Patent Grant
11805091
Imagine a set of virtual elevator doors opening up on your N-th generation smart cellphone screen (where N≥3 here) and imagine an 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. 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”. You already have a notion of where this virtual elevator ride is going to next take you. Soon the doors open up and you find yourself looking at a smartphone screen (the screen of your real life (ReL) intelligent cellphone) having a center area populated with websites related to today's Superbowl™ football game. On the left side of your screen is a list of friends whom you often like to talk to about sports related matters. Next to their names are a strange set of revolving pyramids with red lit bars disposed along the slanted sides of the pyramids. At the top of your screen there is serving tray supporting a set of invitations serving plates where the served stacks or combinations of donut-like objects each invite you to join a recently initiated or soon-to-start online chat and where the user-to-user exchanges of these chats are (or will be) primarily directed to today's game. On the bottom of your screen is another serving tray 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”.
As you muse over this screenful of information that was automatically presented to you and as you muse over what today's date is, as well as considering the real life surroundings where you 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 surprisingly guessed correctly about you, about where you are, your surrounding physical context, what you are thinking about at the moment (your mental context) and what invitations or promotional offerings you are ready to now welcome. Indeed, today is Superbowl™ Sunday and at the moment you are already sitting (in real life) on the couch in your friend's house (Ken's house) getting ready to watch the big game along with a few other like minded colleagues. You surmise that the smart virtual ball inside your smartphone must have used a GPS sensor embedded in the smart cellphone as well as your online digitized calendar to make best-estimate guesses at where you are, 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.
With that thought fading into the back of your subconscious, you start focusing on 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. Meanwhile in your real life background, the TV is already blaring with the pre-game announcements and Ken has started blasting some party music from the kitchen area while he opens bags of pretzels and potato chips. As you focus on the web content presented by your PDA-style (Personal Digital Assistant type) smartphone, a small on-screen advertisement pops up next to the side of the athlete's health-condition reporting frame. The advertisement says: “Pizza: Big Neighborhood Discount Offer, 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 hungry just before the ad popped up. Maybe it was the smell of the opened bags of potato chips. You hadn't eaten pizza in a while and the thought of it starts your mouth salivating. So you pop the advertisement open. It informs you that at least 50 households in your current 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 neighborhood to accept the deal within the next 60 minutes; otherwise the deal lapses. Additional pies and other items are available at different discount rates, first not as good as the opening teaser rate, but then getting better 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 number of pizzas ordered, or number of people who send their email addresses to the promoter or on some other basis that is beneficial to the product vendor.)
This promotional teaser offer 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 a large volume of same-time ordered pizzas to a same one local neighborhood (especially if there are large social gatherings i.e., parties at each) using just one delivery run if the 10 or more households all order in the allotted 60 minutes. 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 all arrive fresh and hot (or at least lukewarm) in the next hour to all the accepting customers in the one neighborhood. Everyone ends up pleased with this deal; customers and promoter. Additionally, the pizza store owner can capture new customers at the party if they are impressed with the speed and quality of the delivery and the taste of the food.
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. Charlie says he wants spicy chicken wings to go along with that. As you hit the virtual acceptance button of the on-screen offer, you begin to wonder; how did the pizza store, or more correctly your smartphone's computer; know this would happen just now—that all these people would welcome the 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 group discount (and/or other promotional offering) before the offer deadline lapses. Next to the sign-up count there is a time countdown indicator decrementing from 60 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 shortly below.
After you place the pizza order, a not-unwelcomed further suggestion 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 using pre-drafted invitation template, 3) Dial their cellphone 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 the persons (A,B,C, etc.) to apply this 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 know this? 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 miss you. Please join.” Further details for this kind of feature will follow below as well.
Your eyes flick back to the news story concerning the health of your favorite sports celebrity. A new frame has now appeared next to it. 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. You know that these “community board” comments have been voted on, ranked as the best liked and/or currently ‘hottest’ and they are all directed to a topic centering on the health condition of your favorite sports celebrity's (e.g., “Is Joe well enough to play full throttle today?”). The best comments have percolated to the top of the list. You have given up trying to figure out how your computer did this too. Details for this kind of feature will 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 aspect unless otherwise explicitly stated herein to the contrary.
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 accompanied by changes of physical state of real physical 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 being carried out in one or more real 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 hardware or firmware implementations including for example implementation of functions by way of field programmable gate arrays (FPGA's) or other such programmable logic devices (PLD's). When 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 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 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.
Background and Further Introduction to Related Technology
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 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 memory and which topic space defining objects can define topic nodes and logical interconnections between those nodes and/or can provide logical links to forums associated with topics of the 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) 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 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, 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 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 customers one at a time). The unsolicited, and thus “pushed” solicitation was not one that generally annoyed them 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 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. 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 memory one or more virtual spaces (data-objects organizing spaces) populated by interrelated data objects 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 to topic-related forums (e.g., online chat rooms) and/or to topic-related other content. The STAN systems can cross match users with respective topic nodes and also with other users (e.g., co-compatible other users) so as to create logical linkages between users that are both 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.
Because people and their interests tend to change with time, location and variation of social context (as examples), the STAN systems are typically structured to adaptively change their focused-upon subareas within topics-defining maps (e.g., hierarchical and/or spatial) and to adaptively change the topics-defining maps themselves (a.k.a. topic spaces, which maps/spaces have physically represented topic nodes or the like defined by data signals recorded in databases or other appropriate memory means 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 helps the STAN systems to keep in tune 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 Vote-Indicating record). CVi's are 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 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, and the like.
In addition to disclosing an adaptively changing topics space/map (topic-to-topic (T2T) associations space), the here 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 the SPEIS′es; which is short for Social/Persona Entities Interrelation Spaces. 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 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 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) 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 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-welcomed solicitations to a corresponding top N ones of the potential offerees who are 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 welcome a second of the brewing group offers. Thus brewing offers are competitively sorted 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. 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. 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.
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 those skilled in the pertinent art prior at 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 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 recent CFi signals, CVi signals recently uploaded for the user and recent topic space (TS) usage patterns or trends detected of the user 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).
In accordance with another aspect of the present disclosure, various user interface techniques are provided for allowing a user to conveniently interface with resources of the STAN system including by means of device tilt, body gesture, head tilt and/or wobble inputs and/or touch screen inputs detected by tablet and/or palmtop data processing units used by STAN system users.
In accordance with 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 and user-relevant topical material (e.g., My Top 5 Now Topics) iconically represented in another subarea 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. 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 same or similar topics to those of current interest to the user or in topics that the user has not yet focused-upon.
Other aspects of the disclosure will become apparent from the below detailed description.
The below detailed description section makes reference to the accompanying drawings, in which:
Some of the detailed description immediately below here is substantially repetitive of detailed description of a
Referring to
The resources of the environment 400 may be used to define so-called, user-to-user associations (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 represented by data signals stored in a SPEIS database area 411 of the system 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 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 instigated by the STAN_3 sub-network system 410.
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.”.
In brief, giF. 1A of the here incorporated ′274 application shows how topics of current interest to (not to be confused with content being currently ‘focused upon’ by) individual online participants may be automatically determined based on detection of certain content 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 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. (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 now 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 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) 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 work-site persona that is different from an at-home or an on-vacation persona and these may have respectively different habits and/or routines. More specifically, one of the many personas that the first user 431 may have is one that predominates in a specific real and/or virtual environment 431e2 (e.g., as geographically detected by integral GPS-2 device of CPU-2). When user 431 is in this environmental context (431e2), that first user 431 may choose to identify him or herself with (or have his CPU device automatically choose for him/her) a different user identification (UAID-2, also 431u2) than the one utilized (UAID-1, also 431u1) when typically interacting in real time with the STAN_3 system 410. A variety of automated tools may be used to detect, analyze and categorize user environment (e.g., place, time, calendar date, velocity, acceleration, surroundings—objects and/or people, etc.). These may include but are not limited to, webcams, IR Beam (IRB) face scanners, GPS locators, electronic time keeper, MEMs, chemical sniffers, etc.
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 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, solo inside that external platform 441-etc. While so interacting 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 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 1st degree “contacts” on the LinkedIn™ platform 444, newly joined groups and so on. The user 431 may them wish to import some of these 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 the friends, un-friends, contacts, buddies etc. are currently focusing-upon. 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 firstly on a brief basis to
Among the objects displayed in the left column area 101 are a sorted list of social entities such as “friends” and/or “family” members and/or groups currently associated with a King-of the-Hill Social Entity (e.g., KoH=“Me” 101a) listed at the top of left column 101. In terms of a more specific example, the displayed circular plate denoted as the “My Friends” group 101c can represent a filtered subset of a current FaceBook™ friends whose identification records have been imported from the corresponding external platform (e.g., 441 of
Yet more specifically, the user of tablet computer 100 (
It is to be understood that the layout and contents of
Referring to still to the illustrative example of
On each face of a revolving pyramid, or polyhedron, or back and forth winding tape reel, etc., the bar graphed (or otherwise graphed) and so-called, temperature parameter (a.k.a. ‘heat’ magnitude) may represent any of a plurality of user-selectable attributes including, but not limited to, degree and/or duration of focus on a topic and/or degree of emotional intensity detected as statistically normalized, averaged, or otherwise statistically massaged for a corresponding social entity (e.g., “Me”, “My Friend”, “My Friends” (a user defined group), “My Family Members”, “My Immediate Family” (a user defined or system defined group), etc.) and as regarding a corresponding set of current top topics of the head entity 101a of the social entities column 101. The current top topics of the head entity (KoH) 101a may be found for example in a current top topics serving plate (or listing) 102a Now displayed elsewhere on the screen 111 (of
Still referring to
Referring back to the left side of
The ability to track the top-N topic(s) that the user and/or other social entity is now focused-upon or has earlier focused-upon is made possible by operations of the STAN_3 system 410 (which system is represented for example in
Not all of
The word “context” is used to mean several different things within this disclosure. Unfortunately, the English language does not offer too 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 “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 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. 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).
One addition provided by the STAN_3 system 410 disclosed here is the database portion 416 which provides “Context” based associations. More specifically, these can be Location-to-User and/or Topic and/or Content associations. The context; if it is location-based for example, can be a real life (ReL) geographic one and/or a virtual one 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 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 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). 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 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 disposed at a given location as well as within a cross-correlated time period, and that the following one or more topics are likely to be associated with the role that the social entity is engaged in due to being in the given “context’ or circumstances: T1, T2, T3, etc.; (2) when a first user is disposed at a given location as well as within a cross-correlated time period, and the following one or more additional social entities 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, and 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, and the following one or more combinations of other social entities, topics, devices and content items are likely to be associated with the first user: U2/T2/D2/C2, U3/T2/D4/C4, etc. The context-to-other association records 416 (e.g., L-to-U/T/C association records 416) may be used to support location-based or otherwise context-based, automated generation of assistance information.
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 and may form different user-to-user associations (U2U) when operating under their various contexts (domains) 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™, hi5™; LinkedIn™, LiveJournal™; MySpace™, NetLog™; Orkut™; 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, California) 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.
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 device, a website's web serving and/or mirroring servers and parts or all of a cloud computing system or equivalent can be ported in whole or in part so as to be hosted in and/or by different one of such physical mechanisms. 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 take place but rather that timely communication and connectivity are provided so that the user experiences substantially same results. 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). Returning back to the digressed-away from method of automatically importing scheduling data to thereby infer at the scheduled dates, the user's more likely environment, a more specific example can be this: 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 corresponding time segment comes around, the STAN_3 system may use such information as one of its gathered encodings for then automatically determining the user's likely mood, surroundings and so forth. For example, between conference events 1 and 3, the user may be likely to seek out a local lunch venue and to seek out nearby friends and/or colleagues to have lunch with. This is where the STAN_3 system 410 can come into play by automatically providing welcomed “offers”. One welcomed offer might be from a local restaurant which proposes a discount if the user brings 3 of his friends/colleagues. Another such welcomed offer might be from one of his friends who asks, “If you are at SNDC today or near the downtown area around lunch time, do you want to do lunch with me? I want to let you in on my latest hot project.” These are examples of location specific, social-interrelation specific, time specific, and/or topic specific event offers which may pop up on the user's tablet screen 111 (
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 interests and 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 moods of the various players (e.g., did each not eat recently and is each in the mood for a business oriented lunch?), (2) infer the current topic(s) of interest most likely on the mind of the individual at the relevant time; (3) infer the type of conversation or other social interaction the 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 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.
