Dynamic Order-Worker Matching Using Multi-Mode Distribution
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Solution Overview
Problem
Distributed network environments face challenges in dynamically matching heterogeneous tasks with heterogeneous client devices, exacerbated by incomplete information, real-time data updates, and client device constraints such as limited processing power and memory, as well as worker unwillingness to provide data or expend time.
Innovation Solution
A work distribution platform utilizing multiple dynamic matching systems that include a worker offering system, platform offering system, and open order system, which operate simultaneously to match orders with workers, leverage machine learning models for inference, and generate training data to improve matching accuracy, while allowing workers more control over order acceptance and pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single matching system is used to pair tasks with workers, then the system complexity is low, but the matching precision and adaptability to heterogeneous tasks and workers deteriorates
Solution Approach 1:
The patent divides the matching system into multiple specialized matching systems, each handling different types of tasks or workers. This segmentation allows each subsystem to optimize for specific matching criteria while the overall system maintains complexity through modular organization rather than monolithic design.
Solution Approach 2:
The platform implements multiple matching systems that can handle different matching scenarios (e.g., skill-based matching, location-based matching, time-based matching). Each system serves a specific function but collectively they provide universal coverage for heterogeneous task-worker pairing requirements.
2Measurement precision
If machine learning models are used to infer missing information, then the matching accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting and preprocessing worker and task data before matching occurs. Machine learning models are trained in advance on historical data, and inference is performed only when needed for actual matching decisions, reducing real-time processing requirements.
Solution Approach 2:
The system applies machine learning selectively rather than universally. It uses ML inference only for cases where information is missing or ambiguous, while relying on direct data comparison for complete cases, thus avoiding excessive computational overhead while maintaining accuracy where needed.
3Productivity
If multiple matching systems operate simultaneously, then the matching efficiency and adaptability improve, but the system complexity and coordination overhead increase
Solution Approach 1:
The patent introduces a coordinator component that acts as an intermediary between multiple matching systems. This coordinator receives task requests, determines which matching systems are applicable, routes requests appropriately, and aggregates results, thereby managing complexity without sacrificing the benefits of multiple simultaneous systems.
4Ease of operation
If workers are given control over order acceptance and pricing, then the worker satisfaction improves, but the platform's ability to optimize matching deteriorates
Solution Approach 1:
The system implements feedback mechanisms where worker preferences and pricing decisions are collected and fed back into the matching algorithms. This allows the platform to learn from worker behavior and adjust optimization strategies accordingly, balancing worker autonomy with system-wide optimization goals.
Data Source
AI summary
A work distribution platform is disclosed that is coupled with client devices associated with workers. The work distribution may include matching systems to match workers with orders. An example matching system is a worker offering system, with which the work distribution platform provides orders to workers so that the workers may submit offers to perform the order. The work distribution platform may include a plurality of matching systems, each of which may perform a different process for matching orders with workers and each of which may be used by the work distribution platform.