Composite ML Item Reconfiguration for Low-Latency Decisions
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Solution Overview
Problem
Existing systems for item reconfiguration are inefficient, reactive, simplistic, and technically deficient, leading to high latency, excessive processing power consumption, and inability to automatically implement item reconfiguration actions or determine field item predictions.
Innovation Solution
A method and apparatus utilizing a composite machine learning model to receive item feature data from internal and external databases, generate a field item feature structure, and identify items for reconfiguration, enabling efficient, proactive, and technically sufficient item reconfiguration actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing item reconfiguration systems are used, then item reconfiguration can be performed, but high latency and excessive processing power consumption occur
Solution Approach 1:
The patent segments the item reconfiguration system into multiple specialized machine learning components (item data hub ML component, item reconfiguration candidate ML component, and multiple reconfiguration ML components). Each component handles specific tasks independently, enabling parallel processing and reducing overall latency compared to monolithic existing systems.
Solution Approach 2:
The system performs preliminary actions by pre-processing item feature data through the item data hub ML component to generate a field item feature structure with predictions before the actual reconfiguration decision is made. This pre-computation reduces processing time during the critical reconfiguration decision phase.
2Productivity
If existing item reconfiguration systems are used, then item reconfiguration can be performed, but excessive processing power consumption occurs
Solution Approach 1:
By dividing the processing workload across multiple specialized ML components, each component can be optimized for its specific function and executed more efficiently. The segmentation allows the system to process only relevant features and predictions for each reconfiguration candidate, reducing redundant computations and overall processing power consumption.
Solution Approach 2:
The system applies partial action by generating only the specific field item predictions needed for reconfiguration decisions rather than processing all possible item attributes. The field item feature structure includes only relevant predicted features, avoiding excessive processing of unnecessary data.
3Extent of automation
If existing item reconfiguration systems are used, then basic reconfiguration can be performed, but inability to automatically implement reconfiguration actions or determine field item predictions occurs
Solution Approach 1:
The machine learning components automatically determine field item predictions and make reconfiguration decisions without human intervention. The system serves itself by using the field item feature structure with predictions to autonomously identify reconfiguration candidates and determine appropriate actions, eliminating the need for manual analysis and implementation.
Solution Approach 2:
The field item feature structure acts as an intermediary data structure that bridges raw item feature data and reconfiguration decisions. It includes predicted features generated by the item data hub ML component, serving as a mediator that enables automated decision-making while managing system complexity through structured data representation.
Data Source
AI summary
A method provided herein includes receiving item feature data representative of a plurality of item configuration features associated with a plurality of items. In some embodiments, the method includes generating a field item feature structure. In some embodiments, the method includes identifying an item of the plurality of items using the field item feature structure and an item reconfiguration candidate machine learning component of a composite machine learning model. In some embodiments, the method includes generating item reconfiguration data for the item of the plurality of items using the field item feature structure and at least one of a plurality of reconfiguration machine learning components of the composite machine learning model. In some embodiments, the method includes initiating performance of one or more item reconfiguration actions based on the item reconfiguration data.