Patents by Inventor Aniya Aggarwal
Aniya Aggarwal 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: 20230168994Abstract: Methods, systems, and computer program products for automatically testing AI models in connection with enterprise-related properties are provided herein.Type: ApplicationFiled: November 29, 2021Publication date: June 1, 2023Inventors: Sandeep Hans, Diptikalyan Saha, Aniya Aggarwal
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Patent number: 11647004Abstract: Preserving distributions of data values of a data asset in a data anonymization operation is provided. Anonymizing data values is performed by transforming sensitive data in a set of columns over rows of the data asset while preserving distribution of the data values in the set of transformed columns to a defined degree using a set of autoencoders and loss function. The autoencoders are base trained from preexisting data in a data assets catalog and actively trained during data dissemination. Parametric coefficients of the loss function are configured and the threshold is generated using policies from an enforcement decision for the data asset and data consumer. The loss function value of a selected row is compared to the threshold. Transformed data values of the selected row are transcribed to an output row when the loss function value is greater than the threshold and disseminated to the data consumer.Type: GrantFiled: March 24, 2021Date of Patent: May 9, 2023Assignee: International Business Machines CorporationInventors: Arjun Natarajan, Ashish Kundu, Roger C. Raphael, Aniya Aggarwal, Rajesh M. Desai, Joshua F. Payne, Mu Qiao
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Publication number: 20230025731Abstract: A computer-implemented method comprising, automatically: analyzing a machine learning dataset which comprises multiple datapoints, to deduce constraints on features of the datapoints; generating a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints; based on a machine learning model which was trained on the dataset, generating a second set of CSP rules that define one or more perturbation candidates among the features of one of the datapoints; formulating a CSP based on the first and second sets of CSP rules; solving the formulated CSP using a solver; and using the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the one datapoint.Type: ApplicationFiled: July 19, 2021Publication date: January 26, 2023Inventors: Michael Vinov, Oleg Blinder, Diptikalyan Saha, Sandeep Hans, Aniya Aggarwal, Omer Yehuda Boehm, Eyal Bin
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LOCALIZATION-BASED TEST GENERATION FOR INDIVIDUAL FAIRNESS TESTING OF ARTIFICIAL INTELLIGENCE MODELS
Publication number: 20220343179Abstract: Methods, systems, and computer program products for localization-based test generation for individual fairness testing of AI models are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and training data related to the at least one artificial intelligence model; identifying one or more boundary regions associated with the at least one artificial intelligence model based at least in part on results of processing at least a portion of the training data using the at least one artificial model; generating, in accordance with at least one of the one or more identified boundary regions, one or more synthetic data points for inclusion with the training data; and executing one or more fairness tests on the at least one artificial intelligence model using at least a portion of the one or more generated synthetic data points and at least a portion of the training data.Type: ApplicationFiled: April 26, 2021Publication date: October 27, 2022Inventors: Diptikalyan Saha, Aniya Aggarwal, Sandeep Hans -
Publication number: 20220311749Abstract: Preserving distributions of data values of a data asset in a data anonymization operation is provided. Anonymizing data values is performed by transforming sensitive data in a set of columns over rows of the data asset while preserving distribution of the data values in the set of transformed columns to a defined degree using a set of autoencoders and loss function. The autoencoders are base trained from preexisting data in a data assets catalog and actively trained during data dissemination. Parametric coefficients of the loss function are configured and the threshold is generated using policies from an enforcement decision for the data asset and data consumer. The loss function value of a selected row is compared to the threshold. Transformed data values of the selected row are transcribed to an output row when the loss function value is greater than the threshold and disseminated to the data consumer.Type: ApplicationFiled: March 24, 2021Publication date: September 29, 2022Inventors: Arjun Natarajan, ASHISH KUNDU, Roger C. Raphael, Aniya Aggarwal, Rajesh M. Desai, Joshua F. Payne, Mu Qiao
