Abstract: Systems and methods of inferential user matching include inferring an interest in matching between the first user and the second user based at least in part on a first profile of a first user and a second profile of a second user. Based at least in part on the inferred interest in matching, the systems and methods match the first user and the second user for a service, and transmit (i) a first representation of the first user to a portable device of the second user, and (ii) a second representation of the second user to a portable device of the first user.

Abstract: A generative interactive video computer-implemented method and system applies trained computer-implemented neural networks to generate a sequence of images, which may comprise a video, and which contains identified objects, and then delivers the sequence of images to a user. The sequence of images may be further generated in accordance with an inference of a preference from user behavioral information. The identified objects may be provided to the system in the form of natural language and/or images. The system then generates and delivers to users natural language-based responses to user requests for information with respect to the identified objects, including attributes that are associated with the identified objects. Users may direct the system to generate video-based virtual environments that include representations of the identified objects.

Abstract: A system and method of advertising based on the automatic determination of an advertising recipient's location and inferences of preferences derived from usage behaviors is disclosed. The advertising recipient's location that is considered in delivering a specific advertisement may be the current location or one or more historical locations. Proximity considerations with regard to other people, locations, or physical objects may influence the advertisement that is to be delivered. The advertisement may be delivered to a mobile device that has location-aware capabilities such as a Global Positioning System-based system.

Abstract: A contextual-based method and system for identifying and revealing objects from video directs a focus of attention to images or sequences of images responsive to a command, interrogative, or inferred preference of a user. A probability is assigned to an object that is inferred to be represented in the images or sequence of images. The probability is generated by application of a computer-implemented neural network. The probability is then updated based upon a context within which the representation of the object is inferred to be situated. The context may be inferred from one or more inferences related to one or more archetypical objects that are associated with the context. In accordance with the updated probability, a communication may be delivered that references attributes of the object and/or the user may direct a command to the representation of the object.

Abstract: An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.

Abstract: A system and method of advertising based on the automatic determination of an advertising recipient's location and inferences of preferences derived from usage behaviors is disclosed. The advertising recipient's location that is considered in delivering a specific advertisement may be the current location or one or more historical locations. Proximity considerations with regard to other people, locations, or physical objects may influence the advertisement that is to be delivered. The advertisement may be delivered to a mobile device that has location-aware capabilities such as a Global Positioning System-based system.

Abstract: A temporally sequenced content recommender method and system generates a first vector of affinities from usage information comprising durations of users' attention toward temporally sequenced content. A second vector of affinities is generated by applying a computer-implemented neural network to content objects. The similarity of the vectors of affinities is determined, which informs the generation of recommendations that are delivered to a user. Beneficial serendipity may be incorporated within the generation of the recommendations by, for example, evaluating contrasting affinities in user affinity vectors, by evaluating levels of available behavioral information for users, or by applying randomized or probabilistic methods. Explanations for the recommendations may be provided to users that include inferred user preferences.

Abstract: An integrated neural network and semantic system applies a neural network to interpret an image, determines a syntactical element corresponding to the image in accordance with the interpretation, and determines a first probability that represents a confidence level that the correspondence is accurate. A semantic chain and associated second probability are then generated based on the syntactical element and the first probability, whereby the second probability represents the system's confidence level that the semantic chain accurately reflects objective reality. A natural language communication is generated for delivery to a user that comprises syntactical elements that are in accordance with the semantic chain and the second probability. The communication may further be expected to result in receiving information that will influence the confidence level that the semantic chain accurately reflects objective reality.

Abstract: Systems and methods of inferential user matching include inferring an interest in matching between the first user and the second user based at least in part on a first profile of a first user and a second profile of a second user. Based at least in part on the inferred interest in matching, the systems and methods match the first user and the second user for a service, and transmit (i) a first representation of the first user to a portable device of the second user, and (ii) a second representation of the second user to a portable device of the first user.

Abstract: A contextual-based method and system for identifying and revealing objects from video directs a focus of attention to images or sequences of images responsive to a command, interrogative, or inferred preference of a user. A probability is assigned to an object that is inferred to be represented in the images or sequence of images. The probability is generated by application of a computer-implemented neural network. The probability is then updated based upon a context within which the representation of the object is inferred to be situated. The context may be inferred from one or more inferences related to one or more archetypical objects that are associated with the context. In accordance with the updated probability, a communication may be delivered that references attributes of the object and/or the user may direct a command to the representation of the object.

