Content Analysis Using Multiple ML Models for Higher Confidence
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Solution Overview
Problem
Existing machine learning models struggle to maintain high levels of confidence in real-world content analysis, particularly for fine-grained tasks involving subtle visual distinctions, despite achieving excellent results on benchmark datasets.
Innovation Solution
A combinatorial decision-making approach using independently configured ML models to enhance overall confidence by combining their individual, relatively low-confidence results, employing conditional logic and hierarchical classification architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single ML model is used for fine-grained content analysis, then the model complexity is low, but the confidence level in outputs deteriorates
Solution Approach 1:
The patent combines multiple independently configured ML models to perform content analysis. Each model independently analyzes the content item and provides a confidence score. The results are merged through a combinatorial decision-making process that aggregates confidence scores from multiple models, thereby achieving higher overall confidence levels while maintaining reasonable model complexity for each individual model.
2Reliability
If multiple ML models are used for content analysis, then the overall confidence level improves, but the system complexity increases
Solution Approach 1:
The system segments the content analysis task into multiple independent model evaluations. Each ML model operates independently as a separate module, analyzing the content item and producing individual confidence scores. This segmentation allows the system to achieve higher reliability through multiple independent assessments while managing complexity by keeping each model relatively simple and independent.
Solution Approach 2:
The system implements a feedback mechanism where confidence scores from multiple models are aggregated and combined to produce an overall confidence level. This combinatorial decision-making process provides feedback that enhances the reliability of the content analysis by leveraging agreement among multiple independent models, while the structured feedback aggregation keeps system complexity manageable.
3Ease of manufacture
If existing ML models are used without modification, then the implementation cost is low, but the performance on fine-grained tasks deteriorates
Solution Approach 1:
The patent combines the outputs of multiple existing ML models to achieve enhanced fine-grained analysis capability. By merging the confidence scores from several models that each perform the same fine-grained content analysis task, the system achieves higher measurement precision and reliability without needing to modify or retrain the individual models, thereby maintaining low implementation cost.
Data Source
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Figure 3
Figure 4A
AI summary
In one aspect a computer-implemented method of processing a content item to extract information from the content item comprises steps of: receiving the content item at a processing stage; and determining whether the content item satisfies a predetermined content condition, by i) providing content of the content item to a plurality of content analysers, each of which applies machine learning content analysis thereto, in order to make an independent determination of whether that predetermined content condition is satisfied, and provides a confidence score for that independent determination, and ii) making an overall determination of whether the content item satisfies that predetermined content condition based on the confidence scores provided by the content analysers for their respective independent determinations.