Time Series Format Conversion From Static Images With GAN Validation
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Solution Overview
Problem
The multiplicity of encoding and recording protocols for time series measurements from various devices hinders the effective use of machine-learning analysis, making data conversion and comprehension cumbersome.
Innovation Solution
An apparatus and method for time series data format conversion using a generator model to parse and align data points, employing a discriminator model to verify alignment with a target domain protocol, and utilizing generative adversarial networks for machine-learning processes to convert static images into dynamic time series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple encoding and recording protocols are used for time series data from various devices, then device compatibility and data source diversity are improved, but data conversion complexity and analysis difficulty increase
Solution Approach 1:
The patent employs an intermediary conversion system that translates time series data from multiple source protocols into a standardized target protocol. The system includes protocol translators and data normalization layers that act as mediators between diverse data sources and the machine learning analysis pipeline, enabling universal data integration without requiring changes to original data collection devices.
Solution Approach 2:
The patent creates a universal data processing framework that can handle multiple encoding and recording protocols through a single standardized interface. The system implements multi-functional capability to process various time series data formats (e.g., CSV, JSON, binary formats from different sensors) and convert them into a unified structure suitable for machine learning analysis, eliminating the need for separate processing pipelines for each data source.
2Measurement precision
If manual data conversion and alignment processes are used for protocol translation, then conversion accuracy can be verified, but processing time and operational effort increase
Solution Approach 1:
The patent replaces manual mechanical data conversion processes with automated computational systems. Machine learning models and algorithmic protocols automatically perform data translation, alignment, and validation tasks that would otherwise require manual intervention. The system uses automated timestamp synchronization, data point matching, and protocol translation algorithms to achieve accurate conversions without human operational input.
Solution Approach 2:
The patent implements self-service capabilities where the data conversion system automatically verifies its own output accuracy through built-in validation mechanisms. The system performs self-checks on converted data including consistency verification, protocol compliance checking, and automatic error detection, eliminating the need for separate manual verification steps while maintaining high conversion accuracy.
3Reliability
If extensive data preprocessing and alignment steps are performed, then data quality for machine learning is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent performs preliminary data preprocessing and alignment operations during the data collection and initial conversion stages. Timestamp synchronization, data normalization, and protocol standardization are executed upfront before data enters the machine learning pipeline. This preliminary action ensures that data quality requirements are met before analysis begins, reducing the need for complex post-processing operations.
Solution Approach 2:
The patent merges multiple preprocessing operations into integrated processing steps. Data conversion, alignment, normalization, and validation operations are combined into unified processing pipelines that execute simultaneously rather than as separate sequential steps. This merging reduces overall processing complexity while maintaining data quality through coordinated execution of multiple preprocessing functions.
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.