Granted Patent B2

US 12,730,968 · App. 18/597,397 · Granted Sep 8, 2026

Inventors: Vipul Khullar (New Delhi, IN); Jan Dumke (Wiesloch, DE)

Assignee: SAP SE

G06F40/284G06F16/243G06F16/252G06F40/205

Abstract

Claims (72)

1 . A computing system comprising:

memory;

one or more hardware processors coupled to the memory; and

one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:

receiving a natural language query input from a user interface;

extracting a target entity from the natural language query input;

identifying a target application programming interface (API) corresponding to the target entity;

formulating an API query using the target API; and

executing the API query to generate a query output on the user interface,

wherein identifying the target API comprises:

generating a vector representation of the target entity;

searching an entity vector database containing vector representations of a plurality of APIs, wherein the searching returns one or more candidate APIs whose vector representations match the vector representation of the target entity, wherein the vector representations of the plurality of APIs correspond to a plurality of entities representing the plurality of APIs in an API graph that defines associations between the plurality of entities; and

prompting a generative artificial intelligence (AI) model to select the target API from the one or more candidate APIs.

2 . The computing system of claim 1 , wherein the operations further comprise creating the entity vector database, wherein creating the entity vector database comprises:

extracting metadata from the plurality of APIs;

generating the API graph based on the metadata extracted from the plurality of APIs;

embedding the plurality of entities into respective vector representations of the plurality of APIs; and

storing the vector representations of the plurality of APIs in the entity vector database.

3 . The computing system of claim 2 , wherein embedding an entity representing an API comprises generating a first vector representation of the API based on metadata of the API and generating a second vector representation of the API based on one or more documents associated with the API.

4 . The computing system of claim 2 , wherein the operations further comprise extracting a parameter value from the natural language query input, wherein formulating the API query comprises mapping the parameter value to a target input value, wherein the mapping comprises:

generating a vector representation of the parameter value;

searching a value vector database containing vector representations of a plurality of input values, wherein the searching returns one or more candidate input values whose vector representations match the vector representation of the parameter value; and

prompting the generative AI model to select the target input value from the one or more candidate input values.

5 . The computing system of claim 4 , wherein the operations further comprise creating the value vector database, wherein creating the value vector database comprises:

identifying the plurality of input values that can be provided as input for parameters of the plurality of APIs;

embedding the plurality of input values into respective vector representations of the plurality of input values; and

storing the vector representations of the plurality of input values into the value vector database.

6 . The computing system of claim 5 , wherein embedding an input value comprises generating a first vector representation of the input value based on a unique identifier of the input value and generating a second vector representation of the input value based on a text description of the input value.

7 . The computing system of claim 4 , wherein extracting the target entity and the parameter value comprises prompting the generative AI model with the natural language query input.

8 . The computing system of claim 4 , wherein formulating the API query comprises prompting the generative AI model to generate an API syntax based on the target API and the target input value.

9 . The computing system of claim 8 , wherein formulating the API query further comprises adding tenant configurations and authentication data to the API syntax.

10 . The computing system of claim 1 , wherein the operations further comprise:

validating the API query prior to executing the API query; and

formatting the query output, wherein the formatting comprises prompting the generative AI model.

11 . A computer-implemented method comprising:

receiving a natural language query input from a user interface;

extracting a target entity from the natural language query input;

identifying a target application programming interface (API) corresponding to the target entity;

formulating an API query using the target API; and

executing the API query to generate a query output on the user interface,

wherein identifying the target API comprises:

generating a vector representation of the target entity;

searching an entity vector database containing vector representations of a plurality of APIs, wherein the searching returns one or more candidate APIs whose vector representations match the vector representation of the target entity, wherein the vector representations of the plurality of APIs correspond to a plurality of entities representing the plurality of APIs in an API graph that defines associations between the plurality of entities; and

prompting a generative artificial intelligence (AI) model to select the target API from the one or more candidate APIs.

12 . The computer-implemented method of claim 11 , further comprising creating the entity vector database, wherein creating the entity vector database comprises:

extracting metadata from the plurality of APIs;

generating the API graph based on the metadata extracted from the plurality of APIs;

embedding the plurality of entities into respective vector representations of the plurality of APIs; and

storing the vector representations of the plurality of APIs in the entity vector database.

13 . The computer-implemented method of claim 12 , wherein embedding an entity representing an API comprises generating a first vector representation of the API based on metadata of the API and generating a second vector representation of the API based on one or more documents associated with the API.

14 . The computer-implemented method of claim 13 , further comprising extracting a parameter value from the natural language query input, wherein formulating the API query comprises mapping the parameter value to a target input value, wherein the mapping comprises:

generating a vector representation of the parameter value;

searching a value vector database containing vector representations of a plurality of input values, wherein the searching returns one or more candidate input values whose vector representations match the vector representation of the parameter value; and

prompting the generative AI model to select the target input value from the one or more candidate input values.

15 . The computer-implemented method of claim 14 , further comprising creating the value vector database, wherein creating the value vector database comprises:

identifying the plurality of input values that can be provided as input for parameters of the plurality of APIs;

embedding the plurality of input values into respective vector representations of the plurality of input values; and

storing the vector representations of the plurality of input values into the value vector database.

16 . The computer-implemented method of claim 15 , wherein embedding an input value comprises generating a first vector representation of the input value based on a unique identifier of the input value and generating a second vector representation of the input value based on a text description of the input value.

17 . The computer-implemented method of claim 14 , wherein extracting the target entity and the parameter value comprises prompting the generative AI model with the natural language query input.

18 . The computer-implemented method of claim 14 , wherein formulating the API query comprises prompting the generative AI model to generate an API syntax based on the target API and the target input value.

19 . The computer-implemented method of claim 11 , further comprising formatting the query output, wherein the formatting comprises prompting the generative AI model to transform the query output from a JSON format to a table format.

20 . One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method, the method comprising:

receiving a natural language query input from a user interface;

extracting a target entity from the natural language query input;

identifying a target application programming interface (API) corresponding to the target entity;

formulating an API query using the target API; and

executing the API query to generate a query output on the user interface,

wherein identifying the target API comprises:

generating a vector representation of the target entity;

searching an entity vector database containing vector representations of a plurality of APIs, wherein the searching returns one or more candidate APIs whose vector representations match the vector representation of the target entity, wherein the vector representations of the plurality of APIs correspond to a plurality of entities representing the plurality of APIs in an API graph that defines associations between the plurality of entities; and

prompting a generative artificial intelligence (AI) model to select the target API from the one or more candidate APIs.

Assignments (2)

Continuity (1)

References Cited (13)