AI-Ranked Data Source Routing for Documentation Gaps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for identifying data sources to fill documentation gaps are inefficient, costly, and time-consuming, often resulting in sub-optimal selections and increased network traffic due to redundant data requests.
Innovation Solution
Utilizing an AI model to determine ranking values for candidate data sources based on attributes, sorting and routing data requests to the highest-ranked sources while blocking lower-ranked sources, thereby optimizing data source selection and reducing redundant requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine data sources, then data source selection can be made, but the process is time-consuming and results in sub-optimal selections
Solution Approach 1:
The patent replaces manual mechanical evaluation of data sources with an automated AI/ML-based system. The system automatically scores and ranks data sources based on multiple criteria (completeness, cost, timeliness, regulatory compliance) without human intervention, dramatically improving selection efficiency while reducing time consumption.
Solution Approach 2:
The patent introduces a multi-parameter scoring system that evaluates data sources based on completeness, cost, timeliness, and regulatory compliance. By changing from subjective manual assessment to objective parameter-based evaluation, the system achieves faster and more optimal data source selection.
2Reliability
If multiple data sources are queried to ensure data availability, then data gaps can be filled, but network traffic increases due to redundant requests
Solution Approach 1:
The patent performs preliminary evaluation and ranking of data sources before actual data requests are made. By pre-identifying the optimal data source for each data gap using AI/ML scoring, the system avoids redundant network requests to multiple sources, reducing network traffic while ensuring data gaps are reliably filled.
Solution Approach 2:
The patent extracts and evaluates specific attributes of each data source (completeness, cost, timeliness, compliance) separately, then combines these into an overall score. This extraction approach allows the system to identify the single best data source for each gap, eliminating the need to query multiple sources and reducing redundant network traffic.
3Ease of operation
If conventional methods are used to evaluate data sources, then data selection can be made, but the process is costly
Solution Approach 1:
The patent replaces costly manual evaluation processes with an automated AI/ML-based evaluation system. The system automatically assesses data sources based on predefined criteria, eliminating the need for expensive human expertise while maintaining or improving evaluation quality.
Solution Approach 2:
The patent establishes objective parameter-based evaluation criteria (completeness, cost, timeliness, regulatory compliance) with assigned weights. This parameterized approach replaces subjective manual evaluation, making the process both easier to operate and more cost-effective through automation and standardization.
Data Source
AI summary
Systems and methods for routing data using an artificial intelligence (AI) model are disclosed. The method includes receiving a data request associated with one or more data gaps, determining, by an AI model, a plurality of ranking values for a plurality of candidate data sources respectively based on one or more attributes, each of the plurality of ranking values indicative of a likelihood of filling the one or more data gaps associated with the data request; and routing, over a network, the data request to a first candidate data source of the plurality of candidate data sources based on a first ranking value of the plurality of ranking values; and blocking routing of the data request over the network to a second candidate data source of the plurality of candidate data sources based on a second ranking value of the plurality of ranking values.