Geographic Entity-Category Opportunity Prediction with Machine Learning

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Solution Overview

Problem

Existing methods for identifying market opportunities for entity categories in geographic areas are often inaccurate and resource-intensive, consuming significant computing resources with limited accuracy.

Innovation Solution

A system utilizing machine learning to analyze event data and distance information between individuals and entities to determine geographic areas associated with opportunities for entity categories, employing unsupervised learning techniques and generative adversarial networks to identify patterns and anomalies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If existing methods are used to identify market opportunities, then the process can be completed, but accuracy is limited and computing resource consumption is high

Engineering Contradiction:

ImproveaccuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent changes the approach by using machine learning models that process transformed data features (distance calculations, event data aggregation) to improve accuracy while optimizing resource usage through efficient algorithms

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces traditional mechanical/computational analysis methods with machine learning models that can process complex patterns in event data and geographic information to achieve higher accuracy with optimized resource consumption

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional analysis methods are used, then resource consumption is high, but the efficiency of market opportunity identification is low

Engineering Contradiction:

ImproveefficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The machine learning model performs self-optimization by automatically learning from event data patterns and adjusting its processing requirements, enabling efficient market opportunity identification without proportionally increasing computing resources

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary data processing and feature extraction (calculating distances, aggregating event data) before feeding into the machine learning model, which pre-computes necessary transformations and reduces the computational burden during opportunity identification

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12452626B2Machine learning model prediction of an opportunity for an entity category in a geographic area

Publication Date: 2025.10.21 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, a device may obtain event data relating to events involving one or more entities and one or more individuals, where the one or more entities are associated with entity categories. The device may determine, based on origin locations of the one or more individuals and locations of the one or more entities, distances between the origin locations of the one or more individuals and the locations of the one or more entities, the distances indicating travel distances of the one or more individuals to perform exchanges in the entity categories associated with the one or more entities. The device may determine, based on the event data and the distances, that a geographic area is associated with an opportunity for an entity category. The device may transmit a notification indicating the opportunity for the entity category in the geographic area.