This disclosure generally relates to systems and methods that facilitate identifying topic(s) from incoming data items from a plurality of data sources, identifying other data items that relate to the topic(s), identifying users that have an interest in the topic(s), identifying availability of the users, rating the respective topics, and presenting a filtered set of topics based upon the identifications and ratings.
User are often inundated with information from a variety of sources, while interacting with client devices, such as social media, social networks, news, email, text messages, chat messages, voicemails, alerts, etc. Oftentimes, there is too much data to manually sort through, and when automatically presented, data items are often not presented at an ideal time for the user. For example, a work related posting may be presented to a user requiring live interaction with coworkers that are currently available, but is presented during non-work hours. In another example, an advertisement for a group discount in connection with dining may be presented when the user is unable interact with friends potentially interested in going to dinner.
A simplified summary is provided herein to help enable a basic or general understanding of various aspects of exemplary, non-limiting embodiments that follow in the more detailed description and the accompanying drawings. This summary is not intended, however, as an extensive or exhaustive overview. Instead, the purpose of this summary is to present some concepts related to some exemplary non-limiting embodiments in simplified form as a prelude to more detailed description of the various embodiments that follow in the disclosure.
In accordance with a non-limiting implementation, a plurality of topics are generated based upon a plurality of data items, respective availability statuses of user identities from a plurality of user identities are determined having an established relationship with each other, the topics are rated based upon at least the respective availability statuses and respective associations of the user identities with the plurality of topics, and a set of topics is selected from the plurality or topics to present to a user identity of the plurality of user identities based upon the respective ratings of the topics.
In accordance with a non-limiting implementation, a topic generation component is configured to generate a plurality of topics based upon a plurality of data items, a user availability component is configured to determine respective availability statuses of user identities from a plurality of user identities having an established relationship with each other, a topic rating component is configured to rate the topics based upon at least the respective availability statuses and respective associations of the user identities with the plurality of topics; and a topic presentation component is configured to select a set of topics from the plurality or topics to present to a user identity of the plurality of user identities based upon the respective ratings of the topics.
These and other implementations and embodiments are described in more detail below.
Various aspects or features of this disclosure are described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In this specification, numerous specific details are set forth in order to provide a thorough understanding of this disclosure. It should be understood, however, that certain aspects of this disclosure may be practiced without these specific details, or with other methods, components, materials, etc. In other instances, well-known structures and devices are shown in block diagram form to facilitate describing this disclosure.
In situations in which systems and methods described herein collect personal information about users, or may make use of personal information, the users can be provided with an opportunity to control whether programs or features collect user information (e.g., information about a user's social network, social actions or activities, profession, a user's preferences, or a user's current location), or to control whether or how to receive content from the content server that may be more relevant to the user. In addition, certain data can be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity can be treated so that no personally identifiable information can be determined for the user, or a user's geographic location can be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. The user can add, delete, or modify information about the user. Thus, the user can control how information is collected about the user and used by a server.
In accordance with various disclosed aspects, a mechanism is provided for presenting data of interest to a user when the user is able to interact with other users in real time that may also have interest in the data. For example, a user can be watching a television show and a discount offer for bowling at a local bowling alley is available. The discount offer can be presented with a list of the user's friends in the local area that are online in a social network and also have an interest in bowling. In another example, a user can be listening to music on their phone around noon, and a discount offer for lunch at a restaurant is available, as well as a posting from a friend asking if anyone is interested in lunch. The discount offer and posting can be presented along with a list of the common friends of the user and the user's friend near the restaurant that are online in a chat system. In a further example, a news article about a technology concept can be published. The news article and a list of friends who have an interest in the technology concept who also are actively engaged with their phones but are not on a phone call can be presented to the user. Thus, determinations or inferences regarding user availability as well as other users' availability and common interest are utilized to optimize dissemination and/or presentation of content to respective users to facilitate optimizing engagement.
A data item can include, for example, video, audio, image, text, or any combination thereof. Data items can be available on an intranet, internet, or can be local content. Furthermore, a user identity is a digital representation of a user in a system, for example, a user account, username, or any other suitable mechanism for representing a user in an electronic system.
With reference to the embodiments described below, an example television device is presented for illustrative purposes only. It is to be appreciated that any suitable type of client device using audio, visual, and or tactile user interfaces can be employed.
