Machine Learning Association System for Electronic Data Records
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Solution Overview
Problem
The challenge lies in efficiently searching and correlating information within large electronic data records, as manual searches are time-intensive and prone to missing relevant information due to the sheer volume of data.
Innovation Solution
A system utilizing machine learning algorithms to generate associations between concepts within electronic data records, accessed from databases, and calculating weight values to represent the strength of these associations, thereby providing an efficient way to correlate information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching is used to find relevant information in large electronic data records, then the user can search for information, but the process becomes time-intensive and relevant information may be missed
Solution Approach 1:
The system performs preliminary actions by pre-generating associations between data elements and organizing them in a structured knowledge graph before queries are submitted. This allows the system to have relationships pre-computed and stored, enabling rapid retrieval without manual searching through large datasets.
Solution Approach 2:
The patent introduces an intermediary layer (the association database and machine learning model) between the user query and the raw electronic data records. This intermediary pre-processes and structures the data relationships, allowing efficient navigation and retrieval without direct manual searching of the underlying large datasets.
2Quantity of substance
If the volume of information in electronic data records increases, then more information is available, but usability challenges increase and relevant information may be missed
Solution Approach 1:
The system segments the large volume of information into structured associations between discrete data elements. By breaking down the data into interconnected nodes and relationships in a knowledge graph, the system makes the information more manageable and navigable while preserving the complete data volume.
Solution Approach 2:
The patent adds a new dimensional layer of associations and relationships over the existing data volume. Instead of simply storing more data, it creates a structured network layer that connects data elements, enabling efficient navigation through the information space without increasing operational complexity.
3Productivity
If machine learning algorithms are used to generate associations between concepts, then information correlation efficiency improves, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where machine learning algorithms automatically generate and update associations between data elements without requiring manual configuration or intervention. The system self-organizes the knowledge graph structure and continuously learns from the data, improving productivity while managing complexity through automation.
Data Source
AI summary
A system is provided for generating machine learning-based associations for electronic data records. In particular, the system may access one or more relevant databases and use machine learning algorithms to automatically generate associations between certain concepts based on the contents and/or hierarchical structures of such databases. The system may further intelligently calculate weight values for each of the associations that may represent the strength of the relatedness or relevance between concepts. Once the associations are generated, the system may, in response to a query for information related to a certain concept, generate and provide a context-relevant view of the concept to the user. In this way, the system provides an efficient way to correlate information within electronic data records.