Abstract: A vector-based search method and system generates affinity vectors within a multidimensional affinity space by applying neural networks to stored content objects. A neural network is applied to content that is input by a user to generate an affinity vector, which is then compared to the affinity vectors corresponding to the stored content objects using a mathematical-based vector comparison algorithm. One or more of the stored content objects are selected and provided to the user based on the comparison. The selecting of the one or more stored content objects may be further performed based on an inference of a preference from user behaviors.

Abstract: A probabilistically tunable conversational method and system receives information and a conversational tuning control setting from a user. The conversational tuning control setting is applied by a probabilistic selection algorithm to select, responsive to the information, a first syntactical element. A second syntactical element is selected in accordance with the first syntactical element and the probabilistic selection algorithm. A communication is generated and delivered to the user that comprises the first and second syntactical elements.

Abstract: An exploratory recommender method and system generates affinity vectors from usage behaviors that are associated with stored content objects. A user's inferred content preferences are embodied within a multidimensional affinity space that is generated in accordance with the affinity vectors. An affinity space exploration algorithm explores areas within the multidimensional affinity space that are outside the user's inferred content preferences. The affinity space exploration algorithm is applied in accordance with the user's inferred content preferences to select a content object from the stored content objects. The content object is provided to the user.

Abstract: A user-described virtual environment method, system, and apparatus obtains a representation of an object and receives a natural language-based communication from a user requesting that a computer-implemented system embody the object within a virtual environment that is described by the user. The natural language description of the virtual environment is interpreted by applying a computer-implemented trained neural network A video stream that embodies the object within a computer-generated virtual environment that is in accordance with the user-described virtual environment is generated by applying a trained neural network and then delivered to the user. The user may then describe desired modifications to the virtual environment and a second video stream is generated in accordance with the desired modifications.

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 reinforcement learning-based semantic method and system interprets content by applying neural networks and then generates and/or updates representations of semantic chains based upon the interpretations. The representations of the semantic chains have associated probabilistic weightings and the semantic chains can comprise causal relationships. Automatic learning occurs as the system assesses the probabilities associated with the semantic chains and focuses its attention accordingly with the intent of increasing its confidence of its inferences. Communications are generated based on the resulting probabilities and a reinforcement learning-based process is then performed with respect to these communications and the probabilities are updated accordingly. A new set of communications is generated based on the updated probabilities. Causal-based explanations for the content of these communications may be provided.

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 generative recommender method and system applies trained neural networks to infer related concepts with respect to segments of temporally sequenced content that are inferred to be of particular interest to users. The inferred related concepts of interest may be embodied, for example, in the form vectorized embeddings of natural language and/or images. The embodied inferred related concepts of interest are then input into a generative process that applies trained neural networks to execute one or more vector embedding-based steps that result in generated content elements such as video that are based upon the related concepts of interest.

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: 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: 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 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 generating adaptive explanations for associated recommendations is disclosed. The adaptive explanations comprise a syntactical structure and associated phrases that are selected in accordance with usage behaviors and/or inferences associated with usage behaviors. The phrases included in an adaptive explanation may be selected through application of a non-deterministic process. The adaptive explanations may be beneficially applied to recommendations that are associated with content, products, and people, including recommendations that comprise advertisements.