To-do task generation method and device, electronic equipment and storage medium
By converting manually marked annotation tasks into machine language data and integrating the task thinking chain to generate to-do tasks, the problem of incomplete and trivial to-do tasks in the conference scenario is solved, and a higher degree of comprehensiveness and intelligibility is achieved.
CN120542385APending Publication Date: 2025-08-26IFLYTEK CO LTD
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Patent Information
- Application Number
- CN202510468155.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-08-26
AI Technical Summary
Technical Problem
When the prior art generates to-do tasks in conference scenarios, there are problems such as incomplete, trivial and low intelligibility, mainly due to the diversity of manual labeling data and the differences between human expression habits and machine expression habits.
Method used
By converting manually marked annotation tasks into machine language data that meets human expectations, a transcription task is generated, and combining annotation tasks and transcription tasks, a task thinking chain is determined, and a to-do tasks that align human cognition is integrated.
Benefits of technology
Improve the comprehensiveness, organization and intelligibility of to-do tasks to ensure that the generated to-do tasks are consistent with the human way of thinking.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of computers, and provides a to-do task generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of labeling tasks and a description text of each labeling task; based on the description text of each labeling task, performing machine language transcription on each labeling task to obtain a transcription task corresponding to each labeling task; determining a task thinking chain based on the description text of each labeling task; and based on the task thinking chain, integrating each labeling task and each transliteration task to obtain a plurality of to-do tasks. According to the method, expression habits and cognitive patterns of human beings can be better captured in the process of converting the labeling tasks into the machine language data, so that the generated to-do tasks can be consistent with thinking modes of the human beings, and the comprehensiveness, orderliness and intelligibility of the to-do tasks are improved.
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