Sensor Data Pre-Processor for Low Power Pattern Recognition
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Solution Overview
Problem
Mobile electronic devices face challenges in providing robust pattern recognition capabilities while maintaining a small form factor and low power consumption, as existing solutions often require additional processors that increase size and cost.
Innovation Solution
A method and apparatus that utilize a finite state machine integrated with sensor hardware, processing sensor data through a data pre-processor and state determiner to apply logical masks and execute pattern recognition algorithms, enabling efficient and cost-effective pattern recognition within a small memory footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additional processors are used for pattern recognition, then pattern recognition capability is improved, but device size and cost increase
Solution Approach 1:
The patent merges the pattern recognition processing functions directly into the sensor hardware by integrating a finite state machine and data pre-processor with the sensor array. This consolidation eliminates the need for separate additional processors, thereby improving pattern recognition capability while avoiding increases in device size and complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer (data pre-processor and finite state machine) that sits between the sensor hardware and the main processor. This intermediary handles pattern recognition tasks locally, reducing the burden on the main processor and enabling robust pattern recognition without requiring additional full-featured processors.
2Adaptability or versatility
If additional processors are used for pattern recognition, then pattern recognition capability is improved, but device cost increases
Solution Approach 1:
The patent combines multiple functions (sensor detection, data preprocessing, and pattern recognition) into a single integrated hardware unit. This merger reduces the total component count and assembly complexity, thereby improving pattern recognition capability while reducing device manufacturing cost.
Solution Approach 2:
The sensor hardware is designed to be self-sufficient by incorporating its own data pre-processor and finite state machine for pattern recognition. This self-service capability eliminates the need for expensive additional processors, enabling robust pattern recognition at lower device cost.
3Adaptability or versatility
If traditional pattern recognition systems are used, then robust pattern recognition is achieved, but power consumption increases
Solution Approach 1:
The patent segments the pattern recognition processing into distinct functional blocks (sensor array, data pre-processor, finite state machine) that operate independently and efficiently. This segmentation allows each component to perform its specific function with minimal power consumption, achieving robust pattern recognition while reducing overall power usage compared to traditional systems that rely on high-power general-purpose processors.
Solution Approach 2:
The integrated sensor hardware performs pattern recognition autonomously using its own embedded finite state machine, eliminating the need to offload processing to high-power external processors. This self-service approach enables robust pattern recognition with significantly reduced power consumption.
Data Source
AI summary
An apparatus for providing pattern recognition may include at least one processor and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the apparatus to at least receive an indication of sensor data descriptive of movement of a user terminal, provide for expansion of the sensor data in a predetermined manner to define outcome values, apply a logical mask to the outcome values to generate selected outcome values for provision to a finite state machine, and utilize a pattern recognition algorithm associated with the finite state machine to determine whether the sensor data corresponds to a pattern identified by proceeding through defined transitions to a final state of the finite state machine. A corresponding computer program product and method are also provided.