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Patent number: 11455554Abstract: Methods, systems, and computer program products for improving trustworthiness of artificial intelligence models in presence of anomalous data are provided herein. A method includes obtaining a machine learning model and a set of training data; determining one or more anomalous data points in said set of training data; for a given one of said anomalous data points, identifying attributes that decrease confidence with respect to at least one output of said machine learning model; determining that a root cause of said decreased confidence corresponds to one of: a class imbalance issue related to said at least one attribute, a confused class issue related to said at least one attribute, a low density issue related to said at least one attribute, and an adversarial issue related to said at least one attribute; and performing step(s) to improve said confidence based at least in part on said determined root cause.Type: GrantFiled: November 25, 2019Date of Patent: September 27, 2022Assignee: International Business Machines CorporationInventors: Pranay Kumar Lohia, Diptikalyan Saha, Aniya Aggarwal, Gagandeep Singh, Rema Ananthanarayanan, Samiulla Zakir Hussain Shaikh, Sandeep Hans
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Patent number: 11379347Abstract: Methods, systems and computer program products for automated test case generation are provided herein. A computer-implemented method includes selecting sample input data as a test case for a system under test, executing the test case on the system under test to obtain a result, and applying the result to a local explainer function to obtain at least a portion of a corresponding decision tree. The method further includes determining at least one path constraint from the decision tree, solving the path constraint to obtain a solution, and generating at least one other test case for the system under test based at least in part on the solution of the path constraint. The steps of the method are illustratively repeated in each of one or more additional iterations until at least one designated stopping criterion is met. The resulting test cases form a test suite for testing of a deep neural network (DNN) or other system.Type: GrantFiled: December 28, 2020Date of Patent: July 5, 2022Assignee: International Business Machines CorporationInventors: Diptikalyan Saha, Aniya Aggarwal, Pranay Lohia, Kuntal Dey
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Patent number: 11321304Abstract: Methods, systems, and computer program products for domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository are provided herein. A computer-implemented method includes obtaining a set of data and information indicative of a domain of said set of data; obtaining constraints from a domain-indexed constraint repository based on said set of data and said information, wherein the domain-indexed constraint repository comprises a knowledge graph having a plurality of nodes, wherein each node comprises an attribute associated with at least one of a plurality of domains and constraints corresponding to the attribute; detecting anomalies in said set of data based on whether portions of said set of data violate said retrieved constraints; generating an explanation corresponding to each of the anomalies that describe the attributes corresponding to the violated constraints; and outputting an indication of the anomalies and the corresponding explanation.Type: GrantFiled: September 27, 2019Date of Patent: May 3, 2022Assignee: International Business Machines CorporationInventors: Sandeep Hans, Samiulla Zakir Hussain Shaikh, Rema Ananthanarayanan, Diptikalyan Saha, Aniya Aggarwal, Gagandeep Singh, Pranay Kumar Lohia, Manish Anand Bhide, Sameep Mehta
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Publication number: 20210158183Abstract: Methods, systems, and computer program products for improving trustworthiness of artificial intelligence models in presence of anomalous data are provided herein. A method includes obtaining a machine learning model and a set of training data; determining one or more anomalous data points in said set of training data; for a given one of said anomalous data points, identifying attributes that decrease confidence with respect to at least one output of said machine learning model; determining that a root cause of said decreased confidence corresponds to one of: a class imbalance issue related to said at least one attribute, a confused class issue related to said at least one attribute, a low density issue related to said at least one attribute, and an adversarial issue related to said at least one attribute; and performing step(s) to improve said confidence based at least in part on said determined root cause.Type: ApplicationFiled: November 25, 2019Publication date: May 27, 2021Inventors: Pranay Kumar Lohia, Diptikalyan Saha, Aniya Aggarwal, Gagandeep Singh, Rema Ananthanarayanan, Samiulla Zakir Hussain Shaikh, Sandeep Hans
