Patents by Inventor SHARAD PAWAR

SHARAD PAWAR 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).

  • Publication number: 20250201010
    Abstract: State of art techniques using moderate sized Language Models (LMs) for text classification need fine-tuning or in-context learning. A method and system providing a two-step classification using moderate-sized (#params?2.7B) causal LM (Gen AI) is disclosed. Firstly, for a text instance to be classified, a set of perplexity and log-likelihood based features are obtained from an LM. Further, a light-weight classifier is trained in the second step to predict the final label. The system enables a new way of exploiting the available labelled instances, in addition to the existing ways like fine-tuning LMs or in-context learning. It neither needs any parameter updates in LMs like fine-tuning nor it is restricted by the number of training examples to be provided in the prompt like in-context learning. The key advantages of the disclosed system are explainability through most suitable key phrases and its applicability in resource poor environment.
    Type: Application
    Filed: November 25, 2024
    Publication date: June 19, 2025
    Applicant: Tata Consultancy Services Limited
    Inventors: Sachin Sharad PAWAR, Nitin Vijaykumar RAMRAKHIYANI, Anubhav SINHA, Manoj Madhav APTE, Girish Keshav PALSHIKAR
  • Publication number: 20250086552
    Abstract: Methods, systems, and media for providing contextual information associated with webpages are provided.
    Type: Application
    Filed: March 22, 2024
    Publication date: March 13, 2025
    Inventors: Vikram Gupta, Karishma Agarwal, Mahesh Nagargoje, Sharad Pawar, Vinay Gaykar
  • Patent number: 12248886
    Abstract: This disclosure relates generally to extraction of cause-effect relation from domain specific text. Cause-effect relation highlights causal relationship among various entities, concepts and processes in a domain specific text. Conventional state-of-the-art methods use named entity recognition for extraction of cause-effect (CE) relation which does not give precise results. Embodiments of the present disclosure provide a knowledge-based approach for automatic extraction of CE relations from domain specific text. The present disclosure method is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. The method extracts the CE relation in the form of a triplet comprising a causal trigger, a cause phrase and an effect phrase identified from the domain specific text. The disclosed method is used for extracting CE relations in biomedical text.
    Type: Grant
    Filed: March 23, 2021
    Date of Patent: March 11, 2025
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Ravina Vinayak More, Sachin Sharad Pawar, Girish Keshav Palshikar, Swapnil Hingmire, Pushpak Bhattacharyya, Vasudeva Varma Kalidindi
  • Patent number: 12242977
    Abstract: This disclosure relates to extraction of tasks from documents based on a weakly supervised classification technique, wherein extraction of tasks is identification of mentions of tasks in a document. There are several prior arts addressing the problem of extraction of events, however due to crucial distinctions between events-tasks, task extraction stands as a separate problem. The disclosure explicitly defines specific characteristics of tasks, creates labelled data at a word-level based on a plurality of linguistic rules to train a word-level weakly supervised model for task extraction. The labelled data is created based on the plurality of linguistic rules for a non-negation aspect, a volitionality aspect, an expertise aspect and a plurality of generic aspects. Further the disclosure also includes a phrase expansion technique to capture the complete meaning expressed by the task instead of merely mentioning the task that may not capture the entire meaning of the sentence.
    Type: Grant
    Filed: July 15, 2022
    Date of Patent: March 4, 2025
    Assignee: Tata Consultancy Services Limited
    Inventors: Sachin Sharad Pawar, Girish Keshav Palshikar, Anindita Sinha Banerjee
  • Publication number: 20240420261
    Abstract: Current approaches for identifying statute facets consider facet type similar to rhetorical roles defined for statute text. However, the nature and content of statutes are quite different from court judgements and established set of rhetorical roles for court judgements are either not applicable for statutes or not sufficient to cover all the key aspects in statutes. Present disclosure provides method and system for extraction and classification of statute facets from legal statutes. The system first takes text of a statute as input. The system then automatically extracts candidate statute facets from statute text using dependency structure and then computes statute specificity for candidate statute facets. Thereafter, the system classifies candidate statute facets into facet types using weak supervision for validation purpose.
    Type: Application
    Filed: May 29, 2024
    Publication date: December 19, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: BASIT ALI, RAMANDEEP SINGH, GIRISH KESHAV PALSHIKAR, SACHIN SHARAD PAWAR
  • Patent number: 12124491
    Abstract: Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, present disclosure provides system and method that address the task of generating audit recommendations that can help auditors to focus their investigations. Adverse remarks, financial variables mentioned in each sentence are extracted/identified from audit reports and category tag is assigned accordingly, thus creating a knowledge base for generating audit recommendations using a trained sentence classifier. In absence of labeled data, the system applies linguistic rule(s) to identify adverse remark sentences, and automatically create labeled training data for training the sentence classifier.
