Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI. (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: A method, system, and computer program product for controlling consumption of a distributed network service in accordance with rights expression information associated with the distributed network service and specifying a manner of use of the distributed network service, including interpreting the rights expression information associated with the distributed network service, the rights expression information indicating a manner of use of the distributed network service; and controlling consumption of the distributed network service based on the rights expression information.

Abstract: Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face. OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g.

Abstract: Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), Big Data analytics, machine learning, training schemes, crowd-sourcing (experts), feature space, clustering, classification, SVM, similarity measures, modified Boltzmann Machines, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g.

Abstract: A system and method for creating a rights expression for association with an item for use in a system for controlling use of the item in accordance with the rights expression, including specifying rights expression information indicating a manner of use of an item, the rights expression information including at least one element, the element having a variable and corresponding value for the variable; and performing an encoding process, including determining an identifier associated with a template corresponding to the rights expression information, extracting from the rights expression information the value for the variable corresponding to the element, and encoding a license adapted to be enforced on a device based on the variable and the identifier, the license including an identification of the template and the value for the variable.

Abstract: In one example, we describe reliable, flexible, low-maintenance, low-overhead, low-cost installation, practical, and easy-to-install structures and components or techniques (methods and systems) for water capture from high humidity sources, e.g., sea or river, for use or consumption by humans, animals, or plants/agriculture/food production. In one example, it is modularized. Thus, it is easier for transportation and maintenance, with less cost and down-time. For example, it can be used in some regions in the Middle East or Africa, with dry land with no or small amount of rain. In one example, we describe the use of renewable energy sources. In one example, we describe the control system for operation of water collection and distribution systems, e.g., for optimization and efficiency or cost. We also describe the mechanisms, techniques, components, and systems to implement various tasks and goals related to these.

Abstract: In one example, we describe reliable, flexible, low-maintenance, low-overhead, low-cost installation, practical, and easy-to-install structures and components or techniques (methods and systems) for water capture from high humidity sources, e.g., sea or river, for use or consumption by humans, animals, or plants/agriculture/food production. In one example, it is modularized. Thus, it is easier for transportation and maintenance, with less cost and down-time. For example, it can be used in some regions in the Middle East or Africa, with dry land with no or small amount of rain. In one example, we describe the use of renewable energy sources. In one example, we describe the control system for operation of water collection and distribution systems, e.g., for optimization and efficiency or cost. We also describe the mechanisms, techniques, components, and systems to implement various tasks and goals related to these.

Abstract: Usage rights for a digital work are established prior to creation of the corresponding content. The rights can be associated with the content after the content is created. A content creation, such as a video recorder or a still camera, device can store labels of the rights and can associate usage rights with content in real time as the content is created.

Abstract: A method, system and computer program product for dynamically assigning usage rights to digital content for use in a system having at least one repository, including specifying a usage right, the usage right specifying an authorized use of digital content and being enforceable by a repository; determining a status of a dynamic condition; and dynamically assigning the usage right to the digital content based on the status of the dynamic condition.

Abstract: A system and method for distribution of digital works in a tree-like structure of devices. A hierarchical right may include a first usage right governing a use for the digital work and a first delegation right governing distribution of the digital work to child nodes of the tree-like structure. A second usage right and/or a second delegation right may be generated based on the hierarchical right, the second usage right governing a use for the digital work and the second delegation right governing distribution of the digital work to child nodes of a first child node of the tree-like structure. The second usage right and/or the second delegation right may be assigned to a version of the digital work, and the second usage right and/or the second delegation right and the version of the digital work may be forwarded to the first child node.

Abstract: A method, system, and computer program product for controlling consumption of a distributed network service in accordance with rights expression information associated with the distributed network service and specifying a manner of use of the distributed network service, including interpreting the rights expression information associated with the distributed network service, the rights expression information indicating a manner of use of the distributed network service; and controlling consumption of the distributed network service based on the rights expression information.

Abstract: A method and system for managing use of items having usage rights associated therewith including a point of capture system adapted to generate content of a future event when the event occurs, a content distributor adapted to generate a rights label having usage rights associated with content of the future event before the content is created, the rights label having a distribution key for encrypting the content as the content is generated, the distribution key being encrypted with a public key. The system also includes a license server adapted to generate a license associate with the content from the rights label before the content is generated, the license including the distribution key encrypted with the public key, and a content distributor adapted to distribute the license before the content is generated.

Abstract: Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like.

Abstract: A self-contained device for protecting content, the content having usage rights associated therewith, includes a usage rights portion having usage rights that control the use of the content; a rights management module that authorizes a request to access to the content based on the usage rights; a rights assignment module that generates and updates the usage rights based on the content access; and an interface module that allows accessing of the content.

Abstract: A method and system for managing use of items having usage rights associated therewith including a point of capture system adapted to generate content of a future event when the event occurs, a content distributor adapted to generate a rights label having usage rights associated with content of the future event before the content is created, the rights label having a distribution key for encrypting the content as the content is generated, the distribution key being encrypted with a public key. The system also includes a license server adapted to generate a license associate with the content from the rights label before the content is generated, the license including the distribution key encrypted with the public key, and a content distributor adapted to distribute the license before the content is generated.