to those predicted by the habits and routines recorded in the then activated PHAFUEL record.
Referring to
Referring to
In step 601, machine readable instructions and/or specifications from a respective sponsor (e.g., vendor of goods and/or services) are fetched by the system and acted upon. The fetched instructions/specifications may directly cause or indirectly influence the formation of an offerees space (e.g., in a system memory area) that is to be populated by recorded identifications of one or more users who are to receive a corresponding promotional offering at an appropriate time and place and/or under other appropriate context. In the cases of
The right side of
At the time of instantiation (655), each of the illustrated, exemplary users, A and B, may already be respectively participating in a respective online chat or other forum participation session or in a game or contest session as is represented by sessions 661 and 662 respectively. During the respective participation by users A and B in their respective sessions (where the respective sessions may include participation in a same chat or other forum participation session and/or same game or contest or lottery), respective CFi's and/or CVi signals are collected from the participating users A and B. The collected CFi's and/or CVi signals may be of relevance to the specification 651′ provided by the sponsor. For example, a sponsor specification may call for populating a corresponding offeree space 652′ only with users who have a participation heat score exceeding a pre-specified minimum value. Those users whose recently received CFi's and/or CVi signals do not provide the desired participation heat score are not allowed into the corresponding offeree space 652′ or are jettisoned from that space. The use of session-obtained CFi's and/or CVi signals is represented in steps 612 and 614 of the process flow chart. Adding in or pruning out of qualifying/non-qualifying users is represented in step 616.
The process of sending out promotional offerings to users may occur even as more users are being hunted for to be added into the offeree space 652′ or pruned out (656) from that space. This is so because offeree space 652′ may be viewed as operating in accordance with a bubble sort mechanism where the best candidates among current offerees within space 652′ bubble to the top on a competitive basis and the least desired ones precipitate down towards the bottom. First offers are sent (620) to the most promising candidates who have managed to bubble to the top of the list and stay there for a pre-specified duration (and/or until their heat scores rise to a pre-specified threshold). At the same time, competitive sorting continues (see feedback path 623) for the less promising candidates who do not get an offering sent to them until it is clear that better candidates will likely not be found before the pre-specified deadline runs out or another offer-ending even occurs.
At step 622, if the time for populating the offeree space 652′ has not run out (or another offer-ending event has not yet happened), control is returned via path 623 to the space populating (or pruning) step 616 and the subsequent sending out in step 620 of an offer to a user is understood to be to another user (B, C, D; not A) who next qualifies as being the best available candidate for receiving such an offer at the time. If the result of testing step 622 is that time has run out or that another offer-ending event has occurred, then the sending out of more offers stops and the offer or deal may then be consummated in step 625.
The right side, data flow diagram of
The above is nonlimiting and by way of a further examples, it is understood that the configuring of user local devices (e.g., 100 of
After this disclosure is lawfully published, the owner of the present patent application has no objection to the reproduction by others of textual and graphic materials contained herein provided such reproduction is for the limited purpose of understanding the present disclosure of invention and of thereby promoting the useful arts and sciences. The owner does not however disclaim any other rights that may be lawfully associated with the disclosed materials, including but not limited to, copyrights in any computer program listings or art works or other works provided herein, and to trademark or trade dress rights that may be associated with coined terms or art works provided herein and to other otherwise-protectable subject matter included herein or otherwise derivable herefrom.
If any disclosures are incorporated herein by reference and such incorporated disclosures conflict in part or whole with the present disclosure, then to the extent of conflict, and/or broader disclosure, and/or broader definition of terms, the present disclosure controls. If such incorporated disclosures conflict in part or whole with one another, then to the extent of conflict, the later-dated disclosure controls.
Unless expressly stated otherwise herein, ordinary terms have their corresponding ordinary meanings within the respective contexts of their presentations, and ordinary terms of art have their corresponding regular meanings within the relevant technical arts and within the respective contexts of their presentations herein. Descriptions above regarding related technologies are not admissions that the technologies or possible relations between them were appreciated by artisans of ordinary skill in the areas of endeavor to which the present disclosure most closely pertains.
Given the above disclosure of general concepts and specific embodiments, the scope of protection sought is to be defined by the claims appended hereto. The issued claims are not to be taken as limiting Applicant's right to claim disclosed, but not yet literally claimed subject matter by way of one or more further applications including those filed pursuant to 35 U.S.C. § 120 and/or 35 U.S.C. § 251.
| Number | Date | Country | |
|---|---|---|---|
| 61485409 | May 2011 | US | |
| 61551338 | Oct 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14192119 | Feb 2014 | US |
| Child | 16196542 | US | |
| Parent | 13367642 | Feb 2012 | US |
| Child | 14192119 | US |
