Patents by Inventor Saurabh Mahadik
Saurabh Mahadik 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: 11341377Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.Type: GrantFiled: December 30, 2019Date of Patent: May 24, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Amioy Kumar, Nagendra K. Kumar, Madhura Shivaram, Suraj Govind Jadhav, Chung-Sheng Li, Saurabh Mahadik
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Patent number: 11282035Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.Type: GrantFiled: June 21, 2017Date of Patent: March 22, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Suraj Govind Jadhav, Saurabh Mahadik, Prakash Ghatage, Guanglei Xiong, Emmanuel Munguia Tapia, Mohammad Jawad Ghorbani, Kyle Johnson, Colin Patrick Connors, Benjamin Nathan Grosof
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Patent number: 10726308Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.Type: GrantFiled: September 26, 2017Date of Patent: July 28, 2020Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Amioy Kumar, Nagendra K. Kumar, Madhura Shivaram, Suraj Govind Jadhav, Chung-Sheng Li, Saurabh Mahadik
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Publication number: 20200151499Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.Type: ApplicationFiled: December 30, 2019Publication date: May 14, 2020Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Amioy KUMAR, Nagendra K. KUMAR, Madhura SHIVARAM, Suraj Govind JADHAV, Chung-Sheng LI, Saurabh MAHADIK
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Patent number: 10298757Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.Type: GrantFiled: February 21, 2018Date of Patent: May 21, 2019Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Guanglei Xiong, Emmanuel Munguia Tapia, Kyle P. Johnson, Christopher Cole, Sachin Aul, Suraj Govind Jadhav, Saurabh Mahadik, Mohammad Ghorbani, Colin Connors, Chinnappa Guggilla, Naveen Bansal, Praveen Maniyan, Sudhanshu A Dwivedi, Ankit Pandey, Madhura Shivaram, Sumeet Sawarkar, Karthik Meenakshisundaram, Nagendra Kumar M R, Hariram Krishnamurth, Karthik Lakshminarayanan
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Publication number: 20190095999Abstract: A claims preprocessor processes claim data to identify claims that are to be adjudicated. Each claim includes at least one claim exception. The claims preprocessor further prioritizes the claim exception of each identified claim based on the claim data. A robotic process automator then orchestrates adjudication of the identified claims based on claim data. Further, a rules engine adjudicates the identified claims based on pre-defined rules. Subsequently, a fall out handler determines if any of the identified claims are incorrectly adjudicated and identify an issue associated with incorrect claims adjudication on determining that any of the identified claims are incorrectly adjudicated. A self learner then provides feedback to rules engine based on a decision tree and information received from fall out handler, the feedback being usable to resolve the issue. The information received from fall out handler is indicative of issue associated with incorrect claims adjudication.Type: ApplicationFiled: January 22, 2018Publication date: March 28, 2019Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Guanglei Xiong, Madhura Shivaram, Soujanya Soni, Ashish Jain, Deepak Kumar Arjun, Sukryool Kang, Rama Veeravalli Santhanam, Clark C. Valera, Melchor F. Dela Cruz, Muthu Venkatesh Prabakaran, Krishna Kummamuru, Joble George, Saurabh Mahadik, Shikhar Vashishtha, Mingzhu Lu, Sanjay Chamoli, Suraj G. Jadhav, Lauren E. Friedman
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Publication number: 20190012568Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.Type: ApplicationFiled: September 26, 2017Publication date: January 10, 2019Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Amioy KUMAR, Nagendra K. KUMAR, Madhura SHIVARAM, Suraj Govind JADHAV, Chung-Sheng LI, Saurabh MAHADIK
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Publication number: 20190005590Abstract: A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.Type: ApplicationFiled: June 30, 2017Publication date: January 3, 2019Inventors: Chung-Sheng LI, Suraj Govind JADHAV, Saurabh MAHADIK, Prakash GHATAGE, Guanglei XIONG, Emmanuel MUNGUIA TAPIA, Mohammad Jawad GHORBANI, Kyle JOHNSON, Colin Patrick CONNORS, Benjamin Nathan GROSOF
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Publication number: 20180374051Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.Type: ApplicationFiled: June 21, 2017Publication date: December 27, 2018Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Suraj Govind JADHAV, Saurabh MAHADIK, Prakash GHATAGE, Guanglei XIONG, Emmanuel Munguia TAPIA, Mohammad Jawad GHORBANI, Kyle JOHNSON, Colin Patrick CONNORS, Benjamin Nathan GROSOF
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Publication number: 20180241881Abstract: A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.Type: ApplicationFiled: February 21, 2018Publication date: August 23, 2018Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Guanglei Xiong, Emmanuel Munguia Tapia, Kyle P. Johnson, Christopher Cole, Sachin Aul, Suraj Govind Jadhav, Saurabh Mahadik, Mohammad Ghorbani, Colin Connors, Chinnappa Guggilla, Naveen Bansal, Praveen Maniyan, Sudhanshu A. Dwivedi, Ankit Pandey, Madhura Shivaram, Sumeet Sawarkar, Karthik Meenakshisundaram, Nagendra Kumar M R, Hariram Krishnamurth, Karthik Lakshminarayanan