Referring now to the drawings,
While only one data source 170 and client device 160 are shown, it should be appreciated that information server 110 can interact with any suitable number of data sources 170 and client devices 160 concurrently. Furthermore, information server 110 and client device 160 can respectively receive input from users to control interaction with, and presentation of data, for example, using input devices, non-limiting examples of which can be found with reference to
Information server 110 and client device 160, each respectively include a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory, a non-limiting example of which can be found with reference to
Information server 110 or client device 160 can be any suitable type of device for generating, interacting with, receiving, accessing, and/or supplying data locally, or remotely over a wired and/or wireless communication link, non-limiting examples of include a wearable device or a non-wearable device. Wearable device can include, for example, heads-up display glasses, a monocle, eyeglasses, contact lens, sunglasses, a headset, a visor, a cap, a helmet, a mask, a headband, clothing, camera, video camera, or any other suitable device capable of recording, generating, interacting with, receiving, accessing, or supplying data that can be worn by a human or non-human user. Non-wearable device can include, for example, a mobile device, a mobile phone, a camera, a camcorder, a video camera, personal data assistant, laptop computer, tablet computer, desktop computer, server system, cable set top box, satellite set top box, cable modem, television set, monitor, media extender device, blu-ray device, DVD (digital versatile disc or digital video disc) device, compact disc device, video game system, portable video game console, audio/video receiver, radio device, portable music player, navigation system, car stereo, motion sensor, infrared sensor, or any other suitable device capable of recording, generating, interacting with, receiving, accessing, and/or supplying data. Moreover, information server 110 and client device 160 can include a user interface (e.g., a web browser or application), that can receive and present displays and content generated locally or remotely.
Referring to
Topic generation component 210 can analyze data items associated with a user identity received and/or accessed from data sources 170 to identify topics related to the data item. A user identity can receive data items from a plurality of data sources 170, such as from subscriptions, memberships, newsfeeds, emails, chat streams, or any other suitable data source. In a non-limiting example, content of, metadata associated with, and/or data derived from the data item can be examined by topic generation component 210, such as for visual, audio, or textual data that is indicative of a topic, for example, using visual, audio, or textual recognition algorithms. For example, a metadata description of the data item, such as “latest record from XYZ music artist” can indicate that the item is related to the topics “music” and “XYZ artist”. In another example, a social network posting can state “Is anyone up for steak tonight” which can indicate the topics “food”, “dinner”, and “steak”. In another non-limiting example, topic generation component 210 can perform a visual analysis to recognize objects an image, such as people, faces, clothing, buildings, cars, a stage, a venue, road signs, or any other suitable visual object that can be employed to generate data that is indicative of a topic. In a further non-limiting example, topic generation component 210 can perform an audio analysis to recognize audio signals, such as music, voices, vehicles, text, language spoken, sounds unique to a location or object, or any other suitable sound that can be employed to generate data that is indicative of a topic. In an embodiment, topic generation component 210 can employ the data that is indicative of a topic in conjunction with a predefined, dynamically determined, and/or user specified taxonomy or list of topics to associate a topic to the data item, such as in a non-limiting example, using a matching or classification algorithm. In another embodiment, topic generation component 210 can automatically generate a new topic from the data that is indicative of a topic and associate the new topic to the data item. For example, a news article discussing the creation of a new country named “newcountry” can result in the creation of a new topic titled “newcountry”. In a further embodiment, topic generation component 210 can prompt for user input to specify a topic to associate with the data item, such as in conjunction with a learning algorithm. The data items, topics, and associations between data items and topics can be stored in data store 150. Moreover, it is to be appreciated that a data item can be associated with more than one topic, and that any suitable mechanism for generating and/or associating topics to data items can be employed. Furthermore, it is to be appreciated that a topic can be pre-defined or user specified in any suitable manner by the system or a user identity. Additionally, it is to be understood that a data item can be a topic. For example, a social network posting stating “Movie tonight?” can be a topic.
Continuing with reference to
Referring to
User presence component 230 also includes user availability component 320 that determines availability status of a user identity to interact in real time, such as in a non-limiting example, by monitoring applications, devices, and/or systems associated with the user identity. For example, user presence component 230 can monitor online status of a user identity on a social network or chat system (e.g. available, not available, visible, invisible, busy, working, or any other suitable status indication), level or type of activity on a device or application associated with a user identity (e.g., making a call, watching television, listening to music, watching a movie, reading news, drafting a document, working in a spreadsheet, taking pictures, recording video or audio, playing video game, no activity, reading email, not holding device, holding mobile device, device is in motion, device is stationary, looking at device, location of device, speed of location change indicative of driving, running, or walking, or any other suitable indication of level or type of activity on the device or application), location of user identity (e.g. work, home, shopping, library, car, friend's house, or any other suitable location), or any other suitable availability criteria indicative of a user identity's availability to interact in real time. It is to be appreciated that user availability component 320 can employ classification algorithms to classify a user identity's availability to interact in real time into a classification model having multiple levels of availability status. For example, a binary classification can be employed having not available and available classes. In another example, classes can include levels of user engagement with a device, such as, device is off, device is on, holding device, looking at device, and using device. It is to be appreciated that any suitable availability criteria, classification model, and/or algorithm can be employed to classify the user identity's availability to interact in real time.
