公开(公告)号：US20230109692A1

公开(公告)日：2023-04-13

申请号：US17822722

申请日：2022-08-26

Applicant: Tata Consultancy Services Limited

Inventor： ANUMITA DASGUPTA ,  INDRAJIT BHATTACHARYA ,  GIRISH KESHAV PALSHIKAR ,  PRATIK SAINI ,  SANGAMESHWAR SURYAKANT PATIL ,  SOHAM DATTA ,  PRABIR MALLICK ,  SAMIRAN PAL ,  SUNIL KUMAR KOPPARAPU ,  AISHWARYA CHHABRA ,  AVINASH KUMAR SINGH ,  KAUSTUV MUKHERJI ,  MEGHNA ABHISHEK PANDHARIPANDE ,  ANIKET PRAMANICK ,  ARPITA KUNDU ,  SUBHASISH GHOSH ,  CHANDRASEKHAR ANANTARAM ,  ANAND SIVASUBRAMANIAM ,  GAUTAM SHROFF

IPC: G06Q10/10 ,  G06Q10/06 ,  H04L12/18

Abstract: This disclosure relates generally to method and system for providing assistance to interviewers. Technical interviewing is immensely important for enterprise but requires significant domain expertise and investment of time. The present disclosure aids assists interviewers with a framework via an interview assistant bot. The method initiates an interview session for a job description by selecting a set of qualified candidates resume to be interviewed. Further, the IA bot recommends each interviewer with a set of question and reference answer pairs prior initiating the interview. At each interview step, the IA bot records interview history and recommends interviewer with the revised set of questions. Further, an assessment score is determined for the candidate using the reference answer extracted from a resource corpus. Additionally, statistics about the interview process is generated, such as number and nature of questions asked, and its variation across to identify outliers for corrective actions.

公开(公告)号：US20240095466A1

公开(公告)日：2024-03-21

申请号：US18450588

申请日：2023-08-16

Applicant: Tata Consultancy Services Limited

Inventor： SUBHASISH GHOSH ,  ARPITA KUNDU ,  INDRAJIT BHATTACHARYA ,  PRATIK SAINI ,  TAPAS NAYAK

IPC: G06F40/40 ,  G06F40/137 ,  G06F40/205 ,  G06Q50/20 ,  G06V30/413

CPC classification number: G06F40/40 ,  G06F40/137 ,  G06F40/205 ,  G06Q50/20 ,  G06V30/413 ,  G06V2201/10

Abstract: The present disclosure a method for document structure based unsupervised long-form technical question generation. Initially, the system receives a textbook document. Further, a PDF metadata is extracted from the textbook document using a Natural Language Processing (NLP) technique. Further, a plurality of structures from the textbook document based on the PDF metadata using an NLP based filtering technique. Further, a plurality of index based question templates and Table of Contents (TOC) based question templates are obtained from a plurality of predefined question templates using the plurality of structures. Further, the generated plurality of long-form technical questions are generated using the obtained index and TOC based question templates. The plurality of long-form technical questions are further evaluated by the system using plurality of metrics. Further, the generated plurality of long-form technical questions are used to finetune a supervised question generation model for generating optimal questions from document structure.

公开(公告)号：US20220342919A1

公开(公告)日：2022-10-27

申请号：US17753514

申请日：2020-03-04

Applicant: Tata Consultancy Services Limited

Inventor： AMIT SANGROYA ,  GAUTAM SHROFF ,  CHANDRASEKHAR ANANTARAM ,  MRINAL RAWAT ,  PRATIK SAINI

IPC: G06F16/33 ,  G06F16/332 ,  G06F40/20

Abstract: For various applications (for example, a Virtual Assistant), mechanisms that are capable of collecting user queries and generating responses are being used. While such systems handle structured queries well, they struggle to or fail to interpret an unstructured Natural Language (NL) query. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for generating responses to unstructured Natural Language (NL) queries. The system collects at least one NL query as input at a time, and generates a sketch, where the sketch is a structured representation of the unstructured NL query. Further by processing the sketch, the system generates one or more database queries. The one or more database queries are then used to search in one or more associated databases and to retrieve matching results, which are then used to generate response to the at least one NL query.