Multi-Modal Time Series Annotation With Domain-Specific Refinement
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Solution Overview
Problem
Existing methods for automatic time series annotation lack the ability to generate high-quality, domain-specific annotations efficiently, often resulting in mislabeling and failing to capture nuanced patterns, especially when precise domain-specific terminology is required.
Innovation Solution
A multi-agent system that includes a general annotation agent and a domain-specific annotation agent, leveraging large language models and reinforcement learning to extract common knowledge from multiple source domains and domain-specific terminology from a target domain, ensuring accurate and contextually appropriate annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing automatic annotation methods are used, then annotation speed is improved, but annotation quality and precision deteriorate
Solution Approach 1:
The patent introduces domain experts as intermediaries between the automatic annotation system and the final annotations. Domain experts review and refine annotations generated by automated methods, ensuring both high speed and high quality. The system acts as an intermediary layer that combines automated efficiency with expert precision.
Solution Approach 2:
The annotation process is segmented into multiple stages: initial automatic annotation, domain-specific refinement, and expert verification. Each stage handles specific aspects of annotation quality, allowing the system to maintain high productivity while progressively improving precision through specialized processing at each level.
2Measurement precision
If manual annotation methods are used, then annotation quality is improved, but time consumption and cost increase
Solution Approach 1:
The system creates and uses domain-specific annotation guides and templates that can be copied and applied consistently across different datasets. These standardized templates encode expert knowledge in reusable formats, maintaining high annotation quality while reducing the time experts need to spend on each individual annotation task.
Solution Approach 2:
Domain-specific terminology and annotation criteria are pre-established and prepared before the actual annotation process. This preliminary preparation work, including creating domain vocabularies and annotation protocols, enables faster execution during the actual annotation phase while maintaining high quality standards.
3Productivity
If generic annotation approaches are used, then processing efficiency is improved, but domain-specific accuracy deteriorates
Solution Approach 1:
The system applies different annotation strategies to different domains and data types. Instead of using a single generic approach, it tailors annotation methods to specific domain requirements while maintaining overall system efficiency. Domain-specific configurations and parameters are applied locally to ensure accuracy without compromising global processing efficiency.
Solution Approach 2:
The annotation system dynamically adjusts parameters such as annotation granularity, terminology vocabulary, and validation criteria based on the specific domain being processed. This allows the system to maintain high processing efficiency by using optimized default parameters while adapting to domain-specific requirements when needed, thereby improving domain-specific accuracy.
Data Source
AI summary
Methods and systems include generating general annotations for input time series data based on annotations from one or more source domains. Domain-specific annotations are generated for the input time series based on annotations from a target domain and based on the general annotations. An action is performed responsive to the domain-specific annotations and the general annotations.