Machine Learning Expertise Mapping for Service Provider Search
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Solution Overview
Problem
The abundance of data sources and information poses a challenge in identifying relevant and useful data, requiring extensive expertise and time, making it incompatible with modern applications' demands for quick and accurate results.
Innovation Solution
A system utilizing a non-transitory computer-readable medium with instructions that execute on processors to identify conditions, determine associated codes, select popular codes, translate them to topics using a machine learning model, and provide service providers based on similarity metrics, optimizing the process for efficient data application across personalized scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual evaluation methods are used to identify relevant data sources, then accuracy and domain expertise are improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent introduces machine learning models as intermediary systems between raw data and human analysts. These models automatically evaluate data sources, extract features, and generate recommendations, serving as a mediator that handles the time-consuming evaluation tasks while preserving accuracy through algorithmic analysis of domain-specific features.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated machine learning systems. Instead of human experts manually reviewing and evaluating data sources, the system uses trained models that automatically assess data quality, relevance, and reliability, substituting human cognitive labor with computational processes that operate faster and at scale.
2Reliability
If comprehensive data analysis processes are implemented, then result accuracy is improved, but complexity of the system increases
Solution Approach 1:
The patent divides the comprehensive data analysis process into distinct modular components: data ingestion modules, feature extraction modules, machine learning model modules, and result generation modules. Each module handles a specific aspect of the analysis, allowing the system to maintain high accuracy through thorough analysis while managing complexity through modular architecture that enables independent development, testing, and deployment of each component.
3Ease of operation
If generalized analytical processes are used, then ease of application is improved, but adaptability to specific scenarios decreases
Solution Approach 1:
The patent implements dynamic configuration capabilities that allow the analytical process to adapt to specific scenarios. The system can dynamically adjust evaluation criteria, select different machine learning models based on the data type, and modify analysis parameters according to the specific application context. This dynamic adaptability enables the same core system to effectively handle diverse scenarios without requiring complete process redesign for each case.
Data Source
AI summary
Methods, systems, and computer-readable media for determining the expertise of service providers to match with users utilizing a service provider search system. The method identifies searched conditions and determine associated codes. The method next determines procedures provided by service providers associated with codes. The method then normalizes codes associated with conditions and selects a subset of them based on the popularity of procedures associated with the codes. The method finally utilizes a machine learning model to translate the subset of codes to topics and calculates similarity metric between the topics and the service providers and tunes the threshold of the metric. The method then an using the tuned threshold outputs a service provider based on a query to a service provider search system.