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: An airport runway arrangement for commercial aircraft comprises a first runway section, a second runway section extending substantially in prolongation of the first runway section and an intermediate section between the first and second runway sections.

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 runway arrangement comprising: a first runway section (202-1) designated as a landing runway section; a second runway section (202-2) designated as a take-off runway section; and a sterile safety area (210); wherein the first and second runway sections are linked by the sterile safety area (210); the runway arrangement comprising a missed approach point for aircraft due to land on the first runway section, displaced from the start of the second runway section (202-2) by a distance greater than H/tan ?1; where H is a safe turning height and ?1 is an angle of ascent following a missed approach; wherein H is greater than 150 m and ?1 is greater than 2°.

Abstract: A runway arrangement comprising: a first runway section (202-1) designated as a landing runway section; a second runway section (202-2) designated as a take-off runway section; and a sterile safety area (210); wherein the first and second runway sections are linked by the sterile safety area (210); the runway arrangement comprising a missed approach point for aircraft due to land on the first runway section, displaced from the start of the second runway section (202-2) by a distance greater than H/tan ?1; where H is a safe turning height and ?1 is an angle of ascent following a missed approach; wherein H is greater than 150 m and ?1 is greater than 2°.

Abstract: Systems and methods are provided for feature detection such that users can apply advanced machine learning and artificial intelligence (AI) without the need for a deep understanding of existing algorithms and techniques. Embodiments of the present disclosure provide systems and methods than enable easy access to a suite of machine learning algorithms and techniques, an intuitive interface for training an AI to recognize image features based on geometric “correct and refine” recursion, and real-time visualizations of training effectiveness.

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 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: 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: 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.

Abstract: A neural network-based content inferencing method and system automatically performs interpretive inferences of content such as video that is associated with a media instance through the application of computer-implemented neural networks. Recommended objects are generated based, at least in part, on the interpretative inferences and are delivered to users. The recommended objects may be further generated based upon inferences of preferences from usage behaviors. 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 an automatic analysis of the user behaviors.

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: Systems and methods are provided for feature detection such that users can apply advanced machine learning and artificial intelligence (AI) without the need for a deep understanding of existing algorithms and techniques. Embodiments of the present disclosure provide systems and methods than enable easy access to a suite of machine learning algorithms and techniques, an intuitive interface for training an AI to recognize image features based on geometric “correct and refine” recursion, and real-time visualizations of training effectiveness.

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.