Time-Series Image Conversion for Cross-Protocol Data Analysis
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Solution Overview
Problem
The multiplicity of different encoding and recording protocols for time series measurements from devices such as environmental sensors and medical devices makes conversion and analysis cumbersome, undermining the effectiveness of machine-learning applications.
Innovation Solution
An apparatus and method using a computing device to convert static time series images into a usable format through machine-learning processes, including parsing, scaling, and aligning data points, and employing generative adversarial networks and deep neural networks to translate between different domain protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different encoding and recording protocols are used for time series measurements from various devices, then device compatibility and data source diversity are improved, but conversion complexity and analysis difficulty increase
Solution Approach 1:
The patent introduces an intermediary conversion system that translates time series data from multiple different encoding and recording protocols into a standardized universal format. This mediator component handles the protocol diversity by providing a common translation layer, thereby maintaining device compatibility while reducing conversion complexity through systematic standardization.
Solution Approach 2:
The patent creates a universal time series data format that can represent and store measurements from various devices regardless of their original protocols. This universal format serves multiple functions by accommodating diverse input sources while providing a single standardized output structure, thus improving adaptability without proportionally increasing complexity.
2Measurement precision
If manual conversion methods are used for time series data between different protocols, then conversion accuracy can be maintained, but processing time and operational effort increase
Solution Approach 1:
The patent replaces manual mechanical conversion processes with an automated computational system. The universal format and structured conversion methodology enable machine-based processing that maintains conversion accuracy through systematic rules while dramatically reducing processing time and operational effort compared to manual methods.
Solution Approach 2:
The patent establishes a pre-defined universal time series format and conversion rules before actual data conversion is needed. This preliminary structuring of the target format and conversion methodology enables rapid automated processing while ensuring accuracy, as the conversion framework is already in place rather than being created during each conversion operation.
3Reliability
If data is stored in proprietary formats from different devices, then device-specific requirements are met, but data interoperability and analysis applicability decrease
Solution Approach 1:
The patent introduces a universal time series format as an intermediary representation that preserves the reliability and integrity of device-specific data while enabling interoperability. The conversion process acts as a mediator that translates proprietary formats into the universal format without losing essential measurement information, thus maintaining device-specific compliance while achieving data interoperability.
Data Source
AI summary
An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.