Method, device, and computer program product for determining service mode

Patent metadata

Field	Value
Publication number	US-2025384068-A1
Application number	US-202418762969-A
Country	US
Kind code	A1
Filing date	Jul 3, 2024
Priority date	Jun 17, 2024
Publication date	Dec 18, 2025
Grant date	—

Abstract

The present disclosure relates to a method, a device, and a computer program product for determining a service mode. The method includes generating an intent parameter by identifying a user intent in a query content input by a user. The method further includes generating an emotion parameter by analyzing a sentiment inclination in the query content. The method further includes generating a confidence parameter by analyzing a similarity between the query content and training data for training an adaptive strategy model. The method further includes determining a service mode for replying to the query content based on the intent parameter, the emotion parameter, and the confidence parameter. In this way, the optimal service mode can be accurately and timely determined without affecting the query process and losing information, ensuring the coherence and consistency of the user experience, thus improving the user experience.

First claim

What is claimed is:

1 . A method for determining a service mode, comprising:
generating an intent parameter by identifying a user intent in a query content input by a user; generating an emotion parameter by analyzing a sentiment inclination in the query content; generating a confidence parameter by analyzing a similarity between the query content and training data for training an adaptive strategy model; and determining a service mode for replying to the query content based on the intent parameter, the emotion parameter, and the confidence parameter.

2 . The method according to claim 1 , further comprising:
in response to receiving the query content input by the user, generating a reply content based on the query content by a predefined strategy model; based on the reply content, determining whether the predefined strategy model has provided a complete reply; and in response to the predefined strategy model having not provided a complete reply, generating a reply content based on the query content by the adaptive strategy model.

3 . The method according to claim 1 , wherein generating the intent parameter comprises:
generating an evaluated intent value based on the user intent and a preset intent set; and generating the intent parameter based on the evaluated intent value and a preset value.

4 . The method according to claim 1 , wherein generating the emotion parameter comprises:
determining an average emotional value based on scores, emoticons and keywords in the query content; and generating the emotion parameter based on the average emotional value and a preset value.

5 . The method according to claim 1 , wherein determining a service mode for replying to the query content comprises:
determining a decision parameter based on the intent parameter, the emotion parameter, and the confidence parameter; and determining whether the service mode is a model service mode using an adaptive strategy model or a direct service mode based on the decision parameter and a preset value.

6 . The method according to claim 5 , wherein determining the decision parameter comprises:
determining evaluation factors corresponding to the intent parameter, the emotion parameter, and the confidence parameter, the evaluation factors being one of a level or a weight; and determining the decision parameter based on the evaluation factors corresponding to the intent parameter, the emotion parameter, and the confidence parameter.

7 . The method according to claim 5 , wherein determining the decision parameter comprises:
determining a service mode corresponding to the intent parameter, the emotion parameter, and the confidence parameter; determining proximity between the service mode and a preset mode; and determining the decision parameter based on the proximity.

8 . The method according to claim 1 , further comprising:
determining whether the query content comprises a preset content; and determining that the service mode is a direct service mode in response to the query content comprising the preset content.

9 . The method according to claim 1 , further comprising:
determining whether the adaptive strategy model is in a preset scenario; and determining that the service mode is the direct service mode in response to the adaptive strategy model being in the preset scenario.

10 . An electronic device, comprising:
at least one processor; and a memory coupled to the at least one processor and having instructions stored therein, wherein the instructions, when executed by the at least one processor, cause the electronic device to perform actions comprising: generating an intent parameter by identifying a user intent in a query content input by a user; generating an emotion parameter by analyzing a sentiment inclination in the query content; generating a confidence parameter by analyzing a similarity between the query content and training data for training an adaptive strategy model; and determining a service mode for replying to the query content based on the intent parameter, the emotion parameter, and the confidence parameter.

11 . The electronic device according to claim 10 , wherein the actions further comprise:
in response to receiving the query content input by the user, generating a reply content based on the query content by a predefined strategy model; based on the reply content, determining whether the predefined strategy model has provided a complete reply; and in response to the predefined strategy model having not provided a complete reply, generating a reply content based on the query content by the adaptive strategy model.

12 . The electronic device according to claim 10 , wherein generating the intent parameter further comprises:
generating an evaluated intent value based on the user intent and a preset intent set; and generating the intent parameter based on the evaluated intent value and a preset value.

13 . The electronic device according to claim 10 , wherein generating the emotion parameter further comprises:
determining an average emotional value based on scores, emoticons and keywords in the query content; and generating the emotion parameter based on the average emotional value and a preset value.

14 . The electronic device according to claim 10 , wherein determining a service mode for replying to the query content further comprises:
determining a decision parameter based on the intent parameter, the emotion parameter, and the confidence parameter; and determining whether the service mode is a model service mode using an adaptive strategy model or a direct service mode based on the decision parameter and a preset value.

15 . The electronic device according to claim 14 , wherein determining the decision parameter comprises:
determining evaluation factors corresponding to the intent parameter, the emotion parameter, and the confidence parameter, the evaluation factors being one of a level or a weight; and determining the decision parameter based on the evaluation factors corresponding to the intent parameter, the emotion parameter, and the confidence parameter.

16 . The electronic device according to claim 14 , wherein determining the decision parameter comprises:
determining a service mode corresponding to the intent parameter, the emotion parameter, and the confidence parameter; determining proximity between the service mode and a preset mode; and determine the decision parameter based on the proximity.

17 . The electronic device according to claim 10 , wherein the actions further comprise:
determining whether the query content comprises a preset content; and determining that the service mode is a direct service mode in response to the query content comprising the preset content.

18 . The electronic device according to claim 10 , wherein the actions further comprise:
determining whether the adaptive strategy model is in a preset scenario; and determining that the service mode is the direct service mode in response to the adaptive strategy model being in the preset scenario.

19 . A computer program product tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, the machine-executable instructions, when executed by a machine, causing the machine to perform actions comprising:
generating an intent parameter by identifying a user intent in a query content input by a user; generating an emotion parameter by analyzing a sentiment inclination in the query content; generating a confi

Assignees

• Dell Products Lp

Inventors

• Wang Zijia
• Wu Wenlei
• Peng Wenqing
• Liu Jinpeng
• Jia Zhen

Classifications

• G06F16/383

  using metadata automatically derived from the content · CPC title

• G06F16/3344 Primary

  using natural language analysis · CPC title