Anomaly Detection Service With Feedback Training
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Solution Overview
Problem
Current manual inspection methods for detecting anomalies in manufactured parts are slow, prone to errors, and costly, often leading to production line shutdowns and significant financial losses.
Innovation Solution
An anomaly detection service utilizing machine learning models that analyze images for potential anomalies, with a feedback-based training mechanism to improve model accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used to detect anomalies in manufactured parts, then human judgment and experience can identify deviations, but the inspection process becomes slow and error-prone leading to production line shutdowns and financial losses
Solution Approach 1:
The patent replaces the mechanical human inspection system with an automated machine learning-based image analysis system. The system uses trained ML models to process images of manufactured parts, automatically detecting anomalies without human intervention. This substitution eliminates human limitations such as fatigue, inconsistency, and slow processing while maintaining high detection accuracy through continuous learning from feedback.
2Measurement precision
If human inspectors are trained to improve judgment consistency and reduce errors, then detection accuracy improves, but training time and labor costs increase
Solution Approach 1:
The machine learning system performs self-improvement through automated feedback training. Instead of requiring external human training, the system automatically learns from its own performance by processing feedback from anomaly detections. The system continuously retrains on new data, improving its measurement precision and consistency autonomously without requiring time-consuming human training interventions.
3Reliability
If more human inspectors are deployed to reduce errors and increase coverage, then detection reliability improves, but labor costs and operational complexity increase
Solution Approach 1:
The patent extracts the detection function from multiple human inspectors and consolidates it into a single automated machine learning system. This extraction eliminates the need for coordinating multiple human workers, reducing operational complexity while maintaining or improving detection coverage. The system processes images independently and consistently, removing the organizational overhead associated with human inspection teams.
4Productivity
If automated image analysis systems are implemented to replace manual inspection, then inspection speed and consistency improve, but the systems lack adaptability to new anomaly types and require extensive retraining
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning system automatically learns from its detection results and new anomaly data. When new anomaly types are encountered, the system receives feedback from the detection outcomes and continuously retrains its models, adapting to new patterns without requiring manual reprogramming. This feedback loop enables the system to maintain high productivity while gaining versatility in detecting diverse anomaly types.
Data Source
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AI summary
Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.