Automated Strategy Adjustment via Reinforcement Learning
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Solution Overview
Problem
The manual process of summarizing, categorizing, and optimizing service strategies based on customer feedback is time-consuming, error-prone, and costly, as it involves manual labor and is impractical for large volumes of feedback from service providers like ride-hailing platforms.
Innovation Solution
A computer-implemented method using Natural Language Processing (NLP) and machine learning to automatically determine characteristics of complaints, classify them, select categories based on complaint frequency, identify candidate strategies causing issues, and optimize these strategies using a reinforcement learning model, such as Monte Carlo Graph Search, to adjust conditions and reduce false positive and false negative rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual process is used to summarize, categorize, and optimize service strategies based on customer feedback, then accuracy and understanding of feedback can be achieved, but the process becomes time-consuming, error-prone, and costly
Solution Approach 1:
The patent replaces the manual mechanical process of summarizing, categorizing, and analyzing feedback with an automated computer system. The system uses natural language processing to extract features from feedback text, machine learning classifiers to categorize feedback automatically, and reinforcement learning to optimize strategies without human intervention, thereby eliminating time loss while maintaining analysis quality
Solution Approach 2:
The system enables the service provider to automatically analyze and optimize their own strategies without requiring external manual analysis. The automated system processes feedback, identifies patterns, and generates strategy optimizations independently, making the organization self-sufficient in its continuous improvement process
2Loss of information
If manual labor is used to process and analyze large volumes of customer feedback, then detailed analysis can be performed, but labor costs increase significantly
Solution Approach 1:
The patent substitutes human labor with an automated computer system that uses natural language processing and machine learning to analyze feedback. The system extracts semantic features, performs sentiment analysis, and categorizes feedback automatically, retaining all detailed information while eliminating the need for expensive human labor
Solution Approach 2:
The system creates digital representations and vector embeddings of feedback content, preserving all informational content in a structured format that can be processed efficiently by algorithms. This digital copying enables comprehensive analysis without the constraints of manual processing capabilities
3Reliability
If comprehensive feedback analysis is performed to identify connections between strategies and feedback, then strategy optimization quality improves, but the complexity of the process increases
Solution Approach 1:
The patent divides the complex feedback analysis process into distinct modular components: natural language processing for feature extraction, machine learning classifiers for categorization, and reinforcement learning for strategy optimization. Each module handles a specific aspect of the analysis, making the overall complex process manageable and maintainable while ensuring comprehensive analysis for high-quality optimization
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically adjusting strategies. One of the methods includes: determining one or more characteristics of a plurality of complaints, wherein each of the complaints corresponds to an order; classifying the plurality of complaints into a plurality of categories based on the one or more characteristics by using a trained classifier; selecting a category from the plurality of categories based on a number of complaints in the selected category; from a group of strategies each associated with one or more conditions and one or more actions, identifying a candidate strategy causing the complaints of the selected category, wherein the one or more actions are executed in response to the one or more conditions being satisfied; and optimizing the candidate strategy using a reinforcement learning model at least based on a plurality of historical orders.