Patents by Inventor Naveen Panwar
Naveen Panwar 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: 11966453Abstract: Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.Type: GrantFiled: February 15, 2021Date of Patent: April 23, 2024Assignee: International Business Machines CorporationInventors: Naveen Panwar, Anush Sankaran, Kuntal Dey, Hima Patel, Sameep Mehta
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Patent number: 11741296Abstract: Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.Type: GrantFiled: February 18, 2021Date of Patent: August 29, 2023Assignee: International Business Machines CorporationInventors: Nishtha Madaan, Naveen Panwar, Deepak Vijaykeerthy, Pranay Kumar Lohia, Diptikalyan Saha
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Patent number: 11734584Abstract: Methods, systems, and computer program products for multi-modal construction of deep learning networks are provided herein. A computer-implemented method includes extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network; generating a deep learning network model, wherein the generating includes inferring multiple details attributed to the deep learning network model based on the one or more extracted items; creating an intermediate representation based on the deep learning network model, wherein the intermediate representation includes (i) one or more items of data pertaining to the deep learning network model and (ii) one or more design details attributed to the deep learning network model; automatically converting the intermediate representation into source code; and outputting the source code to at least one user.Type: GrantFiled: April 19, 2017Date of Patent: August 22, 2023Assignee: International Business Machines CorporationInventors: Rahul A R, Neelamadhav Gantayat, Shreya Khare, Senthil K K Mani, Naveen Panwar, Anush Sankaran
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Patent number: 11694090Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.Type: GrantFiled: April 10, 2019Date of Patent: July 4, 2023Assignee: International Business Machines CorporationInventors: Rahul Aralikatte, Srikanth Govindaraj Tamilselvam, Shreya Khare, Naveen Panwar, Anush Sankaran, Senthil Kumar Kumarasamy Mani
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Patent number: 11605006Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.Type: GrantFiled: May 6, 2019Date of Patent: March 14, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Shreya Khare, Srikanth Govindaraj Tamilselvam, Anush Sankaran, Naveen Panwar, Rahul Rajendra Aralikatte, Senthil Kumar Kumarasamy Mani
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Patent number: 11593642Abstract: Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.Type: GrantFiled: September 30, 2019Date of Patent: February 28, 2023Assignee: International Business Machines CorporationInventors: Jassimran Kaur, Tarun Tater, Anush Sankaran, Naveen Panwar
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Patent number: 11574233Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.Type: GrantFiled: August 30, 2018Date of Patent: February 7, 2023Assignee: International Business Machines CorporationInventors: Anush Sankaran, Naveen Panwar, Srikanth G. Tamilselvam, Shreya Khare, Rahul Aralikatte, Senthil Kumar Kumarasamy Mani
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Publication number: 20220335217Abstract: Methods, systems, and computer program products for detecting contextual bias in text are provided herein. A computer-implemented method includes identifying, by a machine learning network, a protected attribute in one or more data samples; processing the identified data samples using a first sub-network of the machine learning network, wherein the first sub-network is configured to determine a plurality of contexts of the protected attribute across the identified data samples; determining an impact of each of the plurality of contexts on a second sub-network of the machine learning network, wherein the second sub-network of the machine learning network is configured to classify a given data sample into one of a plurality of classes; and adjusting the second sub-network of the machine learning to account for the impact of at least one of the plurality of contexts on the second sub-network.Type: ApplicationFiled: April 19, 2021Publication date: October 20, 2022Inventors: Naveen Panwar, Nishtha Madaan, Deepak Vijaykeerthy, Pranay Kumar Lohia, Diptikalyan Saha
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Publication number: 20220261535Abstract: Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.Type: ApplicationFiled: February 18, 2021Publication date: August 18, 2022Inventors: Nishtha Madaan, Naveen Panwar, Deepak Vijaykeerthy, Pranay Kumar Lohia, Diptikalyan Saha
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Publication number: 20220261597Abstract: Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.Type: ApplicationFiled: February 15, 2021Publication date: August 18, 2022Inventors: Naveen Panwar, Anush Sankaran, Kuntal Dey, Hima Patel, Sameep Mehta
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Publication number: 20220237415Abstract: Methods, systems, and computer program products for priority-based, accuracy-controlled individual fairness of unstructured text are provided herein. A method includes identifying one or more samples in a set of data used to train a machine learning model having at least one attribute; generating counterfactual samples for each of the one or more identified samples; calculating scores for the one or more identified samples based at least in part on output of the machine learning model with respect to the counterfactual samples, wherein the scores indicate a relative level of bias between the one or more identified samples corresponding to the at least one attribute; creating an enhanced set of data at least in part by supplementing at least a portion of the identified samples with the corresponding counterfactual samples based on the calculated scores; and training the machine learning model using the enhanced set of data.Type: ApplicationFiled: January 28, 2021Publication date: July 28, 2022Inventors: Pranay Kumar Lohia, Deepak Vijaykeerthy, Diptikalyan Saha, Nishtha Madaan, Naveen Panwar
