Dynamic Action Classification Using Machine Learning
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Solution Overview
Problem
Conventional resource-related classification techniques rely on static assumptions and limited data types, leading to inaccurate and inconsistent results across varying geographical and temporal parameters.
Innovation Solution
The implementation of dynamic action classification using machine learning techniques, including the use of regression models and classification models to process resource-related data with temporal lag values, enabling the generation of resource-related forecasts and automated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional static assumption-based classification techniques are used, then the system is simple to implement, but the classification accuracy and consistency deteriorate across varying geographical and temporal parameters
Solution Approach 1:
The patent transitions from static classification models to dynamic machine learning models that continuously learn and adapt to changing patterns in resource data. The system uses temporal lag values and evolving data patterns to dynamically adjust classifications, improving accuracy across varying geographical and temporal parameters while managing complexity through automated learning processes.
Solution Approach 2:
The system changes key parameters by incorporating multiple temporal lag values (e.g., 7-day, 14-day, 30-day lags) and using machine learning models that can process and adapt to varying data characteristics. This allows the classification system to handle diverse geographical and temporal variations without requiring manual reconfiguration for each scenario.
2Reliability
If conventional limited data type techniques are used, then the data processing is computationally efficient, but the classification consistency across different temporal parameters deteriorates
Solution Approach 1:
The patent implements a universal machine learning classification system that handles multiple data types and temporal parameters through a single unified model. The system processes various resource data types (energy consumption, production data, weather data) and temporal lag values using the same classification framework, ensuring consistent results across different geographical and temporal scenarios without requiring separate specialized models.
3Measurement precision
If manual adjustments are frequently made to improve accuracy, then the classification precision improves, but the time consumption and operational effort increase
Solution Approach 1:
The patent implements a self-learning classification system where machine learning models automatically improve their accuracy through continuous learning from historical data and feedback. The system performs self-adjustment by learning from past classifications and outcomes, eliminating the need for frequent manual adjustments while maintaining or improving precision over time. The automated learning process continuously optimizes classification accuracy without human intervention.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for dynamic action classification using machine learning techniques are provided herein. An example computer-implemented method includes generating at least one resource-related forecast by processing, using at least one regression model, resource-related data within at least one predetermined temporal period; converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast using one or more machine learning techniques in conjunction with one or more temporal lag values; classifying at least one resource-related action associated with at least a portion of the at least one predetermined temporal period by processing at least a portion of the at least one resource-related action forecast using at least one classification model; and performing one or more automated actions based at least in part on the at least one classified resource-related action.