US 12,353,827 B2
Computer implemented methods for the automated analysis or use of data, including use of a large language model
William Tunstall-Pedoe, Cambridgeshire (GB); Robert Heywood, Cambridgeshire (GB); Seth Warren, Cambridgeshire (GB); Paul Benn, Cambridgeshire (GB); Duncan Reynolds, Cambridgeshire (GB); Ayush Shah, Cambridgeshire (GB); Luci Krnic, Cambridgeshire (GB); and Ziyi Zhu, Cambridgeshire (GB)
Assigned to UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED, Cambridgeshire (GB)
Filed by UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED, Cambridgeshire (GB)
Filed on Oct. 23, 2024, as Appl. No. 18/923,851.
Application 18/923,851 is a continuation of application No. 18/648,788, filed on Apr. 29, 2024, granted, now 12,164,868.
Application 18/648,788 is a continuation of application No. 18/301,615, filed on Apr. 17, 2023, granted, now 11,989,507, issued on May 21, 2024.
Application 18/301,615 is a continuation of application No. PCT/GB2023/050405, filed on Feb. 22, 2023.
Application 18/301,615 is a continuation of application No. 18/001,368, previously published as PCT/GB2021/052196, filed on Aug. 24, 2021.
Claims priority of application No. 2202347 (GB), filed on Feb. 22, 2022; application No. 2219268 (GB), filed on Dec. 20, 2022; application No. 2300624 (GB), filed on Jan. 16, 2023; and application No. 2302085 (GB), filed on Feb. 14, 2023.
Prior Publication US 2025/0045520 A1, Feb. 6, 2025
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/20 (2020.01); G06F 16/334 (2025.01); G06F 40/56 (2020.01)
CPC G06F 40/20 (2020.01) [G06F 16/3344 (2019.01); G06F 40/56 (2020.01)] 30 Claims
OG exemplary drawing
 
1. A method of improving operation of a generative AI large language model (LLM)-based data processing system, in which a large language model is a deep learning model capable of processing natural language, in which the method includes the steps of:
(a) providing an input to a non-LLM data processing system that uses symbolic representations to analyse the input;
(b) the non-LLM system using, accessing or searching a knowledge or data source external to the LLM-based system to construct or enable an enhanced or augmented version of that input;
(c) the non-LLM system providing the enhanced or augmented version of that input to the LLM-based system as a prompt or other context, and
(d) the LLM-based system then using that prompt or other context to generate a continuation or other output that is fact-checked, accurate and reliable.