Granted Patent B2

US 12,379,964 · App. 18/950,323 · Granted Aug 5, 2025

Inventor: Doo Geon Hwang (Busan, KR)

Assignee: PIAMOND CORP.

G06F9/5027G06F21/53G06F40/30H04L9/32

Abstract

Claims (73)

1. An operating method of a mobile-centric agent hub system (MCAHS) implemented with computer device comprising at least one processor, the operating method comprising:

registering and authenticating, by the at least one processor, AI agents included in an external device;

determining, by the at least one processor, a task by analyzing an instruction received from a user;

selecting, by the at least one processor, an AI agent for processing the determined task, among the registered AI agents;

transmitting, by the at least one processor, the determined task to the selected AI agent;

receiving, by the at least one processor, results of processing of the transferred task from the selected AI agent; and

providing, by the at least one processor, a final response generated based on the received results of the processing to the user;

wherein the registering and authenticating of the AI agent comprises:

issuing an authentication token by identifying an identity of the AI agent of the external device that requests a connection through an application programming interface (API) gateway included in the MCAHS;

validating a service that is providable by the AI agent of the external device by receiving service specifications comprising detailed information of the service and the authentication token;

registering the service with a service catalog when the validation of the service is completed and setting access rights and a security policy for the registered service; and

transmitting a registration completion message comprising a unique identifier of the service and an access token to the AI agent of the external device,

wherein the service specifications comprises information on at least one of a name of the service, the unique identifier of the service, a category of the service, a list of functions supported by the service, a description of the functions, a format of an input parameter of the service, a format of a return value of the service, a quality index of the service, rights and an access level required for the service, a version of the service, and an update history of the service.

2. The operating method of claim 1 , wherein:

the determining of the task comprises determining a plurality of tasks for the processing of the instruction,

the selecting of the AI agent comprises selecting a plurality of AI agents for processing the plurality of tasks,

the receiving of the results of the processing comprises receiving a plurality of processing results from the plurality of AI agents, and

the providing of the final response to the user comprises generating the final response based on integrated results of the plurality of processing results and providing the final response to the user.

3. The operating method of claim 1 , wherein:

the selecting of the AI agent comprises selecting a plurality of AI agents for processing the determined task,

the receiving of the results of the processing comprises receiving a plurality of processing results from the plurality of AI agents, and

the providing of the final response to the user comprises selecting one of the plurality of processing results, generating a final response based on the selected processing result, and providing the final response to the user.

4. The operating method of claim 1 , further comprising monitoring, by the at least one processor, a state of the registered AI agent, wherein the selecting of the AI agent comprises selecting an AI agent for processing the determined task based on the monitored state of the AI agent.

5. The operating method of claim 1 , wherein the selecting of the AI agent 6 . comprises:

generating a list of AI agents corresponding to a service area of the task;

aligning the list of AI agents based on preset priority; and

selecting a preset number of AI agents from a highest priority of the list, wherein the preset priority comprises static priority and dynamic priority,

the static priority comprises at least one of basic priority that is set based on user preference and priority according to a service quality index, and

the dynamic priority comprises at least one of priority based on results of real-time performance monitoring, priority based on an analysis of a recent use pattern of the user for an AI agent, and adaptive priority that is set based on context.

6. The operating method of claim 1 , wherein:

the computer device comprises a mobile device of the user, and

the operating method of the MCAHS is performed by the at least one processor under a control of an AI agent that is provided by an operating platform supplier of the mobile device.

7. The operating method of claim 1 , wherein the determining of the task comprises:

determining an intent of the instruction;

determining a domain of the instruction;

analyzing context associated with the instruction;

generating a feature vector based on the determined intent, the determined domain, and information extracted from the determined context;

predicting a type of the instruction by inputting the generated feature vector to a pre-trained machine learning model; and

determining the task based on synthesized results of the determined intent, the determined domain, the determined context, and the predicted type,

wherein the determining of the task based on the synthesized results comprises:

calculating a confidence score of the synthesized results;

comparing the confidence score with a preset threshold; and

requesting the user to check the determined task when the confidence score is less than the threshold.

8. An operating method of a mobile-centric agent hub system (MCAHS) implemented with computer device comprising at least one processor, the operating method comprising:

registering and authenticating, by the at least one processor, AI agents included in an external device;

determining, by the at least one processor, a task by analyzing an instruction received from a user;

selecting, by the at least one processor, an AI agent for processing the determined task, among the registered AI agents;

transmitting, by the at least one processor, the determined task to the selected AI agent;

receiving, by the at least one processor, results of processing of the transferred task from the selected AI agent; and

providing, by the at least one processor, a final response generated based on the received results of the processing to the user;

wherein the selecting of the AI agent comprises:

generating a list of AI agents corresponding to a service area of the task;

aligning the list of AI agents based on preset priority; and

selecting a preset number of AI agents from a highest priority of the list,

wherein the preset priority comprises static priority and dynamic priority, the static priority comprises at least one of basic priority that is set based on user preference and priority according to a service quality index, and the dynamic priority comprises at least one of priority based on results of real-time performance monitoring, priority based on an analysis of a recent use pattern of the user for an AI agent, and adaptive priority that is set based on context.

9. An operating method of a mobile-centric agent hub system (MCAHS) implemented with computer device comprising at least one processor, the operating method comprising:

registering and authenticating, by the at least one processor, AI agents included in an external device;

determining, by the at least one processor, a task by analyzing an instruction received from a user;

selecting, by the at least one processor, an AI agent for processing the determined task, among the registered AI agents;

transmitting, by the at least one processor, the determined task to the selected AI agent;

receiving, by the at least one processor, results of processing of the transferred task from the selected AI agent; and

providing, by the at least one processor, a final response generated based on the received results of the processing to the user;

wherein the determining of the task comprises:

determining an intent of the instruction;

determining a domain of the instruction;

analyzing context associated with the instruction;

generating a feature vector based on the determined intent, the determined domain, and information extracted from the determined context;

predicting a type of the instruction by inputting the generated feature vector to a pre-trained machine learning model; and

determining the task based on synthesized results of the determined intent, the determined domain, the determined context, and the predicted type,

wherein the determining of the task based on the synthesized results comprises:

calculating a confidence score of the synthesized results;

comparing the confidence score with a preset threshold; and

requesting the user to check the determined task when the confidence score is less than the threshold.

Assignments (1)

Priority Claims (4)

Continuity (1)

References Cited (21)