    Type: Grant
    Filed: September 7, 2023
    Date of Patent: October 22, 2024
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Aditi Anil Pawde, Akshada Ananda Shinde, Manoj Madhav Apte, Sachin Sharad Pawar, Sushodhan Sudhir Vaishampayan, Girish Keshav Palshikar
  • Publication number: 20240330349
    Abstract: Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, present disclosure provides system and method that address the task of generating audit recommendations that can help auditors to focus their investigations. Adverse remarks, financial variables mentioned in each sentence are extracted/identified from audit reports and category tag is assigned accordingly, thus creating a knowledge base for generating audit recommendations using a trained sentence classifier. In absence of labeled data, the system applies linguistic rule(s) to identify adverse remark sentences, and automatically create labeled training data for training the sentence classifier.
    Type: Application
    Filed: September 7, 2023
    Publication date: October 3, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: ADITI ANIL PAWDE, AKSHADA ANANDA SHINDE, MANOJ MADHAV APTE, SACHIN SHARAD PAWAR, SUSHODHAN SUDHIR VAISHAMPAYAN, GIRISH KESHAV PALSHIKAR
  • Publication number: 20240320427
    Abstract: Existing approaches for processing and evaluation of documents containing non-fiction narrative texts have the disadvantage that they are comparatively less studied in linguistics, and hence do not provide sufficient data required for evaluations. Method and system are for evaluating non-fiction narrative text documents are provided. The system processes a plurality of non-fiction narrative text documents and computes a plurality of corpus statistics. The plurality of corpus statistics is then used for evaluation of any non-fiction narrative text document that may or may not be collected as real-time input.
    Type: Application
    Filed: February 21, 2024
    Publication date: September 26, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Sachin Sharad PAWAR, Girish Keshav PALSHIKAR, Ankita JAIN, Mahesh Prasad SINGH, Mahesh RANGARAJAN, Aman AGARWAL, Kumar Karan SINGH, Hetal JANI, Vishal KUMAR
  • Patent number: 11755840
    Abstract: Extracting data from documents is challenging due to the variation in structure, content, styles across geographies and functional areas. Further complex relation types are characterized by one or more of N-ary entity mention arguments, cross sentence span of entity mentions for a relation mention, missing entity mention arguments and entity mention arguments being multi-valued. The present disclosure addresses these gaps in the art to extract entity mentions and relation mentions using a joint neural network model including two sequence labelling layers which are trained jointly. The mentions are extracted from documents to facilitate downstream processing. A first RNN layer creates sentence embeddings for each sentence in the document being processed and predicts entity mentions. A second RNN layer predicts labels for each sentence span corresponding to a relation type.
    Type: Grant
    Filed: June 11, 2021
    Date of Patent: September 12, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Sachin Sharad Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar, Anindita Sinha Banerjee, Rajiv Srivastava, Devavrat Shailesh Thosar
  • Patent number: 11734321
    Abstract: This disclosure relates generally to retrieval of prior court cases using witness testimonies. Conventional state-of-the-art methods use supervised techniques for answering basic questions in legal domain using numerous features and do not address interpretability of results and the performance and precision of retrieving prior court cases for these methods are less. Embodiments of the present disclosure obtains an embedded representation for an event structure of a user query and testimony sentences identified from prior court cases using a trained Bi-LSTM classifier and a set of linguistic rules. A similarity is estimated between the embedded representation for the event structure of the user query and the event structure of each testimony sentence from the prior court cases. Further a relevance score is assigned in accordance with the estimated similarity to retrieve the relevant prior court cases. The disclosed method is used to retrieve the relevant prior court cases using witness testimonies.
    Type: Grant
    Filed: March 19, 2021
    Date of Patent: August 22, 2023
    Assignee: Tata Consultancy Services Limited
    Inventors: Kripabandhu Ghosh, Sachin Sharad Pawar, Girish Keshav Palshikar, Pushpak Bhattacharyya, Vasudeva Varma Kalidindi
  • Publication number: 20230229936
    Abstract: This disclosure relates to extraction of tasks from documents based on a weakly supervised classification technique, wherein extraction of tasks is identification of mentions of tasks in a document. There are several prior arts addressing the problem of extraction of events, however due to crucial distinctions between events-tasks, task extraction stands as a separate problem. The disclosure explicitly defines specific characteristics of tasks, creates labelled data at a word-level based on a plurality of linguistic rules to train a word-level weakly supervised model for task extraction. The labelled data is created based on the plurality of linguistic rules for a non-negation aspect, a volitionality aspect, an expertise aspect and a plurality of generic aspects. Further the disclosure also includes a phrase expansion technique to capture the complete meaning expressed by the task instead of merely mentioning the task that may not capture the entire meaning of the sentence.
