Machine Learning Performance Models Using Covaried Service Measures
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Solution Overview
Problem
The abundance of data from various sources poses a challenge in determining the optimal values and sources for use, requiring extensive domain expertise and significant time and resources to evaluate outcomes effectively.
Innovation Solution
A method for generating a statistically covaried machine learning model that involves receiving a configuration file with parameters associated with individuals, parsing it to generate a database query, executing the query to generate tabulated data, determining measures of service providers, and training a machine learning model using statistically covaried measures as training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used to evaluate data sources, then measurement precision can be improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data from multiple sources into structured formats before actual analysis is needed. Configuration files and data schemas are prepared in advance, allowing rapid deployment when evaluation is required without repeating the entire analysis process.
Solution Approach 2:
The patent introduces intermediary components including configuration files that mediate between raw data sources and analysis processes, and standardized data schemas that act as intermediaries between different data formats and the evaluation engine. These intermediaries enable efficient data integration without direct complex processing of source materials.
2Measurement precision
If comprehensive data from multiple sources is collected, then measurement precision improves, but device complexity and difficulty of operation increase
Solution Approach 1:
The system segments the complex data integration task into distinct configuration files for each data source, with each file containing only the necessary parameters and schemas for that specific source. This modular approach allows individual data sources to be configured and evaluated independently, reducing overall system complexity.
Solution Approach 2:
The patent utilizes parameter changes by allowing configuration files to specify different data formats, time periods, and measurement parameters based on the specific analysis needs. This flexibility enables the system to adapt to various data sources without requiring complex transformation logic, simplifying the integration process.
3Adaptability or versatility
If traditional analysis processes are customized for specific circumstances, then adaptability improves, but loss of time and productivity worsen due to modification requirements
Solution Approach 1:
The system implements dynamics by making configuration files and data schemas adjustable and reconfigurable based on specific analysis requirements. Users can modify parameters, time periods, and data source selections without restructuring the entire analysis process, enabling rapid adaptation to different scenarios while maintaining high productivity.
Solution Approach 2:
The patent creates universal configuration file formats and standardized data schemas that can be applied across multiple different data sources and analysis scenarios. This multi-functionality allows the same analytical framework to handle various types of performance measurements without requiring separate customized processes for each situation.
Data Source
AI summary
Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.