IP Library › Granted Patent US 12,694,227
Granted Patent B1
US 12,694,227 · App. 19/291,376 · Granted Jul 28, 2026

System of correcting LLM hallucinations

Inventor: Zeev Barkan (Jerusalem, IL)
G06F40/35G06F40/279G06F40/289G10L15/22
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Quick Facts
Patent No.
US 12,694,227
App. No.
19/291,376
Filed
Aug 5, 2025
Granted
Jul 28, 2026
Kind
B1
Art Unit
2658
USPC
704/9
Abstract

A system of correcting inaccuracies in chatbot responses, the system comprising a processing circuitry (PC) configured to: receive a chatbot's response to a user prompt, and receive data sources associated with the response; present, to the user, the chatbot response, and receive, from the user, one or more text segments, the one or more text segments being associated with a user-suspected hallucination or inaccuracy of the chatbot response; perform, on one or more of the data sources, at least one of: a text search, and a semantic search of one or more of the text segments; and present, to the user, data derivative of the text search and/or semantic search, thereby confirmation or correction of the chatbot response.

Claims (33)

1 . A system of correcting inaccuracies in chatbot responses, the system comprising a processing circuitry (PC) configured to:

receive at least part of a chatbot's response to a user prompt, and receive data indicative of data sources, the data sources being indicated by the chatbot as relevant to the chatbot's response;

present, to the user, data derivative of the chatbot response, and receive, from the user, data indicative of one or more text segments,

the one or more text segments being associated with a user-suspected hallucination or inaccuracy of the chatbot response;

perform, on one or more of the data sources, at least one of:

a text search, and

a semantic search

of one or more of the text segments; and

present, to the user, data derivative of the text search and/or semantic search, thereby providing confirmation or correction of the chatbot response.

2 . The system of claim 1 , wherein at least one of the one or more data sources is provided by the chatbot.

3 . The system of claim 1 , wherein the received data indicative of one or more text segments is derivative of, at least, a user utilization of a device's user interface while viewing the chatbot response.

4 . The system of claim 1 , wherein the data derivative of the text search and/or semantic search is null, thereby being indicative of a hallucination in the chatbot response.

5 . The system of claim 1 , wherein the data derivative of the text search and/or semantic search is a text segment of a data source, thereby providing a clarification, confirmation, or correction of the response.

6 . The system of claim 1 , wherein the chatbot is a large language mode (LLM).

7 . The system of claim 1 , the data derivative of the text search and/or semantic search is: a corrected chatbot response.

8 . A processing circuitry-based method of correcting inaccuracies in chatbot responses, the method comprising:

receiving at least part of a chatbot's response to a user prompt, and receive data indicative of data sources, the data sources being indicated by the chatbot as relevant to the chatbot's response;

presenting, to the user, data derivative of the chatbot response, and receive, from the user, data indicative of one or more text segments,

the one or more text segments being associated with a user-suspected hallucination or inaccuracy of the chatbot response;

performing, on one or more of the data sources, at least one of:

a text search, and

a semantic search

of one or more of the text segments; and

presenting, to the user, data derivative of the text search and/or semantic search, thereby providing confirmation or correction of the chatbot response.

9 . A computer program product comprising a computer readable non-transitory storage medium containing program instructions, which program instructions when read by a processing circuitry, cause the processing circuitry to perform a method of correcting inaccuracies in chatbot responses, the method comprising:

receiving at least part of a chatbot's response to a user prompt, and receive data indicative of data sources, the data sources being indicated by the chatbot as relevant to the chatbot's response;

presenting, to the user, data derivative of the chatbot response, and receive, from the user, data indicative of one or more text segments,

the one or more text segments being associated with a user-suspected hallucination or inaccuracy of the chatbot response;

performing, on one or more of the data sources, at least one of:

a text search, and

a semantic search

of one or more of the text segments; and

presenting, to the user, data derivative of the text search and/or semantic search, thereby providing confirmation or correction of the chatbot response.

References Cited (33)
US 11861320B1 · Gajek · 2024 [cited by examiner]
US 12340238B1 · Stänescu · 2025 [cited by examiner]
US 12340557B1 · Saraee · 2025 [cited by examiner]
US 12379948B1 · Vlasceanu · 2025 [cited by examiner]
US 12393875B2 · Strope · 2025 [cited by examiner]
US 12412138B1 · Geene · 2025 [cited by examiner]
US 20200104746A1 · Strope · 2020 [cited by examiner]
US 20240062111A1 · Strope · 2024 [cited by examiner]
US 20240187522A1 · Yannam · 2024 [cited by examiner]
US 20240202221A1 · Siebel et al. · 2024 [cited by applicant]
US 20240289395A1 · Zhou · 2024 [cited by examiner]
US 20240362418A1 · Peng · 2024 [cited by examiner]
US 20240370709A1 · Siebel · 2024 [cited by examiner]
US 20240370769A1 · Sheth · 2024 [cited by examiner]
US 20240386253A1 · White, Jr. · 2024 [cited by examiner]
US 20240394176A1 · Pean · 2024 [cited by examiner]
US 20240419912A1 · Somech · 2024 [cited by examiner]
US 20250068667A1 · Blum · 2025 [cited by examiner]
US 20250078453A1 · Saraee · 2025 [cited by examiner]
US 20250078454A1 · Saraee · 2025 [cited by examiner]
US 20250193462A1 · Govindarajan · 2025 [cited by examiner]
US 20250284721A1 · Agrawal · 2025 [cited by examiner]
US 20250293998A1 · Courcelle · 2025 [cited by examiner]
CN 118535684A · 2024 [cited by applicant]
CN 118940839A · 2024 [cited by applicant]
CN 118964552A · 2024 [cited by applicant]
CN 118966202A · 2024 [cited by applicant]
WO 2024182285A2 · 2024 [cited by applicant]
Brahmaleen, “Hallucinations in Artificial Intelligence: Origins, Detection, and Mitigation”, International Journal of Science and Research, vol. 14 Issue 1, Jan. 2025, pp. 1-15. [cited by applicant]
Liang, Mengfei, et al. “Thames: An end-to-end tool for hallucination mitigation and evaluation in large language models.” arXiv preprint arXiv:2409.11353 (2024).pp. 1-24. [cited by applicant]
Huynh, “Automatic Hallucination detection with SelfCheckGPT NLI”, Automatic Hallucination detection with SelfCheckGPT NLI, Nov. 2023, ,https://huggingface.co/blog/dhuynh95/automatic-hallucination-detection> online pp. 1… [cited by applicant]
Anonymous “AI Control Plane for LLM Reliability”, Jan. 2025, website <https://www.qualifire.ai/>. [cited by applicant]
Doug Safreno Jan. 2024 , “How To Test For AI Hallucination”, Gentrace pp. 1-8. [cited by applicant]