Reinforcement Learning for Sequential Pattern Extraction
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Solution Overview
Problem
Existing systems face challenges in identifying high-value sequential patterns within large datasets of system interaction events, which are crucial for optimizing user interface configurations and improving online resource management, due to the vast and complex nature of sequential pattern spaces.
Innovation Solution
The use of reinforcement learning techniques to train machine learning models that learn state-action maps containing high-value sequential patterns, where a custom utility measure is applied to calculate rewards and update the model, allowing for the extraction of high-value patterns and subsequent interface modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional pattern mining methods are used to identify sequential patterns in large datasets, then the search process becomes computationally infeasible, but the quality and value of identified patterns cannot be guaranteed
Solution Approach 1:
The patent transforms the pattern mining problem from a static combinatorial search to a dynamic reinforcement learning process. By changing the parameters from exhaustive pattern enumeration to learned policy extraction, the system achieves feasible computational complexity while maintaining high pattern quality through reward-based optimization.
Solution Approach 2:
The patent replaces traditional mechanical pattern mining algorithms with a reinforcement learning agent that learns optimal patterns through interaction with the data environment. This substitution transitions from brute-force computational mechanics to intelligent learning mechanics, resolving the contradiction between search completeness and computational feasibility.
2Measurement precision
If reinforcement learning is used to learn state-action maps, then high-value sequential patterns can be extracted efficiently, but the model training requires significant computational resources
Solution Approach 1:
The reinforcement learning agent performs self-learning through interaction with the environment, automatically identifying high-value patterns without requiring manual feature engineering or exhaustive search. The agent serves itself by learning from rewards, reducing the need for external computational intervention while achieving high precision in pattern identification.
Solution Approach 2:
The patent implements a feedback mechanism where the reinforcement learning agent receives rewards based on the value of discovered patterns. This feedback loop allows the model to iteratively improve its pattern identification accuracy while efficiently allocating computational resources to high-value learning trajectories, balancing precision and resource consumption.
Data Source
AI summary
In some embodiments, techniques for extracting high-value sequential patterns are provided. For example, a process may involve training a machine learning model to learn a state-action map that contains high-utility sequential patterns; extracting at least one high-utility sequential pattern from the trained machine learning model; and causing a user interface of a computing environment to be modified based on information from the at least one high-utility sequential pattern.