Sparse Indicator Processing for Sensor Data Communication
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Solution Overview
Problem
The exponential increase in data sets from various sources, particularly sensor data, poses challenges in efficient processing, as existing methods fail to effectively utilize sparse indicator information to generate meaningful communications about client states or status information.
Innovation Solution
A system comprising hardware processors and a non-transitory computer-readable storage medium that receives queries, identifies sparse indicators, performs status analyses using sensor data, and generates communications indicative of client state transitions, enabling efficient data processing and communication generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data from multiple sources is processed using existing methods, then data processing is performed, but the processing is inefficient and fails to effectively utilize sparse indicator information
Solution Approach 1:
The patent extracts sparse indicator information from sensor data by comparing current sensor readings against reference data sets. This extraction process identifies only the significant deviations or anomalies from normal patterns, rather than processing all sensor data equally. The system queries a data store to identify sparse indicators that represent meaningful differences, thereby efficiently processing large data sets while retaining critical information.
Solution Approach 2:
The patent segments the data processing task into distinct components: (1) querying the data store to identify sparse indicators, (2) retrieving reference data sets, (3) comparing current sensor data against references, and (4) generating communications based on identified deviations. This segmentation allows each component to be optimized independently and improves overall processing efficiency.
2Measurement precision
If all sensor data is processed in detail, then comprehensive analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
Instead of processing all sensor data in detail, the patent extracts only the sparse indicators that represent meaningful deviations from reference patterns. By querying the data store to identify which specific data points differ from references, the system achieves comprehensive analysis of critical information without the computational burden of detailed processing of all data points.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of sensor data - specifically, only those data points that deviate from reference patterns. The system queries the data store to identify sparse indicators and then processes only those specific deviations, rather than performing excessive detailed processing on all sensor data, thereby reducing processing time while maintaining analysis accuracy.
3Reliability
If sparse indicator information is used to generate communications, then communication accuracy improves, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary data store that contains reference data sets and facilitates the identification of sparse indicators. This intermediary structure mediates between the sensor data input and the communication generation output, providing a structured way to compare current data against references and identify meaningful deviations without requiring complex processing logic in the communication generation module.
Solution Approach 2:
The patent segments the system into distinct functional modules: (1) data store for storing reference patterns, (2) query mechanism for identifying sparse indicators, (3) comparison engine for detecting deviations, and (4) communication generator for creating output messages. This segmentation improves communication accuracy by assigning specialized functions to each module while managing system complexity through modular architecture.
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.