Multi-Stage Service Request Matching System
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Solution Overview
Problem
Current online marketplace systems face inefficiencies in matching service requests with suitable service professionals, leading to overloading of professionals with requests, missed opportunities for interested providers, and unintended consequences such as reduced engagement due to limiting systems.
Innovation Solution
A server-based system performs multi-level matching by determining candidate responders based on location, category, interest, and capacity, using machine learning models to compute scores and select a subset of responders for each request, ensuring efficient and relevant responses are sent to interested professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If requests are transmitted to all service professionals that match the requested category and location, then the service professionals have the opportunity to respond to requests, but the service professionals become inundated with requests and the system does not scale with many requests and many service professionals
Solution Approach 1:
The patent segments the service professional population into different groups based on their responsiveness characteristics. The system identifies and separates highly responsive professionals from less responsive ones, then selectively routes requests to appropriate segments. This segmentation allows the system to maintain scalability while ensuring responsive professionals receive sufficient request opportunities.
Solution Approach 2:
The system dynamically changes the parameter of request routing by computing a responsiveness score for each service professional and adjusting the routing strategy based on these scores. Professionals with higher responsiveness scores receive a greater proportion of requests, while those with lower scores receive fewer requests. This parameter-based adjustment enables the system to scale efficiently while optimizing request distribution.
2Productivity
If a simple limiting system is used to send limited requests to service professionals, then the service professionals are not inundated with requests, but service professionals may not be receiving the requests that they were interested in and may stop using the online marketplace
Solution Approach 1:
The system implements a feedback mechanism by computing responsiveness scores based on historical data about service professional behavior, including whether they respond to requests, their response time, and their selection patterns. This feedback loop allows the system to continuously adjust request routing strategies, ensuring that service professionals receive requests they are likely to respond to, thereby maintaining their engagement while managing request volumes effectively.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting the number and type of requests sent to each service professional based on their computed responsiveness score. Rather than applying a uniform limit to all professionals, the system modifies the request routing parameters individually for each professional, allowing highly responsive professionals to receive more requests while limiting those who are less responsive. This maintains engagement while managing overall system productivity.
3Reliability
If manual response generation is used by service professionals, then the responses are customized to individual requests, but the process is time intensive
Solution Approach 1:
The system performs preliminary action by pre-computing responsiveness scores for all service professionals based on their historical behavior and profile information before requests are routed. This advance preparation allows the system to quickly and accurately route requests without requiring time-intensive manual review or customization at the moment of request handling, thereby reducing response generation time while maintaining reliable matching.
Data Source
AI summary
A server automatically generates a response on behalf of second users to a request received from a first user. A first stage of matching is performed that includes determining which of the second users match a requested location and request type. A second stage of matching is performed for each second user that includes computing a value that quantifies a likelihood of the first user selecting that second user to fulfill the request, and determining a capacity of the second user to fulfill the request. Some of the second users are selected based at least in part on the computed value and the determined capacity of each of the second users. A response is automatically generating for each of the selected second users. The generated responses are transmitted to the first user.