Local Cache Entity Resolution for Fewer Vendor API Calls

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Solution Overview

Problem

Existing systems rely heavily on external vendors for entity record verification, leading to high costs, security risks, and inefficient use of network bandwidth, with repeated requests for the same entity record verification being common.

Innovation Solution

Implementing a local cache memory system with a first sector for verified entity records and a second sector for unverified records, utilizing machine learning models to refine data and reduce reliance on external vendors by checking the local cache first for matches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If external vendor API calls are used for entity record verification, then verification accuracy is maintained, but processing time and network bandwidth usage increase significantly

Engineering Contradiction:

Improveverification accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary entity record verification by checking the local cache memory before making external vendor API calls. The cache stores previously verified entity records, allowing the system to quickly determine if a record has already been verified without contacting external vendors, thus reducing processing time while maintaining verification accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The local cache memory acts as an intermediary between the entity record processing system and external vendor APIs. It stores verified entity records and provides them to the system when needed, reducing the frequency of external API calls while ensuring verification accuracy is maintained for cached records.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If external vendor API calls are used for entity record verification, then verification accuracy is maintained, but network bandwidth consumption increases

Engineering Contradiction:

Improveverification accuracyVSAvoidnetwork bandwidth usage

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary checks in the local cache memory before making external vendor API calls. By verifying entity records locally when possible, the system reduces the number of network requests required, thereby maintaining verification accuracy while significantly reducing network bandwidth consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The local cache memory serves as an intermediary that reduces network traffic by providing verified entity records locally. This eliminates the need for repeated external API calls for the same records, maintaining verification accuracy while reducing network bandwidth usage.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If local cache memory is implemented to store verified entity records, then processing efficiency improves, but system complexity increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The cache memory is segmented into different sectors: a first sector for storing verified entity records and a second sector for other cache data. This segmentation allows efficient organization and retrieval of records while maintaining manageable system complexity through structured data management.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system creates copies of verified entity records and stores them in the local cache memory. This allows rapid access to frequently verified records without increasing the complexity of the verification logic itself, as the copying mechanism is a straightforward data storage operation.

Inventive Principle:

Principle #26Copying

4Adaptability or versatility

If machine learning models are used to refine data locally, then reliance on external vendors decreases, but computational requirements increase

Engineering Contradiction:

Improvereliance reductionVSAvoidcomputational energy

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system applies machine learning models selectively rather than universally. Machine learning refinement is applied only when needed - when entity records are not found in the cache or when verification is uncertain - rather than applying it to all records, thus reducing reliance on external vendors while minimizing computational energy consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12505104B2Computer-based systems configured for novel information processing and storage utilizing elastic search environment of a local cache and method and use thereof

Publication Date: 2025.12.23 CAPITAL ONE SERVICES LLC

AI summary

In order to facilitate entity resolution, systems and methods include a processor receiving entity records associated with one or more entities, the system leverages information processing techniques that include utilizing a first sector of a local cache memory to store verified entity records, and store an unverified non-exact match in a second sector of a local cache memory linking the verified and unverified entity records for real-time entity record resolution, the system and methods further order the unverified records to enhance search and reduce reliance on third party systems for entity record verification thus reducing API calls to external vendors.