Granted Patent B2

US 12,619,620 · App. 19/316,841 · Granted May 5, 2026

Inventors: Jaimita Bansal (Jersey City, NJ); Pierre De Belen (Hoboken, NJ)

Assignee: Goldman Sachs & Co. LLC

G06F16/2471

Abstract

Claims (96)

1 . A method comprising:

providing a user query to a self-healing multi-agent artificial intelligence/machine learning (AI/ML) pipeline, the user query requesting a response based on data stored in a data topology, the data topology modeled using a semantic data model;

generating an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline prompting one or more AI/ML models to generate the initial data access query;

determining that the initial data access query includes a hallucination or error;

performing an automatic loop one or more times, wherein the automatic loop includes:

providing additional information to the AI/ML pipeline;

generating an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline prompting at least one of the one or more AI/ML models based on the additional information;

determining whether the updated data access query includes a hallucination or error; and

if the updated data access query includes a hallucination or error, repeating the automatic loop;

using a final data access query with no hallucination or error to retrieve the data from the data topology; and

generating the response using the data retrieved from the data topology.

2 . The method of claim 1 , wherein:

the semantic data model represents the data topology and identifies dataspaces, classes, and properties associated with the data topology;

agents of the AI/ML pipeline use the semantic data model to identify a specific dataspace, one or more specific classes, and one or more specific properties associated with the user query; and

each data access query is generated based on the specific dataspace, the one or more specific classes, and the one or more specific properties.

3 . The method of claim 2 , wherein:

the semantic data model provides context to the agents of the AI/ML pipeline;

the agents comprise the one or more AI/ML models that generate responses when prompted by the agents; and

the responses from the one or more AI/ML models identify the specific dataspace, the one or more specific classes, the one or more specific properties, and the data access queries.

4 . The method of claim 2 , wherein:

the data topology includes tabular data; and

the semantic data model allows the AI/ML pipeline to understand columns of data in the tabular data.

5 . The method of claim 1 , wherein the semantic data model models the data topology using multiple classes and associated properties that are semantically aligned with natural language on which the AI/ML pipeline is trained.

6 . The method of claim 5 , wherein at least some of the classes are associated with multiple associations in the semantic data model.

7 . The method of claim 1 , wherein:

the AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent;

the dataspace agent identifies one of multiple dataspaces associated with the user query;

the class agent identifies at least one of multiple classes associated with the identified dataspace, the at least one identified class mapped to the data topology;

the property agent identifies at least one of multiple properties within the at least one identified class; and

the query agent generates each data access query based on at least one of: the at least one identified class and the at least one identified property.

8 . The method of claim 7 , wherein the self-healing agent determines, for each data access query, whether:

a syntax of the data access query has one or more errors;

at least one property in the data access query exists;

one or more values in the data access query are proper; and

a data type of a value in the data access query matches an expected data type.

9 . The method of claim 1 , wherein at least one of the data access queries is based on one or more of:

filtering of at least one of classes and properties defined in the semantic data model based on the user query; and

joining of at least one of classes and properties defined at multiple levels in the semantic data model based on the user query.

10 . An apparatus comprising:

at least one processing device configured to:

provide a user query to a self-healing multi-agent artificial intelligence/machine learning (AI/ML) pipeline, the user query requesting a response based on data stored in a data topology, the data topology modeled using a semantic data model;

generate an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline configured to prompt one or more AI/ML models to generate the initial data access query;

determine that the initial data access query includes a hallucination or error;

perform an automatic loop one or more times, wherein, to perform the automatic loop, the at least one processing device is configured to:

provide additional information to the AI/ML pipeline;

generate an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline configured to prompt at least one of the one or more AI/ML models based on the additional information;

determine whether the updated data access query includes a hallucination or error; and

if the updated data access query includes a hallucination or error, repeat the automatic loop;

use a final data access query with no hallucination or error to retrieve the data from the data topology; and

generate the response using the data retrieved from the data topology.

