Granted Patent B1

US 12,639,274 · App. 19/073,414 · Granted May 26, 2026

Inventors: Dimitri C. Critsinelis (Houston, TX); Gunjan Neopaney (Katy, TX); Edwyn S. Bougre (West Palm Beach, FL); Nishitha Reddy Narreddi (Austin, TX)

Assignee: Inventus Holdings, LLC

G06F16/212G06F16/24522G06F16/258

Abstract

Claims (73)

1 . A non-transitory machine-readable medium storing instructions that, when executed by a processor, cause the processor to execute operations, the operations comprising:

receiving information identifying a database table;

preprocessing the database table, the preprocessing comprising:

extracting primary and foreign key relationships; and

generating sample data by combining frequency-based sampling of most common values with random sampling;

generating, using an LLM (large language model), a unified metadata schema, the generating comprising:

processing the preprocessed database table and the sample data using domain-specific prompts; and

outputting the unified metadata schema in a standardized format;

embedding and indexing the unified metadata schema;

converting a natural language question output from an end-user device into a SQL (structured query language) query, the converting comprising:

identifying relevant metadata in the unified metadata schema matching query intent using semantic search;

filtering and prioritizing metadata based on query relevance; and

generating the SQL query using the filtered metadata;

executing the SQL query against the database; and

providing query results to the end-user device as a response to the natural language question.

2 . The non-transitory machine-readable medium of claim 1 , wherein the operations further comprise logging user feedback and query execution operations to enable improvement in generating the SQL query.

3 . The non-transitory machine-readable medium of claim 1 , wherein preprocessing the database table comprises applying an elbow method to identify a point of diminishing returns in data completeness at a threshold percentage of non-null values and removing columns having null values meeting or exceeding the threshold percentage.

4 . The non-transitory machine-readable medium of claim 1 , wherein generating the sample data comprises collecting data points for each column by combining most frequently occurring values with random samples.

5 . The non-transitory machine-readable medium of claim 1 , wherein the domain-specific prompts incorporate industry and database context information to improve metadata generation accuracy.

6 . The non-transitory machine-readable medium of claim 1 , wherein the unified metadata schema comprises:

table name and description;

column names, data types and descriptions;

key terms and sample data; and

table relationships and constraints.

7 . The non-transitory machine-readable medium of claim 1 , wherein embedding the metadata comprises creating vector representations to enable semantic search capabilities.

8 . The non-transitory machine-readable medium of claim 1 , wherein converting the natural language question comprises validating query accuracy using metadata context and returning error messages rather than hallucinating responses when unable to generate a valid SQL query.

9 . The non-transitory machine-readable medium of claim 1 , wherein the operations further comprise processing one database table at a time to curtail hallucinations.

10 . A system for automated metadata generation and natural language database querying, comprising:

a metadata generation module operating on one or more computing platforms that:

receives information identifying a database table;

preprocesses the database table by extracting primary and foreign key relationships;

generates balanced sample data by combining frequency-based sampling of most common values with random sampling; and

generates, using an LLM (large language model), a unified metadata schema using domain-specific prompts and outputs the unified metadata schema in a standardized format;

a retrieval augmented generation module operating on the one or more computing platforms that:

embeds and indexes the unified metadata schema;

identifies relevant metadata in the unified metadata schema matching user intent of a question using a semantic search; and

filters and prioritizes metadata based on query relevance;

a data retrieval agent operating on the one or more computing platforms that:

receives a natural language question from an end-user device; and

converts the natural language question into a SQL (structured query language) query using the filtered metadata;

executes the SQL query against the database; and

provides query results to the end-user device as a response to the natural language question.

11 . The system of claim 10 , wherein preprocessing the database table comprises applying an elbow method to identify a point of diminishing returns in data completeness at a threshold percentage of non-null values and removing columns having null values meeting or exceeding the threshold percentage.

12 . The system of claim 10 , wherein generating the balanced sample data comprises data points for each column by combining most frequently occurring values with random samples.

13 . The system of claim 10 , wherein the unified metadata schema comprises table name and description, column names and data types, key terms and sample data and table relationships and constraints.

14 . The system of claim 10 , wherein the metadata generation module processes one database table at a time to avoid hallucinations.

15 . The system of claim 10 , wherein the data retrieval agent validates query accuracy using metadata context and returns an error when unable to generate a valid SQL query.

16 . The system of claim 10 , wherein the data retrieval agent further logs user feedback and query execution operation, wherein the user feedback characterizes approval or disapproval ratings for answers to natural language questions.

17 . The system of claim 10 , wherein the domain-specific prompts incorporate industry and database context information to improve metadata generation accuracy.

18 . A method for automated metadata generation and natural language database querying, comprising:

preprocessing, by a metadata generation module operating on one or more computing platforms, a database table, the preprocessing comprising:

extracting primary and foreign key relationships; and

generating sample data by combining frequency-based sampling of most common values with random sampling; and

generating, using an LLM (large language model), a unified metadata schema, wherein the generating comprises:

processing the preprocessed database table and the sample data using domain-specific prompts; and

outputting the unified metadata schema in a standardized format;

embedding and indexing the unified metadata schema with a retrieval augmented generation module operating on the one or more computing platforms;

converting, by a data retrieval agent operating on the one or more computing platforms a natural language question provided from an end-user device into an SQL (structured query language) query, wherein the converting comprises:

identifying relevant metadata in the unified metadata schema matching query intent using semantic search;

filtering and prioritizing metadata based on query relevance; and

generating the SQL query using the filtered metadata;

executing, by the data retrieval agent the SQL query against the database; and

outputting, by the data retrieval agent, query results to the end-user device as a response to the natural language question.

19 . The method of claim 18 , wherein preprocessing the database table comprises:

analyzing data completeness patterns using an elbow method to identify a set of columns that have at least a threshold percentage of null values;

removing the set of columns that having null values meeting or exceeding the threshold percentage; and

collecting data points for each retained column by combining most frequently occurring values with random samples.

20 . The method of claim 18 , wherein generating the unified metadata schema comprises:

incorporating table name, description and business function;

documenting column names, data types, descriptions and constraints;

specifying relationships between tables and foreign key connections;

storing the metadata in JSON format to support interoperability across platforms; and

processing one database table at a time to curtail hallucination.

Assignments (2)

References Cited (17)