In a non-limiting example, user presence component 230 can limit identification and determination of availability status to user identities that already have an established relationship with a user identity to which topics, data items, and/or currently available user identities will be presented. In a non-limiting example, an established relationship can include a connection in a social network, an email exchange, a coworker, a part of a direct or extended family, friends, a contact or buddy list, a phone call, a text exchange, a chat message exchange, common membership to a group, common subscription to a publication, or any other suitable criteria indicative of a previously established relationship between two or more user identities. Furthermore, criteria for established relationships can be predefined, dynamically generated, and/or user specified. In addition, user presence component 230 can assign an affinity rating between two user identities based upon one or more established relationships between the two user identities, such as based type of relationship(s) between user identities (e.g. connection in a social network, an email exchange, a coworker, a part of a direct or extended family, friends, a contact or buddy list, a phone call, a text exchange, a chat message exchange, common membership to a group, common subscription to a publication, or any other suitable criteria indicative of a type of previously established relationship between two or more user identities). Moreover, user presence component 230 can weight the type of relationship(s) between user identities, for example, using predefined, dynamically determined, and/or user specified weights. It is to be appreciated that there can be more than one established relationship between two user identities, and a suitable formula or algorithm can be employed to determine the affinity rating, for example, based upon types of established relationships between the two user identities and their associated weights. An affinity rating is an indicator of the strength of the established relationships between two user identities.
Referring back to
Topic presentation component 250 can generate, for a user identity, a list of topics, one or more data items associated with the respective topics, and/or one or more other user identities associated with the respective topics that are currently available to interact with the user identity. Referring to
Referring to
Referring to
Referring back to
It is to be appreciated that any selection, determination, matching, classification, or inference criteria, functions, or algorithms discussed herein can employ suitable thresholds that can be predefined, dynamically generated, and/or user specified.
Referring to
Referring to
One of ordinary skill in the art can appreciate that the various embodiments described herein can be implemented in connection with any computer or other client or server device, which can be deployed as part of a computer network or in a distributed computing environment, and can be connected to any kind of data store where media may be found. In this regard, the various embodiments described herein can be implemented in any computer system or environment having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units. This includes, but is not limited to, an environment with server computers and client computers deployed in a network environment or a distributed computing environment, having remote or local storage.
Distributed computing provides sharing of computer resources and services by communicative exchange among computing devices and systems. These resources and services include the exchange of information, cache storage and disk storage for objects, such as files. These resources and services can also include the sharing of processing power across multiple processing units for load balancing, expansion of resources, specialization of processing, and the like. Distributed computing takes advantage of network connectivity, allowing clients to leverage their collective power to benefit the entire enterprise. In this regard, a variety of devices may have applications, objects or resources that may participate in the various embodiments of this disclosure.
Each computing object 910, 912, etc. and computing objects or devices 920, 922, 924, 926, 928, etc. can communicate with one or more other computing objects 910, 912, etc. and computing objects or devices 920, 922, 924, 926, 928, etc. by way of the communications network 940, either directly or indirectly. Even though illustrated as a single element in
There are a variety of systems, components, and network configurations that support distributed computing environments. For example, computing systems can be connected together by wired or wireless systems, by local networks or widely distributed networks. Currently, many networks are coupled to the Internet, which provides an infrastructure for widely distributed computing and encompasses many different networks, though any suitable network infrastructure can be used for exemplary communications made incident to the systems as described in various embodiments herein.
Thus, a host of network topologies and network infrastructures, such as client/server, peer-to-peer, or hybrid architectures, can be utilized. The “client” is a member of a class or group that uses the services of another class or group. A client can be a computer process, e.g., roughly a set of instructions or tasks, that requests a service provided by another program or process. A client process may utilize the requested service without having to “know” all working details about the other program or the service itself.
In a client/server architecture, particularly a networked system, a client can be a computer that accesses shared network resources provided by another computer, e.g., a server. In the illustration of
A server is typically a remote computer system accessible over a remote or local network, such as the Internet or wireless network infrastructures. The client process may be active in a first computer system, and the server process may be active in a second computer system, communicating with one another over a communications medium, thus providing distributed functionality and allowing multiple clients to take advantage of the information-gathering capabilities of the server. Any software objects utilized pursuant to the techniques described herein can be provided standalone, or distributed across multiple computing devices or objects.
In a network environment in which the communications network/bus 940 is the Internet, for example, the computing objects 910, 912, etc. can be Web servers, file servers, media servers, etc. with which the client computing objects or devices 920, 922, 924, 926, 928, etc. communicate via any of a number of known protocols, such as the hypertext transfer protocol (HTTP). Objects 910, 912, etc. may also serve as client computing objects or devices 920, 922, 924, 926, 928, etc., as may be characteristic of a distributed computing environment.
