Granted Patent B2

US 12,626,166 · App. 17/807,039 · Granted May 12, 2026

Inventors: Vagner Figueredo de Santana (São Paulo, BR); Ana Fucs (Rio de Janeiro, BR); Vinicius Costa Villas Boas Segura (Rio de Janeiro, BR); Daniel Brugnaro De Moraes (São Paulo, BR); Renato Fontoura de Gusmao Cerqueira (Rio de Janeiro, BR)

Assignee: International Business Machines Corporation

G06N5/045G06F11/3438G06F18/214G06F18/22

Abstract

Claims (40)

1 . A computer-implemented method of selecting an artificial intelligence algorithm, executable by a processor, comprising:

receiving received data corresponding to a user interaction in a user interface with an artificial intelligence system, wherein the received data includes a dataset selected by a user in the user interface, wherein the user interaction includes an event and a target;

creating embeddings based on the received data and a usage graph used to train a model training module, wherein the usage graph comprises events and targets of users interacting with the artificial intelligence system;

calculating a similarity value between the embeddings;

identifying a need by the user for an explanation associated with the artificial intelligence system based on the similarity value;

selecting an artificial intelligence algorithm from among a plurality of artificial intelligence algorithms, wherein the selected artificial intelligence algorithm includes an explainable artificial intelligence component;

creating a model for the selected artificial intelligence algorithm corresponding to the received data; and

providing feedback related to the created model back to the user through the user interface.

2 . The computer-implemented method of claim 1 , wherein the artificial intelligence algorithm is selected based on previous user interactions with the artificial intelligence system selecting an artificial intelligence algorithm having an explainable artificial intelligence component.

3 . The computer-implemented method of claim 1 , further comprising training the model based on measuring a similarity value between the received data and data corresponding to previous user interactions having a need for an explanation associated with the artificial intelligence system.

4 . The computer-implemented method of claim 1 , wherein the similarity value corresponds to a cosine similarity value between vectors associated with the created embeddings.

5 . The computer-implemented method of claim 1 , further comprising providing an explanation of the artificial intelligence algorithm to the user.

6 . The computer-implemented method of claim 1 , wherein the data corresponding to the user interaction with the artificial intelligence system includes data corresponding to an identifier, a timestamp, an event, a target, and metadata.

7 . A computer system for selecting an artificial intelligence algorithm, the computer system comprising:

one or more computer-readable storage media configured to store computer program code; and

one or more computer processors configured to access said computer program code and operate as instructed by said computer program code, said computer program code including:

receiving code configured to cause the one or more computer processors to receive received data corresponding to a user interaction in a user interface with an artificial intelligence system, wherein the received data includes a dataset selected by a user in the user interface, wherein the user interaction includes an event and a target;

creating code configured to cause the one or more computer processors to create embeddings based on the received data and a usage graph used to train a model training module, wherein the usage graph comprises events and targets of users interacting with the artificial intelligence system;

calculating code configured to cause the one or more computer processors to calculate a similarity value between the embeddings;

identifying code configured to cause the one or more computer processors to identify a need by the user for an explanation associated with the artificial intelligence system based on the similarity value;

selecting code configured to cause the one or more computer processors to select an artificial intelligence algorithm from among a plurality of artificial intelligence algorithms, wherein the selected artificial intelligence algorithm includes an explainable artificial intelligence component;

creating code configured to cause the one or more computer processors to create a model for the selected artificial intelligence algorithm corresponding to the received data; and

providing feedback related to the created model back to the user through the user interface.

8 . The computer system of claim 7 , wherein the artificial intelligence algorithm is selected based on previous user interactions with the artificial intelligence system selecting an artificial intelligence algorithm having an explainable artificial intelligence component.

9 . The computer system of claim 7 , further comprising training code configured to cause the one or more computer processors to train the model based on measuring a similarity value between the received data and data corresponding to previous user interactions having a need for an explanation associated with the artificial intelligence system.

10 . The computer system of claim 7 , wherein the similarity value corresponds to a cosine similarity value between vectors associated with the created embeddings.

11 . The computer system of claim 7 , further comprising providing code configured to cause the one or more computer processors to provide an explanation of the artificial intelligence algorithm to the user.

12 . The computer system of claim 7 , wherein the data corresponding to the user interaction with the artificial intelligence system includes data corresponding to an identifier, a timestamp, an event, a target, and metadata.

13 . A computer readable medium having stored thereon a computer program for selecting an artificial intelligence algorithm, the computer program configured to cause one or more computer processors to:

receive received data corresponding to a user interaction in a user interface with an artificial intelligence system, wherein the received data includes a dataset selected by a user in the user interface, wherein the user interaction includes an event and a target;

create embeddings based on the received data and a usage graph used to train a model training module, wherein the usage graph comprises events and targets of users interacting with the artificial intelligence system;

calculate a similarity value between the embeddings;

identify a need by the user for an explanation associated with the artificial intelligence system based on the similarity value;

select an artificial intelligence algorithm from among a plurality of artificial intelligence algorithms, wherein the selected artificial intelligence algorithm includes an explainable artificial intelligence component;

create a model for the selected artificial intelligence algorithm corresponding to the received data; and

providing feedback related to the created model back to the user through the user interface.

14 . The computer readable medium of claim 13 , wherein the artificial intelligence algorithm is selected based on previous user interactions with the artificial intelligence system selecting an artificial intelligence algorithm having an explainable artificial intelligence component.

15 . The computer readable medium of claim 13 , wherein the computer program is further configured to cause the one or more computer processors to train the model based on measuring a similarity value between the received data and data corresponding to previous user interactions having a need for an explanation associated with the artificial intelligence system.

16 . The computer readable medium of claim 13 , wherein the similarity value corresponds to a cosine similarity value between vectors associated with the created embeddings.

17 . The computer readable medium of claim 13 , wherein the computer program is further configured to cause the one or more computer processors to provide an explanation of the artificial intelligence algorithm to the user.

Assignments (1)

Continuity (1)

References Cited (29)