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 the present disclosure are persistently testing or sensing for change of user mood (and thus change of active PEEP and/or other profiles), the same mood determining algorithms may be used for automatically formulating group invitations based on mood. Since STAN systems are also persistently testing for change of current user location or current surroundings, the same user location/context determining algorithms may be used for automatically formulating group invitations based on current user location and/or other current user context. Since STAN systems are also persistently testing for change of user's current likely topic(s) of interest, the same user topic(s) determining algorithms may be used for automatically formulating group invitations based on user topic(s) being currently focused-upon. Since STAN systems are also persistently checking their users' scheduling calendars for open time slots and pressing obligations, the same algorithms may assist in the automated formulating of group invitations based on open time slots and based on competing other obligations. In other words, much of the underlying data processing is already occurring in the background for the STAN systems to support their primary job of delivering online invitations to STAN users to join on-topic (or other) online forums. It is thus a relatively small extension to add other types of group offers to the process, where the other types of offers can include invitations to join in a real world social interactions (e.g., lunch, dinner, movie, show, bowling, etc.) or to join in on a real world or virtual world business oriented venture (e.g., group discount coupon, group collaboration project).
In one embodiment, user PEEP records (Personal Emotion Expression Profiles) are augmented with user PHAFUEL records (Personal Habits And Favorites/Unfavorites Expression Logs) which indicate various life style habits of the respective users such as, but not limited to: (1) what types of foods he/she likes to eat, when and where (e.g., favorite restaurants or restaurant types); (2) what types of sports activities he/she likes to engage in, when and where (e.g., favorite gym or exercise equipment); (3) what types of non-sport activities he/she likes to engage in, when and where (e.g., favorite movies, movie houses, theaters, actors, etc.); (4) what are the usual sleep, eat, work and recreational time patterns of the individuals are (e.g., typically sleeps 11 pm-6 am, gym 7-8, breakfast 8-8:30, work 9-12, 1-5, dinner 7 pm, etc.) during normal work weeks, when on vacation, when on business oriented trips, etc. The combination of such PEEP records and PHAFUEL records can be used to automatically formulate event invitations that are in tune with each individual's life style habits.
In line with this, automated life style planning tools such as the Microsoft Outlook™ product 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
Better yet, the corresponding group event offer (e.g., let's have lunch together) may be augmented by a local merchant's add-on advertisement. For example, 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) is automatically augmented by the STAN_3 system 410 to have attached thereto a group discount offer (e.g., “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 formulate discount offer templates which they want to have matched 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 they 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 (in real life and/or online) the STAN_3 system 410 automatically reminds its user members of the original and 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 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 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 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. As seen in
In one embodiment, after passage of a predetermined amount of time the My Top-5 Topics Now plate 102a_Now automatically becomes a My Top-5 Topics Earlier plate 102a′_Earlier which is covered up by a slightly translucent but newer My Top Topics Now plate 102a_Now. If the user wants to see the older, My Top Topics Earlier plate 102a′_Earlier, he may click on a protruding out small portion of that older plate or use other menu means for shuffling it to the front. Behind the My Top Topics Earlier plate 102a′_Earlier there is an even earlier in time 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-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.)
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.
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 notes that the header entity is “Me”.
Next, the virtual ball (also referred to herein as the Magic Marble 108) outputs a virtual spot light onto a small topic flag icon 101ts sticking up from the “Me” header object 101a. A balloon (not shown) temporarily opens up and displays the guessed-at most prominent (top) topic that the 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 shines another virtual spotlight on invitation dot 102i at the left end of the also-displayed, My Top Topics Now plate 102a_Now. Then the Magic Marble 108 rolls over to the right side of the screen 111 and parks itself in a ball parking area 108z.
Unseen by the user during this exercise (wherein the Magic Marble 108 rolls diagonally from one corner (113) to the other (113a) and then across to Ball Park 108z) is that the user's tablet computer 100 was watching him while he was watching it. Two spaced apart sensors, 106 and 109, are provided along an upper edge of the tablet computer 100. (There could be more, 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 extract best-guess hints that the user is likely attending the “Superbowl™ Sunday Party” at his friend's house (Ken's). It similarly provided the system 410 with hints that the user would soon welcome an unsolicited Group Coupon offering 104a for fresh hot pizza. But again the story is leap frogging ahead of itself. The guessed at, social context “Ken's Superbowl™ Sunday Party” also allowed the system 410 to pre-formulate the layout of the 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 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 tilt and jiggle sensor 107. This can be in the form of an opto-electronically implemented gyroscopic sensor and/or MEMs type acceleration sensors. The tilt and jiggle sensor 107 determines what angles the flat panel display screen 111 is at relative to gravity. 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 or both). 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 of the Magic Marble 108. 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. Then, whatever the Magic Marble 108 or cursor 135 or both is/are pointing to, can be highlighted and indicated as activating a user-controllable menu function (136) or set of such functions. In the illustrated example of menu 136, the user has preset the control-right key press function to cause two actions to simultaneously happen. First, if there is a pre-associated topic (topic node) associated with the pointed-to on-screen item, an icon representing the associated topic will be pointed to. More specifically, if the user moves cursor 135 to point to keyword 115a7 (the key.a5 word of phrase), connector beam 115a6 grows backwards from the pointed-to object (key.a5) to an on-topic invitation and/or suggestion (e.g., 102m) in the top tray 102. Second, if there are certain friends or family members or other social entities pre-associated with the pointed-to object (e.g., key.a5) and there are on-screen icons (e.g., 101a, . . . , 101d) representing those social entities, the corresponding icons (e.g., 101a, . . . , 101d) will glow or otherwise be highlighted. Hence, with a simple hot key combination (e.g., a control right click), the user can quickly come to appreciate object-to-topic relations and/or object-to-person relations as between a pointed-to object (e.g., key.a5 in
Let it be assumed for sake of illustration and as a hypothetical that when the user control-right clicks on the key.a5 object, the My Family icon 101b glows. Let it also be assumed that in response to this, 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 N topics?). This cannot be done with the illustrated configuration of
In addition to the topic flag icon (e.g., 101ts) provided with each social entity representing object (101a, . . . , 101d) and the shuffle up tool (98+, except for topmost entity 101a), each social entity representing object (101a, . . . , 101d) may be provided with a show-me-more details tool 99+ (e.g., the starburst plus sign for example in circle 101d of
Aside from causing a user-selected hot key combination (e.g., control right click) to provide 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 information about one or more of other logical entities, such as, but not limited to, associated forums (e.g., platforms 103) and associated group events (e.g., professional conference, lunch date, etc.) and/or invitations 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 computer 100 to have other or additional sensors. For example, 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.
More specifically, in the case 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 router 427 for more refined determination of what specific unsolicited offers the user (and current friends) are likely to accept based on one or more the current topic(s) on his/their minds, current location(s) where he/they are situated, and so on. The so sorted outputs of the Topic/Other Router 427 are then forwarded to current offer sponsors (e.g., food vendors, paraphernalia vendors) who will have their own criteria as to which users or user groups will qualify for certain offers and these are applied as further match-making criteria until specific users or user groups have been shuffled into an offerees group that is pre-associated with a group offer they are very likely to accept. The purpose of this welcomeness filtering and routing and shuffling is so that STAN_3 users are not annoyed with unwelcome solicitations and so that offer sponsors are not disappointed with low acceptance rates (or too high of an acceptance rate if alternatively that is one of their goals). More will be detailed about this below.
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.) 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 which 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 records) of the respective users and thereafter used by the general welcomeness filter 426 of the system 410 or by like other means to block unwelcomed solicitations from being made too often to STAN users. After sufficient training time has passed, users begin to feel as if the system 410 somehow magically knows when unsolicited event offers (e.g., 104t, 104a) will be welcomed and when not. Hence in the above given example of the hypothetical “Superbowl™ Sunday Party”, the STAN_3 system 410 had beforehand developed one or more PHAFUEL records (Personal Habits And Favorites/Unfavorites Expression Profiles) for the given user indicating for example what foods he likes or dislikes under different circumstances, when he likes to eat lunch, when he is likely to be with a group of other people and so on. The combination of the pre-developed PHAFUEL records and the welcome/unwelcomed heuristics for the unsolicited event offers (e.g., 104t, 104a) can be used by the STAN_3 system 410 to know when are likely times and circumstances that such unsolicited event offers will be welcome by the user and what kinds of unsolicited event offers will be welcome or not. More specifically, the PHAFUEL records of respective STAN users can indicate what things the user least likes or hates as well what they normally like and accept. So if the user of the above hypothecated “Superbowl™ Sunday Party” hates pizza (or is likely to reject it under current circumstances, e.g., because he just had pizza 2 hours ago) the match between vendor offer and the given user and/or his forming social interaction group will be given a low score and generally will not be presented to the given user and/or his forming social interaction group. Incidentally, active PHAFUEL records for different users may automatically change as a function of time, mood, context, etc. Accordingly, even though a first user may have a currently active PHAFUEL record (Personal Habit Expression Profiles) indicating he now is likely to reject a pizza-related offer; that same first user may have a later activated PHAFUEL record which is activated in another context and when so activated indicates the first user is likely to then accept the pizza-related offer.
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 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, cooperation agreements are 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.) that permit automated agents output by the STAN_3 system 410 or live agents coached by the STAN_3 system to enter the other platforms 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 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) and passwords (if given) 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 dates on when these relationships were originated or last modified or last destroyed (e.g., by de-friending) 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
Relationships between social entities (e.g., real life persons) 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 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 topic-related co-chatterers in one or more online forums sponsored or tracked by the STAN_3 system 410. 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. The code sequences are organized so that the most common of relationships 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 relationships (e.g., FaceBook™ friend of, MySpace™ friend of, father of, son of, brother of, 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. 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. At times, Jr. 492 wants to keep track of what new top topics KK 493 is currently focusing-upon 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). 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 based on the user specified rules. 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. However, if 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, he can 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. 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 of the last 2 weeks; my in-STAN top topic friends of the last 8 days 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, 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. In other words, what platforms do user(B) and user(C) have shared interests in? 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 as487c, 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 in
The “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) call “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 “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 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 to
As another example, the system 410 may have guessed wrong as to 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. In the latter case, if the Magic Marble 108 had incorrectly taken the user to the Superbowl™ Sunday floor of the metaphorical high rise building, the user can pop the Magic Marble 108 out of its usual parking area 108z, roll it down to the virtual elevator doors 113, and have it take him to the Help Grandma floor, one or a few stories above. This time when the virtual elevator doors open, the user's left side column 101 is automatically populated with social entities who are likely to be able to help him with fixing Grandma's refrigerator, the invitations tray 102 is automatically populated by invitations to chat rooms or other forums directed to the repair of certain name brand appliances (GE™, Whirlpool™, etc.) and the lower tray offers 104 may include solicitations such as: Hey if you can't do it yourself by half-time, I am a local appliance repair person who can be at Grandma's house in 15 minutes to fix her refrigerator at an acceptable price.
If the mistaken 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 “training” one which the system 410 is to use to heuristically re-adjust its context determining decision makings on in the future.
Referring 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 plate-packing tool 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 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 hit pointing 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 12, which for the first time in Cousin Wendy's list of currently focused-upon topics, points to an area topic space far away from the Health Maintenance region. This will 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 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 heat 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., TSRS, 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 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; 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 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; and (5) other STAN users who are now currently contactable by means of cellphone texting or other such socially less-intrusive-than direct-talking techniques.
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 proffered 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.
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 does not want to be now bothered, he can click 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 More-Options button 482g provides user 432 with more action options and/or more information. The other social networking (SN) button 482f is similar to 482e but guesses as to an alternate external network account which user 432 might now want to share information about. In one embodiment, each of the more-specific affirmation (OK) buttons 482e and 482f includes a user modifiable options section 482s. More specifically, when a user affirms (OK) that he or she wants to let the STAN_3 system import data from the user's FaceBook™ account(s) or other external platform account(s), the user may simultaneously wish to agree to permit the STAN_3 system to automatically export (in response to import requests from those identified external accounts) some or all of shareable data from the user's STAN_3 account(s). In other words, it is conceivable that in the future, external platforms such as FaceBook™, MySpace™, LinkedIn™, GoogleWave™, GoogleBuzz™, Google Social Search™, FriendFeed™, blogs, ClearSpring™, YahooPulse™, Friendster™, Bebo™, etc. might evolve so as to automatically seek cross-pollination data (e.g., user-to-user associations (U2U) data) from the STAN_3 system and by future agreements such is made legally possible. In that case, the STAN_3 user might wish to leave the illustrated default of “2-way Sharing is OK” as is. Alternatively, the user may activate the options scroll down sub-button within area 482s of OK virtual button 482e and pick another option (e.g., “2-way Sharing between platforms NOT OK”—option not shown).
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 identity, 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 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.
