Patents by Inventor Ananya Honnedevasthana ASHOK
Ananya Honnedevasthana ASHOK 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: 11875264Abstract: An event detection method can include encoding a plurality of training video snippets into low dimensional descriptors of the training video snippets in a code space. The low dimensional descriptors of the training video snippets can be decoded into corresponding reconstructed video snippets. One or more parameters of the encoding and decoding can be adjusted based on one or more a loss functions to reduce a reconstruction error between the one or more training video snippets and the corresponding one or more reconstructed video snippets, to reduce a class entropy of the plurality of event classes of the code space, to increase fit of the training video snippet, and/or to increase compactness of the code space. The method can further include encoding one or more labeled video snippets of a plurality of event classes into low dimensional descriptors of the labeled video snippets in the code space.Type: GrantFiled: March 18, 2020Date of Patent: January 16, 2024Assignee: R4N63R Capital LLCInventors: Krishnendu Chaudhury, Ananya Honnedevasthana Ashok, Sujay Narumanchi, Devashish Shankar, Ashish Mehra
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Patent number: 11615359Abstract: Techniques for detecting cycle data can include determining object properties and motion properties in a set of consecutive frames of a sensor stream. The cycle data can be determined from the object properties and motion properties without detecting constituent objects. The object properties and motion properties enable improved detection of cycle data in the presence of different object poses, different positions of the object, partial occlusion of the object, varying illumination, variation in the background, and or the like.Type: GrantFiled: April 1, 2022Date of Patent: March 28, 2023Assignee: Drishti Technologies, Inc.Inventors: Krishnendu Chaudhury, Ananya Honnedevasthana Ashok, Sujay Narumanchi, Devashish Shankar, Ritesh Jain
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Publication number: 20220222939Abstract: Techniques for detecting cycle data can include determining object properties and motion properties in a set of consecutive frames of a sensor stream. The cycle data can be determined from the object properties and motion properties without detecting constituent objects. The object properties and motion properties enable improved detection of cycle data in the presence of different object poses, different positions of the object, partial occlusion of the object, varying illumination, variation in the background, and or the like.Type: ApplicationFiled: April 1, 2022Publication date: July 14, 2022Inventors: Krishnendu CHAUDHURY, Ananya Honnedevasthana ASHOK, Sujay NARUMANCHI, Devashish SHANKAR, Ritesh JAIN
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Patent number: 11321944Abstract: Techniques for detecting cycle data can include determining object properties and motion properties in a set of consecutive frames of a sensor stream. The cycle data can be determined from the object properties and motion properties without detecting constituent objects. The object properties and motion properties enable improved detection of cycle data in the presence of different object poses, different positions of the object, partial occlusion of the object, varying illumination, variation in the background, and or the like.Type: GrantFiled: October 17, 2019Date of Patent: May 3, 2022Assignee: Drishti Technologies, Inc.Inventors: Krishnendu Chaudhury, Ananya Honnedevasthana Ashok, Sujay Narumanchi, Devashish Shankar, Ritesh Jain
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Publication number: 20210216777Abstract: An event detection method can include encoding a plurality of training video snippets into low dimensional descriptors of the training video snippets in a code space. The low dimensional descriptors of the training video snippets can be decoded into corresponding reconstructed video snippets. One or more parameters of the encoding and decoding can be adjusted based on one or more a loss functions to reduce a reconstruction error between the one or more training video snippets and the corresponding one or more reconstructed video snippets, to reduce a class entropy of the plurality of event classes of the code space, to increase fit of the training video snippet, and/or to increase compactness of the code space. The method can further include encoding one or more labeled video snippets of a plurality of event classes into low dimensional descriptors of the labeled video snippets in the code space.Type: ApplicationFiled: March 18, 2020Publication date: July 15, 2021Inventors: Krishnendu CHAUDHURY, Ananya Honnedevasthana ASHOK, Sujay NARUMANCHI, Devashish SHANKAR, Ashish MEHRA
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Publication number: 20210117684Abstract: Techniques for detecting cycle data can include determining object properties and motion properties in a set of consecutive frames of a sensor stream. The cycle data can be determined from the object properties and motion properties without detecting constituent objects. The object properties and motion properties enable improved detection of cycle data in the presence of different object poses, different positions of the object, partial occlusion of the object, varying illumination, variation in the background, and or the like.Type: ApplicationFiled: October 17, 2019Publication date: April 22, 2021Inventors: Krishnendu CHAUDHURY, Ananya Honnedevasthana ASHOK, Sujay NARUMANCHI, Devashish SHANKAR, Ritesh JAIN
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Patent number: 10890898Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing, restaurants and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for establishing traceability.Type: GrantFiled: November 5, 2018Date of Patent: January 12, 2021Assignee: Drishti Technologies, Inc.Inventors: Prasad Narasimha Akella, Ananya Honnedevasthana Ashok, Krishnendu Chaudhury, Sujay Venkata Krishna Narumanchi, Devashish Shankar, Ananth Uggirala
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Publication number: 20190138676Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for analyzing ergonomic data from the one or more sensor streams.Type: ApplicationFiled: November 5, 2018Publication date: May 9, 2019Inventors: Prasad Narasimha AKELLA, Ananya Honnedevasthana ASHOK, Zakaria Ibrahim ASSOUL, Krishnendu CHAUDHURY, Sameer GUPTA, Ananth UGGIRALA
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Publication number: 20190138932Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for process validation, anomaly detection and in-process quality assurance.Type: ApplicationFiled: November 5, 2018Publication date: May 9, 2019Inventors: Prasad Narasimha AKELLA, Ananya Honnedevasthana ASHOK, Krishnendu CHAUDHURY, Ashish GUPTA, Sujay Venkata Krishna NARUMANCHI, David Scott PRAGER, Devashish SHANKAR, Ananth UGGIRALA
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Publication number: 20190138623Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of birth certificates for each instance of a subject product or service. The birth certificate can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.Type: ApplicationFiled: November 5, 2018Publication date: May 9, 2019Inventors: Prasad Narasimha AKELLA, Ananya Honnedevasthana ASHOK, Zakaria Ibrahim ASSOUL, Krishnendu CHAUDHURY, Sameer GUPTA, Sujay Venkata Krishna NARUMANCHI, David Scott PRAGER, Devashish SHANKAR, Ananth UGGIRALA, Yash Raj CHHABRA
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Publication number: 20190138905Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing, restaurants and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for establishing traceability.Type: ApplicationFiled: November 5, 2018Publication date: May 9, 2019Inventors: Prasad Narasimha AKELLA, Ananya Honnedevasthana ASHOK, Krishnendu CHAUDHURY, Sujay Venkata Krishna NARUMANCHI, Devashish Shankar, Ananth UGGIRALA