Patents by Inventor Dennis G. Urech
Dennis G. Urech has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11991194Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.Type: GrantFiled: July 6, 2021Date of Patent: May 21, 2024Assignee: Intellective Ai, Inc.Inventors: Ming-Jung Seow, Wesley Kenneth Cobb, Gang Xu, Tao Yang, Aaron Poffenberger, Lon W. Risinger, Kishor Adinath Saitwal, Michael S. Yantosca, David M. Solum, Alex David Hemsath, Dennis G. Urech, Duy Trong Nguyen, Charles Richard Morgan
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Publication number: 20230419669Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.Type: ApplicationFiled: June 23, 2023Publication date: December 28, 2023Applicant: Intellective Ai, Inc.Inventors: Wesley Kenneth COBB, Ming-Jung SEOW, Gang XU, Kishor Adinath SAITWAL, Anthony AKINS, Kerry JOSEPH, Dennis G. URECH
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Patent number: 11727689Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.Type: GrantFiled: July 16, 2021Date of Patent: August 15, 2023Assignee: Intellective Ai, Inc.Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Gang Xu, Kishor Adinath Saitwal, Anthony Akins, Kerry Joseph, Dennis G. Urech
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Publication number: 20220006825Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.Type: ApplicationFiled: July 6, 2021Publication date: January 6, 2022Applicant: Intellective Ai, Inc.Inventors: Ming-Jung SEOW, Wesley Kenneth COBB, Gang XU, Tao YANG, Aaron POFFENBERGER, Lon W. RISINGER, Kishor Adinath SAITWAL, Michael S. YANTOSCA, David M. SOLUM, Alex David HEMSATH, Dennis G. URECH, Duy Trong NGUYEN, Charles Richard MORGAN
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Publication number: 20210398418Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.Type: ApplicationFiled: July 16, 2021Publication date: December 23, 2021Applicant: Intellective Ai, Inc.Inventors: Wesley Kenneth COBB, Ming-Jung SEOW, Gang XU, Kishor Adinath SAITWAL, Anthony AKINS, Kerry JOSEPH, Dennis G. URECH
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Patent number: 11017236Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.Type: GrantFiled: July 26, 2019Date of Patent: May 25, 2021Assignee: Intellective Ai, Inc.Inventors: Kishor Adinath Saitwal, Dennis G. Urech, Wesley Kenneth Cobb
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Patent number: 10706284Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: GrantFiled: August 20, 2019Date of Patent: July 7, 2020Assignee: AVIGILON PATENT HOLDING 1 CORPORATIONInventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
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Publication number: 20190377951Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: ApplicationFiled: August 20, 2019Publication date: December 12, 2019Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis G. URECH, David S. FRIEDLANDER, Gang XU, Ming-Jung SEOW, Lon W. RISINGER, David M. SOLUM, Tao YANG, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL
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Patent number: 10423835Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: GrantFiled: December 19, 2018Date of Patent: September 24, 2019Assignee: AVIGILON PATENT HOLDING 1 CORPORATIONInventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
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Patent number: 10410058Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.Type: GrantFiled: January 18, 2018Date of Patent: September 10, 2019Assignee: Omni AI, Inc.Inventors: Kishor Adinath Saitwal, Dennis G. Urech, Wesley Kenneth Cobb
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Publication number: 20190230108Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.Type: ApplicationFiled: December 11, 2018Publication date: July 25, 2019Applicant: Omni AI, Inc.Inventors: Ming-Jung SEOW, Wesley Kenneth COBB, Gang XU, Tao YANG, Aaron POFFENBERGER, Lon W. RISINGER, Kishor Adinath SAITWAL, Michael S. YANTOSCA, David M. SOLUM, Alex David HEMSATH, Dennis G. URECH, Duy Trong NGUYEN, Charles Richard MORGAN
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Publication number: 20190188998Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.Type: ApplicationFiled: August 31, 2018Publication date: June 20, 2019Applicant: Omni AI, Inc.Inventors: Wesley Kenneth COBB, Ming-Jung SEOW, Gang XU, Kishor Adinath SAITWAL, Anthony AKINS, Kerry JOSEPH, Dennis G. URECH
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Publication number: 20190122048Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: ApplicationFiled: December 19, 2018Publication date: April 25, 2019Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis G. URECH, David S. FRIEDLANDER, Gang XU, Ming-Jung SEOW, Lon W. RISINGER, David M. SOLUM, Tao YANG, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL
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Patent number: 10198636Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: GrantFiled: March 14, 2018Date of Patent: February 5, 2019Assignee: AVIGILON PATENT HOLDING 1 CORPORATIONInventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
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Patent number: 10187415Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.Type: GrantFiled: March 26, 2017Date of Patent: January 22, 2019Assignee: Omni AI, Inc.Inventors: Ming-Jung Seow, Wesley Kenneth Cobb, Gang Xu, Tao Yang, Aaron Poffenberger, Lon W. Risinger, Kishor Adinath Saitwal, Michael S. Yantosca, David M. Solum, Alex David Hemsath, Dennis G. Urech, Duy Trong Nguyen, Charles Richard Morgan
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Patent number: 10096235Abstract: Alert directives and focused alert directives allow a user to provide feedback to a behavioral recognition system to always or never publish an alert for certain events. Such an approach bypasses the normal publication methods of the behavioral recognition system yet does not obstruct the system's learning procedures.Type: GrantFiled: March 15, 2013Date of Patent: October 9, 2018Assignee: Omni AI, Inc.Inventors: Wesley Kenneth Cobb, Ming-Jung Seow, Gang Xu, Kishor Adinath Saitwal, Anthony Akins, Kerry Joseph, Dennis G. Urech
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Publication number: 20180204068Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: ApplicationFiled: March 14, 2018Publication date: July 19, 2018Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis G. URECH, David S. FRIEDLANDER, Gang XU, Ming-Jung SEOW, Lon W. RISINGER, David M. SOLUM, Tao YANG, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL
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Patent number: 9946934Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.Type: GrantFiled: April 21, 2017Date of Patent: April 17, 2018Assignee: AVIGILON PATENT HOLDING 1 CORPORATIONInventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis G. Urech, David S. Friedlander, Gang Xu, Ming-Jung Seow, Lon W. Risinger, David M. Solum, Tao Yang, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal
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Patent number: 9911043Abstract: Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.Type: GrantFiled: June 28, 2013Date of Patent: March 6, 2018Assignee: Omni AI, Inc.Inventors: Kishor Adinath Saitwal, Dennis G. Urech, Wesley Kenneth Cobb
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Publication number: 20180046613Abstract: Embodiments presented herein describe techniques for generating a linguistic model of input data obtained from a data source (e.g., a video camera). According to one embodiment of the present disclosure, a sequence of symbols is generated based on an ordered stream of normalized vectors generated from the input data. A dictionary of words is generated from combinations of the ordered sequence of symbols based on a frequency at which combinations of symbols appear in the ordered sequence of symbols. A plurality of phrases is generated based an ordered sequence of words from the dictionary observed in the ordered sequence of symbols based on a frequency by which combinations of words in ordered sequence of words appear relative to one another.Type: ApplicationFiled: March 26, 2017Publication date: February 15, 2018Applicant: Omni AI, Inc.Inventors: Ming-Jung SEOW, Wesley Kenneth COBB, Gang XU, Tao YANG, Aaron POFFENBERGER, Lon W. RISINGER, Kishor Adinath SAITWAL, Michael S. YANTOSCA, David M. SOLUM, Alex David HEMSATH, Dennis G. URECH, Duy Trong NGUYEN, Charles Richard MORGAN