Patents by Inventor Raghuveer Chanda
Raghuveer Chanda 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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Publication number: 20240144022Abstract: A method includes receiving first data elements of a first data type and second data elements of a second data type. The first data type is text data and the second data type is at least one of image data or video data. The method also includes identifying first features of each of the first data elements, identifying second features of each of the second data elements, and generating merged features by combining a first feature of the first features of each of the first data elements with a second feature of the second features of one of the second data elements. The first feature and the second feature each represent a common feature. The method also includes generating a model based on the common features and at least a portion of the first features and the second features.Type: ApplicationFiled: December 22, 2023Publication date: May 2, 2024Inventors: Girija Narlikar, Yemao Zeng, Raghuveer Chanda, Abhishek Sethi
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Patent number: 11841786Abstract: Embodiments of the invention are directed to techniques for detecting anomalous values in data streams using forecasting models. In some embodiments, a computer can receive a value of a data stream comprising a plurality of data values, where the received value corresponds to a time interval and previously received values each correspond to a previous time interval. Models can be selected based on the time interval, where each of the models has a different periodicity. For each of the selected models, the computer may generate a score by generating a prediction value based on the model and generating the score based on the prediction value and the received value. A final score can then be generated based on the scores. Next, a score threshold can be generated. If the final score exceeds the score threshold, the computer may generate a notification that indicates that the data value is an anomaly.Type: GrantFiled: December 20, 2021Date of Patent: December 12, 2023Assignee: Visa International Service AssociationInventors: Raghuveer Chanda, Himanshu Ojha, Abdul Hadi Shakir, Subash Prabanantham, Vipul Valamjee
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Publication number: 20230334328Abstract: Systems, methods, and computer readable storage media that may be used to train a model based on merged common features of two or more different data types. One method includes receiving a plurality of first data elements of a first data type and a plurality of second data elements of a second data type, identifying first features of each of the plurality of first data elements, identifying second features of each of the plurality of second data elements, generating merged features by combining a first feature of the first features of each of the plurality of first data elements with a second feature of the second features of one of the plurality of second data elements, wherein the first feature and the second feature each represent a common feature, and training a model based on the merged features and at least a portion of the first features and the second features.Type: ApplicationFiled: July 14, 2020Publication date: October 19, 2023Applicant: GOOGLE LLCInventors: Girija Narlikar, Yemao Zeng, Raghuveer Chanda, Abhishek Sethi
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Publication number: 20230260303Abstract: Methods, systems, and storage media for classifying content across media formats based on weak supervision and cross-modal training are disclosed. The system can maintain a first feature classifier and a second feature classifier that classifies features of content having a first and second media format, respectively. The system can extract a feature space from a content item using the first feature classifier and the second feature classifier. The system can apply a set of content rules to the feature space to determine content metrics. The system can correlate a set of known labelled data to the feature space to construct determinative training data. The system can train a discrimination model using the content item and the determinative training data. The system can classify content using the discrimination model to assign a content policy to the second content item.Type: ApplicationFiled: February 7, 2023Publication date: August 17, 2023Inventors: Girija Narlikar, Abishek Sethi, Sahaana Suri, Raghuveer Chanda
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Patent number: 11574145Abstract: Methods, systems, and storage media for classifying content across media formats based on weak supervision and cross-modal training are disclosed. The system can maintain a first feature classifier and a second feature classifier that classifies features of content having a first and second media format, respectively. The system can extract a feature space from a content item using the first feature classifier and the second feature classifier. The system can apply a set of content rules to the feature space to determine content metrics. The system can correlate a set of known labelled data to the feature space to construct determinative training data. The system can train a discrimination model using the content item and the determinative training data. The system can classify content using the discrimination model to assign a content policy to the second content item.Type: GrantFiled: June 30, 2020Date of Patent: February 7, 2023Assignee: GOOGLE LLCInventors: Girija Narlikar, Abishek Sethi, Sahaana Suri, Raghuveer Chanda
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Publication number: 20220114074Abstract: Embodiments of the invention are directed to techniques for detecting anomalous values in data streams using forecasting models. In some embodiments, a computer can receive a value of a data stream comprising a plurality of data values, where the received value corresponds to a time interval and previously received values each correspond to a previous time interval. Models can be selected based on the time interval, where each of the models has a different periodicity. For each of the selected models, the computer may generate a score by generating a prediction value based on the model and generating the score based on the prediction value and the received value. A final score can then be generated based on the scores. Next, a score threshold can be generated. If the final score exceeds the score threshold, the computer may generate a notification that indicates that the data value is an anomaly.Type: ApplicationFiled: December 20, 2021Publication date: April 14, 2022Inventors: Raghuveer Chanda, Himanshu Ojha, Abdul Hadi Shakir, Subash Prabanantham, Vipul Valamjee
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Patent number: 11237939Abstract: Embodiments of the invention are directed to techniques for detecting anomalous values in data streams using forecasting models. In some embodiments, a computer can receive a value of a data stream comprising a plurality of data values, where the received value corresponds to a time interval and previously received values each correspond to a previous time interval. Models can be selected based on the time interval, where each of the models has a different periodicity. For each of the selected models, the computer may generate a score by generating a prediction value based on the model and generating the score based on the prediction value and the received value. A final score can then be generated based on the scores. Next, a score threshold can be generated. If the final score exceeds the score threshold, the computer may generate a notification that indicates that the data value is an anomaly.Type: GrantFiled: March 1, 2017Date of Patent: February 1, 2022Assignee: Visa International Service AssociationInventors: Raghuveer Chanda, Himanshu Ojha, Abdul Hadi Shakir, Subash Prabanantham, Vipul Valamjee
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Publication number: 20210406601Abstract: Methods, systems, and storage media for classifying content across media formats based on weak supervision and cross-modal training are disclosed. The system can maintain a first feature classifier and a second feature classifier that classifies features of content having a first and second media format, respectively. The system can extract a feature space from a content item using the first feature classifier and the second feature classifier. The system can apply a set of content rules to the feature space to determine content metrics. The system can correlate a set of known labelled data to the feature space to construct determinative training data. The system can train a discrimination model using the content item and the determinative training data. The system can classify content using the discrimination model to assign a content policy to the second content item.Type: ApplicationFiled: June 30, 2020Publication date: December 30, 2021Applicant: GOOGLE LLCInventors: Girija NARLIKAR, Abishek SETHI, Sahaana SURI, Raghuveer CHANDA
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Publication number: 20200065212Abstract: Embodiments of the invention are directed to techniques for detecting anomalous values in data streams using forecasting models. In some embodiments, a computer can receive a value of a data stream comprising a plurality of data values, where the received value corresponds to a time interval and previously received values each correspond to a previous time interval. Models can be selected based on the time interval, where each of the models has a different periodicity. For each of the selected models, the computer may generate a score by generating a prediction value based on the model and generating the score based on the prediction value and the received value. A final score can then be generated based on the scores. Next, a score threshold can be generated. If the final score exceeds the score threshold, the computer may generate a notification that indicates that the data value is an anomaly.Type: ApplicationFiled: March 1, 2017Publication date: February 27, 2020Inventors: Raghuveer Chanda, Himanshu Ojha, Abdul Hadi Shakir, Subash Prabanantham, Vipul Valamjee