Artificial Hierarchical Memory for Real-Time Operator Intent Prediction
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Solution Overview
Problem
Current assisting systems in industries like automotive and aviation face challenges in handling and analyzing the increasing amount of data from sensors and actuators to recognize operational situations in real-time, particularly in providing robust and sensitive monitoring of human intentions in dynamic environments.
Innovation Solution
A Perceptual/Cognitive architecture with an enhanced artificial hierarchical memory system, utilizing a Memory Prediction Framework, which includes spatial temporal nodes and a recursive growing self-organizing neural network for time series analysis, capable of learning frequently seen input patterns and sequences, and predicting operator commands based on temporal events management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of sensors and actuators is increased to monitor more components, then the monitoring coverage and system safety are improved, but the amount of data to be handled and analyzed increases significantly
Solution Approach 1:
The system segments data processing by creating multiple levels of abstraction: raw sensor data is divided into discrete events, which are then grouped into temporal sequences, and finally synthesized into situational contexts. This hierarchical segmentation allows the system to manage large volumes of sensor data from increased sensors and actuators without overwhelming the analysis capacity.
Solution Approach 2:
The patent introduces an intermediary layer between raw sensor data and high-level decision-making. The HTM-based cognitive architecture acts as a mediator that automatically processes, filters, and interprets sensor signals, transforming them into meaningful events and patterns. This intermediary processing layer reduces the burden on downstream systems while maintaining comprehensive monitoring coverage.
2Productivity
If traditional data processing methods are used to handle sensor data, then the system complexity is low, but the system cannot provide robust and sensitive monitoring of human intentions in real-time
Solution Approach 1:
The patent replaces traditional mechanical or rule-based data processing systems with a biologically-inspired HTM-based cognitive architecture. This neural network approach substitutes conventional algorithms with distributed neural computations that can dynamically adapt to patterns in sensor data, enabling robust detection of human intentions while maintaining real-time performance.
Solution Approach 2:
The system implements dynamic processing through the HTM architecture's ability to adaptively learn and update patterns over time. The cognitive model dynamically adjusts its interpretation of sensor data based on temporal sequences and contextual information, allowing the system to respond sensitively to changing human intentions without requiring static, pre-programmed rules for every possible scenario.
3Measurement precision
If all sensor data is analyzed in detail to recognize operational situations, then the recognition accuracy is improved, but the processing time increases and real-time performance deteriorates
Solution Approach 1:
The system extracts only the most relevant features and events from the complete sensor data stream. The HTM architecture selectively identifies significant patterns and temporal sequences while filtering out redundant or less important information. This extraction approach maintains high recognition accuracy by focusing computational resources on critical data elements rather than processing every detail equally.
Solution Approach 2:
The patent applies partial processing by analyzing subsets of sensor data at different levels of detail. The hierarchical architecture performs coarse-grained analysis at higher levels and fine-grained analysis only when necessary at lower levels. This selective processing maintains situation recognition accuracy while reducing overall processing time by avoiding exhaustive analysis of all sensor data in all circumstances.
Data Source
AI summary
This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.