    Type: Application
    Filed: July 15, 2022
    Publication date: July 20, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: SACHIN SHARAD PAWAR, GIRISH KESHAV PALSHIKAR, ANINDITA SINHA BANERJEE
  • Publication number: 20230196296
    Abstract: This disclosure relates generally to predicting proficiency level of a person from resume. The proficiency levels obtained using state-of-the-art methods tends to overestimate proficiency. Moreover, the estimated proficiency levels do not satisfy several constraints that are considered key by subject matter experts. Embodiments of the present disclosure extract skills and other related information automatically from resume and capture skill related information in terms of a feature vector. A skill estimation function is learned to predict the proficiency level of the skill from the feature vector using any one of two models. A first model is learned using a constraint loss function to combine label information with domain specific constraints and a second model is learned using a clustering based technique. The disclosure predicts skill proficiency using only resume and can be used for predicting proficiency level of skills of employees from their resumes, for suitable job recommendations from job portal.
    Type: Application
    Filed: October 25, 2022
    Publication date: June 22, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: ANINDITA SINHA BANERJEE, SACHIN SHARAD PAWAR, GIRISH KESHAV PALSHIKAR
  • Publication number: 20220284192
    Abstract: Extracting data from documents is challenging due to the variation in structure, content, styles across geographies and functional areas. Further complex relation types are characterized by one or more of N-ary entity mention arguments, cross sentence span of entity mentions for a relation mention, missing entity mention arguments and entity mention arguments being multi-valued. The present disclosure addresses these gaps in the art to extract entity mentions and relation mentions using a joint neural network model including two sequence labelling layers which are trained jointly. The mentions are extracted from documents to facilitate downstream processing. A first RNN layer creates sentence embeddings for each sentence in the document being processed and predicts entity mentions. A second RNN layer predicts labels for each sentence span corresponding to a relation type.
    Type: Application
    Filed: June 11, 2021
    Publication date: September 8, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Sachin Sharad PAWAR, Nitin Ramrakhiyani, Girish Keshav Palshikar, Anindita Sinha Banerjee, Rajiv Srivastava, Devavrat Shailesh Thosar
  • Patent number: 11400843
    Abstract: A vehicle seat assembly with a release mechanism for a leg support member, a release mechanism, and a method of installing a release mechanism for a leg support connected to a seat assembly are provided. The release mechanism has a support member to connect to one of a seat base and a leg support member. An arm has a proximal end region rotatably connected to the support member and a distal end region, The arm defines a cam surface. A biasing member is connected to the support member, and is in contact with the distal end region of the arm. A follower is provided to connect to the other of the seat base and the leg support member, and is engageable with the cam surface of the arm when the leg support member is rotated between a deployed position and a stowed position.
    Type: Grant
    Filed: February 15, 2021
    Date of Patent: August 2, 2022
    Assignee: Lear Corporation
    Inventors: Rafal Pater, Kishore Tarade, Parashuram Rangolli, Sharad Pawar
  • Publication number: 20220207400
    Abstract: This disclosure relates generally to extraction of cause-effect relation from domain specific text. Cause-effect relation highlights causal relationship among various entities, concepts and processes in a domain specific text. Conventional state-of-the-art methods use named entity recognition for extraction of cause-effect (CE) relation which does not give precise results. Embodiments of the present disclosure provide a knowledge-based approach for automatic extraction of CE relations from domain specific text. The present disclosure method is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. The method extracts the CE relation in the form of a triplet comprising a causal trigger, a cause phrase and an effect phrase identified from the domain specific text. The disclosed method is used for extracting CE relations in biomedical text.