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Publication number: 20210117314Abstract: Methods, systems and computer program products for automated test case generation are provided herein. A computer-implemented method includes selecting sample input data as a test case for a system under test, executing the test case on the system under test to obtain a result, and applying the result to a local explainer function to obtain at least a portion of a corresponding decision tree. The method further includes determining at least one path constraint from the decision tree, solving the path constraint to obtain a solution, and generating at least one other test case for the system under test based at least in part on the solution of the path constraint. The steps of the method are illustratively repeated in each of one or more additional iterations until at least one designated stopping criterion is met. The resulting test cases form a test suite for testing of a deep neural network (DNN) or other system.Type: ApplicationFiled: December 28, 2020Publication date: April 22, 2021Inventors: Diptikalyan Saha, Aniya Aggarwal, Pranay Lohia, Kuntal Dey
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Publication number: 20210097052Abstract: Methods, systems, and computer program products for domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository are provided herein. A computer-implemented method includes obtaining a set of data and information indicative of a domain of said set of data; obtaining constraints from a domain-indexed constraint repository based on said set of data and said information, wherein the domain-indexed constraint repository comprises a knowledge graph having a plurality of nodes, wherein each node comprises an attribute associated with at least one of a plurality of domains and constraints corresponding to the attribute; detecting anomalies in said set of data based on whether portions of said set of data violate said retrieved constraints; generating an explanation corresponding to each of the anomalies that describe the attributes corresponding to the violated constraints; and outputting an indication of the anomalies and the corresponding explanation.Type: ApplicationFiled: September 27, 2019Publication date: April 1, 2021Inventors: Sandeep Hans, Samiulla Zakir Hussain Shaikh, Rema Ananthanarayanan, Diptikalyan Saha, Aniya Aggarwal, Gagandeep Singh, Pranay Kumar Lohia, Manish Anand Bhide, Sameep Mehta
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Patent number: 10956310Abstract: Methods, systems and computer program products for automated test case generation are provided herein. A computer-implemented method includes selecting sample input data as a test case for a system under test, executing the test case on the system under test to obtain a result, and applying the result to a local explainer function to obtain at least a portion of a corresponding decision tree. The method further includes determining at least one path constraint from the decision tree, solving the path constraint to obtain a solution, and generating at least one other test case for the system under test based at least in part on the solution of the path constraint. The steps of the method are illustratively repeated in each of one or more additional iterations until at least one designated stopping criterion is met. The resulting test cases form a test suite for testing of a deep neural network (DNN) or other system.Type: GrantFiled: August 30, 2018Date of Patent: March 23, 2021Assignee: International Business Machines CorporationInventors: Diptikalyan Saha, Aniya Aggarwal, Pranay Lohia, Kuntal Dey
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Publication number: 20210019349Abstract: One embodiment provides a method, including: identifying at least one protected attribute of a task of annotating information, wherein the task is assigned to at least one crowdsourced worker for performance of the task; generating at least one question for detecting bias of the at least one crowdsourced worker with respect to the at least one protected attribute, the at least one question comprising a previously provided annotation; providing the at least one question to the at least one crowdsourced worker at a period during performance of the task; computing a bias of the at least one crowdsourced worker with respect to the at least one protected attribute by comparing (i) an annotation provided by the crowdsourced worker to the at least one question and (ii) the previously provided annotation; and updating a profile of the at least one crowdsourced worker with the computed bias.Type: ApplicationFiled: July 19, 2019Publication date: January 21, 2021Inventors: Aniya Aggarwal, Diptikalyan Saha, Kalapriya Kannan, Kuntal Dey, Pranay Kumar Lohia, Seema Nagar
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Patent number: 10795671Abstract: Audiovisual documentation of source code in an integrated development environment. A computing device initiates a knowledge transfer session for discussion of source code and generation of audiovisual source code documentation explaining segments of source code from a code base. An audiovisual interface containing a segment of code from the code base is displayed within the integrated development environment. Audio during the knowledge transfer session is recorded with a recording device. Code tracking indicators from an optical tracking device operated by a user are received when the user is reviewing and focused on the segment of code. The computing device determines via the code tracking indicators a module of the segment of code under review. Portions of the recorded audio are associated with the determined module of the segment of code to generate audiovisual source code documentation. The knowledge transfer session is terminated.Type: GrantFiled: November 21, 2017Date of Patent: October 6, 2020Assignee: International Business Machines CorporationInventors: Aniya Aggarwal, Danish Contractor, Varun Parashar
