Machine Learning Response Feedback Loop for Faster Model Training
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Solution Overview
Problem
Conventional technologies for automated responses require significant human labor, computer resources, and training time, necessitating high costs for data preparation and model training.
Innovation Solution
An information processing system that utilizes a machine learning model to generate responses, allows user corrections, and updates the model using these corrections, leveraging a network of users for data collection and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined rules or large volume of training data are used to construct machine learning models for automated responses, then response generation capability is improved, but human labor costs and training time increase significantly
Solution Approach 1:
The system enables users to correct model outputs directly through the interface, and these corrections are automatically fed back into the machine learning model for retraining. This self-service mechanism allows the model to improve itself without requiring external data preparation or manual training interventions, significantly reducing training time and human labor costs while maintaining response quality
Solution Approach 2:
The patent implements a feedback loop where user corrections of model outputs are captured and used to retrain the machine learning model. This continuous feedback mechanism allows the model to learn from actual usage patterns and improve its responses over time without requiring initial large-scale training datasets, thereby reducing both training time and data preparation requirements
2Reliability
If predefined rules or large volume of training data are used to construct machine learning models for automated responses, then response generation capability is improved, but computer resources and human labor costs increase significantly
Solution Approach 1:
The system enables users to correct model outputs directly through the interface, and these corrections are automatically fed back into the machine learning model for retraining. This self-service mechanism allows the model to improve itself without requiring external data preparation or manual training interventions, significantly reducing training time and human labor costs while maintaining response quality
Solution Approach 2:
Instead of discarding incorrect model outputs as waste data, the system recovers them by capturing user corrections and utilizing these corrected outputs for model retraining. This transforms what would be erroneous data into valuable training material, eliminating the need for extensive clean data preparation while improving model performance
3Measurement precision
If machine learning models are trained with extensive data to improve accuracy, then response quality is improved, but the time and resources required for initial setup increase
Solution Approach 1:
The system performs preliminary actions by enabling users to correct model outputs in real-time during normal operation. These corrections are automatically collected and used for subsequent model retraining, allowing the model to improve accuracy progressively without requiring extensive initial training data or setup time. The model leverages actual usage patterns to refine its responses over time
Data Source
AI summary
An information processing system enabling efficient collection of data comprises: a learning model storage storing a trained machine learning model; an input receiving unit configured to receive input data from a first user; a processing circuitry configured to provide the input data to the trained model to generate output data; an output unit configured to output the output data in a manner viewable by the first user and a second user different from the first user, or by the second user only; and a correction result receiving unit configured to receive a correction result in which the second user has revised the output data.