System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform

    公开(公告)号:US20240241924A1

    公开(公告)日:2024-07-18

    申请号:US18587343

    申请日:2024-02-26

    CPC classification number: G06F18/2185 G06F16/43 G06F16/953 G06N3/006 G06N3/043

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.

    System and method for extremely efficient image and pattern recognition and artificial intelligence platform

    公开(公告)号:US11074495B2

    公开(公告)日:2021-07-27

    申请号:US15919170

    申请日:2018-03-12

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. tracking); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; and Image Ad and Referral Networks.

    System and method for analyzing ambiguities in language for natural language processing
    3.
    发明授权
    System and method for analyzing ambiguities in language for natural language processing 有权
    用于分析自然语言处理语言模糊的系统和方法

    公开(公告)号:US08949170B2

    公开(公告)日:2015-02-03

    申请号:US14201974

    申请日:2014-03-10

    Applicant: Lotfi A. Zadeh

    Inventor: Lotfi A. Zadeh

    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), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, 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. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

    Abstract translation: 规范涵盖了用于人工智能,软计算和深度学习/识别的新算法,方法和系统,例如图像识别(例如,用于动作,手势,情感,表达,生物识别,指纹,面部,OCR(文本),背景 ,关系,位置,模式和对象),大量图像(“大数据”)分析,机器学习,培训计划,人群采购(使用专家或人类),特征空间,聚类,分类,相似性度量,优化 ,搜索引擎,排名,问答系统,语言的软(模糊或不清晰)边界/不精确/模糊/模糊性,自然语言处理(NLP),计算单词(CWW),解析,机器翻译,声音和 语音识别,视频搜索和分析(例如跟踪),图像注释,几何抽象,图像校正,语义网,上下文分析,数据可靠性(例如,使用Z号码(例如“约45分钟;非常肯定”)) 规则 发动机,控制系统,自主车辆,自我诊断和自我修复机器人,系统诊断,医疗诊断,生物医学,数据挖掘,事件预测,财务预测,经济学,风险评估,电子邮件管理,数据库管理,索引和加入 操作,内存管理和数据压缩。

    System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform

    公开(公告)号:US20200184278A1

    公开(公告)日:2020-06-11

    申请号:US16729944

    申请日:2019-12-30

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g., “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.

    Method and system for analyzing and recognition of an emotion or expression from multimedia, text, or sound track
    5.
    发明授权
    Method and system for analyzing and recognition of an emotion or expression from multimedia, text, or sound track 有权
    用于分析和识别多媒体,文本或声轨的情感或表达的方法和系统

    公开(公告)号:US09262688B1

    公开(公告)日:2016-02-16

    申请号:US14847916

    申请日:2015-09-08

    Applicant: Lotfi A. Zadeh

    Inventor: Lotfi A. Zadeh

    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), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, 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. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

    Abstract translation: 规范涵盖了用于人工智能,软计算和深度学习/识别的新算法,方法和系统,例如图像识别(例如,用于动作,手势,情感,表达,生物识别,指纹,面部,OCR(文本),背景 ,关系,位置,模式和对象),大量图像(“大数据”)分析,机器学习,培训计划,人群采购(使用专家或人类),特征空间,聚类,分类,相似性度量,优化 ,搜索引擎,排名,问答系统,语言的软(模糊或不清晰)边界/不精确/模糊/模糊性,自然语言处理(NLP),计算单词(CWW),解析,机器翻译,声音和 语音识别,视频搜索和分析(例如跟踪),图像注释,几何抽象,图像校正,语义网,上下文分析,数据可靠性(例如,使用Z号码(例如“约45分钟;非常肯定”)) 规则 发动机,控制系统,自主车辆,自我诊断和自我修复机器人,系统诊断,医疗诊断,生物医学,数据挖掘,事件预测,财务预测,经济学,风险评估,电子邮件管理,数据库管理,索引和加入 操作,内存管理和数据压缩。

    Analyzing or resolving ambiguities in an image for object or pattern recognition
    6.
    发明授权
    Analyzing or resolving ambiguities in an image for object or pattern recognition 有权
    分析或解决图像中的模糊识别对象或模式识别

    公开(公告)号:US09171261B1

    公开(公告)日:2015-10-27

    申请号:US14746815

    申请日:2015-06-22

    Applicant: Lotfi A. Zadeh

    Inventor: Lotfi A. Zadeh

    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), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, 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. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

    Abstract translation: 规范涵盖了用于人工智能,软计算和深度学习/识别的新算法,方法和系统,例如图像识别(例如,用于动作,手势,情感,表达,生物识别,指纹,面部,OCR(文本),背景 ,关系,位置,模式和对象),大量图像(“大数据”)分析,机器学习,培训计划,人群采购(使用专家或人类),特征空间,聚类,分类,相似性度量,优化 ,搜索引擎,排名,问答系统,语言的软(模糊或不清晰)边界/不精确/模糊/模糊性,自然语言处理(NLP),计算单词(CWW),解析,机器翻译,声音和 语音识别,视频搜索和分析(例如跟踪),图像注释,几何抽象,图像校正,语义网,上下文分析,数据可靠性(例如,使用Z号码(例如“约45分钟;非常肯定”)) 规则 发动机,控制系统,自主车辆,自我诊断和自我修复机器人,系统诊断,医疗诊断,生物医学,数据挖掘,事件预测,财务预测,经济学,风险评估,电子邮件管理,数据库管理,索引和加入 操作,内存管理和数据压缩。

    System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform

    公开(公告)号:US20220121884A1

    公开(公告)日:2022-04-21

    申请号:US17543485

    申请日:2021-12-06

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.

    System and method for extremely efficient image and pattern recognition and artificial intelligence platform

    公开(公告)号:US11195057B2

    公开(公告)日:2021-12-07

    申请号:US16729944

    申请日:2019-12-30

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g., “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.

    System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform

    公开(公告)号:US20180204111A1

    公开(公告)日:2018-07-19

    申请号:US15919170

    申请日:2018-03-12

    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., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. tracking); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor: rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; and Image Ad and Referral Networks.

    Method and system for predicting an outcome of an event
    10.
    发明授权
    Method and system for predicting an outcome of an event 有权
    用于预测事件结果的方法和系统

    公开(公告)号:US09424533B1

    公开(公告)日:2016-08-23

    申请号:US14986673

    申请日:2016-01-02

    Applicant: Lotfi A. Zadeh

    Inventor: Lotfi A. Zadeh

    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), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, 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. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

    Abstract translation: 规范涵盖了用于人工智能,软计算和深度学习/识别的新算法,方法和系统,例如图像识别(例如,用于动作,手势,情感,表达,生物识别,指纹,面部,OCR(文本),背景 ,关系,位置,模式和对象),大量图像(“大数据”)分析,机器学习,培训计划,人群采购(使用专家或人类),特征空间,聚类,分类,相似性度量,优化 ,搜索引擎,排名,问答系统,语言的软(模糊或不清晰)边界/不精确/模糊/模糊性,自然语言处理(NLP),计算单词(CWW),解析,机器翻译,声音和 语音识别,视频搜索和分析(例如跟踪),图像注释,几何抽象,图像校正,语义网,上下文分析,数据可靠性(例如,使用Z号码(例如“约45分钟;非常肯定”)) 规则 发动机,控制系统,自主车辆,自我诊断和自我修复机器人,系统诊断,医疗诊断,生物医学,数据挖掘,事件预测,财务预测,经济学,风险评估,电子邮件管理,数据库管理,索引和加入 操作,内存管理和数据压缩。

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