Patents by Inventor Sumit Chopra
Sumit Chopra 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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Systems and methods for determining video feature descriptors based on convolutional neural networks
Patent number: 9858484Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.Type: GrantFiled: December 30, 2014Date of Patent: January 2, 2018Assignee: Facebook, Inc.Inventors: Du Le Hong Tran, Balamanohar Paluri, Lubomir Bourdev, Robert D. Fergus, Sumit Chopra -
Publication number: 20170311020Abstract: A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.Type: ApplicationFiled: July 10, 2017Publication date: October 26, 2017Inventors: Suhrid Balakrishnan, DAVID LEE APPLEGATE, SUMIT CHOPRA, SIMON URBANEK
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Patent number: 9736519Abstract: A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.Type: GrantFiled: July 22, 2015Date of Patent: August 15, 2017Assignee: AT&T Intellectual Property I, L.P.Inventors: Suhrid Balakrishnan, David Lee Applegate, Sumit Chopra, Simon Urbanek
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Patent number: 9728183Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for combining frame and segment level processing, via temporal pooling, for phonetic classification. A frame processor unit receives an input and extracts the time-dependent features from the input. A plurality of pooling interface units generates a plurality of feature vectors based on pooling the time-dependent features and selecting a plurality of time-dependent features according to a plurality of selection strategies. Next, a plurality of segmental classification units generates scores for the feature vectors. Each segmental classification unit (SCU) can be dedicated to a specific pooling interface unit (PIU) to form a PIU-SCU combination. Multiple PIU-SCU combinations can be further combined to form an ensemble of combinations, and the ensemble can be diversified by varying the pooling operations used by the PIU-SCU combinations.Type: GrantFiled: November 10, 2015Date of Patent: August 8, 2017Assignee: AT&T Intellectual Property I, L.P.Inventors: Sumit Chopra, Dimitrios Dimitriadis, Patrick Haffner
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Patent number: 9720907Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for learning latent representations for natural language tasks. A system configured to practice the method analyzes, for a first natural language processing task, a first natural language corpus to generate a latent representation for words in the first corpus. Then the system analyzes, for a second natural language processing task, a second natural language corpus having a target word, and predicts a label for the target word based on the latent representation. In one variation, the target word is one or more word such as a rare word and/or a word not encountered in the first natural language corpus. The system can optionally assigning the label to the target word. The system can operate according to a connectionist model that includes a learnable linear mapping that maps each word in the first corpus to a low dimensional latent space.Type: GrantFiled: September 14, 2015Date of Patent: August 1, 2017Assignee: Nuance Communications, Inc.Inventors: Srinivas Bangalore, Sumit Chopra
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Publication number: 20170193390Abstract: In one embodiment, a method includes accessing a first set of entities, with which a user has interacted, and a second set of entities in a social-networking system. A first set of vector representations of the first set of entities are determined using a deep-learning model. A target entity is selected from the first set of entities, and the vector representation of the target entity is removed from the first set. The remaining vector representations in the first set are combined to determine a vector representation of the user. A second set of vector representations of the second set of entities are determined using the deep-learning model. Similarity scores are computed between the user and each of the target entity and the entities in the second set of entities. Vector representations of entities in the second set of entities are updated based on the similarity scores using the deep-learning model.Type: ApplicationFiled: December 30, 2015Publication date: July 6, 2017Inventors: Jason E. Weston, Keith Adams, Sumit Chopra
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Publication number: 20170178006Abstract: Methods, systems, and products adapt recommender systems with pairwise feedback. A pairwise question is posed to a user. A response is received that selects a preference for a pair of items in the pairwise question. A latent factor model is adapted to incorporate the response, and an item is recommended to the user based on the response.Type: ApplicationFiled: January 4, 2017Publication date: June 22, 2017Inventors: Suhrid Balakrishnan, Sumit Chopra
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Publication number: 20170125075Abstract: A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.Type: ApplicationFiled: January 13, 2017Publication date: May 4, 2017Applicant: MICRON TECHNOLOGY, INC.Inventors: Aidan Shori, Sumit Chopra
