Multi-modal enhancement of large language models without retraining
US12399923B1 · kind B1 · utility
Inventors
Key dates
| Filing date | Sep 16, 2024 |
| Grant date | Aug 26, 2025 |
| Priority date | — |
| Expiry date | Sep 16, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/3347
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method for enhancing query responses from large language models without retraining by converting a query into a query vector; using a proximity metric to measure a proximity from the query vector to a plurality of vector embeddings stored in a vector database; ranking the plurality of vector embeddings based on proximity to the query vector; mapping the query to a homogenized context vector from a plurality of homogenized context vectors; using an augmented proximity metric to convert the proximity to an augmented proximity for each vector embeddings; performing an augmented ranking to refine the vector embeddings to those most relevant to the query; creating a prompt for a large language model comprising the query and the text data corresponding to refined vector embeddings as context; and feeding the prompt to the large language model to generate a response to the query.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.