As mentioned, advantageously, the techniques described herein can be applied to any suitable device. It is to be understood, therefore, that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the various embodiments. Accordingly, the computer described below in
Although not required, embodiments can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates to perform one or more functional aspects of the various embodiments described herein. Software may be described in the general context of computer executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers or other devices. Those skilled in the art will appreciate that computer systems have a variety of configurations and protocols that can be used to communicate data, and thus, no particular configuration or protocol is to be considered limiting.
With reference to
Computer 1010 typically includes a variety of computer readable media and can be any available media that can be accessed by computer 1010. The system memory 1030 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). By way of example, and not limitation, system memory 1030 may also include an operating system, application programs, other program modules, and program data.
A user can enter commands and information into the computer 1010 through input devices 1040, non-limiting examples of which can include a keyboard, keypad, a pointing device, a mouse, stylus, touchpad, touchscreen, trackball, motion detector, camera, microphone, joystick, game pad, scanner, or any other device that allows the user to interact with computer 1010. A monitor or other type of display device is also connected to the system bus 1022 via an interface, such as output interface 1050. In addition to a monitor, computers can also include other peripheral output devices such as speakers and a printer, which may be connected through output interface 1050.
The computer 1010 may operate in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer 1070. The remote computer 1070 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, or any other remote media consumption or transmission device, and may include any or all of the elements described above relative to the computer 1010. The logical connections depicted in
As mentioned above, while exemplary embodiments have been described in connection with various computing devices and network architectures, the underlying concepts may be applied to any network system and any computing device or system in which it is desirable to publish or consume media in a flexible way.
Also, there are multiple ways to implement the same or similar functionality, e.g., an appropriate API, tool kit, driver code, operating system, control, standalone or downloadable software object, etc. which enables applications and services to take advantage of the techniques described herein. Thus, embodiments herein are contemplated from the standpoint of an API (or other software object), as well as from a software or hardware object that implements one or more aspects described herein. Thus, various embodiments described herein can have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software.
The word “exemplary” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the aspects disclosed herein are not limited by such examples. In addition, any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, for the avoidance of doubt, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
Computing devices typically include a variety of media, which can include computer-readable storage media and/or communications media, in which these two terms are used herein differently from one another as follows. Computer-readable storage media can be any available storage media that can be accessed by the computer, is typically of a non-transitory nature, and can include both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data, or unstructured data. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible and/or non-transitory media which can be used to store desired information. Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.
On the other hand, communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
As mentioned, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. As used herein, the terms “component,” “system” and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Further, a “device” can come in the form of specially designed hardware; generalized hardware made specialized by the execution of software thereon that enables the hardware to perform specific function (e.g., coding and/or decoding); software stored on a computer readable medium; or a combination thereof.
The aforementioned systems have been described with respect to interaction between several components. It can be appreciated that such systems and components can include those components or specified sub-components, some of the specified components or sub-components, and/or additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical). Additionally, it is to be noted that one or more components may be combined into a single component providing aggregate functionality or divided into several separate sub-components, and that any one or more middle layers, such as a management layer, may be provided to communicatively couple to such sub-components in order to provide integrated functionality. Any components described herein may also interact with one or more other components not specifically described herein but generally known by those of skill in the art.
In order to provide for or aid in the numerous inferences described herein (e.g. inferring relationships between metadata or inferring topics of interest to users), components described herein can examine the entirety or a subset of the data to which it is granted access and can provide for reasoning about or infer states of the system, environment, etc. from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data.
Such inference can result in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification (explicitly and/or implicitly trained) schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) can be employed in connection with performing automatic and/or inferred action in connection with the claimed subject matter.
A classifier can map an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, as by f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hyper-surface in the space of possible inputs, where the hyper-surface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches include, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.
In view of the exemplary systems described above, methodologies that may be implemented in accordance with the described subject matter will be better appreciated with reference to the flowcharts of the various figures. While for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Where non-sequential, or branched, flow is illustrated via flowchart, it can be appreciated that various other branches, flow paths, and orders of the blocks, may be implemented which achieve the same or a similar result. Moreover, not all illustrated blocks may be required to implement the methodologies described hereinafter.
In addition to the various embodiments described herein, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiment(s) for performing the same or equivalent function of the corresponding embodiment(s) without deviating there from. Still further, multiple processing chips or multiple devices can share the performance of one or more functions described herein, and similarly, storage can be effected across a plurality of devices. Accordingly, the invention is not to be limited to any single embodiment, but rather can be construed in breadth, spirit and scope in accordance with the appended claims.