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Publication number: 20170103324Abstract: Embodiments are disclosed for providing a machine-generated response (e.g., answer) to an input (e.g., question) based on long-term memory information. A method according to some embodiments include receiving an input; converting the input into an input feature vector in an internal feature representation space; updating a memory data structure by incorporating the input feature vector into the memory data structure; generating an output feature vector in the internal feature representation space, based on the updated memory data structure and the input feature vector; converting the output feature vector into an output object; and providing an output based on the output object as a response to the input.Type: ApplicationFiled: October 13, 2015Publication date: April 13, 2017Inventors: Jason E. Weston, Sumit Chopra, Antoine Bordes
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Patent number: 9601168Abstract: A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.Type: GrantFiled: March 18, 2013Date of Patent: March 21, 2017Assignee: Micron Technology, Inc.Inventors: Aidan Shori, Sumit Chopra
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Publication number: 20170061294Abstract: In one embodiment, a method includes receiving text query that includes n-grams. A vector representation of each n-gram is determined using a deep-learning model. A nonlinear combination of the vector representations of the n-grams is determined, and an embedding of the text query is determined based on the nonlinear combination. The embedding of the text query corresponds to a point in an embedding space, and the embedding space includes a plurality of points corresponding to a plurality of label embeddings. Each label embedding is based on a vector representation of a respective label determined using the deep-learning model. Label embeddings are identified as being relevant to the text query by applying a search algorithm to the embedding space. Points corresponding to the identified label embeddings are within a threshold distance of the point corresponding to the embedding of the text query in the embedding space.Type: ApplicationFiled: November 23, 2015Publication date: March 2, 2017Inventors: Jason E. Weston, Keith Adams, Sumit Chopra
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Patent number: 9576247Abstract: Methods, systems, and products adapt recommender systems with pairwise feedback. A pairwise question is posed to a user. A response is received that selects a preference for a pair of items in the pairwise question. A latent factor model is adapted to incorporate the response, and an item is recommended to the user based on the response.Type: GrantFiled: October 12, 2013Date of Patent: February 21, 2017Assignee: LinkedIn CorporationInventors: Suhrid Balakrishnan, Sumit Chopra
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SYSTEMS AND METHODS FOR DETERMINING VIDEO FEATURE DESCRIPTORS BASED ON CONVOLUTIONAL NEURAL NETWORKS
Publication number: 20160189009Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.Type: ApplicationFiled: December 30, 2014Publication date: June 30, 2016Inventors: Du Le Hong Tran, Balamanohar Paluri, Lubomir Bourdev, Robert D. Fergus, Sumit Chopra -
Publication number: 20160078869Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for performing speaker verification. A system configured to practice the method receives a request to verify a speaker, generates a text challenge that is unique to the request, and, in response to the request, prompts the speaker to utter the text challenge. Then the system records a dynamic image feature of the speaker as the speaker utters the text challenge, and performs speaker verification based on the dynamic image feature and the text challenge. Recording the dynamic image feature of the speaker can include recording video of the speaker while speaking the text challenge. The dynamic feature can include a movement pattern of head, lips, mouth, eyes, and/or eyebrows of the speaker. The dynamic image feature can relate to phonetic content of the speaker speaking the challenge, speech prosody, and the speaker's facial expression responding to content of the challenge.Type: ApplicationFiled: November 30, 2015Publication date: March 17, 2016Inventors: Ann K. SYRDAL, Sumit CHOPRA, Patrick HAFFNER, Taniya MISHRA, Ilija ZELJKOVIC, Eric ZAVESKY
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Publication number: 20160063991Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for combining frame and segment level processing, via temporal pooling, for phonetic classification. A frame processor unit receives an input and extracts the time-dependent features from the input. A plurality of pooling interface units generates a plurality of feature vectors based on pooling the time-dependent features and selecting a plurality of time-dependent features according to a plurality of selection strategies. Next, a plurality of segmental classification units generates scores for the feature vectors. Each segmental classification unit (SCU) can be dedicated to a specific pooling interface unit (PIU) to form a PIU-SCU combination. Multiple PIU-SCU combinations can be further combined to form an ensemble of combinations, and the ensemble can be diversified by varying the pooling operations used by the PIU-SCU combinations.Type: ApplicationFiled: November 10, 2015Publication date: March 3, 2016Inventors: Sumit CHOPRA, Dimitrios DIMITRIADIS, Patrick HAFFNER