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
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 includes a topic-to-topic (T2T) associations graph which latter entity includes a parent-to-child hierarchy of topic nodes. In
As a first user (131) makes implied visitations (131a) through the illustrated section 146a of topic space during a corresponding first time period (first time slot t0-t1), he can spend different amounts of time making direct ‘touchings’ on different ones of the illustrated topic nodes and he can optionally spend different amounts of time (and/or otherwise cast different amounts of ‘heat’ energies) making indirect ‘touchings’ on such topic nodes. An example of a hierarchical indirect touching is where user 131 is deemed (by the STAN_3 system 410) to have ‘directly’ touched child node Tn01 and, because of a then existing halo effect (see 132h of
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. The time spent and/or the emotional or other energy intensity that are deemed to have been cast by indirect touchings may be attenuated based on a predetermined halo diminution function (decays with hierarchical step distance of spatial radial distance—not necessarily at same decay rate in all directions). 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, 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 activations would be examples of direct ‘touching’ in URL space and in chat room space. Although traveler 132 is not yet clicking 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 will be responding to his nonetheless energetic skimmings through web content and will be concurrently determining most likely topic nodes to attribute to this energetic (even if low level energetic) activity of user 132. Each topic node that is deemed to be a currently more likely than not, now focused-upon node in system 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 where the total will indicate how much time the user 132 just spent in directly touching′ various ones of the topic nodes.
The first and third journey subparts 132a3 and 132a5 of traveler 132 are shown to have extended into a next time slot 147b (slot t1-2). Here the extended journeys are denoted as further journey subparts 132a6 and 132a8. The second journey, 132a4 ended in the first time slot (t0-1). During the second time slot 147b (slot t1-2), corresponding journey subparts 132a6 and 132a8 respectively touch corresponding nodes (or topic space cluster regions (TScRs) if such ‘touchings’ are being tracked) with different percentages of consumed time and/or spent energies (e.g., emotional intensities determined by CFi's). More specifically, the detected ‘touchings’ of journey subparts 132a6 and 132a8 are on nodes within topic space planes or regions TSp2r6 and TSp0r8. There can be yet more time slots following the illustrated second time slot (t1-2). The illustration of just two is merely for sake of simplified example. At the end of a predetermined total duration (e.g., t0 to t2), percentages (or other normalized scores) attributed to the detected ‘touchings’ are sorted relative to one another within each time slot box (e.g., 146b), for example from largest to smallest. This produces a ranking or an assigned sort number for each directly or indirectly ‘touched’ topic node or clustering of topic nodes. Then predetermined weights are applied on a time-slot-by slot basis to the sort numbers (rankings) of the respective time slots so that, for example the most recent time slot is more heavily weighted than an earlier one. The weights could be equal. Then the weighted sort values are added on a node-by-node basis (or other topic region by topic region basis) to see which node (or topic region) gets the highest preference value, which the lowest and which somewhere in between. Then the identifications of the visited nodes (or topic regions) are sorted again (e.g., in unit 148b) according to their respective summed scores (weighted rankings) to thereby generate a second-time sorted list (e.g., 149b) extending from most preferred (top most) topic node to least preferred (least most) of the directly and/or indirectly visited topic nodes. This list is recorded for example in Top-N Nodes Now list 149b for the case of social entity 132 and respective other list 149a for the case of social entity 131. Thus the respective top 5 (or other number of) topic nodes or topic regions currently being focused-upon now by social entity 131 might be listed in memory means 149a of
Accordingly, by using a process such as that of
Just as lists of top N topic nodes or topic space region (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) 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 within other types of system “spaces” can be automatically generated where the lists indicate for example, top N″ URL's 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
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 thread) like illustrated ring 426′ yet formed in that space 425. Next, a single ring-creating user 403′ of space 421 (membership support space) starts things going by launching (for example in a figurative 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′ into group discussion support space 425. In the LinkedIn™ environment this action is known as simply starting a 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 426p is merely an identifier of 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 branch with dated nodes attached to it, 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, 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 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. Irrespective of how many other 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′. 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 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
Detailed description about how an initially launched (instantiated) and anchored (moored) Social Notes Exchange (SNE) ring can become a drifting one that swings Tarzan-style from one anchoring node (TC) to a next, in other words, it becomes a drifting dSNE 416d; have been provided in the STAN_1 and STAN_2 applications that are incorporated herein. As such the same details will not be repeated here.
Additionally, in the here incorporated STAN_2 application, it was disclosed how topic space can be both hierarchical and spatial and can have fixed points in a multidimensional reference frame (e.g., 413xyz) as well as how topic space can be defined by parent and child hierarchical graphs (as well as non-hierarchical other association graphs). As such the same will not be repeated here except to note that it is within the contemplation of the present disclosure to use spatial halos in place of or in addition to the above described, hierarchical touchings halo to determine what topic nodes have been directly or indirectly touched by the journeys through topic space of a STAN_3 monitored user (e.g., 131 or 132 of
Additionally, in the here incorporated STAN_2 application, it was disclosed how cross language and cross-jargon dictionaries may be used to locate persons and/or groups that likely share a common topic of interest. As such the same will not be repeated here except to note that it is within the contemplation of the present disclosure to use similar cross language and cross-jargon dictionaries to expand definitions of user-to-user association (U2U) types such as those shown for example in area 490.12 of
Additionally, an example given in
That is one way of keeping friends in one's radar scope and seeing what topics they are now focused-upon. However that might call for each friend having his own individual radar scope, thus cluttering up screen space 111 of
Referring to
Moreover, in one embodiment, the distance-wise decaying 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, only one shown) are provided for allowing certified experts having expertise, good reputation and/or credentials within different topic areas to edit and/or vote (approvingly or disapprovingly) with respect to topic nodes that are controlled by Wiki-like collaboration governance groups, where the Wiki-like collaborated over topic nodes (not explicitly shown in
In addition to hierarchical trees that link to all (universal) 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) can each establish their own hierarchical and/or non-hierarchical and universal, although generally they will be semi-universal linking trees that link at least to topic nodes controlled by the Wiki-like collaboration project governance body. The Wiki-like collaboration project governance body can be an open type or a limited access type of body. By open type, it is meant here that any STAN user can serve on such a Wiki-like collaboration project governance body if he or she so chooses. Basically, it mimics the collaboration of the open-to-public Wikipedia™ project for example. On the other hand, other Wiki-like collaboration projects supported by the STAN_3 system 410 can be of the limited access type, meaning that only pre-approved STAN users can log in with special permissions and edit attributes of the project-owned topic nodes and/or attributes of the project-owned topic trees and/or vote on collaboration issues.
More specifically, and referring to
Such a full-privileges member of the Wiki-like collaboration project can also modify others of the data-object organizing or mapping mechanisms 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.
Returning to description of general usage members of the STAN_3 community and their ‘touchings’ with system resources such as system topic space (413) or other system 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 nodes or subregions of one or more data categorizing 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
When the 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′), then the heats produced by their respective halos will usually add up to thereby define cumulatively increased heats for the so-‘touched’ nodes. This can give a global indication of how ‘hot’ each of the topic nodes is. However, the detection that certain social entities (e.g., 431′, 432″) are both crossing through a same topic node during a predetermined time period may be an event that warrants adding even more heat 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) are predetermined to be influential or Tipping Point Persons (TPP's) 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″) within a predetermined time duration (e.g., same week), then it may be of value to track the steps that brought those 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 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 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″) 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 498 is configured to automatically detect when the not-yet-finished ‘significant journeys’ of new 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 498 sends alerts to subscribed promoters of the presence of the new users whose more recent but not-yet-finished ‘significant journeys’ are taking them along paths similar to those of the trail-blazing pioneers (e.g., Tipping Point Persons). The alerted promoters may then wish to make promotional offerings to the in-transit new travelers based on 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 498 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 498 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 498 for locating close or crossing ‘touching’ paths in other data-objects organizing 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. 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 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. That 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) ‘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 heats 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 case where relevant social entities (e.g., friends and family) who are relevant to them ‘touched’ upon nodes or topic space regions relevant to them (e.g., My Top 5 Topics). This concept will be explored again when filters of a mechanism that can generate clustering mappings (
With the above as introductory background, details of a ‘relevant’ heats measuring system 150 in accordance with
“Heats” can come in many types, where type depends on mixtures of weights, baselines and optional normalizations picked when generating the respective “heats”. As it processes in-coming CFi and like streamlets in pipelined fashion, the heats measuring subsystem 150 (
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 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 and/or within a certain geographic region but not regarded as so much of an influencer with a younger generation and/or outside the certain geographic region. Accordingly, when the particular 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. 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 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 so-specified 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 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 my 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, assume 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 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’.
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) 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 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 1510 from module 151. Output signals 1510 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 which top row includes modules 151 and 153 continues here with module 155. As matches are made by module 153 between co-compatible STAN users and the topic nodes they are currently focusing-upon, and the specific chat rooms (or other TCONEs—see dSNE 416d in
Such adaptive changes in topic space, including ever changing population concentrations (clusterings, see
Once a history of recent changes to topic 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
In a next step, the heat parameters formulating module 160 automatically determines which of its input parameters it will instruct a downstream engine (e.g., 170) to use, what weights will be assigned to each and which will not be used (e.g., a zero weight) or which will be negatively used (a negative weight). In one embodiment, the heat parameters formulating module 160 uses a generalized topic region lookup table (LUT, not shown) assigned to a relative large region of topic space within which the corresponding, subset topic region (e.g., A1) of a next-described heat formulating engine 170 resides. In other words, system operators of the STAN_3 system 410 may have prefilled the generalized topic region lookup table (LUT, not shown) to indicate something like: IF subset topic region (e.g., A1) is mostly inside larger topic region A, use the following A-space parameters and weights for feeding summation unit 175 with: Param1(A), wt1(A), Param2(A), wt2(A), etc., but do not use these other parameters and weights: Param3(A), wt3(A), Param4(A), wt4(A), etc., ELSE IF subset topic region (e.g., B1) is mostly inside larger topic region B, use the following B-space parameters and weights: Param5(B), wt5(B), Param6(B), wt6(B), etc., to define signals (e.g., 171o, 172o, etc.) which will be fed into summation unit 175 . . . , etc. The system operators in this case will have manually determined which heat parameters and weights are the ones best to use in the given portion of the overall topic space (413′). In an alternate embodiment, governing STAN users who have been voted into governance position by users of hierarchically lower topic nodes define the heat parameters and weights to be used in the corresponding quadrant of topic space. In one embodiment, a community boards mechanism of
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.1, wt.2, 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 1710 (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 no 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., TnxyA1) relative for example, to a baseline duration as summed with a predetermined constant (e.g., +1). In other words, if they are spending more time focusing-upon this topic area than normal, that works to increase the ‘heat’ values that will ultimately be calculated. The optionally normalized durations of focus of strangers can also be included as augmenting coloration in the computation. A wide variety of other optionally normalized and/or optionally weighted attributes W can be factored in as represented in the schematic of engine 170 by multiplier unit 17wx, by it inputs 17w and by its respective weight factor wt.W and its output signal 17wo.
The output signal 176 produced by summation unit 175 of engine 170 can therefore represent a relative amount of so-called ‘heat’ energy that has been recently cast by STAN users on the subject topic space region (e.g., TSR TnxyA1) 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) 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 data-objects organizing 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
Specific time durations and/o specific spaces or subspaces are merely some examples of how heats may be filtered so as to provide more focused information about how others are behaving (and/or how the user himself has been behaving). Heat information may also be generated while filtering on the basis of context. More specifically, a given user may be asked by his boss to report on what he has been doing on the job this past month or past business quarter. The user may refresh his or her memory by inputting a request to the STAN_3 system 410 to show the one user's heats over the past month and as further filtered to count only ‘touchings’ that occurred within the context and/or geographic location basis of being at work or on the job. In other words, the user's ‘touchings’ that occurred outside the specified context (e.g., of being at work or on the job) will not be counted. In another situation, the user may be interested in collecting information about heats cast by him/herself and/or others while within a specified one or more geographic locations (e.g., as determined by GPS). In another situation, the user may be interested in collecting information about heats cast by him/herself and/or others while focusing-upon a specified kind of content (e.g., as determined by CFi's that report focus upon one or more specified URL's). In another situation, the user may be interested in collecting information about heats cast by him/herself and/or others while engaged in certain activities involving group dynamics (see briefly
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 and Hj(T1) 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
Referring back to
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 backboard item (187c) to a forefront board 186 and where to place it 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 to promote a backboard item (187c) to being forefront item (186c) 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 item; (2) reputations and/or credentials of people who voted to promote the item versus that of those who voted against its promotion; (3) rapidity with which people voted to promote (or demote) the 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 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) 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 other-wise whole or abbreviated snippets of user-originated 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 item (e.g., number of net positive votes), 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 186c1; (2) a more complete copy of the originated comment (where the snippet may be an abstracted/abbreviated version of the original full comment), (3) information about when the shown item (e.g., comment, tweet, abstracted comment, etc.) in its whole was originated; (4) information about where the shown item (186c1) in its original whole form was originated; where this location information can be: (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.
