US 12,737,361 B2Granted

Machine learning-based processing of queries using structured data converted to natural language documents

AssigneeDell Products L.P.

InventorsThirumaleshwara Adyanadka Shama, Shibi Panikkar

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Abstract

An apparatus comprises at least one processing device configured to obtain, at an application interface, a query directed to structured data, to convert the structured data into natural language documents, to associate the natural language documents with context and differentiating entities in a hierarchical database defining a hierarchy of the differentiating and context entities, and to determine a tenant boundary of the query specifying at least one of the context entities associated with at least one of the differentiating entities in the hierarchical database. The at least one processing device is further configured to generate a prompt identifying the query and a subset of the natural language documents selected based at least in part on the determined tenant boundary, to process the prompt utilizing at least one machine learning model to generate an answer for the query, and to provide, via the application interface, the generated answer to the query.

Claims20

Dependent claims

An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain, at an application interface, a query, the query being directed to structured data in one or more structured data sources;
to generate one or more natural language linker configuration templates for the one or more structured data sources, the one or more natural language linker configuration templates specifying (i) one or more differentiating entities in the one or more structured data sources and (ii) links between the one or more differentiating entities in the one or more structured data sources and one or more columns in the one or more structured data sources to create natural language text;
to convert at least a portion of the structured data extracted from the one or more structured data sources into one or more natural language documents, wherein converting said at least a portion of the structured data extracted from the one or more structured data sources into the one or more natural language documents comprises applying the generated one or more natural language linker configuration templates to generate, for each of at least a subset of the one or more differentiating entities in the one or more structured data sources, one or more separate natural language documents for each of one or more context entities associated with the subset of the one or more differentiating entities;
to associate the one or more natural language documents with vector indexes in a hierarchical database, the hierarchical database defining a hierarchy of the one or more differentiating entities and the one or more context entities;
to store the one or more natural language documents in a vectorized format in a vector database, the one or more natural language documents being associated with the vector indexes in the hierarchical database, the vector indexes being associated with respective ones of the one or more context entities in the hierarchical database;
to determine a tenant boundary of the query, the tenant boundary specifying at least one of the one or more context entities associated with at least one of the one or more differentiating entities in the hierarchical database;
to generate a prompt for processing utilizing at least one machine learning model, the prompt identifying the query and a subset of the one or more natural language documents selected based at least in part on the determined tenant boundary, wherein generating the prompt comprises retrieving the vectorized formats of the identified subset of the one or more natural language documents from the vector database and utilizing the vectorized formats of the identified subset of the one or more natural language documents in generating the prompt;
to process the prompt utilizing the at least one machine learning model to generate an answer for the query;
and
to provide, via the application interface, the generated answer to the query.

9 more elements

A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

to obtain, at an application interface, a query, the query being directed to structured data in one or more structured data sources;
to generate one or more natural language linker configuration templates for the one or more structured data sources, the one or more natural language linker configuration templates specifying (i) one or more differentiating entities in the one or more structured data sources and (ii) links between the one or more differentiating entities in the one or more structured data sources and one or more columns in the one or more structured data sources to create natural language text;
to convert at least a portion of the structured data extracted from the one or more structured data sources into one or more natural language documents, wherein converting said at least a portion of the structured data extracted from the one or more structured data sources into the one or more natural language documents comprises applying the generated one or more natural language linker configuration templates to generate, for each of at least a subset of the one or more differentiating entities in the one or more structured data sources, one or more separate natural language documents for each of one or more context entities associated with the subset of the one or more differentiating entities;
to associate the one or more natural language documents with vector indexes in a hierarchical database, the hierarchical database defining a hierarchy of the one or more differentiating entities and the one or more context entities;
to store the one or more natural language documents in a vectorized format in a vector database, the one or more natural language documents being associated with the vector indexes in the hierarchical database, the vector indexes being associated with respective ones of the one or more context entities in the hierarchical database;
to determine a tenant boundary of the query, the tenant boundary specifying at least one of the one or more context entities associated with at least one of the one or more differentiating entities in the hierarchical database;
to generate a prompt for processing utilizing at least one machine learning model, the prompt identifying the query and a subset of the one or more natural language documents selected based at least in part on the determined tenant boundary, wherein generating the prompt comprises retrieving the vectorized formats of the identified subset of the one or more natural language documents from the vector database and utilizing the vectorized formats of the identified subset of the one or more natural language documents in generating the prompt;
to process the prompt utilizing the at least one machine learning model to generate an answer for the query;
and
to provide, via the application interface, the generated answer to the query.

7 more elements

A method comprising:

obtaining, at an application interface, a query, the query being directed to structured data in one or more structured data sources;
generating one or more natural language linker configuration templates for the one or more structured data sources, the one or more natural language linker configuration templates specifying (i) one or more differentiating entities in the one or more structured data sources and (ii) links between the one or more differentiating entities in the one or more structured data sources and one or more columns in the one or more structured data sources to create natural language text;
converting at least a portion of the structured data extracted from the one or more structured data sources into one or more natural language documents, wherein converting said at least a portion of the structured data extracted from the one or more structured data sources into the one or more natural language documents comprises applying the generated one or more natural language linker configuration templates to generate, for each of at least a subset of the one or more differentiating entities in the one or more structured data sources, one or more separate natural language documents for each of one or more context entities associated with the subset of the one or more differentiating entities;
associating the one or more natural language documents with vector indexes in a hierarchical database, the hierarchical database defining a hierarchy of the one or more differentiating entities and the one or more context entities;
storing the one or more natural language documents in a vectorized format in a vector database, the one or more natural language documents being associated with the vector indexes in the hierarchical database, the vector indexes being associated with respective ones of the one or more context entities in the hierarchical database;
determining a tenant boundary of the query, the tenant boundary specifying at least one of the one or more context entities associated with at least one of the one or more differentiating entities in the hierarchical database;
generating a prompt for processing utilizing at least one machine learning model, the prompt identifying the query and a subset of the one or more natural language documents selected based at least in part on the determined tenant boundary, wherein generating the prompt comprises retrieving the vectorized formats of the identified subset of the one or more natural language documents from the vector database and utilizing the vectorized formats of the identified subset of the one or more natural language documents in generating the prompt;
processing the prompt utilizing the at least one machine learning model to generate an answer for the query;
and
providing, via the application interface, the generated answer to the query;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

8 more elements

Dependencies are inferred from the claim text.