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Publication number: 20160004690Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for learning latent representations for natural language tasks. A system configured to practice the method analyzes, for a first natural language processing task, a first natural language corpus to generate a latent representation for words in the first corpus. Then the system analyzes, for a second natural language processing task, a second natural language corpus having a target word, and predicts a label for the target word based on the latent representation. In one variation, the target word is one or more word such as a rare word and/or a word not encountered in the first natural language corpus. The system can optionally assigning the label to the target word. The system can operate according to a connectionist model that includes a learnable linear mapping that maps each word in the first corpus to a low dimensional latent space.Type: ApplicationFiled: September 14, 2015Publication date: January 7, 2016Inventors: Srinivas BANGALORE, Sumit Chopra
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Patent number: 9218815Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for performing speaker verification. A system configured to practice the method receives a request to verify a speaker, generates a text challenge that is unique to the request, and, in response to the request, prompts the speaker to utter the text challenge. Then the system records a dynamic image feature of the speaker as the speaker utters the text challenge, and performs speaker verification based on the dynamic image feature and the text challenge. Recording the dynamic image feature of the speaker can include recording video of the speaker while speaking the text challenge. The dynamic feature can include a movement pattern of head, lips, mouth, eyes, and/or eyebrows of the speaker. The dynamic image feature can relate to phonetic content of the speaker speaking the challenge, speech prosody, and the speaker's facial expression responding to content of the challenge.Type: GrantFiled: November 24, 2014Date of Patent: December 22, 2015Assignee: AT&T Intellectual Property I, L.P.Inventors: Ann K. Syrdal, Sumit Chopra, Patrick Haffner, Taniya Mishra, Ilija Zeljkovic, Eric Zavesky
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Patent number: 9208778Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for combining frame and segment level processing, via temporal pooling, for phonetic classification. A frame processor unit receives an input and extracts the time-dependent features from the input. A plurality of pooling interface units generates a plurality of feature vectors based on pooling the time-dependent features and selecting a plurality of time-dependent features according to a plurality of selection strategies. Next, a plurality of segmental classification units generates scores for the feature vectors. Each segmental classification unit (SCU) can be dedicated to a specific pooling interface unit (PIU) to form a PIU-SCU combination. Multiple PIU-SCU combinations can be further combined to form an ensemble of combinations, and the ensemble can be diversified by varying the pooling operations used by the PIU-SCU combinations.Type: GrantFiled: November 10, 2014Date of Patent: December 8, 2015Assignee: AT&T Intellectual Property I, L.P.Inventors: Sumit Chopra, Dimitrios Dimitriadis, Patrick Haffner
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Publication number: 20150326905Abstract: A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.Type: ApplicationFiled: July 22, 2015Publication date: November 12, 2015Inventors: Suhrid Balakrishnan, DAVID LEE APPLEGATE, SUMIT CHOPRA, SIMON URBANEK
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Patent number: 9135241Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for learning latent representations for natural language tasks. A system configured to practice the method analyzes, for a first natural language processing task, a first natural language corpus to generate a latent representation for words in the first corpus. Then the system analyzes, for a second natural language processing task, a second natural language corpus having a target word, and predicts a label for the target word based on the latent representation. In one variation, the target word is one or more word such as a rare word and/or a word not encountered in the first natural language corpus. The system can optionally assigning the label to the target word. The system can operate according to a connectionist model that includes a learnable linear mapping that maps each word in the first corpus to a low dimensional latent space.Type: GrantFiled: December 8, 2010Date of Patent: September 15, 2015Assignee: AT&T Intellectual Property I, L.P.Inventors: Srinivas Bangalore, Sumit Chopra