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 stream of attention method, system, and apparatus determines a first focus of attention by applying a probabilistic analysis to a first set of syntactical elements. The syntactical elements may be derived from external communications or from communications generated internally by a computer-implemented system. Alternatively, the syntactical elements may be determined based on their association with images accessed from an external or internal source. Actions are performed by the computer-implemented system that are expected to reduce uncertainty with respect to the focus of attention. Such actions may include posing syntactical-based interrogatives directed to external entities or internally, searching a corpus of content, and/or moving to a new location and receiving input at the new location.

Abstract: An inferential media tagging method and system automatically performs inferences from audio content that is associated with a media instance through the application of, for example, Bayesian-based algorithms or computer-implemented neural networks. Recommended objects are generated based, at least in part, on the inferences, and are delivered to users. The recommended objects may be further generated based upon inference tuning controls and inferences of preferences from usage behaviors and may be delivered in a temporal sequence. User behaviors associated with users interacting with the recommended objects are accessed and elements of the media instance are selected for delivery to users based on the user behaviors.

Abstract: A body position-based recommender system infers user preferences from user behaviors and generates adaptive recommendations based upon the inferred user preferences and user body position information. The adaptive recommendations may be delivered kinesthetically. The inferences of user preferences may be based upon mobility inferences. The inferred preferences may be based upon the application of inference weightings that are determined in accordance with usage behavior priority rules that are applied to the user behaviors. Computer-implemented neural networks may be applied to infer user preferences from pictorial-based information. Natural language-based explanations that include the reasoning for the recommendations may be delivered to the user.

Abstract: An inferential-based physical object arrangement method and system infers user preferences from user behaviors and automatically selects computer-implemented objects that represent physical objects based upon the inferred preferences. A media instance is generated for delivery to a user that comprises spatially arranged representations of a selected computer-implemented object and representations of other physical objects that are accessed from a digital map. Computer-implemented neural networks may be applied to infer user preferences by interpreting pictorial-based information and/or to interpret from pictorial-based information the physical objects that are included in the media instances. Natural language-based explanations comprising the reasoning for the delivery of a media instance to a user may be delivered to the user.

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: A peer-to-peer activity sequence structure method and system generates computer-implemented objects comprising a reference to a first system user, a reference to an activity performed by the first system user, and a timestamp associated with the activity performed by the first system user. The computer-implemented objects are syndicated to multiple activity sequence structures on a peer-to-peer basis and then linked to temporally preceding objects within each of the multiple activity sequence structures. An evaluation function that may apply temporal criteria is applied to select a specific activity sequence structure for access and for subsequent linkage to additional computer-implemented objects.

Abstract: A self-assembling learning system and apparatus determines self-assembling actions that are expected to reduce uncertainties that are embodied as probabilities and then updates the probabilities in accordance with information that results from the performing of the self-assembling actions. The updated probabilities inform the determination of subsequent self-assembling actions. Neural networks, simulations of multiple potential self-assembling actions, and expected values of information may be applied in determining the self-assembling actions that are to be performed. Sensors may be applied to receive information that inform the updating of probabilities, and the self-assembling apparatus may be a robotic device. Self-assembling actions may comprise modifications to relationships between elements of a computer-implemented system.

Abstract: An inferential people recommender method and system infers the preferences of users from usage behaviors and generates recommendations of people based upon the matching of the inferred preferences. Natural language-based explanations that provide the reasoning that is applied in making the recommendations are also provided. The inferences of preferences may be determined by inference weightings that are in accordance with behavioral-based priority rules. The inferences may be further based on factors such as proximity, and duration of proximity, of people to physical objects, including other people.

Abstract: Adaptive recommendations that reference users of computer-implemented systems are generated based upon subscription-based relationships among the users of the computer-implemented systems and the recommendations are further based upon inferences of user preferences from user behavioral information. Behavioral-based priority rules may be applied in generating the inferences. Natural language-based explanations that explain the reasoning for the recommendations may be delivered to recommendation recipients. The explanations may further reference subscription relationships among users.

