Patents by Inventor Indrajit KAR
Indrajit KAR 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: 20230316702Abstract: An Artificial Intelligence (AI) based automatic damage detection and estimation system receives images of a damaged object. The images are converted into monochrome versions if needed and analyzed by an ensemble machine learning (ML) cause prediction model that includes a plurality of sub-models that are each trained to identify a cause of damage to a corresponding portion for the damaged object from a plurality of causes. In addition, an explanation for the selection of the cause from the plurality of causes is also provided. The explanation includes image portions and pixels of images that enabled the cause prediction model to select the cause of damage. An ML parts identification model is also employed to identify and labels parts of the damaged object which are repairable and parts that are damaged and need replacement. The cost estimation for the repair and restoration of the damaged object can also be generated.Type: ApplicationFiled: May 10, 2023Publication date: October 5, 2023Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Indrajit KAR, Mohammed C. SALMAN, Ankit VASHISHTA, Vishal D. PANDEY
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Patent number: 11676365Abstract: An Artificial Intelligence (AI) based automatic damage detection and estimation system receives images of a damaged object. The images are converted into monochrome versions if needed and analyzed by an ensemble machine learning (ML) cause prediction model that includes a plurality of sub-models that are each trained to identify a cause of damage to a corresponding portion for the damaged object from a plurality of causes. In addition, an explanation for the selection of the cause from the plurality of causes is also provided. The explanation includes image portions and pixels of images that enabled the cause prediction model to select the cause of damage. An ML parts identification model is also employed to identify and labels parts of the damaged object which are repairable and parts that are damaged and need replacement. The cost estimation for the repair and restoration of the damaged object can also be generated.Type: GrantFiled: December 16, 2019Date of Patent: June 13, 2023Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Indrajit Kar, Mohammed C. Salman, Ankit Vashishta, Vishal D. Pandey
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Patent number: 11531829Abstract: An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).Type: GrantFiled: July 24, 2020Date of Patent: December 20, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Sridhar Murugaraj, Indrajit Kar, Vishal Pandey, Sushresulagna Rath
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Patent number: 11436443Abstract: A model testing system administers tests to machine learning (ML) models to test the accuracy and the robustness of the ML models. A user interface (UI) associated with the model testing system receives selections of one or more of a plurality of tests to be administered to a ML model under test. Test data produced by one or more of a plurality of testing ML models that correspond to the plurality of tests is provided to the ML model under test based on the selected tests. One or more of a generative patches test, a generative perturbations test and a counterfeit data test can be administered to the ML model under test based on the selections.Type: GrantFiled: May 5, 2020Date of Patent: September 6, 2022Assignee: ACCENTURE GLOBAT, SOLUTIONS LIMITEDInventors: Indrajit Kar, Shalini Agarwal, Vishal Pandey, Mohammed C. Salman, Sushresulagna Rath
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Patent number: 11269760Abstract: Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.Type: GrantFiled: January 2, 2020Date of Patent: March 8, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chandrasekhar Sheshadri, Shalini Agarwal, Indrajit Kar, Vishal Pandey, Saloni Tewari, Dhiraj Suresh Panjwani, Ebrahim Abdullah Plumber, Rizwan Ahmed Saifudduza Siddiqui
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Publication number: 20220027666Abstract: An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).Type: ApplicationFiled: July 24, 2020Publication date: January 27, 2022Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Sridhar MURUGARAJ, Indrajit KAR, Vishal PANDEY, Sushresulagna RATH
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Publication number: 20210287050Abstract: A model testing system administers tests to machine learning (ML) models to test the accuracy and the robustness of the ML models. A user interface (UI) associated with the model testing system receives selections of one or more of a plurality of tests to be administered to a ML model under test. Test data produced by one or more of a plurality of testing ML models that correspond to the plurality of tests is provided to the ML model under test based on the selected tests. One or more of a generative patches test, a generative perturbations test and a counterfeit data test can be administered to the ML model under test based on the selections.Type: ApplicationFiled: May 5, 2020Publication date: September 16, 2021Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Indrajit Kar, Shalini Agarwal, Vishal Pandey, Mohammed C. Salman, Sushresulagna Rath
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Publication number: 20210209011Abstract: Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.Type: ApplicationFiled: January 2, 2020Publication date: July 8, 2021Inventors: Chandrasekhar Sheshadri, Shalini Agarwal, Indrajit Kar, Vishal Pandey, Saloni Tewari, Dhiraj Suresh Panjwani, Ebrahim Abdullah Plumber, Rizwan Ahmed Saifudduza Siddiqui
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Publication number: 20210182713Abstract: An Artificial Intelligence (AI) based automatic damage detection and estimation system receives images of a damaged object. The images are converted into monochrome versions if needed and analyzed by an ensemble machine learning (ML) cause prediction model that includes a plurality of sub-models that are each trained to identify a cause of damage to a corresponding portion for the damaged object from a plurality of causes. In addition, an explanation for the selection of the cause from the plurality of causes is also provided. The explanation includes image portions and pixels of images that enabled the cause prediction model to select the cause of damage. An ML parts identification model is also employed to identify and labels parts of the damaged object which are repairable and parts that are damaged and need replacement. The cost estimation for the repair and restoration of the damaged object can also be generated.Type: ApplicationFiled: December 16, 2019Publication date: June 17, 2021Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Indrajit KAR, Mohammed C. SALMAN, Ankit VASHISHTA, Vishal D. PANDEY