Automated Human-Machine System Optimization via Hierarchical Action Classification
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Solution Overview
Problem
Current methods for managing and optimizing operations within organizations involving both humans and machines lack the ability to automatically sense, classify, and analyze actions in real-time, leading to inefficiencies and suboptimal decision-making.
Innovation Solution
A system that automatically senses, abstracts, and classifies entity actions using a network of sensors, machine learning, and signal processing techniques to create real-time or near-real-time models, enabling predictive simulations and optimization of organizational processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated sensing and classification systems are implemented, then productivity and decision-making quality improve, but device complexity and implementation cost increase
Solution Approach 1:
The system segments operational analysis into distinct hierarchical levels: gesture recognition (basic movements), behavior identification (patterns of gestures), activity classification (sequences of behaviors), and accomplishment tracking (completed tasks). This segmentation allows complex operational data to be processed in manageable, modular units that can be independently optimized and implemented.
Solution Approach 2:
The patent introduces computational models as intermediary layers between raw sensor data and decision-making systems. These models automatically infer causal rules and relationships from sensed data, serving as mediators that translate complex operational observations into actionable insights without requiring direct human analysis of raw data streams.
2Adaptability or versatility
If real-time action classification is implemented, then responsiveness and adaptability improve, but measurement precision requirements and processing demands increase
Solution Approach 1:
The system performs preliminary classification of operational data into hierarchical categories (gestures, behaviors, activities, accomplishments) before detailed analysis. This preliminary structuring of data enables faster subsequent processing and reduces the computational burden of real-time precision classification by pre-organizing information in meaningful groups.
Solution Approach 2:
The patent adds temporal and hierarchical dimensions to action classification, transforming static action recognition into dynamic multi-level categorization. By organizing actions across multiple hierarchical levels and time sequences, the system achieves precise real-time classification through dimensional expansion rather than relying solely on increased measurement precision at a single level.
3Reliability
If comprehensive entity modeling is implemented, then predictive capability and optimization potential improve, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts and focuses on key causal relationships and critical patterns from comprehensive operational data, rather than processing all available data equally. By identifying and isolating the most significant causal rules governing entity behavior, the system achieves reliable predictive capability while reducing overall data processing volume through selective extraction of essential information.
Solution Approach 2:
The patent transforms raw operational data into standardized parametric models with defined causal relationships. By converting diverse operational observations into consistent parameterized entity models with predictable behavioral rules, the system enables efficient computational processing while maintaining high predictive accuracy through parameterized representations rather than raw data manipulation.
Data Source
AI summary
A method of analysing and tracking machine systems has the steps of sensing operational data from equipment, the operational data comprising at least location, time, and one or more operational condition data related to the equipment; analysing the operational data to identify data patterns; logging the data patterns as events in a database; comparing the events to a database of predetermined patterns to classify each data pattern as a known event or an unknown event; updating the database to include a new data pattern related to any unknown events; and alerting a user to further classify the unknown events manually.