Sparse Indicator Data Processing for Sensor State Analysis
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Solution Overview
Problem
The exponential increase in data sets from various sources, including sensors, poses a challenge in efficiently processing and analyzing this information to generate effective communications, as existing methods struggle to compactly represent and utilize differences in data sets for state analysis.
Innovation Solution
A system and method that utilize sparse indicator information in combination with sensor data to perform status analyses, generating communications indicative of the likelihood of clients transitioning to specific states by identifying differences from reference data sets and integrating sensor data for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to analyze complete data sets, then comprehensive state analysis can be performed, but processing time and computational resources increase exponentially with data set size
Solution Approach 1:
The patent extracts only the sparse indicators (differences) from complete data sets rather than processing entire data sets. By identifying and isolating the specific elements that differ from reference data sets, the system achieves comprehensive state analysis using only the necessary information, dramatically reducing processing time while maintaining analysis accuracy.
Solution Approach 2:
The patent segments the data processing task into identifying sparse indicators separately from processing complete data sets. By dividing the analysis into reference data comparison and sparse indicator extraction, the system processes only the relevant differences rather than entire data sets, reducing computational complexity.
2Loss of information
If complete data sets are stored and processed, then full information is available for analysis, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts sparse indicators (differences) from complete data sets and stores only these extracted elements. This extraction approach maintains information completeness for state analysis while dramatically reducing storage requirements and processing complexity, as only the divergent elements need to be retained and processed.
Solution Approach 2:
The patent creates a compact representation (copy) of the essential information through sparse indicators rather than storing complete data sets. This sparse indicator copy contains all necessary information for state analysis while being significantly more compact and easier to process than full data sets.
3Measurement precision
If sensor data is integrated with sparse indicators, then state transition likelihood accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges sensor data with sparse indicators in a unified processing framework. By combining these two data sources and applying integrated processing logic, the system achieves improved state transition likelihood accuracy while managing processing complexity through the existing sparse indicator structure.
Solution Approach 2:
The patent uses sparse indicators as an intermediary between complete data sets and state analysis. This intermediary structure facilitates the integration of sensor data by providing a standardized format for combining multiple data sources, simplifying the processing complexity while maintaining accuracy.
Data Source
AI summary
Techniques, systems, and products for analyzing sparse indicators and sensor data and generating communications are disclosed. The sensors may be associated with or incorporated into devices that may automatically relay sensor data for use in analyses and communication generation.