公开(公告)号：US20230061773A1

公开(公告)日：2023-03-02

申请号：US17822714

申请日：2022-08-26

申请人： Tata Consultancy Services Limited

发明人： SANGAMESHWAR SURYAKANT PATIL ,  SAMIRAN PAL ,  AVINASH KUMAR SINGH ,  SOHAM DATTA ,  GIRISH KESHAV PALSHIKAR ,  INDRAJIT BHATTACHARYA ,  HARSIMRAN BEDI ,  YASH AGRAWAL ,  VASUDEVA VARMA KALIDINDI

IPC分类号： G06F16/332 ,  G06F16/35 ,  G06F40/30 ,  G06F40/295

摘要： Questions play a central role in assessment of a candidate's expertise during an interview or examination. However, generating such questions from input text documents manually needs specialized expertise and experience. Further, techniques that are available for automated question generation require input sentence as well as an answer phrase in that sentence to generate question. This in-turn requires large training datasets consisting tuples of input sentence answer-phrase and the corresponding question. Additionally, training datasets are available are for general purpose text, but not for technical text. Present application provides systems and methods for generating technical questions from technical documents. The system extracts meta information and linguistic information of text data present in technical documents. The system then identifies relationships that exist in provided text data. The system further creates one or more graphs based on the identified relationships. The created graphs are the used by the system to generate technical questions.

公开(公告)号：US20230305549A1

公开(公告)日：2023-09-28

申请号：US18174383

申请日：2023-02-24

申请人： Tata Consultancy Services Limited

发明人： SANGAMESHWAR SURYAKANT PATIL ,  NITIN VIJAYKUMAR RAMRAKHIYANI ,  SWAPNIL VISHVESHWAR HINGMIRE ,  ALOK KUMAR ,  HARSIMRAN BEDI ,  MANIDEEP JELLA ,  GIRISH KESHAV PALSHIKAR

IPC分类号： G05B23/02

CPC分类号： G05B23/024 ,  G05B23/0221

摘要： This disclosure relates to the field of incident analysis, and, more particularly, to systems and methods for similarity analysis in incident reports using event timeline representations. Conventionally, processing of repositories of incident reports to identify similar incidents is challenging due to use of unstructured text data in describing the incident reports. Timeline representation is an important knowledge representation which captures chronological ordering of the events. The timeline representation becomes useful in process of root cause analysis as causes would temporally precede the effect. To construct event timeline representations, chronological ordering of events is required. The present disclosure provides a temporal relation identification technique to obtain a timeline representation of the events. Further, a similarity identification approach is used that makes use of neural embeddings to identify similar timeline representations and in turn, similar incident reports. The similar incident reports help to devise best practices to provide better post-incident remedial measures.