Patents by Inventor Colin Connors
Colin Connors 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: 11586955Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.Type: GrantFiled: July 17, 2018Date of Patent: February 21, 2023Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Guanglei Xiong, Mohammad Ghorbani, Emmanuel Munguia Tapia, Sukryool Kang, Benjamin Nathan Grosof, Ashish Jain, Colin Connors
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Patent number: 11507914Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.Type: GrantFiled: March 27, 2019Date of Patent: November 22, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Emmanuel Munguia Tapia, Jingyun Fan, Cynthia Michelle Barrera, Scott Gillette, Colin Connors, Kayhan Moharreri
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Patent number: 11494441Abstract: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.Type: GrantFiled: August 4, 2020Date of Patent: November 8, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Ditty Mathew, Colin Connors, Tapia Emmanuel Munguia, Chinnappa Guggilla, Anwitha Paruchuri
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Patent number: 11308545Abstract: Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.Type: GrantFiled: October 12, 2018Date of Patent: April 19, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Emmanuel Munguia Tapia, Jingyun Fan, Danielle Moffat, Colin Connors, Kayhan Moharreri
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Patent number: 11270253Abstract: Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.Type: GrantFiled: January 7, 2019Date of Patent: March 8, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Guruprasad Dasappa, Krishna Kummamuru, Colin Connors, Guanglei Xiong, Christopher Banschbach, Thomas Michael Fahey
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Publication number: 20220043864Abstract: A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.Type: ApplicationFiled: August 4, 2020Publication date: February 10, 2022Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Ditty MATHEW, Colin CONNORS, Tapia Emmanuel MUNGUIA, Chinnappa GUGGILLA, Anwitha PARUCHURI
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Patent number: 11227183Abstract: A data extraction and expansion system receives documents with data to be processed, extracts a set of a specific type of entities from the received documents, expands the set of entities by retrieving additional entities of the specific type from an ontology and other external data sources to improve the match between the received documents. The ontology includes data regarding entities and relationships between entities. The ontology is built by extracting the entity and relationship information from external data sources and can be constantly updated. If the additional entities to expand the set of entities cannot be retrieved from the ontology then a real-time search of the external data sources is executed to retrieve the additional entities from the external data sources.Type: GrantFiled: August 31, 2020Date of Patent: January 18, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Colin Connors, Ditty Mathew, Emmanuel Munguia Tapia, Anwitha Paruchuri, Anshuma Chandak, Tsunghan Wu
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Patent number: 10963700Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.Type: GrantFiled: July 16, 2019Date of Patent: March 30, 2021Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Neeru Narang, Guanglei Xiong, Colin Connors, Sukryool Kang, Chung-Sheng Li
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Patent number: 10846294Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.Type: GrantFiled: July 17, 2018Date of Patent: November 24, 2020Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Benjamin Nathan Grosof, Madhura Shivaram, Guanglei Xiong, Colin Connors, Kyle Patrick Johnson, Emmanuel Munguia Tapia, Mingzhu Lu, Golnaz Ghasemiesfeh, Tsunghan Wu, Neeru Narang, Sukryool Kang, Kayhan Moharreri
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Publication number: 20200311668Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.Type: ApplicationFiled: March 27, 2019Publication date: October 1, 2020Inventors: Chung-Sheng LI, Emmanuel Munguia Tapia, Jingyun Fan, Cynthia Michelle Barrera, Scott Gillette, Colin Connors, Kayhan Moharreri
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Publication number: 20200219040Abstract: Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.Type: ApplicationFiled: January 7, 2019Publication date: July 9, 2020Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Guruprasad Dasappa, Krishna Kummamuru, Colin Connors, Guanglei Xiong, Christopher Banschbach, Thomas Michael Fahey
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Publication number: 20200118195Abstract: Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.Type: ApplicationFiled: October 12, 2018Publication date: April 16, 2020Applicant: Accenture Global Solutions LimitedInventors: Chung-Sheng Li, Emmanuel Munguia Tapia, Jingyun Fan, Danielle Moffat, Colin Connors, Kayhan Moharreri
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Publication number: 20200089962Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.Type: ApplicationFiled: July 16, 2019Publication date: March 19, 2020Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Neeru NARANG, Guanglei XIONG, Colin CONNORS, Sukryool KANG, Chung-Sheng LI
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Publication number: 20200026770Abstract: A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.Type: ApplicationFiled: July 17, 2018Publication date: January 23, 2020Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Benjamin Nathan Grosof, Madhura Shivaram, Guanglei Xiong, Colin Connors, Kyle Patrick Johnson, Emmanuel Munguia Tapia, Mingzhu Lu, Golnaz Ghasemiesfeh, Tsunghan Wu, Neeru Narang, Sukryool Kang, Kayhan Moharreri
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Patent number: 10482540Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.Type: GrantFiled: February 2, 2018Date of Patent: November 19, 2019Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng Li, Guanglei Xiong, Sukryool Kang, Ashish Jain, Colin Connors, Benjamin Nathan Grosof, Neeru Narang
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Publication number: 20190244121Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.Type: ApplicationFiled: July 17, 2018Publication date: August 8, 2019Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Guanglei Xiong, Mohammad Ghorbani, Emmanuel Munguia Tapia, Sukryool Kang, Benjamin Nathan Grosof, Ashish Jain, Colin Connors
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Publication number: 20190244300Abstract: A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.Type: ApplicationFiled: February 2, 2018Publication date: August 8, 2019Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Chung-Sheng LI, Guanglei XIONG, Sukryool KANG, Ashish JAIN, Colin CONNORS, Benjamin Nathan GROSOF, Neeru NARANG
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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: 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