An explanatory integrity evaluation method and system evaluates potential facts and associated potential conclusions that are embodied by syntactical elements that are generated by one or more computer-implemented neural networks that are trained on content that includes a plurality of syntactical elements. The explanatory integrity evaluations may include fact sensitivity and causal factor analyses, assessing probabilistic reasoning, performing searches, and/or evaluating and selecting from alternative explanations. Probabilities that the potential facts and associated potential conclusions represent object reality may be determined. Explanatory quality scores may be generated with respect to combinations of potential facts and potential conclusions, which may inform communications to users.
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.
G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p. ex. de visages similaires sur les réseaux sociaux
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
G06V 30/262 - Techniques de post-traitement, p. ex. correction des résultats de la reconnaissance utilisant l’analyse contextuelle, p. ex. le contexte lexical, syntaxique ou sémantique
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.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
H04N 21/258 - Gestion de données liées aux clients ou aux utilisateurs finaux, p. ex. gestion des capacités des clients, préférences ou données démographiques des utilisateurs, traitement des multiples préférences des utilisateurs finaux pour générer des données collaboratives
4.
Semantic-based navigation of temporally sequenced content
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.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G10L 13/02 - Procédés d'élaboration de parole synthétiqueSynthétiseurs de parole
G06F 16/71 - IndexationStructures de données à cet effetStructures de stockage
G10L 15/25 - Reconnaissance de la parole utilisant des caractéristiques non acoustiques utilisant la position des lèvres, le mouvement des lèvres ou l’analyse du visage
5.
Explanatory Integrity Determination Method and System
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.
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.
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.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
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.
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.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
11.
Method and device for temporally sequenced adaptive recommendations of activities
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.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06F 16/60 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données audio
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Advisory services relating to business management and business operations [Educational services, namely, conducting seminars and conferences in the field of information technology trends]
A computer-implemented adaptive experimentation method and system is described that automatically selects and executes information gathering actions. The adaptive experimentation method and system integrates value of information considerations, experimental design, and inferences from experimental results. The experimental results may include behaviors of users of a computer-based system. The process enables an automatic, adaptive process for attaining additional information and applying the attained information in making subsequent experiment decisions.
A system and method for adaptive commerce is disclosed. Adaptive commerce enables recommendations of products or services based on usage behaviors and commercial contextual information. Commercial contextual information may include the business environment of the recommendation recipient, purchase histories, and product or service attributes. Bundles of products and/or services, or specific product or service configurations may be recommended. Corresponding prices may be determined in accordance with behavioral inferences and commercial contextual information.