A shown in the voting/commenting options column 186d of
Expansion tool 186b+(e.g., a starburst+) allows the user to view the basis of, or re-define the basis by which the #1, #2, etc. rankings are provided in left column 186b of community board 186. There is however, another tool 186b2 (Sorts) which allows the user to keep the ranking number associated with each board item (e.g., 186c1) unchanged but to also sort the sequence in which the rows are presented according to one or more sort criteria. For example, if the ranking numbers (e.g., #1, #2, etc.) in column 186b are by popularity and the user wants to retain those rankings numbers, but at the same time the user wants his list re-sorted on a chronological basis (e.g., which postings were commented most recently by way of My-2-cents postings—see column 186d) and/or resorted on the basis of which have the greater number of such My-2-cents postings, then the user can employ the sorts-and-searches tool 186b3 of board 186 to resort its rows accordingly or to search through its content for identified search terms. Each community board, 186, 187, etc. has its own sorts-and-searches tool 186b3.
It should be recalled that window 185 unfurled (as 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. Referring briefly to
Returning again to
Referring to the process flow chart of
There are two process initiation threads in
Assuming an instance of step 184.0 has been instantiated by the STAN_3 system 410 when bandwidth so allows, the computer will jump to step 184.2 of 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). 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. Other members of the same TCONE decide that the locally originated content is worthy of praise and promotion. So they give it a thumbs up or other such positive vote. 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). 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 other members who decide. When such 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, 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) 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’.
Assuming that in step 184.2, the computer decides the original remark is worthy of promotion, in next step 184.3 of
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 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 promotion.
Several different things can happen once a comment is promoted up to one or more community boards. First, the originator of the promoted remark might 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.
Second, the local TCONE members who voted the item up for posting on the local and/or other community board may 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.
Once a comment (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.
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. 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 positing (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 ignore 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′ of the STAN_3 system 410′, it is to be appreciated that the topic space (413′) is a living, breathing and evolving kind of data space. Most of its topic nodes are movable/variable topic nodes in that the governing users can vote to move the corresponding topic node (and its tethered thereto TCONE's) to a different position hierarchically and/or spatially within topic space. They may vote to cleave into two spaced apart topic nodes. They may vote to merge with another topic node and thus form an enlarged one topic node where before there had been two separate ones. For each topic node, the memberships of the tethered thereto TCONE's may also vote to bifurcate the TCONE, merge with other TCONE's, drift off to other topic nodes and so on. All these robust and constant changes to the living, breathing and constantly evolving, adapting topic space mean that original community boards of merging topic nodes become merged and re-ranked; original community boards of cleaving topic nodes become cleaved and re-ranked; and when new, substantially empty topic nodes are born as a result of a rebellious one or more TCONE's leaving their original topic node, a new and substantially empty community board is born for each newly born topic node.
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, etc., the STAN_3 system 410 has a computer initiated, board populating process flow per steps 188.0, 188.2, etc. Step 188.2 is relatively similar to earlier described 184.2 except that here the computer relies on implicit voting (e.g., CFi's and/or CVi's) to automatically determine if an in-TCONE comment deserves promotion to a local subsidiary community board (e.g., 187 of
Some of the automated notifications that happen with people promoted comments also happen with computer-promoted comments. For example, after step 188.4, the originator of the comment is notified in step 189.5. 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 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 further promoted (or demoted if the others do not like it).
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 (“G” for Go). If and when the user clicks or otherwise activates this instant join tool 113c1g (e.g., by clicking 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.)
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 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., the user is automatically taken by Layer-vator 113″ to the correct floor 113b″ merely by popping open his calm shell style smart phone (—as an example—or more generally by clicking 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, emails 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 telephone 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 otherwise activate an adjacent next column of options such as 113b2 (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 he user click or otherwise activates a hot key combination (e.g., control-right mouse type button).
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) 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 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 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 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
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 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). A user may click 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. 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 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.
Similarly, a second displayed driver icon 113d1 shows a respective one or more participants (in this case just digress DB) 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 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 to insert 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 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 to 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. 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 other such attentive informational outputs. 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 is 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 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 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
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.). 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.
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 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.
Other roles that may be of value for determining where room dynamics are heading is by identifying entities who fit 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 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. This does not mean that all participants must be agreeing with each other. Rather it means that the persons who are matched up for starting a new room are a substantially balanced group of active listeners and active talkers. Ideally, each person would have a 50%/50% balance as between preferring to be an active talker and being an active listener. But the real world doesn't work out as smoothly as that. Some people are very aggressive or vocal and have tendencies towards say, 90% talker and 10% (or less) active listener. Some people are very reserved and have tendencies towards say, 90% active listener and 10% (or less) active talker. If everyone is for most part a 90% talker and only a 1% listener, the exchanges in the room will likely not result in any advancement of understanding and insight; just a lot of people in a room all basically talking to themselves merely for the pleasure of hearing their own voices (even if in the form of just text). On the other hand, if everyone in the room is for most part a 90% listener (and not necessarily an “active” listener but rather merely a “lurker”) and only a 1% talker, then progress in the room will also not likely move fast or anywhere at all. So the STAN_3 system 410 in one embodiment thereof, includes a listener/talker recipe mixing engine (not shown) that automatically determines from the then-active CpCCp's, DsCCp's, PEEP's, PHAFUEL's (personal habits and routines log), and PSDIP's (Personal Social Dynamics Interaction Profiles) of STAN users who are candidates for being collectively invited into a chat or other forum participation opportunity, which combinations of potential invitees will result in a relatively harmonious mix of active talkers (e.g., texters) and active listeners (e.g., readers). The preceding applies to topics that draw many participants (e.g., hundreds). Of course if the candidate population for peopling a room directed to an esoteric topic is sparse, then a beggars can't be choosers approach is adopted and the invited STAN users for that nascent room will likely be all the potential candidates except that super-trolls (100% ranting talker, 0% listener) may still be automatically excluded from the invitations list. In a more sophisticated invitations mix generating engine, not only are the habitual talker versus active/passive listeners tendencies of candidates considered but also the leader, follower, rebel and other such tendencies are also automatically factored in by the engine. A room that has just one leader and a passive choir being sung to by that one leader can be quite dull. But throw in the “spice” of a rebel or two (e.g., loyal or disloyal opposition) and the flavor of the room dynamics is greatly enhanced. Accordingly, the social mixing engine that automatically composes invitations to would-be-participants of each STAN-spawned room has a set of predetermined social mix recipes it draws from in order to make each party “interesting” but not too interesting (not to the point of fostering social breakdown and complete disharmony).
Although in one embodiment, the social mixing engine (described elsewhere herein—see 555-557 of
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). 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 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 or otherwise activate a more menus options arrow (down arrow in box 190b) to see and select other more popular options of his or of the system 410. Alternatively, if the user wants more flexible and complex selection tool options, the user 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 one embodiment, the user of computer 100′″ can click 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 mean 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.
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. 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 190.5) for the user to look at. In one embodiment, clicking 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.
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 while they wait for a next, in-real life (ReL) event; 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″″ of
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 or other such activation. In one embodiment, a context determining module of the system 410 automatically determines what card the user will most likely want to be first presented with 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 Angeles freeway for example). Alternatively, the user may utilize the Layer-Vator tool 113″″ 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. The expected duration can alter the nature of which topics are offered as possibilities, 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). 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 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 the one or more out 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 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 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.
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) is transmitted to an in-cloud object recognizing module (not shown). The object recognizing module then automatically produces descriptive keywords and the like for logical association with the camera captured imagery (e.g., 198). Then the produced descriptive keywords are automatically forwarded to topic lookup modules (e.g., 151 of
In the illustrated embodiment 200, the device screen 211 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 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) 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 sensors for detecting what the user 201A is saying and/or what music or other background noises the user may be listening to. In one embodiment, detected background music and/or other background noises are used as possibly focused-upon CFi reporting signals (see 298′ of
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.
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 energetic attention giving activities ei(t, x, f, {TS, XS, . . . }) of the user, where here, ei denotes 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 with other social entities where those other social entities (not explicitly shown) are knowingly there because the first user 301A knows they are attentively there, and such knowledge 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).
Referring in yet more detail to possible elements of the 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 and/or typing and/or other touching signal streams produced by the user 301A in corresponding time periods (t) and within corresponding physical space (x) domains where the latter click/etc. streams or the like are input into at least one local data 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, focus-indicating 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 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 second signals 298 that are indicative of energetic 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 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 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 actions of the user). The energetic attention giving activities ei (t, x, f, {TS, XS, . . . }) of the user may alternatively or additionally include 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.
In the illustrated first environment 300A, at least one of the user's local data processing devices (299) is 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 presenting (e.g., displaying) browser generated content to the user, where the browser-provided content 299xt can have one or more of positioning (x), timing (t) and 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 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 content (299xt, see also window 117 of
When the STAN_3 system portion 310 receives the combination (322) of the content-identifying signals (e.g., time, place and/or data of 299xt) and the signals representing user-expended energies and/or user-aware-of context (EO(x,t, . . . ), ei(x,t, . . . ), CX(x,t, . . . ), etc.), the STAN_3 system portion 310 can treat the same in a manner similar to how it treats CFi's (current focus indicator records) of the user 301A and the STAN_3 system portion 310 can therefore produce responsive result signals 324 such as, but not limited to, identifications of the most likely topic nodes or topic space regions (TSR's) within the system topic space (413′) that correspond with the received combination 322 of content and focus representing signals. In one embodiment, the number returned as likely, topic node identifications is limited to a predetermined number such as N=1,2,3, . . . and therefore the returned topic node identifications may be referred to as the top N topic node/region ID's in
As explained in the here-incorporated STAN_1 and STAN_2 applications, each topic node 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 v on-topic topic content that could be suggested as further research areas to STAN users who are currently focused-upon the topic of the corresponding node. The linked-to suggestable 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-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 online or real life (ReL) conferences, most relevant on-topic social groups (of online and/or real life gathering kinds), and so on.
The produced responsive result signals 324 of the STAN_3 system portion 310 can then be processed by the 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 may have been a news compilation web page that was originated from the net 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 displayable information (or otherwise presentable information) that the user may first need to approve before final presentation (e.g., by the user accepting a corresponding invitation) or that the user is automatically treated to without need for invitation acceptance.
Yet more specifically, in the case of the news compilation web page (e.g., displayed in area 299xt at first time t1), once the system automatically determines what topics and/or sub-portions of initially available content the user 301A is currently focused-upon (e.g., energetically paying attention to and/or energetically responding to), the initially presented news compilation transforms shortly thereafter (e.g., within a minute or less) into a “living” news compilation that seems to magically know what the user 301A is currently focusing-upon and which then serves up correlated additional content 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, the system 299-310 may shortly thereafter automatically pop open a live chat room where like-minded other STAN users are starting to discuss a particular aspect regarding X that happened to now be on the first user's (301A) mind. The way that the system 299-310 came to infer what was most likely on the first user's (301A) mind is by utilizing a host triangulating or mapping mechanisms that home in on the most likely topics on the user's mind based on 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 attention giving activities of the user such as, but not limited to, staring for a time duration in excess of a predetermined threshold amount at an 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 contextual attributes of the user such as, but not limited to: his/her geographic location, his/her economic 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 next carried out step 355 of
In next carried out step 356 of
In next carried out step 357 of
Before moving on to the details of
Referring now to
Because various semantic spins can be inferred from the “context” or “contextual state” from under which each word 301w is originated (e.g., “Please”), from under which each facial configuration (e.g., raised eyebrows, flared nostrils) and/or head gesture (e.g., tilted head) 301g arises, from under which each sequence of words (e.g., “How about . . . ?”) is assembled, from under which each sequence of mouse clicks or other user-to-machine input activations evolves, and so forth; proper resolution of current user context to one degree of specificity or another can be helpful in determining what semantic spin is more likely to be associated with one or more of the user's energetic input ei(x,t,f, . . . ) and/or output EO(x,t,f, . . . ) activities and/or which CFi and/or CVi signals are to be grouped with one another when parsing received CFi, CVi signal streamlets (e.g., 151i2 of
In one embodiment, a fail-safe default or checkpoint switching system 301s (controlled by module 301pvp) is employed. A predetermined-to-be-safe set of default or checkpoint profile selections 301d is automatically resorted to in place of profile selections indicated by a current output 316o of the system's perceived-context mapping mechanism 316″ if recent feedback signals from the user (301A′) indicate that invitations (e.g., 102i of
After the default state 301d has been established during system initialization or user PoV state reset, switch 301s is automatically flipped into its normal mode wherein context indicating signals 316o, produced and output from a context space mapping mechanism (Xs) 316″, participate in determining which user profiles 301p will be the 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., 199 of
It is to be noted here that interactions between the knowledge base rules (KBR's) subsystem and the current context defining output, 316o of context mapping mechanism 316″ can 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. 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 active profile. For example, one of the knowledge base rules (KBR's) within a currently active profile may read as follows: “IF Current Context signals 316o include an active pointer to context space subregion XSR2 THEN Switch to PEEP profile number PEEP5.7 as being the currently active PEEP 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 and the two systems complement each other rather than conflicting with one another. The dependency may flow the other way incidentally, wherein the context mapping mechanism 316″ uses a context resolving KBR algorithm that may read as follows: “IF Current PHAFUEL profile is number PHA6.8 THEN exclude context subregion XSR3, ELSE . . . ” and this profile-dependent algorithm then controls how other profiles will be selected or not.