    Type: Application
    Filed: March 23, 2021
    Publication date: June 30, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Ravina Vinayak MORE, Sachin Sharad PAWAR, Girish Keshav PALSHIKAR, Swapnil HINGMIRE, Pushpak BHATTACHARYYA, Vasudeva VARMA KALIDINDI
  • Publication number: 20220067076
    Abstract: This disclosure relates generally to retrieval of prior court cases using witness testimonies. Conventional state-of-the-art methods use supervised techniques for answering basic questions in legal domain using numerous features and do not address interpretability of results and the performance and precision of retrieving prior court cases for these methods are less. Embodiments of the present disclosure obtains an embedded representation for an event structure of a user query and testimony sentences identified from prior court cases using a trained Bi-LSTM classifier and a set of linguistic rules. A similarity is estimated between the embedded representation for the event structure of the user query and the event structure of each testimony sentence from the prior court cases. Further a relevance score is assigned in accordance with the estimated similarity to retrieve the relevant prior court cases. The disclosed method is used to retrieve the relevant prior court cases using witness testimonies.
    Type: Application
    Filed: March 19, 2021
    Publication date: March 3, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Kripabandhu GHOSH, Sachin Sharad PAWAR, Girish Keshav PALSHIKAR, Pushpak BHATTACHARYYA, Vasudeva Varma KALIDINDI
  • Patent number: 11210472
    Abstract: Narrative texts contain rich knowledge about actors and interactions among them. It is often useful to extract and visualize these interactions through a set of inter-related timelines in which an actor has participated. Current approaches utilize labeled datasets and implement supervised techniques and thus are not suitable. Embodiments of the present disclosure implement systems and methods for automated extraction of Message Sequence Chart (MSC) from textual description by identifying verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. The present disclosure further employs an optimization-based approach to temporally re-order these interactions.
    Type: Grant
    Filed: March 10, 2020
    Date of Patent: December 28, 2021
    Assignee: Tata Consultancy Services Limited
    Inventors: Sangameshwar Suryakant Patil, Swapnil Vishweshwar Hingmire, Nitin Vijaykumar Ramrakhiyani, Sachin Sharad Pawar, Harsimran Bedi, Girish Keshav Palshikar, Pushpak Bhattacharyya, Vasudeva Varma Kalidindi
  • Publication number: 20210339668
    Abstract: A vehicle seat assembly with a release mechanism for a leg support member, a release mechanism, and a method of installing a release mechanism for a leg support connected to a seat assembly are provided. The release mechanism has a support member to connect to one of a seat base and a leg support member. An arm has a proximal end region rotatably connected to the support member and a distal end region, The arm defines a cam surface. A biasing member is connected to the support member, and is in contact with the distal end region of the arm. A follower is provided to connect to the other of the seat base and the leg support member, and is engageable with the cam surface of the arm when the leg support member is rotated between a deployed position and a stowed position.
    Type: Application
    Filed: February 15, 2021
    Publication date: November 4, 2021
    Applicant: LEAR CORPORATION
    Inventors: Rafal PATER, Kishore TARADE, Parashuram RANGOLLI, Sharad PAWAR
  • Publication number: 20200394365
    Abstract: Narrative texts contain rich knowledge about actors and interactions among them. It is often useful to extract and visualize these interactions through a set of inter-related timelines in which an actor has participated. Current approaches utilize labeled datasets and implement supervised techniques and thus are not suitable. Embodiments of the present disclosure implement systems and methods for automated extraction of Message Sequence Chart (MSC) from textual description by identifying verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. The present disclosure further employs an optimization-based approach to temporally re-order these interactions.
    Type: Application
    Filed: March 10, 2020
    Publication date: December 17, 2020
    Applicant: Tata Consultancy Services Limited
    Inventors: Sangameshwar Suryakant Patil, Swapnil Vishweshwar Hingmire, Nitin Vijaykumar Ramrakhiyani, Sachin Sharad Pawar, Harsimran Bedi, Girish Keshav Palshikar, Pushpak Bhattacharyya, Vasudeva Varma Kalidindi
  • Patent number: 10810244
    Abstract: The present invention relates to system and method for evaluating reviewer's ability to provide feedback. The system receives feedback given by the reviewer that includes qualitative feedback and quantitative feedback. The system performs scoring of qualitative feedback to evaluate level of noise, suggestions, appreciation, specificity and duplicate comments in the qualitative feedback. Further, the system performs scoring of quantitative feedback that includes realistic score, softness score and critical nature score. Subsequently, the scores of qualitative feedback and quantitative feedback are aggregated to provide a rank to the reviewer for the reviewer's ability to provide feedback.
    Type: Grant
    Filed: October 14, 2016
    Date of Patent: October 20, 2020
    Assignee: Tata Cunsultancy Services Limited
    Inventors: Manoj Madhav Apte, Sachin Sharad Pawar, Girish Keshav Palshikar, Swapnil Vishveshwar Hingmire