Unified Machine Learning Model for Outlier Detection
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Solution Overview
Problem
Conventional systems require excessive processing power to detect outliers across multiple areas of an entity, as each area may have different definitions of outliers, necessitating multiple machine learning models and coordinating their results, which is inefficient and resource-intensive.
Innovation Solution
A machine learning model clusters data from entity operations to identify outliers without being dedicated to any specific area, allowing operations from multiple sub-entities to be input, enabling detection of outliers for the entity as a whole without the need for multiple models or additional processing, and dynamically adjusts criteria for outlier identification based on feedback and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are used to detect outliers in different areas of an entity, then outlier detection accuracy is improved, but processing power consumption increases excessively
Solution Approach 1:
The patent combines multiple area-specific outlier detection models into a single unified machine learning model that processes data from multiple areas simultaneously. This consolidation maintains the ability to detect outliers across different entity areas while eliminating the need to run separate models, thereby reducing processing power consumption while preserving detection accuracy.
Solution Approach 2:
The patent creates a universal machine learning model that can detect outliers across multiple different areas of an entity through a single model instance. This multi-functional model replaces the need for area-specific models, achieving broad outlier detection capability without the computational overhead of maintaining and executing multiple separate models.
2Reliability
If multiple machine learning models are used for different entity areas, then comprehensive outlier coverage is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple area-specific detection models into a single unified model that handles data from multiple entity areas. This consolidation maintains comprehensive outlier coverage across all areas while eliminating the complexity of coordinating results from multiple separate models, as the unified model produces a single integrated output.
3Measurement precision
If area-specific machine learning models are used, then localized outlier detection precision is improved, but loss of time for coordinating results increases
Solution Approach 1:
The patent combines multiple area-specific models into a unified model that processes data from multiple areas in a single operation. This approach maintains the precision of localized outlier detection while eliminating the time-consuming process of coordinating results from multiple separate models, as the unified model produces integrated results directly.
Data Source
AI summary
A system and a method are disclosed herein for machine-learned detection of outliers within payload requests. An entity management system uses machine learning to cluster data characterizing requests from entities to route payloads, and determines one or more data clusters that are outliers. The system receives a request to route a payload to a destination, and applies a supervised machine learning model to size and type information indicated by the payload. The supervised machine learning model applies a label to the payload data (e.g., indicating that the payload routing request is an outlier). This outlier detection may drive a validation process to address detected outliers. The system may receive an indication to perform a validation function and transmit the payload to a validation destination. The system may leverage payload data and feedback received from an entity to optimize machine learning techniques to the entity.