- Number
- 20260220177
- Published
- 2026-07-30
- Filed
- 2025-01-24
- Assignee
- Disney Enterprises, Inc.
- Inventors
- PAYNE; Tyler, LUCAS; Ella E., WRIGHT; Andrew M., WISEMAN; John J., MALINA; Xavier, KENNEDY; James R.
- CPC
- G06F16/243; G06F16/334; G06F16/211; G06F16/2237; G06F16/24522; G06F16/332
- Verdict
- Set aside nlp/localization, business-ops
- In edition
- 2026-W31
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Techniques for machine learning-based database querying are provided.
Abstract
Techniques for machine learning-based database querying are provided. A natural language request for information contained in a database is received. Based on the natural language request and a natural language meta-schema for the database, a relevant schema comprising at least one of a subset of tables from a plurality of tables of the database, a subset of columns from a plurality of columns in the plurality of tables, or a subset of values from a plurality of values in the plurality of columns is identified. A database query is generated based on prompting one or more language models (LMs) using the relevant schema and the natural language request. A result from the database is retrieved using the database query, and a natural language response to the natural language request is generated based on the result and using the one or more LMs.
Background
BACKGROUND
In a wide variety of environments, many projects and processes begin with (or at least can benefit from) evaluating comparable historical information from prior projects or processes. For example, when starting work on designing a new system (e.g., automobiles, aircraft, computing deployments, and the like), it may be beneficial to review prior information about the design and/or development of prior comparable systems. In many cases, records of such information are stored in structured databases, thereby relying on technical expertise in the structured query language(s) and database schema(s) to access and analyze the relevant data. Though making the data accessible through less complex means (e.g., via natural language requests) would be advantageous, existing approaches fail to provide adequate access without also introducing high failure rates and concerns relating to hallucination or inaccurate output.
Claims
1. A method, comprising: receiving a natural language request for information contained in a database; identifying, based on the natural language request and a natural language meta-schema for the database, a relevant schema comprising: a subset of tables from a plurality of tables of the database, wherein the subset of tables excludes at least one table of the plurality of tables and is identified based on a first plurality of natural language labels for the plurality of tables, a subset of columns from a plurality of columns in the subset of tables, wherein the subset of columns excludes at least one column of the plurality of columns and is identified based on a second plurality of natural language labels for the plurality of columns, and a subset of values from a plurality of values in the subset of columns, wherein the subset of values excludes at least one value of the plurality of values and is identified based on semantic indices for the plurality of values; generating a database query based on prompting one or more language models (LMs) using the relevant schema and the natural language request; retrieving a result from the database using the database query; and generating a natural language response to the natural language request based on the result and using the one or more LMs. ||
9. 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 an operation comprising: receiving a natural language request for information contained in a database; identifying, based on the natural language request and a natural language meta-schema for the database, a relevant schema comprising: a subset of tables from a plurality of tables of the database, wherein the subset of tables excludes at least one table of the plurality of tables and is identified based on a first plurality of natural language labels for the plurality of tables, a subset of columns from a plurality of columns in the subset of tables, wherein the subset of columns excludes at least one column of the plurality of columns and is identified based on a second plurality of natural language labels for the plurality of columns, and a subset of values from a plurality of values in the subset of columns, wherein the subset of values excludes at least one value of the plurality of values and is identified based on semantic indices for the plurality of values; generating a database query based on prompting one or more language models (LMs) using the relevant schema and the natural language request; retrieving a result from the database using the database query; and generating a natural language response to the natural language request based on the result and using the one or more LMs. ||
15. 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: receiving a natural language request for information contained in a database; identifying, based on the natural language request and a natural language meta-schema for the database, a relevant schema comprising: a subset of tables from a plurality of tables of the database, wherein the subset of tables excludes at least one table of the plurality of tables and is identified based on a first plurality of natural language labels for the plurality of tables, a subset of columns from a plurality of columns in the subset of tables, wherein the subset of columns excludes at least one column of the plurality of columns and is identified based on a second plurality of natural language labels for the plurality of columns, and a subset of values from a plurality of values in the subset of columns, wherein the subset of values excludes at least one value of the plurality of values and is identified based on semantic indices for the plurality of values; generating a database query based on prompting one or more language models (LMs) using the relevant schema and the natural language request; retrieving a result from the database using the database query; and generating a natural language response to the natural language request based on the result and using the one or more LMs.