User Data Matching With Temporal Overlap Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Matching complex user data with third-party data structures, such as clinical trial programs, is challenging due to specific eligibility criteria, geographic limitations, time commitment, potential side effects, and limited accessibility, making it difficult for patients to find suitable trials that match their medical conditions and treatment needs.
Innovation Solution
An apparatus and method using a processor and memory to create a query data structure from user data, including attributes like medical condition, temporal and positional information, to identify matches in a data repository by computing overlaps and generating a recommended course of action, facilitated by machine-learning models like large language models and overlap calculation algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual screening processes are used to match volunteers with clinical trials, then eligibility criteria can be thoroughly evaluated, but the process becomes time-consuming and complex for patients to navigate
Solution Approach 1:
The patent introduces an intermediary system comprising a processor and memory that automatically matches user data with third-party data structures. This intermediary handles the complex comparison between user eligibility criteria and clinical trial requirements, eliminating the need for patients to manually navigate complex screening processes while maintaining high matching accuracy through structured data comparison.
Solution Approach 2:
The patent replaces the manual mechanical screening process with an automated computational system. The processor executes algorithms that automatically compare user data against clinical trial criteria, substituting human manual evaluation with machine-based data processing. This reduces complexity for patients while preserving thorough eligibility assessment through systematic data matching.
2Reliability
If comprehensive eligibility criteria are enforced for clinical trials, then trial quality and safety are improved, but accessibility for patients is reduced
Solution Approach 1:
The patent enables the system to automatically perform the eligibility assessment without requiring patients to manually verify complex criteria. The processor self-service compares user data with trial requirements and generates match results, allowing patients to access suitable trials without navigating complex eligibility verification processes themselves, thus maintaining safety standards while improving accessibility.
Solution Approach 2:
The system provides feedback to patients regarding their eligibility status and matching results. By automatically evaluating comprehensive criteria and returning clear match information, the system maintains rigorous safety standards while making the process accessible to patients who receive direct feedback about their qualification status without having to interpret complex eligibility requirements.
3Measurement precision
If detailed user data collection is performed to ensure accurate matching, then matching precision is improved, but data processing time and system complexity increase
Solution Approach 1:
The patent segments the data matching process into distinct operational components: data reception, structured comparison, overlap computation, and result generation. By dividing the comprehensive data processing into modular segments executed by the processor, the system achieves high matching precision through detailed data analysis while reducing overall processing time through efficient organized execution of each segment.
Solution Approach 2:
The patent transforms detailed user data into standardized parameters that can be efficiently processed and compared against trial criteria. By converting comprehensive user information into structured data parameters, the system maintains high matching precision through detailed data collection while enabling faster processing through parameter-based comparison operations rather than raw data analysis.
Data Source
AI summary
Apparatus for matching user data with third-party data structures include a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive user data, create a query data structure as a function of the user data, query a data repository using the query data structure to identify a match, and generate a recommended course of action as a function of the match. The query data structure includes a query attribute and a first temporal attribute. The data repository includes a plurality of third-party data structures, wherein each third-party data structure includes a data feature and a second temporal attribute. Identifying the matches includes computing an overlap score and comparing the overlap score with a matching threshold.