11 . The apparatus of claim 10 , wherein:

the semantic data model represents the data topology and identifies dataspaces, classes, and properties associated with the data topology;

agents of the AI/ML pipeline are configured to use the semantic data model to identify a specific dataspace, one or more specific classes, and one or more specific properties associated with the user query; and

the AI/ML pipeline is configured to generate each data access query based on the specific dataspace, the one or more specific classes, and the one or more specific properties.

12 . The apparatus of claim 11 , wherein:

the semantic data model provides context to the agents of the AI/ML pipeline;

the agents comprise the one or more AI/ML models configured to generate responses when prompted by the agents; and

the responses from the one or more AI/ML models identify the specific dataspace, the one or more specific classes, the one or more specific properties, and the data access queries.

13 . The apparatus of claim 10 , wherein the semantic data model models the data topology using multiple classes and associated properties that are semantically aligned with natural language on which the AI/ML pipeline is trained.

14 . The apparatus of claim 13 , wherein at least some of the classes are associated with multiple associations in the semantic data model.

15 . The apparatus of claim 10 , wherein:

the AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent;

the dataspace agent is configured to identify one of multiple dataspaces associated with the user query;

the class agent is configured to identify at least one of multiple classes associated with the identified dataspace, the at least one identified class mapped to the data topology;

the property agent is configured to identify at least one of multiple properties within the at least one identified class; and

the query agent is configured to generate each data access query based on at least one of: the at least one identified class and the at least one identified property.

16 . The apparatus of claim 15 , wherein the self-healing agent is configured to determine, for each data access query, whether:

a syntax of the data access query has one or more errors;

at least one property in the data access query exists;

one or more values in the data access query are proper; and

a data type of a value in the data access query matches an expected data type.

17 . A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:

provide a user query to a self-healing multi-agent artificial intelligence/machine learning (AI/ML) pipeline, the user query requesting a response based on data stored in a data topology, the data topology modeled using a semantic data model;

generate an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline configured to prompt one or more AI/ML models to generate the initial data access query;

determine that the initial data access query includes a hallucination or error;

perform an automatic loop one or more times, wherein the instructions when executed cause the at least one processor, during the automatic loop, to:

provide additional information to the AI/ML pipeline;

generate an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model, the AI/ML pipeline configured to prompt at least one of the one or more AI/ML models based on the additional information;

determine whether the updated data access query includes a hallucination or error; and

if the updated data access query includes a hallucination or error, repeat the automatic loop;

use a final data access query with no hallucination or error to retrieve the data from the data topology; and

generate the response using the data retrieved from the data topology.

18 . The non-transitory computer readable medium of claim 17 , wherein:

the semantic data model represents the data topology and identifies dataspaces, classes, and properties associated with the data topology;

agents of the AI/ML pipeline are configured to use the semantic data model to identify a specific dataspace, one or more specific classes, and one or more specific properties associated with the user query; and

the AI/ML pipeline is configured to generate each data access query based on the specific dataspace, the one or more specific classes, and the one or more specific properties.

19 . The non-transitory computer readable medium of claim 18 , wherein:

the semantic data model provides context to the agents of the AI/ML pipeline;

the agents comprise the one or more AI/ML models configured to generate responses when prompted by the agents; and

the responses from the one or more AI/ML models identify the specific dataspace, the one or more specific classes, the one or more specific properties, and the data access queries.

20 . The non-transitory computer readable medium of claim 17 , wherein:

the AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent;

the dataspace agent is configured to identify one of multiple dataspaces associated with the user query;

the class agent is configured to identify at least one of multiple classes associated with the identified dataspace, the at least one identified class mapped to the data topology;

the property agent is configured to identify at least one of multiple properties within the at least one identified class; and

the query agent is configured to generate each data access query based on at least one of: the at least one identified class and the at least one identified property.

Assignments (1)

Continuity (3)

References Cited (11)