Dynamic Rule Matching Engine for Unstructured Data

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Solution Overview

Problem

Traditional computer matching engines rely on predefined rules and structured data, making them ineffective when dealing with unstructured inputs and dynamically changing rules, particularly in identifying matches that are not exact or precisely defined.

Innovation Solution

A computer-implemented method and system that dynamically modifies a rule-based matching system by using a processor to receive a rule set, locate matching data entities, and revise the rules based on binary rater assessments, allowing for fuzzy-matching and dynamic scaling of computing resources, enabling the system to handle loosely defined terms and changing rules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional predefined rules are used for matching, then structured data can be processed efficiently, but unstructured inputs and dynamically changing rules cannot be handled effectively

Engineering Contradiction:

Improveability to handle unstructured inputs and dynamically changing rulesVSAvoidcomplexity of the matching system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic rule modification capabilities that allow the matching engine to adapt rules during runtime based on incoming data characteristics. The system transitions from static predefined rules to dynamic rules that can be created, modified, and deleted automatically, enabling the system to handle unstructured inputs and changing requirements without manual intervention.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The matching engine incorporates self-learning capabilities where the system automatically analyzes incoming data, identifies patterns, and generates new rules without external input. The engine serves itself by autonomously improving its rule set based on performance feedback and data characteristics, reducing the need for manual rule configuration and maintenance.

Inventive Principle:

Principle #25Self-service

2Reliability

If exact matching criteria are enforced, then precision is maintained, but matches that are not exact or precisely defined are missed

Engineering Contradiction:

Improveaccuracy of matching resultsVSAvoidnumber of matches identified

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent implements fuzzy matching capabilities that allow the system to vary matching criteria based on data characteristics and confidence levels. Instead of enforcing strict exact matching, the system adjusts matching parameters dynamically, allowing for approximate matches when appropriate while maintaining high confidence matches when possible, thus balancing precision with recall.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The matching system dynamically adjusts its strictness based on the type of data being processed, the confidence level of potential matches, and performance metrics. The system can switch between exact matching and fuzzy matching modes, and adjust threshold values dynamically to optimize the balance between match accuracy and match volume.

Inventive Principle:

Principle #15Dynamics

3Productivity

If computing resources are fixed, then system stability is maintained, but the system cannot scale to handle varying workloads

Engineering Contradiction:

Improveprocessing capacityVSAvoidresource management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements dynamic resource allocation that allows the matching engine to scale computing resources based on workload demands. The system can automatically provision additional processing capacity during peak loads and release resources during low-utilization periods, enabling flexible scaling without manual intervention while maintaining system stability through controlled resource management.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The matching engine is designed with a modular architecture that can utilize diverse computing resources including CPU, GPU, and distributed computing clusters. The system can dynamically allocate different types of computing resources based on the specific matching tasks at hand, making the resource management system versatile and adaptable to various workload types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11138269B1Optimizing database query processes with supervised independent autonomy through a dynamically scaling matching and priority engine

Publication Date: 2021.10.05 WELLS FARGO BANK NA

AI summary

A system and related method dynamically modify a rule-based matching system. The process comprises receiving a rule set and a source data entity (SDE), and locating a plurality of matching data entities (MDEs) by searching a data node. The process further comprises, (a) determining a best MDE from the plurality of MDEs based on an MDE score created using the rule set, (b) receiving, a binary rater assessment rating that utilizes a factor that is independent of the rule set, the rater assessment comprising a binary degree of matching, (c) when the received binary rater assessment is yes, then designating the best MDE as an accepted MDE and updating information associated with the SDE, (d) when the received binary rater assessment is no, then eliminating the best MDE from the plurality of MDEs and repeating operations (a)-(d). The rule set is revised to dynamically produce a revised rule set.