From the above, it can be seen that, in accordance with one aspect of the present disclosure, context guessing signals 316o are produced and output from a context space mapping mechanism (Xs) 316″ which mechanism (Xs) is schematically shown in
Just as having a large number of differentiating “fuzzy” pointer vectors 316v (vector signals 316v) helps to metaphorically home in or resolve down to 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 differentiating vector signals (e.g., 316v) decreases, the tendency is for the resolving power of the metaphorical “fuzzy” pointer vectors to decrease whereby, in hindsight, it appears as if the “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, where those coarser nodes and/or subregions are conceptually located near the more “coarsely-resolved” apex portion of the inverted hierarchical pyramids rather than near the more “finely-resolved” base layers of the corresponding inverted hierarchical pyramids depicted in
As indicated, the input vector signals (e.g., 316v) are not actually “fuzzy” pointer vectors because the result of their application to the corresponding mapping mechanism (e.g., 316″) is usually not known until after the mapping mechanism (e.g., 316″) has processed the supplied vector signals (e.g., 316v) and has generated corresponding output signals (e.g., 316o) which do identify the best fitting nodes and/or subregions. In one embodiment, the output signals (e.g., 316o) of each mapping mechanism (e.g., context mapping mechanism 316″) are output as a sorted list that provides the identifications of the best fitted-to and more hierarchically refined nodes and/or subregions first (e.g., at the top of the list) and that provides the identifications of the poorly fitted-to and less hierarchically refined nodes and/or subregions last (e.g., at the bottom of the list). The output, resolving signals (e.g., 316o) may also include indications of how well or poorly the resolution process executed. If the resolution process is indicated to have executed more poorly than a predetermined acceptable level, the STAN_3 system 410 may elect to not generate any invitations (and/or promotional offerings) on the basis of the subpar resolutions of current, focused-upon nodes and/or subregions within the corresponding space (e.g., context space (Xs) or topic space (Ts)).
The input vector signals (e.g., 316v) that are supplied to the various mapping mechanisms (e.g., to context space 316″, to topic space 313″) 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-categorized” first CFi signals 302o produced by a first CFi categorizing-mechanism 302″, (2) pre-categorized second CFi signals 298o produced by a second CFi categorizing-mechanism (298″), (3) physical context indicating signals 301x′ derived from sensors that sense physical surroundings and/or physical states 301x of the user, and (4) context indicating or suggesting signals 301p′ obtained from currently active profiles 310p of the user 301A′ (e.g., from executing KBR's within those currently active profiles 310p). This aspect is represented in
While not shown in the drawings for all the various mapping mechanisms, it is to be observed that in general, each mapping mechanism 312″-316″ has a mapped result signals output (e.g., 312o) which outputs results signals (also denoted as 312o for example) that can define a sorted list of identifications of nodes and/or subregions within that space that are most likely for a given time period (e.g., “Now”) to indicate a focused mindset of the respective social entity (e.g., STAN user) with regard to attributes (e.g., topics, context, keywords, etc.) that are categorized within that mapped space. Since these mapping mechanism result signals (e.g., 312o) correspond to specific social entity (e.g., an identified STAN user) and to a defined time duration, the result signals (e.g., 312o) will generally include and/or logically link to social entity identification signals (e.g., User-ID) that identify a corresponding one or more users or user groups and to time duration identification signals that identify a corresponding one or more time durations in which the identified nodes and/or subregions can be considered to valid.
At this point in the disclosure, an important observation that was made above is repeated with slightly different wording. The user (e.g., 301A′) is part of the context from under which his or her various actions emanate. 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 context from under which user actions emanate. Also, often, the user's current physical surroundings and/or body states (301x) are part of the context from under which user 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) surrounding physical images, (2) surrounding physical sounds, (3) surrounding physical odors or chemicals, (3) 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′ 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 user 301A′ based on previously guessed-at mind states. The real physical surrounding context signals 301x′ of the user are often 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? 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 current mind set of the user. Moreover, fresh and newly received CFi signals (302e′, 298′) 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.
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, 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 310d. In that case, the context mapping mechanism 316″ 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 context space as new CFi signals 302e′, 298e′ are received and processed by CFi categorizing-mechanisms 302″ and 298″.
It will now be explained in yet more detail how input vector signals (like 316v) for the mapping mechanisms (e.g., 316″, 313″) are generated from raw CFi signals and the like. There are at least two different kinds of energetic activities the user (301A′ of
In accordance with the system 300.D of
Incidentally, just as each user may have one or more unique 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 (e.g., unique keywords, unique topic names, etc.) that they personally use to represent things that the more general populace expresses with use of other, more-universally accepted expressions (e.g., popular keywords, popular topic names, etc.). In accordance with one aspect of the disclosure, one or more of the user profiles 301p can include expression-translating lookup tables (LUT's) and/or knowledge base rules (KBR's) that provide translation from abnormal CFi expressions produced by the respective individual user into more universally understood, normal CFi expressions. This expression normalizing process is represented in
In addition to replacing and/or supplementing ‘abnormal’ 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 the unique pattern of CFi-carried data at about the same time, this signaling that perhaps a new pattern or permutation is being adopted by the user community (e.g., by influential early-adopter or Tipping Point Persons within that community) and that it is not a misspelling or an individually unique pattern (e.g., pet 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 keyword expressions space and/or another such space (e.g., hybrid keyword plus context space) as may be appropriate so that the new-permutation no longer appears to modules 302qe′ and 302qe2′ as being an abnormal or misspelled pattern. The node (corresponding to the early-adopted new CFi pattern) can be inserted into keyword expressions space and/or another such space (e.g., hybrid keyword plus context space) even before a topic node is optionally created for new CFi pattern. Later, if and when a new topic node is created for a topic related to the new CFi pattern, there already exists in the system's keyword expressions space and/or another such space (e.g., hybrid keyword plus context space), a non-topic node to which the newly-created topic node can be logically linked. In other words, the system can automatically start laying down an infra-structure (e.g., keyword primitives; which will be explained in conjunction with 371 of
Each of the CFi generating units 302b′ and 298a′ includes a current focus-indicator(s) packaging subunit (not shown) which packages raw telemetry signals from the corresponding tracking sensors into time-stamped, location-stamped, user-ID stamped and/or otherwise stamped and transmission ready data packets. These data packets are received by appropriate CFi processing servers in the cloud and processed in accordance with their user-ID (and/or local device-ID) and time and location (and/or other stampings). One of the basic processings that the data packet receiving servers (or automated services) perform is to group received packets of respective users and/or data-originating devices according to user-ID (and/or according to local originating device-ID) and to also group received packets belonging to different times of origination and/or times transmission into respective chronologically ordered groups. The so pre-processed CFi signals are then normalized by normalizing modules like 302qe′-302qe2′ and then fed into the CFi categorizing-mechanisms 302″ and 298″ for further processing.
The first set of sensors 298a′ have already been substantially described above. 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 actions of the user, such as the user uttering words (301w), consciously nodding or shaking or wobbling his head, typing on a keyboard, making hand gestures, clicking 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 earphone), unique and abnormal output expressions (e.g., pet names for things) are run through expression-translating lookup tables (LUT's) and/or knowledge base rules (KBR's) of then active PEEP and/or other profiles for translating such raw expressions into more normalized, Active Attention Evidencing Energy (AAEE) indicator signals of the outputting kind. The normalized AAEE indicator signals 298e′ of the inputting kind have already been described.
The normalized Active Attention Evidencing Energy (AAEE) signals, 302e′ and 298e′ are next inputted into corresponding first and second CFi categorizing mechanisms 302″ and 298″ as already mentioned. These categorizing mechanisms organize the received CFi signals (302e′ and 298e′) into yet more usable groupings and/or categories than just having them grouped according to user-ID and/or time or telemetry origination and/or location of telemetry origination.
This improved grouping process is best explained with a few 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 can be automatically determined from the time stamps of the corresponding CFi data packet signals.) One problem for CFi categorizing mechanism 302″ is how to resolve whether each of the three keyword expressions: KWE1, KWE2 and KWE3 is directed to a respective separate topic or whether all are directed to a same topic 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). This is referred to here as the CFi grouping and parsing problem. Which CFi's belong with each other and which belong to another group or stand by themselves? (By way of a more specific example, assume that KWE1=“Lincoln” and KWE3=“address” while KWE2=“Goldwater” although perhaps the user 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.)
A second problem for the CFi categorizing mechanism 302″ to resolve is 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? 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 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 and/or culture. If the current context state is determined by the STAN_3 system 410 to be one where the user 310A′ is engaged in a live video web conference with persons of a Western culture, the up-and-down head nod may be taken as an expression of intentional affirmation (yes, agreed to) to the others if the nod is pronounced enough. On the other hand, if the user 301A′ is simply reading some text to himself (a different context) and he nods his head up and down or side to side and with less pronouncement, that may mean something different, dependent on the currently active PEEP profile. The same would apply to the grunting noise.
In general, the CFi receiving and categorizing mechanisms 302″/298″ first cooperatively assign incoming CFi signals (normalized 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 information inputting activities (298′) of the user 301A′. If the CFi receiving and categorizing mechanisms 302″/298″ cannot parse as between the two, they copy the same received CFi signals to both sides. Next, the CFi receiving and categorizing mechanisms 302″/298″ try to categorize the received CFi signals into predetermined subcategories unique to that side of the combined categorizing mechanism 302″/298″. Keywords versus URL expressions would be one example of such categorizing operations. URL expressions can be automatically categorizing as such by their prefix and/or suffix strings (e.g., by having a “dot.com” character string embedded therein). Other such categorization parsing include but are not limited to: distinguishing as between meta-tag type CFi's, image types, sounds, emphasized text runs, 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 . . . ”), limbicly-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: (1) neo-cortically directed expressions (i.e., those appealing to the intellect), (2) limbicly-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 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 limbicly-directed expressions are automatically allocated for processing 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 a biological/medical state(s) mapping mechanism (see exemplary primitive data object of
The automated and augmenting categorization of incoming CFi's is performed with the aid of one or more CFi categorizing and inferencing engines 310′ where the inferencing engines 310′ have access to categorizing nodes and/or subregions within, for example, topic and context 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 mapping mechanisms. The inferencing engines 310′ receive as their inputs, last known state signals from various ones of the state mapping mechanisms. 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 content materials are represented by cs signals received from the output 314o of the content mapping mechanism 314″ (where 314″ stores representations of content that is available to be focused-upon by the user 301A′); the previously determined to be most-likely CFi categorizations are received as “cfis” 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 from an output 315o 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 future. In other words, the most recently determined states xs, es, cfis of the user (or of another social entity that is being processed) are used for categorizing the more likely categories for next incoming new CFi signals 302e′ and 298e′. The “cs” signals tell the inferencing engines 310′ what content was available to the user 310A′ at the time one of the CFi's was generated (time stamped CFi signals) for being then perceived by the user. More specifically, if a search engine input box was displayed in a given screen area, and the user inputted a character string expression into that area at that time, then the expression is determined to most likely be a keyword expression (KWE). If a particular sound was being then output by a sound outputting device near the user, then a detected sound at that time (e.g., music) is determined to most likely be a music and/or other sound CFi the user was exposed to at the time of telemetry origination. By categorizing the received (and optionally normalized) CFi's in this manner it becomes easier to subsequently parse them, and group logically interrelated ones of them together before transmitting the parsed and grouped CFi's as input vector signals into appropriate ones of the mapping mechanisms.
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, the CFi categorizing and inferencing engines 310′ can parse and group 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 associated non-emotional CFi's (e.g., keywords) because that is later 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.
In terms of a 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 Mecrury/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.)
