Abstract: A method, system, and device for audio-based identification interfaces for selecting objects from video generates and stores frequency-based audio identifiers associated with segments of an audio stream that is integrated with a video stream. The generation of the frequency-based audio identifiers may be performed by a hashing function applied to audio frequencies within audio segments. The video stream comprises identified objects that may be identified by application of a trained neural network. An audio segment is received from a user and a corresponding frequency-based audio identifier is generated and matched against stored frequency-based audio identifiers. The matching determines an audio segment and a temporally corresponding identified object, which is then embodied within an interactive user interface.

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 method and system for virtual environment-based interfaces applied to selected objects from video directs a system's focus of attention to an image within a first video stream and identifies an object in the image by applying a trained neural network. In response to a communication from a user comprising language and/or images describing a virtual environment, a second trained neural network is applied to generate a second video stream that embodies the identified object within a virtual environment that is in accordance with the user-described virtual environment. The second video stream is then delivered to the user. The system's focus of attention and/or generation of the virtual environment may be informed by user preferences that are inferred from user behaviors.

Abstract: A method and system for incentivized neural network training and assurance processes provides incentives to object miners to identify objects in video streams for the purposes of enhancing the training of computer-implemented neural networks on the identified objects and/or augmenting the results of automatic object identification by trained neural networks. An object mining user interface and process is provided to object miners that provides incentives for identifying objects in video streams and technical capabilities for designating identified objects within multiple multi-dimensional regions of pixels. Incentives may be token-based and in accordance with end user interactions within a visual user interface with representations of the miner-identified objects within a video stream.

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: A method and system for semantic-based navigation of temporally sequenced content such as videos interprets the image and audio-based content by applying computer-implemented neural networks and performs multi-modal inferences of temporally aligned content. The multi-modal inferences may be performed by means of the application of vectorized embeddings and/or by application of semantic chaining techniques. The multi-modal inferences are applied to generate navigational indicators and/or responses to user inputs that comprise natural language or images. The navigational indicators and responses to user inputs may be personalized based upon user behaviors.

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: A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

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 semantic method and system interprets content by applying neural networks and then generates and/or updates generalized semantic chains based upon the interpretations. The generalized semantics chains have associated weightings or probabilities and the semantic chains can comprise generalizations related to categorization and causation. Automatic learning occurs as the system assess the probabilities associated with the semantic chains and focuses its attention accordingly with the intent of increasing its confidence of its generalizations. The auto-learned generalizations are applied in generating communications that may be directed internally to the system as well as externally.

Abstract: An explanatory integrity determination method and system determines the explanatory integrity of content by analyzing factors that include intentional deception, conscious and unconscious biases, and explanatory gaps. The analyzing is performed by an ensemble of machine learning-based models, including linguistic analysis, semantic chaining, and deep learning. The determined explanatory integrity of an item of content is delivered to a consumer of the content through user interfaces such as a graphical presentations and/or natural language interfaces and/or is applied as an element of decision making by a computer-implemented recommender system.

Abstract: A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

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.