Predictive Anomaly Modeling for Monitored Unit Diversion
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Solution Overview
Problem
The exponential growth of data in various environments leads to inefficiencies as much of it is stored or ignored, creating undesirable outcomes due to the time required for sorting and the potential abandonment of valuable data.
Innovation Solution
A system is developed to aggregate data from disparate sources using a transformative processing engine that integrates, processes, and stores data from various components and user devices, enabling predictive modeling to identify anomaly events by analyzing data attributes from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored or ignored due to exponential growth, then data storage capacity is sufficient, but time efficiency deteriorates and valuable data may be abandoned
Solution Approach 1:
The patent applies preliminary action by pre-processing and tagging data during ingestion, creating metadata labels that enable rapid retrieval later. The system performs data classification, anomaly detection, and tagging upfront rather than sorting through all stored data when needed, thus resolving the contradiction between having sufficient storage capacity and maintaining time efficiency for data retrieval
Solution Approach 2:
The patent introduces an intermediary layer of metadata and tags that mediates between the large volume of stored data and the need for quick retrieval. This intermediary structure allows the system to handle exponential data growth without proportionally increasing sorting time, as queries can filter through metadata rather than examining every stored data point
2Reliability
If data is sorted through manually or using traditional methods, then data can be evaluated, but productivity decreases and desirable outcomes are lost
Solution Approach 1:
The patent replaces manual or traditional mechanical data sorting methods with automated machine learning models and algorithms. These systems automatically evaluate data for anomalies, patterns, and significance, maintaining high evaluation accuracy while dramatically improving productivity by processing data at machine speed rather than human pace
Solution Approach 2:
The patent implements self-service through autonomous data evaluation systems that automatically assess, tag, and prioritize data without human intervention. The machine learning models independently identify valuable data, detect anomalies, and generate insights, thereby maintaining reliability while achieving high productivity through automated self-evaluation
3Device complexity
If traditional data processing methods are used, then system complexity is low, but the ability to identify anomaly events deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the complexity parameter - intentionally increasing system complexity through machine learning models, multiple data sources, and advanced algorithms to achieve superior anomaly detection accuracy. The system accepts higher computational complexity as necessary to detect subtle anomalies that traditional methods would miss, thereby resolving the contradiction by shifting complexity from an unwanted attribute to an enabler of precision
Data Source
AI summary
Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.