Query augmentation

Patent metadata

Field	Value
Publication number	US-2025390515-A1
Application number	US-202418750618-A
Country	US
Kind code	A1
Filing date	Jun 21, 2024
Priority date	Jun 21, 2024
Publication date	Dec 25, 2025
Grant date	—

Abstract

Aspects of the present disclosure relate to an online resource that can initiate a conversation between a user and an automated assistant provided by the online resource. The online resource identifies a plurality of queries from the user during the conversation between the user and the automated assistant and determines a context for each query. The online resource selects an agent for each query based on its context, and then sends the queries to their respective selected agents to generate responses. The online resource combines the responses received from the selected agents to form an answer to the query, and then provides the answer to the user.

First claim

What is claimed is:

1 . A method for routing user requests from an automated assistant associated with an online resource, the method performed by one or more processors of a computing system associated with the online resource and comprising:
receiving, from the user over a communications network coupled to the computing system, a request for an automated assistant; initiating a conversation, over the communications network, between the user and the automated assistant in response to the request; identifying a plurality of queries from the user during a portion of the conversation; determining a context for each of the plurality of queries; selecting, for each of the plurality of queries, one agent of a plurality of agents based on the determined context for the respective query; sending each of the plurality of queries to a respective agent of the selected agents; and receiving, from each of the selected agents, a response to the respective query of the plurality of queries.

2 . The method of claim 1 , wherein the context is based at least in part on one or more previous portions of the conversation.

3 . The method of claim 1 , wherein the context includes a browsing history of the user within a user assistance page or web site associated with the online resource.

4 . The method of claim 1 , wherein the context is based at least in part on a type of application through which the user sends the request to the online resource.

5 . The method of claim 1 , wherein different agents of the plurality of agents are configured to generate responses to different queries associated with different contexts or different groups of contexts.

6 . The method of claim 1 , wherein each of the plurality of agents is associated with a corresponding large language model (LLM) trained using query-and-response training data associated with a unique context or a unique group of contexts.

7 . The method of claim 1 , wherein selecting the agent for a respective query includes comparing the context for the respective query with an agent description associated with the selected agent.

8 . The method of claim 7 , wherein selecting the agent further comprises:
determining a degree of similarity between the context and the agent descriptions for the plurality of agents; and selecting the agent associated with the highest degree of similarity to generate the response for the respective sub-query.

9 . The method of claim 1 , further comprising:
combining the responses from the selected agents into an answer responsive to the plurality of queries; and transmitting the answer to the user over the communications network.

10 . The method of claim 9 , further comprising:
presenting the answer to the user as part of the conversation between the automated assistant and the user.

11 . A computing system associated with an online resource, the computing system comprising:
one or more processors; and a memory communicatively coupled with the one or more processors and storing instructions that, when executed by the one or more processors, causes the computing system to:
receive, from the user over a communications network coupled to the computing system, a request for an automated assistant;
initiate a conversation, over the communications network, between the user and the automated assistant in response to the request;
identify a plurality of queries from the user during a portion of the conversation;
determine a context for each of the plurality of queries;
select, for each of the plurality of queries, one agent of a plurality of agents based on the determined context for the respective query;
send each of the plurality of queries to a respective agent of the selected agents; and
receive, from each of the selected agents, a response to the respective query of the plurality of queries.

12 . The computing system of claim 11 , wherein the context is based at least in part on one or more previous portions of the conversation.

13 . The computing system of claim 11 , wherein the context includes a browsing history of the user within a user assistance page or web site associated with the online resource.

14 . The computing system of claim 11 , wherein the context is based at least in part on a type of application through which the user sends the request to the online resource.

15 . The computing system of claim 11 , wherein different agents of the plurality of agents are configured to generate responses to different queries associated with different contexts or different groups of contexts.

16 . The computing system of claim 11 , wherein each of the plurality of agents is associated with a corresponding large language model (LLM) trained using query-and-response training data associated with a unique context or a unique group of contexts.

17 . The computing system of claim 11 , wherein execution of the instructions to select the agent for a respective query causes the computing system to compare the context for the respective query with an agent description associated with the selected agent.

18 . The computing system of claim 17 , wherein execution of the instructions to select the agent causes the computing system to:
determine a degree of similarity between the context and the agent descriptions for the plurality of agents; and select the agent associated with the highest degree of similarity to generate the response for the respective sub-query.

19 . The computing system of claim 11 , wherein execution of the instructions further causes the computing system to:
combine the responses from the selected agents into an answer responsive to the plurality of queries; and transmit the answer to the user over the communications network.

20 . The computing system of claim 19 , wherein execution of the instructions further causes the computing system to:
present the answer to the user as part of the conversation between the automated assistant and the user.

Assignees

• Intuit Inc

Inventors

• Bosnjakovic Dusan
• Lamba Prarit
• Khoshnevisan Farzaneh
• Kang Byungkyu
• Joshi Anmol

Classifications

• G06F16/3344

  using natural language analysis · CPC title

• G06F16/3329 Primary

  Natural language query formulation · CPC title

• G06F16/345 Primary

  Summarisation for human users · CPC title