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”). The question for the machine system to resolve in this example is which of the possible permutations of KWE1, KWE2 and KWE3 did the user become “anxious” over and thus project increased “heat” on the associated topic nodes? Was it KWE1 taken alone or all of KWE1, KWE2 and KWE3 taken in combination or a subcombination of that? For sake of example, let it be assumed that KWE2 (e.g., =“Goldwater”) was a typographic error input by the user. He meant at the time to enter KWE3 instead, but through inadvertence, he caused an erroneous KWE2 to be submitted to his search engine. In other words, the middle keyword expression, KWE2 is just an unintended noise string that got accidentally thrown in between the relevant combination of just KWE1 and KWE3. How does the system automatically determine that KWE2 is an unintended noise string, while KWE1 and KWE3 belong together? The answer is that, at first, the machine system 410 does not know. However, embedded within a keyword expressions space (see briefly 370 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 the inferencing engines 310′ are configured to submit some of their entertained keyword permutations to the one or more online search 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 to just one search engine) and to determine the quality (and/or quantity) of matches found so as to thereby automatically determine the likelihood that the entertained keyword permutation is a valid one as opposed to being a set of unrelated terms.
Eventually, the inferencing engines 310′ automatically entertain the keyword permutation represented by “KWE1 AND KWE3”. In this example, the inferencing engines 310′ find one or more corresponding nodes and/or subregions in keyword and context hybrid space (e.g., “Lincoln's Address”) where some are identified as being more likely than others, 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 by the topic and/or other mapping mechanisms (313″ or others) so as to produce a current state output signal (e.g., 3130) 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 valid permutation while “KWE2 AND KWE3” is not (because KWE2 is accidentally interjected noise) is that the timing of emotion development (e.g., user 301A′ becoming “anxious”) began 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 of keywords and/or other CFi's that deserve further processing while filtering out unreasonable groupings that will likely waste processing bandwidth in the downstream mapping mechanisms (e.g., topic space 313″) without producing useful results (e.g., valid topic identifying signals 313o).
The categorized, parsed and reasonably grouped CFi permutations are then selected 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
Still referring a bit longer to
The types of raw or categorized CFi's that two or more STAN users have substantially in common are not limited to text-based information. It could instead be musical information (see briefly
Referring now to
Before describing details of the illustrated keyword expressions space 370, a quick return tour is provided here through the hierarchical and plural tree branches-occupied 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′, a mid-layer topic node named, Tn62 (see also the enlarged view in
Additionally, in
Parent node Tn51 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 break off from, and drifting away from, that mooring is relatively difficult) or on a relatively weak anchored basis (whereby stretch away from and/or break off of the corresponding forum (e.g., chat room) from, and drifting away from that mooring point is relatively easier). Recall that 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 center as forum membership and circumstances change. In general, topic space 313′ can be a constantly and robustly changing combination of interlinked nodes and/or 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.
The illustrated plurality of forum sessions 30E.50 are hosting 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 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 ‘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 are clustered around keystone expressions and/or are clustered according to Thesaurus™ like sense 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. Instead, the “lincoln*” expression according to that other sense would be covered by another primitive node 371.5 that is clustered in addressable memory space near nodes (371.6) for yet other keyword expressions (e.g., Kw6?*) related to that alternate sense of “Lincoln”. Such Thesaurus™ like or semantic contextual like clustering is used in this embodiment for the sake of reducing bit lengths of digital pointers that point to the keyword primitives.
Assume for sake of example that a second node 371.2 is disposed in the primitives holding layer 371 fairly close, in terms of memory address number to the location where the first node 371.1 is stored. Assume moreover, that the keyword expression (Kw2) of the second node 371.2 covers the expression, “*Abe” and by so doing 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”. An operator node, as the term is used herein, functions somewhat similarly to an ordinary node in a hierarchical tree structure except that it generally does not store directly within it, a definition of its intended, combined-primitives attribute. More specifically, if a first operator node 372.1 shown in sequences/combinations layer were an ordinary node rather than an operator node, that node would directly store within it, the expression, “lincoln*” AND “*Abe” (if the Abe Lincoln example is continued here). However, in accordance with one aspect of the present disclosure, node 372.1 contains references to one or more predefined functional “operators” (e.g., AND, OR, NOT, parenthesis, Nearby(number of words), After, Before, NotNearby( ) NotBefore, and so on) and pointers as substitutes for variables that are to be operated on by the referenced functional “operators”. One of the pointers (e.g., 370.1) can be a long or absolute or base pointer having a relatively large number of bits and another of the pointers (e.g., 370.12) can be a short or relative or offset pointer having a substantially smaller number of bits. This allows the memory space consumed by various combinations of primitives (two primitives, three primitives, four, . . . 10, 100, etc.) to be made relatively small in cases where the plural ones of the pointed-to primitives (e.g., Kw1 and Kw2) are clustered together, address-wise in the primitives holding layer (e.g., 371). In other words, rather than using two long-form pointers, 370.1 and 370.2 to define the “AND”ed combination of Kw1 and Kw2, the first operator node 372.1 may contain just one long-form pointer, 370.1, and associated therewith, one or more short-form pointers (e.g., 370.12) that point to the same clustering region of the primitives holding layer (e.g., 371) but use the one long-form pointer (e.g., 370.1) as a base or reference point for addressing the corresponding other primitive object (e.g., Kw2 371.2) with a fewer number of bits because the other primitive object (e.g., Kw2 node 371.2) is clustered in a Thesaurus™ like or semantic contextual like clustering way to one or more keystone primitives (e.g., Kw1 node 371.1). While
Referring to
Referring back to
As further shown in
As further shown in
In view of the above, it may be seen that the cross-spaces bi-directional link 370.6 of
The scores contributed by the cross-spaces logical links (e.g., 370.6) need not indicate or merely indicate what topic nodes/subregions the STAN user (e.g., user 301A′) appears to be focusing-upon based on received raw or categorized CFi's. They can alternatively or additionally indicate what nodes and/or subregions in user-to-user associations (U2U) space the user (e.g., user 301A′) appears to be focusing-upon and to what degree of likelihood. They can alternatively or additionally indicate what emotions or behavioral states in emotions/behavioral states space the user (e.g., user 301A′) appears to be focusing-upon and to what degree of comparative likelihood. They can alternatively or additionally indicate what context nodes and/or subregions in context space (see 316″ of
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 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
Referring to
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 can be (2) a second field 30J.2 to informal role names or role states or activity names. The reason for this second field 30J.2 is because the formal names assigned to some roles (e.g., Vice President) can often be for sake of ego rather than reality. Someone can be formally referred to as Vice President or manager of Data Reproduction when in fact they operate the company's photocopying machine. Therefore cross-links 30J.2 to the informal but more accurate definitions of the actor's role may be helpful in more accurately defining the user's context. The pointed-to informal role can simply be another context primitive defining object like 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 in the context of being an acting manager (as defined by field 30J.1), corresponding third field 30J.3 may include a pointer pointing to an operator node object in context space or in an activities space 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. 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 may include pointers pointing to one or more expected-wise cross-correlated nodes in topic space. The pointers of fourth field 30J.4 may alternatively or additionally point to knowledge base rules (KBR's) that exclude or include various nodes and/or subregions of topic space. More specifically, if the role or user context is Fifth Grade Student, one of the pointed-to KBR's may exclude or substantially downgrade in match score, topic nodes directed to purchase, driving or other uses of automobiles.
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 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 “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).
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 users space (not shown). 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. More specifically, a social entity with the role of “Fifth Grade Teacher” may be specified as a role who is likely giving current attention to the inhabitant who holds the role of primitive 30J.0 (e.g., “Fifth Grade Student”). The context of a STAN user can often include a current expectation that other users are casting attention on that first user. people may cat differently when alone as opposed to when they believe others are watching them.
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) as indicated by eighth area 30J.8 of data structure 30J.0.
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
Referring to
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 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). 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 usual 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′ in the case of the keyword expressions space).
Referring to
Once the identifications (e.g., signals 551o2) of the identified social entities are pooled together into respective pooling areas (e.g., 504), another module 553 fetches a copy of the identifications (as signals 551o1) and uses the same to scan the currently active, sessions preferences profiles (e.g., 501p) of those social entities where the sessions preferences profiles (501p) indicate currently active preferences of the pooled persons (or other social entities), such as for example, 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), the level of expertise or credentials of other participants that they desire, the personality types of other participants whom they wish to avoid or whom they wish to join with, and so on. The preferences collecting module 553 forwards its results 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 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 an “interesting” mix of co-compatible personalities so that a socially “interesting” interchange will hopefully develop when invitees (those waiting in pool 504) are invited to join into the, soon-to be launched forums (565) and a statistically predictable subpopulation of them 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 which respectively define different mixes of personality types that usually 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 requests 555i2 into the recipe assigning/formulating engine 555. In one embodiment, a listing of the current top topics identified by module 551 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. 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 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 needed by the initially generated recipes and 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 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. 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., room leaders) 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 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 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 deal with possible bottlenecks or shortages in desired personality types. The 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 by respective entities in pool 504 for invitations sent to them by the automated recipes modifying and invitations sending engine 557.
Some chat rooms or other forums will receive an insufficient number of the right kinds of acceptances (e.g., a critically needed room leader does not sign up). If that happens, an RSVP receiving engine 559 trashes the room (flow 569) and sends apologies to the invitees 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, 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 recipes holding area 555i4. The FB4 feedback signal (e.g., percentage of acceptances and/or types of acceptances) are used by the recipes perfecting engine (of module 555i4) to tweak the existing recipes so they better conform to actual results 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 (of 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. 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, the auction winners 564 can 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 (FBI) 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 (FBI) to determine whether or not to pitch the same to the room's general population (with risk of losing goodwill if the pitch is poorly received) and/or 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—285—noted that as time progresses 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 may not be the same as at a time period represented by 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 (FB2, a.k.a. signal 57502) looks at the way that participants actually behave in the rooms. These actual behavior reports are transmitted to another engine (not shown) which compares the actual behavior reports 575o2 against the traits and habit recorded in the respective user's current profiles 501p. The profiles versus actual behavior comparing engine (not shown, associated with signals 575o2) either reports variances as between actual behavior and profile-predicted behavior or automatically tweaks the profiles 501p to better reflect the observed actual behavior patterns. Another feedback signal (FB3) sent back from engine 575 to the variance reporting/correcting engine (not shown) is one relating to the verification of the alleged street credentials of certain Tipping Point Persons or the like. These credential verification signals are derived from votes (e.g., (CVi's) cast by in-room participants other than the persons whose credentials are being verified. Another feedback signal (57503) sent back from engine 575 goes to the recipes tweaking engine (not shown) of holding area 555i4. These downstream feedback signals (575o3) indicate how the spawned room performs later downstream, long after it has been launched but before it fades out (576). The downstream feedback signals (575o3) may be used to improve recipes for longevity as opposed to good performance merely soon after launch (570) of the rooms (of the TCONEs).
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 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″) for each of potential recipients of promotional offerings. Accordingly, the engine 575 can better predict when is the best timing 564c to pitch the offering based on latest reports about the user's contextual state (and/or other mapped states, e.g., physiological/emotional/habitual states=hungry and in mood for pizza).
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:
Per
Per
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 Facbook™ 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 on 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.
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. The geographic clusterings shown in
Referring to
In one embodiment, the system 410 automatically determines if availability is such that users can have meetings based on local events, or on happenstance clusterings or groupings of like focused people. These automated determinations may be optionally filtered to assure proper personhood co-compatibilities and/or dispositions in user-defined proper vicinities. In an embodiment, the system provides the user with zoom in and out function for the displayed clusterings map.
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); (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; (3) level of attentions-giving capability. For example, if a first user is multi-tasking, such as watching TV and trying to follow a chat at same time and so not really going to be very attentively involved in the chat, just passive vs. him totally looking at this) then the attentions-giving capability may be indicated along a spectrum of possibilities 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?). 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.
In one embodiment, the system 410 not only automatically serves up automatically labeled serving plates and/or user-labeled serving plates (e.g., 102b″ of
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 temproary pseudoname) invites one or more other persons (also each initially identified only by his/her temproary pseudoname) who appear to the first person to be 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 premanent 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 invitators 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 recipinet while using the target's non-pseudoname identification (the more premanent identification). The system-forwared message to the non-pseudoname-wise identified target may declare something such as: “I am open to talking online about potentially embarassing 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 targetted 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 tahnk you for suggestig 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 situtated and/or resides. These automatically collected CFi's are run 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-screeen 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 realted 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 soled 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 pain 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 actaully 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 realted 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, 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.
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
Reservation of Extra-Patent Rights, Resolution of Conflicts, and Interpretation of Terms
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.