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Publication number: 20200073788Abstract: Methods, systems and computer program products for automated test case generation are provided herein. A computer-implemented method includes selecting sample input data as a test case for a system under test, executing the test case on the system under test to obtain a result, and applying the result to a local explainer function to obtain at least a portion of a corresponding decision tree. The method further includes determining at least one path constraint from the decision tree, solving the path constraint to obtain a solution, and generating at least one other test case for the system under test based at least in part on the solution of the path constraint. The steps of the method are illustratively repeated in each of one or more additional iterations until at least one designated stopping criterion is met. The resulting test cases form a test suite for testing of a deep neural network (DNN) or other system.Type: ApplicationFiled: August 30, 2018Publication date: March 5, 2020Inventors: Diptikalyan Saha, Aniya Aggarwal, Pranay Lohia, Kuntal Dey
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Publication number: 20190155600Abstract: Audiovisual documentation of source code in an integrated development environment. A computing device initiates a knowledge transfer session for discussion of source code and generation of audiovisual source code documentation explaining segments of source code from a code base. An audiovisual interface containing a segment of code from the code base is displayed within the integrated development environment. Audio during the knowledge transfer session is recorded with a recording device. Code tracking indicators from an optical tracking device operated by a user are received when the user is reviewing and focused on the segment of code. The computing device determines via the code tracking indicators a module of the segment of code under review. Portions of the recorded audio are associated with the determined module of the segment of code to generate audiovisual source code documentation. The knowledge transfer session is terminated.Type: ApplicationFiled: November 21, 2017Publication date: May 23, 2019Inventors: Aniya Aggarwal, Danish Contractor, Varun Parashar
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Patent number: 9460078Abstract: A device may obtain text to be analyzed to identify glossary terms. The device may analyze a linguistic unit to generate multiple linguistic units related to the linguistic unit. The device may analyze the multiple linguistic units to generate potential glossary terms. The device may perform a glossary term analysis on the potential glossary terms to generate glossary terms that include a subset of the potential glossary terms. The device may identify included terms that are included in the glossary terms. The device may identify excluded terms that are excluded from the glossary terms. The device may determine a semantic relatedness score between at least one excluded term and at least one included term. The device may selectively add the excluded linguistic term to the glossary terms to form a final set of glossary terms based on the semantic relatedness score, and may output the final set of glossary terms.Type: GrantFiled: November 27, 2013Date of Patent: October 4, 2016Assignee: Accenture Global Services LimitedInventors: Anurag Dwarakanath, Roshni R. Ramnani, Shubhashis Sengupta, Aniya Aggarwal
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Publication number: 20140163966Abstract: A device may obtain text to be analyzed to identify glossary terms. The device may analyze a linguistic unit to generate multiple linguistic units related to the linguistic unit. The device may analyze the multiple linguistic units to generate potential glossary terms. The device may perform a glossary term analysis on the potential glossary terms to generate glossary terms that include a subset of the potential glossary terms. The device may identify included terms that are included in the glossary terms. The device may identify excluded terms that are excluded from the glossary terms. The device may determine a semantic relatedness score between at least one excluded term and at least one included term. The device may selectively add the excluded linguistic term to the glossary terms to form a final set of glossary terms based on the semantic relatedness score, and may output the final set of glossary terms.Type: ApplicationFiled: November 27, 2013Publication date: June 12, 2014Applicant: Accenture Global Services LimitedInventors: Anurag DWARAKANATH, Roshni R. Ramnani, Shubhashis Sengupta, Aniya Aggarwal