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Publication number: 20220101182Abstract: One embodiment provides a method, including: obtaining a dataset for use in building a machine-learning model; assessing a quality of the dataset, wherein the quality is assessed in view of an effect of the dataset on a performance of the machine-learning model, wherein the assessing comprises scoring the dataset with respect to each of a plurality of attributes of the dataset; for each of the plurality of attributes having a low quality score, providing at least one recommendation for increasing the quality of the dataset with respect to the attribute having a low quality score; and for each of the plurality of attributes having a low quality score, providing an explanation explaining a cause of the low quality score for the attribute having a low quality score.Type: ApplicationFiled: September 28, 2020Publication date: March 31, 2022Inventors: Hima Patel, Lokesh Nagalapatti, Naveen Panwar, Nitin Gupta, Ruhi Sharma Mittal, Sameep Mehta, Shanmukha Chaitanya Guttula, Shazia Afzal
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Publication number: 20210264283Abstract: One embodiment provides a method, including: receiving a training dataset to be utilized for training a deep-learning model; identifying a plurality of aspects of the training dataset, wherein each of the plurality of aspects corresponds to one of a plurality of categories of operations that can be performed on the training dataset; measuring, for each of the plurality of aspects, an amount of variance of the aspect within the training dataset; creating additional data to be incorporated into the training dataset, wherein the additional data comprise data generated for each of the aspects having a variance less than a predetermined amount, wherein the data generated for an aspect results in the corresponding aspect having an amount of variance at least equal to the predetermined amount; and incorporating the additional data into the training dataset.Type: ApplicationFiled: February 24, 2020Publication date: August 26, 2021Inventors: Srikanth Govindaraj Tamilselvam, Senthil Kumar Kumarasamy Mani, Jassimran Kaur, Utkarsh Milind Desai, Shreya Khare, Anush Sankaran, Naveen Panwar, Akshay Sethi
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Publication number: 20210097383Abstract: Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.Type: ApplicationFiled: September 30, 2019Publication date: April 1, 2021Inventors: Jassimran Kaur, Tarun Tater, Anush Sankaran, Naveen Panwar
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Patent number: 10885347Abstract: One embodiment provides a method, comprising: identifying, using a processor, an individual in a video segment; ascertaining, from at least the video segment, a viewpoint expressed by the individual on a topic, wherein the viewpoint comprises at least one of: a stance of the individual and a sentiment of the individual toward the topic; identifying, using a processor, a superset video comprising the video segment, wherein the superset video is an originally published video; ascertaining, based at least in part on the superset video, an overarching viewpoint of the individual on the topic; determining whether an inconsistency exists between the viewpoint expressed by the individual in the video segment and the overarching viewpoint of the individual ascertained in the superset video; and alerting, responsive to determining that an inconsistency exists, a user that the video segment contains the inconsistency.Type: GrantFiled: September 18, 2019Date of Patent: January 5, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Tarun Tater, Anush Sankaran, Srikanth Govindaraj Tamilselvam, Naveen Panwar
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Publication number: 20200356868Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.Type: ApplicationFiled: May 6, 2019Publication date: November 12, 2020Inventors: Shreya Khare, Srikanth Govindaraj Tamilselvam, Anush Sankaran, Naveen Panwar, Rahul Rajendra Aralikatte, Senthil Kumar Kumarasamy Mani
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Publication number: 20200327420Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.Type: ApplicationFiled: April 10, 2019Publication date: October 15, 2020Inventors: Rahul Aralikatte, Srikanth Govindaraj Tamilselvam, Shreya Khare, Naveen Panwar, Anush Sankaran, Senthil Kumar Kumarasamy Mani
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Publication number: 20200184261Abstract: One embodiment provides a method, including: providing, at a collaborative deep learning model authoring tool, a dialog window that (i) receives user inputs discussing deep learning model aspects and (ii) provides recommendations from the collaborative deep learning model authoring tool; providing, at the collaborative deep learning model authoring tool, a consensus view indicating (i) a conflicting aspect identified as an aspect where more than one user selected a different aspect and (ii) the aspect selected for implementation within the deep learning model based upon that aspect having the most user selections; providing, at the collaborative deep learning model authoring tool, a model view displaying layers of the deep learning model based upon (i) aspects selected by the users in the dialog window and (ii) the aspect selected for implementation in the consensus view; and providing, at the collaborative deep learning model authoring tool, a deployment view that displays an execution of the deep learning mType: ApplicationFiled: December 5, 2018Publication date: June 11, 2020Inventors: Anush Sankaran, Rahul Rajendra Aralikatte, Shreya Khare, Naveen Panwar, Senthil Kumar Kumarasamy Mani, Srikanth Govindaraj Tamilselvam
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Publication number: 20200074347Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.Type: ApplicationFiled: August 30, 2018Publication date: March 5, 2020Inventors: Anush Sankaran, Naveen Panwar, Srikanth Govindaraj Tamilselvam, Shreya Khare, Rahul Aralikatte, Senthil Kumar Kumarasamy Mani
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Publication number: 20180307978Abstract: Methods, systems, and computer program products for multi-modal construction of deep learning networks are provided herein. A computer-implemented method includes extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network; generating a deep learning network model, wherein said generating comprises inferring multiple details attributed to the deep learning network model based on the one or more extracted items; creating an intermediate representation based on the deep learning network model, wherein the intermediate representation comprises (i) one or more items of data pertaining to the deep learning network model and (ii) one or more design details attributed to the deep learning network model; automatically converting the intermediate representation into source code; and outputting the source code to at least one user.Type: ApplicationFiled: April 19, 2017Publication date: October 25, 2018Inventors: Rahul AR, Neelamadhav Gantayat, Shreya Khare, Senthil Kk Mani, Naveen Panwar, Anush Sankaran