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, can gather co-compatible users on-the-fly into corresponding online chat or other forum participation sessions based on user context and/or more likely topics currently being focused-upon; and can additionally provide transaction offerings to groups of people based on detected context and on their usage of the STAN systems. Yet more specifically one such offering may be a promotional offering such as group discount coupon that becomes effective if a minimum number of offerees commit to using the offered online coupon before a predetermined deadline expires. This patent application claims priority as a Continuation of U.S. patent application Ser. No. 17/714,802, filed on Apr. 6, 2022; which claims the benefit as a Continuation of U.S. patent application Ser. No. 16/196,542, filed on Nov. 20, 2018; which claims the benefit as a Continuation of Ser. No. 14/192,119, filed on Feb. 27, 2014; which claims the benefit as a Continuation of Ser. No. 13/367,642, filed on Feb. 7, 2012; which claims the benefit of provisional patent application having Ser. No. 61/485,409, filed May 12, 2011 and provisonal patent application having Ser. No. 61/551,338, filed on Oct. 25, 2011; the aforementioned applications being incorporated by reference in their entirety. 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 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.
| Number | Name | Date | Kind |
|---|---|---|---|
| 1996516 | Kinkead | Apr 1935 | A |
| 2870026 | Keller | Jan 1959 | A |
| 3180760 | Rauter | Apr 1965 | A |
| 3676937 | Janson | Jul 1972 | A |
| 3749870 | Oakes | Jul 1973 | A |
| 5047363 | Hickernell | Sep 1991 | A |
| 5337233 | Hofert | Aug 1994 | A |
| 5659742 | Beattie | Aug 1997 | A |
| 5754939 | Herz et al. | May 1998 | A |
| 5793365 | Tang et al. | Aug 1998 | A |
| 5828839 | Moncreiff | Oct 1998 | A |
| 5848396 | Gerace | Dec 1998 | A |
| 5873076 | Barr | Feb 1999 | A |
| 5890152 | Rapaport et al. | Mar 1999 | A |
| 5930474 | Dunworth et al. | Jul 1999 | A |
| 5950200 | Sudai et al. | Sep 1999 | A |
| 5961332 | Joao | Oct 1999 | A |
| 6041311 | Chisienko | Mar 2000 | A |
| 6047363 | Lewchuk | Apr 2000 | A |
| 6061716 | Moncrieff | May 2000 | A |
| 6064971 | Hartnett | May 2000 | A |
| 6081830 | Schindler | Jun 2000 | A |
| 6154213 | Rennison | Nov 2000 | A |
| 6180760 | Takai et al. | Jan 2001 | B1 |
| 6229542 | Miller | May 2001 | B1 |
| 6256633 | Dharap | Jul 2001 | B1 |
| 6272467 | Durand et al. | Aug 2001 | B1 |
| 6425012 | Trovato et al. | Jul 2002 | B1 |
| 6442450 | Inoue | Aug 2002 | B1 |
| 6446113 | Ozzie et al. | Sep 2002 | B1 |
| 6480885 | Olivier | Nov 2002 | B1 |
| 6496851 | Morris | Dec 2002 | B1 |
| 6577329 | Flickner et al. | Jun 2003 | B1 |
| 6611881 | Gottfurcht | Aug 2003 | B1 |
| 6618593 | Drutman et al. | Sep 2003 | B1 |
| 6633852 | Heckerman | Oct 2003 | B1 |
| 6651086 | Manber et al. | Nov 2003 | B1 |
| 6745178 | Emens et al. | Jun 2004 | B1 |
| 6757682 | Naimark et al. | Jun 2004 | B1 |
| 6766374 | Trovato et al. | Jul 2004 | B2 |
| 6873314 | Campbell | Mar 2005 | B1 |
| 6879994 | Matsliach et al. | Apr 2005 | B1 |
| 6978292 | Murakami et al. | Dec 2005 | B1 |
| 6981021 | Takakura et al. | Dec 2005 | B2 |
| 6981040 | Konig | Dec 2005 | B1 |
| 7034691 | Rapaport | Apr 2006 | B1 |
| 7219303 | Fish | May 2007 | B2 |
| 7386796 | Simpson et al. | Jun 2008 | B1 |
| 7394388 | Light et al. | Jul 2008 | B1 |
| 7395507 | Robarts et al. | Jul 2008 | B2 |
| 7401098 | Baker | Jul 2008 | B2 |
| 7424541 | Bourne | Sep 2008 | B2 |
| 7430315 | Yang et al. | Sep 2008 | B2 |
| 7472352 | Liversidge et al. | Dec 2008 | B2 |
| 7610287 | Dean et al. | Oct 2009 | B1 |
| 7630986 | Herz et al. | Dec 2009 | B1 |
| 7640304 | Goldscheider | Dec 2009 | B1 |
| 7647098 | Prichep | Jan 2010 | B2 |
| 7720784 | Froloff | May 2010 | B1 |
| 7730030 | Xu | Jun 2010 | B1 |
| 7788260 | Lunt et al. | Aug 2010 | B2 |
| 7848960 | Rampeli et al. | Dec 2010 | B2 |
| 7853881 | Aly Assal et al. | Dec 2010 | B1 |
| 7860928 | Anderson | Dec 2010 | B1 |
| 7865553 | Anderson | Jan 2011 | B1 |
| 7870026 | Krishnan et al. | Jan 2011 | B2 |
| 7878390 | Batten | Feb 2011 | B1 |
| 7881315 | Haveson et al. | Feb 2011 | B2 |
| 7899915 | Reisman | Mar 2011 | B2 |
| 7904500 | Anderson | Mar 2011 | B1 |
| 7945861 | Karam | May 2011 | B1 |
| 7966565 | Dawson et al. | Jun 2011 | B2 |
| 8024328 | Dolin et al. | Sep 2011 | B2 |
| 8150868 | Richardson et al. | Apr 2012 | B2 |
| 8180760 | Carver | May 2012 | B1 |
| 8249898 | Schoenberg | Aug 2012 | B2 |
| 8274377 | Smith et al. | Sep 2012 | B2 |
| 8380902 | Howard | Feb 2013 | B2 |
| 8484191 | Maghoul | Jul 2013 | B2 |
| 8572094 | Luo | Oct 2013 | B2 |
| 8583617 | Stemeseder | Nov 2013 | B2 |
| 8676937 | Rapaport | Mar 2014 | B2 |
| 8732605 | Falaki | May 2014 | B1 |
| 8743708 | Robertson | Jun 2014 | B1 |
| 8843835 | Busey et al. | Sep 2014 | B1 |
| 8874477 | Hoffberg | Oct 2014 | B2 |
| 8949250 | Garg | Feb 2015 | B1 |
| 9015093 | Commons | Apr 2015 | B1 |
| 9081823 | Luo | Jul 2015 | B2 |
| 9135663 | Heiferman | Sep 2015 | B1 |
| 9183285 | Brown | Nov 2015 | B1 |
| 9251500 | Cathcart | Feb 2016 | B2 |
| 9367629 | Garg | Jun 2016 | B2 |
| 9460215 | Garg | Oct 2016 | B2 |
| 9712457 | Stemeseder | Jul 2017 | B2 |
| 9749870 | Kadel | Aug 2017 | B2 |
| 9959320 | Garg | May 2018 | B2 |
| 10169390 | Luo | Jan 2019 | B2 |
| 10268733 | Garg | Apr 2019 | B2 |
| 10303696 | Cathcart | May 2019 | B2 |
| 10360227 | Garg | Jul 2019 | B2 |
| 11539657 | Rapaport | Dec 2022 | B2 |
| 20010053694 | Igarashi | Dec 2001 | A1 |
| 20020072955 | Brock | Jun 2002 | A1 |
| 20030037110 | Yamamoto | Feb 2003 | A1 |
| 20030052911 | Cohen-solal | Mar 2003 | A1 |
| 20030055897 | Brown et al. | Mar 2003 | A1 |
| 20030069900 | Hind | Apr 2003 | A1 |
| 20030076352 | Uhlig | Apr 2003 | A1 |
| 20030078972 | Tapissier | Apr 2003 | A1 |
| 20030092428 | Awada | May 2003 | A1 |
| 20030154186 | Goodwin | Aug 2003 | A1 |
| 20030160815 | Muschetto | Aug 2003 | A1 |
| 20030195928 | Kamijo et al. | Oct 2003 | A1 |
| 20030225833 | Pilat et al. | Dec 2003 | A1 |
| 20030234952 | Dawson et al. | Dec 2003 | A1 |
| 20040075677 | Loyall | Apr 2004 | A1 |
| 20040076936 | Horvitz | Apr 2004 | A1 |
| 20040078432 | Manber et al. | Apr 2004 | A1 |
| 20040174971 | Guan | Sep 2004 | A1 |
| 20040205651 | Dutta et al. | Oct 2004 | A1 |
| 20040228531 | Fernandez et al. | Nov 2004 | A1 |
| 20050004923 | Park | Jan 2005 | A1 |
| 20050054381 | Lee | Mar 2005 | A1 |
| 20050086610 | MacKinlay et al. | Apr 2005 | A1 |
| 20050149459 | Kofman et al. | Jul 2005 | A1 |
| 20050154693 | Ebert | Jul 2005 | A1 |
| 20050246165 | Pettinelli | Nov 2005 | A1 |
| 20050259035 | Iwaki et al. | Nov 2005 | A1 |
| 20060020662 | Robinson | Jan 2006 | A1 |
| 20060026111 | Athelogou | Feb 2006 | A1 |
| 20060026152 | Zeng | Feb 2006 | A1 |
| 20060080613 | Savant | Apr 2006 | A1 |
| 20060093998 | Vertegaal | Jul 2006 | A1 |
| 20060156326 | Goronzy et al. | Jul 2006 | A1 |
| 20060161457 | Rapaport | Jul 2006 | A1 |
| 20060176831 | Greenberg et al. | Aug 2006 | A1 |
| 20060184566 | Lo | Aug 2006 | A1 |
| 20060213976 | Inakoshi et al. | Sep 2006 | A1 |
| 20060224593 | Benton et al. | Oct 2006 | A1 |
| 20060270419 | Crowley | Nov 2006 | A1 |
| 20070005425 | Bennett | Jan 2007 | A1 |
| 20070013652 | Kim | Jan 2007 | A1 |
| 20070016585 | Nickell et al. | Jan 2007 | A1 |
| 20070036292 | Selbie et al. | Feb 2007 | A1 |
| 20070094601 | Greenberg | Apr 2007 | A1 |
| 20070100938 | Bagley et al. | May 2007 | A1 |
| 20070112719 | Reich | May 2007 | A1 |
| 20070113181 | Blattner | May 2007 | A1 |
| 20070149214 | Walsh | Jun 2007 | A1 |
| 20070150281 | Hoff | Jun 2007 | A1 |
| 20070150916 | Begole et al. | Jun 2007 | A1 |
| 20070168446 | Keohane et al. | Jul 2007 | A1 |
| 20070168448 | Garbow et al. | Jul 2007 | A1 |
| 20070168863 | Blattner | Jul 2007 | A1 |
| 20070171716 | Wright | Jul 2007 | A1 |
| 20070214077 | Barnes | Sep 2007 | A1 |
| 20070239566 | Dunnaboo et al. | Oct 2007 | A1 |
| 20070265507 | de Lemos | Nov 2007 | A1 |
| 20070273558 | Smith | Nov 2007 | A1 |
| 20070282724 | Barnes | Dec 2007 | A1 |
| 20080005252 | Della Pasqua | Jan 2008 | A1 |
| 20080034040 | Wherry | Feb 2008 | A1 |
| 20080034309 | Louch et al. | Feb 2008 | A1 |
| 20080040474 | Zuckerberg | Feb 2008 | A1 |
| 20080052742 | Kopf et al. | Feb 2008 | A1 |
| 20080065468 | Berg et al. | Mar 2008 | A1 |
| 20080082548 | Betts | Apr 2008 | A1 |
| 20080091512 | Marci et al. | Apr 2008 | A1 |
| 20080097235 | Ofek et al. | Apr 2008 | A1 |
| 20080114737 | Neely et al. | May 2008 | A1 |
| 20080114755 | Wolters et al. | May 2008 | A1 |
| 20080133664 | Lentz | Jun 2008 | A1 |
| 20080154883 | Chowdhury | Jun 2008 | A1 |
| 20080168376 | Tien | Jul 2008 | A1 |
| 20080183750 | Lee | Jul 2008 | A1 |
| 20080189367 | Okumura | Aug 2008 | A1 |
| 20080209343 | Macadam et al. | Aug 2008 | A1 |
| 20080209350 | Sobotka et al. | Aug 2008 | A1 |
| 20080222295 | Robinson | Sep 2008 | A1 |
