The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 24, 2026

Filed:

Oct. 02, 2023
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Prabir Mallick, Kolkata, IN;

Samiran Pal, Kolkata, IN;

Avinash Kumar Singh, Pune, IN;

Anumita Dasgupta, Kolkata, IN;

Soham Datta, Kolkata, IN;

Kaamraan Khan, Hyderabad, IN;

Tapas Nayak, Kolkata, IN;

Indrajit Bhattacharya, Kolkata, IN;

Girish Keshav Palshikar, Pune, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/3329 (2025.01); G06F 16/334 (2025.01); G06F 40/186 (2020.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01);
U.S. Cl.
CPC ...
G06F 16/3329 (2019.01); G06F 16/3344 (2019.01); G06F 40/186 (2020.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01);
Abstract

Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score. Finally, a longform technical question and answer dataset is generated based on the combined plurality of questions and optimal answers.


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