User Task Scheduling With Triggered Follow-Up Execution
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Solution Overview
Problem
Existing machine learning models require continuous user interaction to execute tasks that cannot be completed within a predetermined time period, leading to resource waste and limited functionality.
Innovation Solution
A method and apparatus that create subsequent tasks based on user inputs, allowing execution results to be provided when triggering conditions are met, such as at a future time or upon task completion, thereby reducing the need for continuous user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning model waits for the user to maintain dialogue until task completion, then the execution result can be provided accurately, but the user is prevented from executing other tasks and resources are wasted
Solution Approach 1:
The patent segments the task execution process into two independent parts: (1) receiving the user input and creating a subsequent task, and (2) executing the task when triggering conditions are met. This segmentation allows the system to provide the execution result without requiring continuous user interaction, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system performs preliminary action by creating a subsequent task immediately when receiving user input, rather than waiting for the user to maintain dialogue. The task is prepared in advance with defined triggering conditions, allowing execution to occur automatically when conditions are met, thereby improving user productivity while maintaining result accuracy.
2Ease of operation
If the machine learning model requires continuous user interaction to execute tasks, then the execution can be controlled in real time, but resource waste occurs and functionality is limited
Solution Approach 1:
The system implements self-service by automatically executing tasks when triggering conditions are met, without requiring continuous user interaction. The subsequent task is designed to monitor and respond to conditions autonomously, reducing resource waste associated with maintaining continuous dialogue while preserving the ability to control execution through condition definition.
Solution Approach 2:
Instead of continuous user interaction, the system uses periodic action by checking triggering conditions at defined intervals or events. This allows real-time control through condition-based triggering while significantly reducing resource consumption compared to continuous interaction models.
3Productivity
If the task execution is delayed to a future time or until conditions are met, then user interaction is reduced and resources are optimized, but the system complexity increases
Solution Approach 1:
The patent applies nesting by embedding the triggering condition logic and execution mechanism within the subsequent task structure. This nested architecture allows delayed execution functionality to be integrated seamlessly into the existing task management system, reducing the perceived complexity while enabling reduced user interaction and optimized resource usage.
Data Source
AI summary
A method, apparatus, device, and medium for processing a user task are provided. In one method, a user input represented in a natural language is received from a user. The user input indicates a task to be executed. A task type of the task is determined based on the user input. In response to determining that the task type indicates an execution result of the task is unable to be obtained within a predetermined time period, a subsequent task of the task is created to provide the execution result of the task by a machine learning model when a triggering condition for the task is met. With exemplary implementations of the present disclosure, user tasks that cannot obtain the execution result of the task within a predetermined time period can be processed in a more flexible and efficient manner.