Unpaired Time Series Translation Across Device Protocols
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Solution Overview
Problem
The multiplicity of different encoding and recording protocols from various devices capturing time series measurements hinders the effective training and use of machine-learning analysis, making it inapplicable for most data sets.
Innovation Solution
An apparatus and method for unpaired time series translation using generative adversarial networks and diffusion-based machine-learning processes to convert time series data from an initial domain to a usable format in another domain, validated against a confidence threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different encoding and recording protocols are used from various devices, then device compatibility and data source diversity are improved, but data usability for machine-learning analysis deteriorates
Solution Approach 1:
The patent introduces an intermediary translation system that converts time series data from multiple different encoding and recording protocols into a unified format suitable for machine-learning analysis. This mediator layer allows diverse device protocols to be compatible without requiring changes to the underlying devices or the analysis system, resolving the contradiction between maintaining protocol diversity and ensuring data usability.
2Device complexity
If protocol conversion is implemented without pairing methods, then implementation complexity is reduced, but translation accuracy deteriorates
Solution Approach 1:
The patent employs unsupervised pairing methods that are performed in advance during the training phase, before actual translation is needed. This preliminary action involves automatically pairing source and target domain data without manual intervention, reducing implementation complexity while maintaining translation accuracy through pre-established correspondence relationships.
Solution Approach 2:
The patent implements feedback mechanisms where the translation system continuously evaluates and refines its pairing strategies based on translation results. This feedback loop allows the system to improve translation accuracy over time while maintaining relatively simple implementation by automatically adjusting pairing methods rather than requiring complex predefined rules.
3Measurement precision
If manual pairing methods are used for domain correspondence, then translation precision is improved, but processing time and labor increase
Solution Approach 1:
The patent implements self-service automated pairing methods that enable the system to automatically establish and maintain domain correspondences without requiring manual pairing of source and target data. The unsupervised learning approach allows the system to autonomously identify and establish meaningful relationships between different data domains, achieving both high translation precision and efficient processing.
Solution Approach 2:
The patent performs pairing operations in advance during the training phase, creating reusable pairing models that can be applied to subsequent translation tasks. This preliminary automated pairing eliminates the need for time-consuming manual pairing during actual translation operations, significantly reducing processing time while maintaining precision through the pre-established pairing relationships.
Data Source
AI summary
An apparatus and method for unpaired time series to time series translation is disclosed. The apparatus comprises at least a processor configured to receive an automated analysis of a time series, convert that time series from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.