| 20080234976 | Wittkowski | Sep 2008 | A1 |
| 20080262364 | Aarts | Oct 2008 | A1 |
| 20080266118 | Pierson et al. | Oct 2008 | A1 |
| 20080281783 | Papkoff et al. | Nov 2008 | A1 |
| 20080288437 | Siregar | Nov 2008 | A1 |
| 20080313108 | Carrabis | Dec 2008 | A1 |
| 20080319827 | Yee | Dec 2008 | A1 |
| 20080320082 | Kuhike et al. | Dec 2008 | A1 |
| 20090037443 | Athale | Feb 2009 | A1 |
| 20090070700 | Johanson | Mar 2009 | A1 |
| 20090077064 | Daigle | Mar 2009 | A1 |
| 20090089296 | Stemeseder | Apr 2009 | A1 |
| 20090089678 | Sacco et al. | Apr 2009 | A1 |
| 20090094088 | Chen | Apr 2009 | A1 |
| 20090100469 | Conradt et al. | Apr 2009 | A1 |
| 20090112696 | Jung et al. | Apr 2009 | A1 |
| 20090112713 | Jung et al. | Apr 2009 | A1 |
| 20090119173 | Parsons et al. | May 2009 | A1 |
| 20090119584 | Herbst | May 2009 | A1 |
| 20090164916 | Jeong | Jun 2009 | A1 |
| 20090179983 | Schindler | Jul 2009 | A1 |
| 20090198566 | Greenberg | Aug 2009 | A1 |
| 20090204714 | Ferrara | Aug 2009 | A1 |
| 20090215469 | Fisher | Aug 2009 | A1 |
| 20090216773 | Konopnicki | Aug 2009 | A1 |
| 20090233623 | Johnson | Sep 2009 | A1 |
| 20090234727 | Petty | Sep 2009 | A1 |
| 20090234876 | Schigel et al. | Sep 2009 | A1 |
| 20090249244 | Robinson et al. | Oct 2009 | A1 |
| 20090254662 | Lee | Oct 2009 | A1 |
| 20090260060 | Smith et al. | Oct 2009 | A1 |
| 20090276705 | Ozdemir | Nov 2009 | A1 |
| 20090288012 | Hertel et al. | Nov 2009 | A1 |
| 20090325615 | McKay | Dec 2009 | A1 |
| 20090327417 | Chakra et al. | Dec 2009 | A1 |
| 20100030734 | Chunilal | Feb 2010 | A1 |
| 20100037277 | Flynn-Ripley et al. | Feb 2010 | A1 |
| 20100057857 | Szeto | Mar 2010 | A1 |
| 20100058183 | Hamilton, II | Mar 2010 | A1 |
| 20100063993 | Higgins | Mar 2010 | A1 |
| 20100070448 | Omoigui | Mar 2010 | A1 |
| 20100070758 | Low et al. | Mar 2010 | A1 |
| 20100070875 | Turski | Mar 2010 | A1 |
| 20100073133 | Conreux | Mar 2010 | A1 |
| 20100094797 | Monteverde | Apr 2010 | A1 |
| 20100114684 | Neged | May 2010 | A1 |
| 20100138452 | Henkin | Jun 2010 | A1 |
| 20100153453 | Knowles | Jun 2010 | A1 |
| 20100159909 | Stifelman | Jun 2010 | A1 |
| 20100164956 | Hyndman et al. | Jul 2010 | A1 |
| 20100169766 | Duarte et al. | Jul 2010 | A1 |
| 20100180217 | Li | Jul 2010 | A1 |
| 20100191727 | Malik | Jul 2010 | A1 |
| 20100191741 | Stefik | Jul 2010 | A1 |
| 20100191742 | Stefik | Jul 2010 | A1 |
| 20100198633 | Guy et al. | Aug 2010 | A1 |
| 20100205541 | Rapaport | Aug 2010 | A1 |
| 20100217757 | Fujioka | Aug 2010 | A1 |
| 20100223157 | Kalsi | Sep 2010 | A1 |
| 20100250497 | Redlich | Sep 2010 | A1 |
| 20100293104 | Olsson et al. | Nov 2010 | A1 |
| 20100306249 | Hill | Dec 2010 | A1 |
| 20110016121 | Sambrani | Jan 2011 | A1 |
| 20110022602 | Luo | Jan 2011 | A1 |
| 20110029898 | Malik | Feb 2011 | A1 |
| 20110040155 | Guzak | Feb 2011 | A1 |
| 20110041153 | Simon | Feb 2011 | A1 |
| 20110047119 | Kaplan | Feb 2011 | A1 |
| 20110047487 | DeWeese et al. | Feb 2011 | A1 |
| 20110055017 | Solomon | Mar 2011 | A1 |
| 20110055734 | Borst et al. | Mar 2011 | A1 |
| 20110055735 | Wood et al. | Mar 2011 | A1 |
| 20110070758 | Low et al. | Mar 2011 | A1 |
| 20110082907 | Anderson | Apr 2011 | A1 |
| 20110087540 | Krishnan et al. | Apr 2011 | A1 |
| 20110087735 | Anderson | Apr 2011 | A1 |
| 20110125661 | Hull et al. | May 2011 | A1 |
| 20110137690 | Louch et al. | Jun 2011 | A1 |
| 20110137921 | Inagaki | Jun 2011 | A1 |
| 20110137951 | Baker | Jun 2011 | A1 |
| 20110142016 | Chatterjee | Jun 2011 | A1 |
| 20110145570 | Gressel et al. | Jun 2011 | A1 |
| 20110153761 | Anderson | Jun 2011 | A1 |
| 20110154224 | Bates et al. | Jun 2011 | A1 |
| 20110161164 | Anderson | Jun 2011 | A1 |
| 20110161177 | Anderson | Jun 2011 | A1 |
| 20110179125 | Lee et al. | Jul 2011 | A1 |
| 20110184886 | Shoham | Jul 2011 | A1 |
| 20110185025 | Cherukuri et al. | Jul 2011 | A1 |
| 20110197123 | Caine et al. | Aug 2011 | A1 |
| 20110197146 | Goto et al. | Aug 2011 | A1 |
| 20110201423 | Borst et al. | Aug 2011 | A1 |
| 20110219015 | Kim | Sep 2011 | A1 |
| 20110246306 | Blackhurst | Oct 2011 | A1 |
| 20110246908 | Akram et al. | Oct 2011 | A1 |
| 20110246920 | Lebrun | Oct 2011 | A1 |
| 20110252121 | Borgs et al. | Oct 2011 | A1 |
| 20110270618 | Banerjee | Nov 2011 | A1 |
| 20110270830 | Stefik | Nov 2011 | A1 |
| 20120042263 | Rapaport et al. | Feb 2012 | A1 |
| 20120047447 | Haq | Feb 2012 | A1 |
| 20120079045 | Plotkin | Mar 2012 | A1 |
| 20120095819 | Li | Apr 2012 | A1 |
| 20120102130 | Guyot | Apr 2012 | A1 |
| 20120124486 | Robinson et al. | May 2012 | A1 |
| 20120158633 | Eder | Jun 2012 | A1 |
| 20120158715 | Maghoul et al. | Jun 2012 | A1 |
| 20120166432 | Tseng | Jun 2012 | A1 |
| 20120259240 | Llewellynn | Oct 2012 | A1 |
| 20120265528 | Gruber et al. | Oct 2012 | A1 |
| 20120284105 | Li | Nov 2012 | A1 |
| 20120323691 | McLaughlin | Dec 2012 | A1 |
| 20120323928 | Bhatia | Dec 2012 | A1 |
| 20130018685 | Parnaby | Jan 2013 | A1 |
| 20130041696 | Richard | Feb 2013 | A1 |
| 20130079149 | Fletcher | Mar 2013 | A1 |
| 20130086063 | Chen | Apr 2013 | A1 |
| 20130110827 | Nabar | May 2013 | A1 |
| 20130124626 | Cathcart | May 2013 | A1 |
| 20130196685 | Griff | Aug 2013 | A1 |
| 20130275405 | Maghoul | Oct 2013 | A1 |
| 20130325755 | Arquette | Dec 2013 | A1 |
| 20140108428 | Luo | Apr 2014 | A1 |
| 20140136713 | Stemeseder | May 2014 | A1 |
| 20140233472 | Kadel | Aug 2014 | A1 |
| 20140282646 | McCoy | Sep 2014 | A1 |
| 20140309782 | Sharpe | Oct 2014 | A1 |
| 20140321839 | Armstrong | Oct 2014 | A1 |
| 20150026260 | Worthley | Jan 2015 | A1 |
| 20150046588 | Martini | Feb 2015 | A1 |
| 20150066910 | Bleach | Mar 2015 | A1 |
| 20150121495 | Gao | Apr 2015 | A1 |
| 20150125147 | Zhang | May 2015 | A1 |
| 20150178283 | Garg | Jun 2015 | A1 |
| 20150178284 | Garg | Jun 2015 | A1 |
| 20150178397 | Garg | Jun 2015 | A1 |
| 20150220995 | Guyot | Aug 2015 | A1 |
| 20150262430 | Farrelly | Sep 2015 | A1 |
| 20150296369 | Berionne | Oct 2015 | A1 |
| 20150339335 | Luo | Nov 2015 | A1 |
| 20160132570 | Cathcart | May 2016 | A1 |
| 20160150260 | Ovide | May 2016 | A1 |
| 20160246890 | Garg | Aug 2016 | A1 |
| 20160275801 | Kopardekar | Sep 2016 | A1 |
| 20160335270 | Garg | Nov 2016 | A1 |
| 20160353274 | Chichierchia | Dec 2016 | A1 |
| 20170034178 | Hansen | Feb 2017 | A1 |
| 20170083180 | Nelson | Mar 2017 | A1 |
| 20170115992 | Krishnamurthy | Apr 2017 | A1 |
| 20170134948 | Gao | May 2017 | A1 |
| 20170171142 | Arquette | Jun 2017 | A1 |
| 20180210886 | Garg | Jul 2018 | A1 |
| Number | Date | Country |
|---|---|---|
| 2017200893 | Mar 2017 | AU |
| 2017200893 | Jun 2018 | AU |
| 2956463 | Jun 2015 | CA |
| 102004999 | Apr 2011 | CN |
| 1736902 | Dec 2006 | EP |
| 3232344 | Oct 2017 | EP |
| 200533337 | Dec 2005 | JP |
| 2005333374 | Dec 2005 | JP |
| 2007004807 | Jan 2007 | JP |
| 2017102950 | Jun 2017 | JP |
| 2018113049 | Jul 2018 | JP |
| Entry |
|---|
| Mark-Shane Scale. “Facebook as a Social Search Engine and the Implications for Libraries in the 21st Century.” (Nov. 2008). Retrieved online Jun. 14, 2022. https://www.researchgate.net/publication/235322898_Facebook_as_a_social_search_engine_and_the_implications_for_libraries_in_the_twenty-first_century (Year: 2008). |
| Ioannis Arapakis et al. “Predicting User Engagement with Direct Displays Using Mouse Cursor Information.” (2016). Retrieved online Jun. 14, 2022. https://iarapakis.github.io/papers/SIGIR16.pdf (Year: 2016). |
| Sitecore. “Engagement Analytics Configuration Reference Guide.” (Jan. 2, 2010). Retrieved online Feb. 10, 2023. https://doc.sitecore.com/xp/en/sdnarchive/upload/sitecore6/65/engagement_analytics_configuration_reference_sc65-usletter.pdf (Year: 2010). |
| PCT International Preliminary Report on Patentability, PCT US2010/023731, dated Aug. 16, 2011. |
| Advance E-Mail. PCT Notification Transmittal of International Preliminary Report on Patentability, PCT US2010/023731, dated Aug. 25, 2011. |
| Joshua Schnell, Macgasm, http://www.macgasm.net/2011/06/09/apple-smartphones-smarter-patent/, Oct. 6, 2011. |
| PCT Search Report, PCT/US2010/023731, dated Jun. 4, 2010. |
| D. Bottazzi et al., “Context-Aware Middleware for Anytime, Anywhere Social Networks”, IEEE Computer Society, pp. 23-32 (2007). |
| Miluzzo et al., “Sensing Meets Mobile Social Networks: The Design, Implement. and Evaluat. of the CenceMe Appl.”, ACM, Nov. 2008, p. 337. |
| Mark-Shane Scale.“Facebook as a Social Search Engine and the Implications for Libraries in the 21st Century”. (Nov. 2008) https://www.researchgate.net/publication/235322898. |
| Number | Date | Country | |
|---|---|---|---|
| 61551338 | Oct 2011 | US | |
| 61485409 | May 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17714802 | Apr 2022 | US |
| Child | 17971588 | US | |
| Parent | 16196542 | Nov 2018 | US |
| Child | 17714802 | US | |
| Parent | 14192119 | Feb 2014 | US |
| Child | 16196542 | US | |
| Parent | 13367642 | Feb 2012 | US |
| Child | 14192119 | US |
