AI Data Routing for Ranking Candidate Sources and Blocking Redundancy
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Solution Overview
Problem
Conventional methods for determining optimal data sources to fill data gaps are costly, time-consuming, and often result in sub-optimal selections due to manual processes and lack of efficient data source evaluation, leading to redundant data requests and increased network traffic.
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, and blocking lower-ranked sources to optimize data source selection and reduce redundant requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine optimal data sources, then flexibility and control are maintained, but the process becomes costly and time-consuming with sub-optimal selections
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an AI-based automated system. The AI model automatically evaluates data sources against multiple constraints (cost, timeliness, regulatory compliance) and selects optimal sources, eliminating manual labor while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The system transforms the evaluation process by changing from manual parameter assessment to automated AI-based parameter evaluation. The AI model processes multiple parameters (cost, timeliness, compliance) simultaneously and objectively, improving selection accuracy and speed compared to manual methods.
2Reliability
If multiple data sources are evaluated to ensure optimal selection, then data quality improves, but network traffic and computational resources increase
Solution Approach 1:
The AI model performs preliminary evaluation and ranking of data sources before actual data retrieval. By pre-assessing multiple data sources against required constraints and ranking them, the system identifies the optimal source in advance, avoiding unnecessary network traffic and computational resources for evaluating all possible sources exhaustively.
Solution Approach 2:
The AI model acts as an intermediary between data requests and data sources. It mediates the selection process by evaluating and ranking candidate sources, then directing requests to the optimal source. This intermediary function reduces direct network traffic and computational overhead by filtering out sub-optimal sources before data retrieval attempts.
3Loss of information
If data requests are sent to multiple data sources to ensure completeness, then data gaps are filled, but redundant requests increase costs
Solution Approach 1:
The system changes the approach from sending requests to multiple sources to evaluating and selecting the single optimal source based on multiple parameters (cost, timeliness, compliance, data availability). The AI model determines which data source is most likely to provide complete data without redundancy, adjusting the selection parameters to maximize data gap coverage while minimizing redundant requests.
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.