Abstract: Adaptively interacting neural network-based systems interact with each other and then automatically analyze the results of the interactions by application of computer implemented neural networks. Structural subsets of the systems are then automatically modified based upon the results of the analysis. Information that results of from the interactions may comprise text-based and/or pictorial-based information. Modifications of the structural subsets may include iteratively modifying relationships among the elements of the systems and/or modifying relationship indicators that are associated with relationships among elements of the systems.

Abstract: Methods and systems for video-based adaptive recommendations deliver recommendations to users that are based upon inferences from user behaviors, and further, from interpretations of pictorial-based information associated with video content through the application of computer-implemented neural networks. The recommendations may refer to information that is interpreted from the pictorial-based information such as representations of physical objects. Natural language-based explanations for the delivered recommendations that explain the reasoning that is applied in making the recommendations may be provided to recommendation recipients.

Abstract: A method and system for self-propelled vehicles infers mobility aspects, such as speed and direction, of physical objects represented in digital maps. The physical objects may be further interpreted from pictorial-based information through the application of computer-implemented neural networks. Information is delivered to adaptive systems embodied within self-propelled vehicles that is based upon the mobility inferences and the interpretations from the pictorial information. Explanations for the delivered information comprising the system's reasoning and/or references to mobility inferences may also be delivered.

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: An inferential people matching method, system, and device infers people's mutual interests in making contact with each other from usage behaviors associated with computer-implemented systems. The usage behaviors may comprise monitored geographic location information that is associated with portable processor-based devices. In accordance with the inference of mutual interest, recommendations of each of two people are generated for delivery to the other of the two people. Expressions of interest between the two people are determined based on usage behaviors exhibited by the two people in response to the recommendations. These post-recommendation usage behaviors may include gestures and physiological responses. A bilateral expression of interest is revealed to the two people if the bilateral expression of interest that is determined from the expressions of interest is sufficient.

Abstract: A neural network-based inferential advertising system and method delivers interactive advertisements to advertisement recipients in accordance with user-selected restrictions that inform the behavioral information upon which advertising preference inferences are made, as well as advertising preference inferences that are made based upon interpretations made from content by a computer-implemented neural network. The behavioral information upon which advertising inferences are made may include bodily movements of advertisement recipients that are performed without physical interaction with a device. Interactions by advertising recipients with delivered advertisements may include oral and gesture-based interactions.

Abstract: A physical object-based people matching method, system, and device recommends a first person to a second person based on the geographic proximity of a location associated with the first person to one or more physical objects that are associated with the second person. The geographic proximity may be determined from the use of a location-aware portable device. The recommendation may be further informed by a mobility inference. The first person is enabled to send an expression of interest to the second person in response to the recommendation, and the second person may be enabled to send an expression of interest to the first person in response to receiving the expression of interest. An explanation for the recommendation may be delivered to the first person that references a mobility inference or a computer-implemented map.

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 self-assembling learning system and apparatus determines self-assembling actions that are expected to reduce uncertainties that are embodied as probabilities and then updates the probabilities in accordance with information that results from the performing of the self-assembling actions. The updated probabilities inform the determination of subsequent self-assembling actions. Neural networks, simulations of multiple potential self-assembling actions, and expected values of information may be applied in determining the self-assembling actions that are to be performed. Sensors may be applied to receive information that inform the updating of probabilities, and the self-assembling apparatus may be a robotic device. Self-assembling actions may comprise modifications to relationships between elements of a computer-implemented system.

Abstract: A serendipity generating method, system, and device generates recommendations by identifying contrasting corresponding topic affinity values between users who more generally have a relatively high degree of similarity between corresponding topic affinity values. The topic affinity values may represent user interests with respect to topics and the topic affinity values may be inferred from usage behaviors, including geographic location information. Recommendations may be further in accordance with an assessment of the amount or quality of the usage behaviors from which the topic affinity values are derived and/or in accordance with the application of a probabilistic process. The recommendations may comprise computer-implemented objects that have relatively high affinities to topics associated with the contrasting corresponding topic affinity values.