Abstract: An inferential user matching system can determine a mutual interest between a first user and a second user. Based on determining the mutual interest, the system can transmit an expression of interest indication to a portable device(s) of the first and/or second user.

Abstract: A method, system, and apparatus for identifying and revealing objects from video identifies or infers objects from images or sequences of images upon a command, interrogative, or inferred interest of a user. The system is trained to identify or infer the objects using neural network or statistical learning-based models. The system may respond to the user in accordance with an expected user latency in requesting the system to reveal a specific object to the user. Semantic techniques may be applied to enable the system to interpret contextual or object attribute information that is provided by the users' communications to the system. The system may be applied in product promotional processes that are with respect to video that is associated with television, movies, and other video content.

Abstract: An optimizing data-to-learning-to-action method and system identifies uncertainties embodied as probability distributions that influence a sequence of decisions. The uncertainties are mapped to a simulation of a computer-based infrastructure that supports the execution of the decisions. Actions with respect to the infrastructure that are expected to reduce the uncertainties are simulated. The probability distributions are updated accordingly for each simulated action and an associated net value of information for each simulated action is generated. The action with the greatest net value of information is implemented and the simulated infrastructure is updated accordingly. The process may then be re-run based upon the updated simulated infrastructure.

Abstract: An auto-learning recommender method and system delivers recommendations to users and analyzes the resulting usage behaviors by applying a computer-implemented neural network. Probabilities are automatically determined based upon the analysis that may correspond to inferred preferences. The probabilities inform the generation of additional recommendations that are delivered to users. The generation of the additional recommendations may be further informed by value of information calculations and/or analysis of intrinsic patterns within content.

Abstract: An inferential user matching system can determine a mutual interest between a first user and a second user. Based on determining the mutual interest, the system can transmit an expression of interest indication to a portable device(s) of the first and/or second user.

Abstract: A method, system, and apparatus for identifying and revealing objects from video identifies or infers objects from images or sequences of images upon a command, interrogative, or inferred interest of a user. The system is trained to identify or infer the objects using neural network or statistical learning-based models. The system may respond to the user in accordance with an expected user latency in requesting the system to reveal a specific object to the user. Semantic techniques may be applied to enable the system to interpret contextual or object attribute information that is provided by the users' communications to the system. The system may be applied in product promotional processes that are with respect to video that is associated with television, movies, and other video content.

Abstract: A method and device for temporally sequenced recommendations of activities delivers to users temporally sequenced objects comprising user activities, wherein the delivered objects are selected based, at least in part, on inferences of preferences from usage behaviors. The delivered objects may include activities associated with processor-based devices in addition to human activities. Variations of the system and method include delivering the temporally sequenced objects in accordance with the contents of the objects and user feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Abstract: An inferential user matching system can determine a mutual interest between a first user and a second user based on inferred trends corresponding to a set of usage behaviors by a plurality of users utilizing the system. Based on determining the mutual interest, the system can transmit, over one or more networks, a representation of the first and/or second user to the portable computing device of other user and monitor the first and/or second user to detect an expression of interest. Based on detecting the expression of interest, the system can transmit an expression of interest indication to a portable device(s) of the first and/or second user.

Abstract: A method and system of inferential-based communications applies two sets of values, whereby the first set of values are determined from inferences of the relevancy of textual elements with respect to text-based content and the second set of values are determined from behavioral-based inferences with respect to topics, so as to select appropriate words to be included in communications that are generated for delivery to a user. The inferences that are with respect to text-based content may be determined by applying analytic methods such as Bayesian or statistical learning-based methods. The values that are determined from behavioral-based inferences with respect to topics may correspond to inferences of interest and/or expertise. Words in the communications may be selected and/or arranged in accordance with syntactical rules and may reference elements of the text-based content and/or behavioral inferences.

Abstract: A neural network-based recommender method and system applies computer-implemented neural networks to match information within computer-implemented content objects that contain, for example, text and/or images. The matching of the information is combined with an evaluation of the computer-implemented objects based upon user behaviors to generate computer-implemented topics. Affinities among the topics or among users and the topics are generated based on the matching of the information and the evaluation of the computer-implemented objects. Recommendations are generated for delivery to users based upon the generated topics and affinities. The topics and/or affinities may be based upon inferences of expertise levels.