- Number
- 12675471
- Published
- 2026-07-07
- Filed
- 2024-07-31
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Raal; David DeWet, Cox; Jason Alexander
- CPC
- G06F16/243; G06F16/2468; G06F16/9024
- Verdict
- Set aside nlp/localization, business-ops
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Techniques for generating a result for a search query.
Abstract
Techniques for generating a result for a search query. These techniques include identifying a complex search query including more than two parameters, dividing the complex search query into one or more components, and determining an intent for each of the one or more components using machine learning (ML), the ML including at least one of: a large language model (LLM) or natural language processing (NLP) neural network. The techniques further include generating a result for the search query based on routing each component through a pipeline using the respective intent, the pipeline including both a query against a graph database and a search against a vector database.
Background
BACKGROUND (1) With the rapid emergence of sophisticated chat-based artificial intelligence (AI) and machine learning (ML) models, many users now expect to ask natural language questions and receive back coherent detailed answers. In the public domain, a growing number of ML based Large Language Models (LLMs) provide this. For proprietary data, however, or data with a real-time component, these LLMs are often unable to provide coherent or current answers to questions.
Claims
1. A method, comprising: identifying a complex search query comprising more than two parameters; dividing the complex search query into a first component and a second component; determining a first intent for the first component by processing the first component using machine learning (ML), the ML comprising at least one of: a large language model (LLM) or natural language processing (NLP) neural network; determining a second intent for the second component by processing the second component using the ML; and generating a result for the complex search query, comprising routing the first and second components through a pipeline based on the first and second intents, comprising: based on the first intent for the first component, generating and executing a query against a graph database; and based on the second intent for the second component, generating and executing a search against a vector database. ||
11. A non-transitory computer program product comprising: one or more non-transitory computer readable media containing, in any combination, computer program code that, when executed by operation of any combination of one or more processors, performs operations comprising: identifying a complex search query comprising more than two parameters; dividing the complex search query into a first component and a second component; determining a first intent for the first component using by processing the first component using machine learning (ML), the ML comprising at least one of: a large language model (LLM) or natural language processing (NLP) neural network; determining a second intent for the second component by processing the second component using the ML; and generating a result for the complex search query, comprising routing the first and second components through a pipeline based on the first and second intents, comprising: based on the first intent for the first component, generating and executing a query against a graph database; and based on the second intent for the second component, generating and executing a search against a vector database. ||
16. A system, comprising: one or more processors; and one or more memories storing a program, which, when executed on any combination of the one or more processors, performs operations, the operations comprising: identifying a complex search query comprising more than two parameters; dividing the complex search query into a first component and a second component; determining a first intent for the first component by processing the first component using machine learning (ML), the ML comprising at least one of: a large language model (LLM) or natural language processing (NLP) neural network; determining a second intent for the second component by processing the second component using the ML; and generating a result for the complex search query, comprising routing the first and second components through a pipeline based on the first and second intents, comprising: based on the first intent for the first component, generating and executing a query against a graph database; and based on the second intent for the second component-of the one, generating and executing a search against a vector database.