ML Resource Allocation Using Similar Item Matching
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Solution Overview
Problem
Existing resource allocation systems are inefficient, time-consuming, inaccurate, and unable to account for dynamic resource utilization, leading to high latency and excessive processing power consumption, and are unable to automatically implement optimized resource allocation actions.
Innovation Solution
A method that includes identifying resource data using an item matching machine learning model to generate item similarity prediction data, analyzing feature data to select an optimal similar item, and initiating optimized resource allocation actions, utilizing a resource allocation optimization machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource allocation systems are used, then comprehensive resource analysis can be performed, but the system suffers from high latency and excessive processing power consumption
Solution Approach 1:
The patent segments the resource allocation system into multiple specialized machine learning models: item matching model for similarity detection, feature data analysis model for characteristic evaluation, and optimization model for allocation decisions. This segmentation allows each model to specialize in specific tasks, improving overall efficiency and reducing processing time while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system performs preliminary actions by pre-processing resource data and item features before actual allocation decisions. The item matching model pre-identifies similar items, and the feature analysis model pre-evaluates characteristics, so that when allocation decisions are needed, the optimization model receives pre-prepared data, significantly reducing latency.
2Measurement precision
If traditional resource allocation systems are used, then resource analysis can be performed, but the system consumes excessive processing power
Solution Approach 1:
By dividing the processing workload across multiple specialized machine learning models (item matching, feature analysis, optimization), each model handles specific computational tasks efficiently. This segmentation prevents any single model from consuming excessive processing power while maintaining comprehensive resource analysis accuracy.
Solution Approach 2:
The patent introduces intermediary processing layers between raw data and final allocation decisions. The item matching model and feature analysis model act as intermediaries that pre-process and structure data, reducing the computational burden on the final optimization model and overall system processing power consumption.
3Measurement precision
If manual resource allocation methods are used, then detailed analysis can be performed, but automatic optimization cannot be implemented
Solution Approach 1:
The system implements self-service automation through machine learning models that automatically perform resource matching, feature analysis, and allocation optimization without manual intervention. The models learn from historical data and autonomously make allocation decisions, eliminating the need for manual resource allocation while maintaining high accuracy through automated analysis.
Solution Approach 2:
The patent incorporates feedback mechanisms where the optimization model receives input from item matching and feature analysis models, and where allocation outcomes feed back into the system for continuous learning and improvement. This feedback loop enables automatic optimization while maintaining detailed analytical accuracy through iterative refinement.
4Measurement precision
If comprehensive resource data analysis is performed, then accurate allocation decisions can be made, but processing time increases
Solution Approach 1:
The comprehensive resource data analysis is segmented across multiple specialized models that process different aspects simultaneously. The item matching model handles similarity detection, the feature analysis model handles characteristic evaluation, and the optimization model handles allocation decisions. This parallel segmentation maintains comprehensive analysis accuracy while reducing total processing time.
Solution Approach 2:
The system performs preliminary data processing and feature extraction before final allocation decisions. By pre-processing resource data and item characteristics in advance through the matching and feature analysis models, the system maintains accurate decision-making capability while reducing the time required for actual allocation decisions.
Data Source
AI summary
Embodiments of the present disclosure provide optimized resource allocation. Resource data associated with a plurality of items may be identified. Item similarity prediction data may be generated by applying the resource data to an item matching machine learning model. The item similarity prediction data may comprise at least one predicted similar items subset from the plurality of items. The at least one predicted similar items subset may comprise at least one target item from the plurality of items and one or more similar items from the plurality of items. Item feature data associated with the at least one predicted similar items subset may be identified via one or more external sources. Optimization data may be generated by analyzing the item similarity prediction data with respect to the item feature data. The optimization data may comprise an optimal similar item selected from the one or more similar items.