Machine Learning Task Generation with Feedback Retraining
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Solution Overview
Problem
Current collaborative messaging systems lack efficient techniques for automatically generating tasks from text messages, leading to increased user workload, cognitive load, and potential manual mistakes in task management.
Innovation Solution
A machine learning module processes input text messages to generate task information, including intended actions and associated users, with feedback mechanisms to retrain the module, enhancing the accuracy of task generation by reinforcing correct outputs and reducing incorrect ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual task generation is used, then user control over task creation is maintained, but user workload and cognitive load increase
Solution Approach 1:
The system performs self-service by automatically generating tasks from message conversations without requiring manual user intervention. The machine learning module autonomously processes messages, identifies task information, and creates task objects, thereby eliminating the need for users to manually create tasks while maintaining user control through feedback mechanisms.
Solution Approach 2:
The system implements feedback by allowing users to review automatically generated tasks and provide corrections. User feedback is captured and used to retrain the machine learning module, creating a continuous improvement loop that enhances automation accuracy while preserving user authority over task creation.
2Reliability
If manual task creation is performed, then accuracy can be controlled by the user, but manual mistakes and errors increase
Solution Approach 1:
The system replaces the mechanical manual process of task creation with an automated machine learning-based system. The machine learning module processes message conversations and generates tasks automatically, eliminating manual errors while maintaining accuracy through continuous learning from user feedback.
Solution Approach 2:
The system uses feedback mechanisms where users can correct automatically generated tasks. These corrections are fed back into the machine learning module for retraining, continuously improving accuracy and reducing errors while maintaining high productivity through automation.
3Productivity
If automated task generation is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The machine learning module serves multiple functions: it processes message conversations, identifies task information, extracts relevant details, and generates task objects. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated module, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The feedback mechanism allows the system to learn from user corrections and improve automatically, reducing the need for complex manual configuration and maintenance. This self-improving capability simplifies system operation while maintaining high productivity through continuous optimization.
4Measurement precision
If machine learning module is retrained continuously, then task generation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs partial retraining by selectively updating the machine learning module using only the feedback from recently generated tasks rather than retraining on all historical data. This partial action approach improves accuracy for current tasks while minimizing the time and computational resources required for retraining.
Solution Approach 2:
The system prepares feedback data in advance and structures it for efficient processing. By pre-processing and organizing user feedback corrections, the system can perform rapid retraining operations that minimize time loss while maximizing accuracy improvements.
Data Source
AI summary
Provided are a computer program product, system, and method for generation of tasks and retraining machine learning modules to generate tasks based on feedback for the generated tasks. A machine learning module processes an input text message sent in the communication channel to output task information including an intended action and a set of associated users. A task message is generated including the output task information of a task to perform. The task message is sent to a user interface panel in a user computer. Feedback is received from the user computer on the output task information in the task message. The machine learning module is retrained to output task information from the input text message based on the feedback to reinforce likelihood correct task information is outputted and reinforce lower likelihood incorrect task information is outputted.