Work Management Platform Matching Workers to Orders
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current work management techniques, especially in the gig economy and shipping industries, face challenges in efficiently matching workers with orders due to scheduling conflicts, communication issues, and the need to balance cost and quality, often leading to suboptimal outcomes and increased costs.
Innovation Solution
A work management platform that uses machine learning models to calculate order-worker scores based on order characteristics and past worker behavior, optimizing the matching process to predict selection and completion likelihoods, and dynamically adjusts assignments to improve workflow efficiency and worker satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional heuristic techniques are used to match workers with orders, then the matching process is simple and fast, but the matching accuracy is low leading to suboptimal outcomes
Solution Approach 1:
The patent replaces traditional heuristic matching methods with machine learning models that use historical data and worker characteristics to predict order completion likelihood. This substitution of mechanical/heuristic systems with intelligent algorithms resolves the contradiction by achieving higher matching accuracy through data-driven predictions while managing complexity through automated model training and inference processes.
Solution Approach 2:
The system implements feedback loops where actual order completion data is continuously fed back to retrain and improve the machine learning models. This feedback mechanism enables the system to learn from past performance and progressively improve matching accuracy over time, resolving the contradiction between achieving high precision and managing system complexity through iterative optimization.
2Reliability
If more offers and incentives are provided to workers, then the likelihood of order completion increases, but the costs increase
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict which workers are most likely to complete orders before making offers. By identifying high-probability candidates in advance through data analysis, the system can target offers more precisely, increasing completion rates while reducing the need for widespread incentivizing, thus resolving the contradiction between reliability and cost.
Solution Approach 2:
The system dynamically adjusts offer parameters such as incentives and compensation based on predicted worker responsiveness and order characteristics. By changing these parameters optimally rather than uniformly, the system achieves higher completion rates for critical orders while minimizing unnecessary spending on orders or workers where completion is already highly likely, resolving the cost-reliability tradeoff.
3Adaptability or versatility
If the platform makes multiple offers to workers for the same order, then the likelihood of finding a suitable worker increases, but the time and resources consumed increase
Solution Approach 1:
The system performs preliminary filtering using machine learning models to identify a small set of highly probable candidate workers before making offers. This preliminary action based on historical data and worker characteristics reduces the need for multiple sequential offers, maintaining adaptability in worker selection while significantly reducing the time and resources spent on the matching process.
4Ease of operation
If the platform assigns orders based on simple criteria, then the assignment process is fast, but the worker satisfaction and engagement decrease
Solution Approach 1:
The patent replaces simple assignment criteria with machine learning-based predictions that consider worker preferences, historical performance, and order characteristics. This substitution maintains operational speed through automated processing while significantly improving worker engagement by making assignments that are more likely to match worker preferences and capabilities, thus resolving the contradiction between ease of operation and reliability of engagement.
Data Source
AI summary
A platform for managing work is disclosed. In example embodiments, the platform may match orders and workers. In some embodiments, the platform may use one or more models that have inputs related to order characteristics or past worker behavior. In some embodiments, the platform may receive future orders and actualize future orders. To actualize future orders, the platform may use one or more models that have inputs related to order characteristics or past worker behavior. In some examples, orders may be shopping orders or deliveries. In some examples, workers may be shoppers or drivers.