Deep Learning Machine Vision for Locality Lead Scoring

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Solution Overview

Problem

Enterprises face challenges in effectively assessing and optimizing marketing campaigns across different localities, as existing methods lack efficient ways to analyze geographic data for lead generation and campaign effectiveness.

Innovation Solution

A deep learning machine vision system is configured to analyze map images and street view images of localities, generating locality profile scores and economic categorizations, grouping similar localities, extracting relevant entities, and calculating lead scores based on historical data and campaign performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to analyze geographic data for marketing campaigns, then implementation simplicity is maintained, but measurement precision and productivity of lead generation are insufficient

Engineering Contradiction:

Improvelocality analysis precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/manual geographic data analysis methods with a neural network-based machine vision system. The system automatically processes map images and street view images to extract locality characteristics, entity types, and economic indicators, eliminating manual analysis while achieving high precision in locality profiling and lead scoring.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates digital copies of physical geographic environments by processing map images and street view images. These image copies are then analyzed by neural networks to extract meaningful data about localities, entities, and economic characteristics without requiring physical site visits or manual surveys.

Inventive Principle:

Principle #26Copying

2Measurement precision

If comprehensive historical data is collected for all entities, then lead score accuracy is improved, but loss of time and use of energy increase

Engineering Contradiction:

Improvelead score accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing key characteristics of localities and entities in advance. Neural networks analyze map images and street view images beforehand to create locality profiles and entity categorizations that are stored for rapid retrieval during lead scoring, eliminating the need for real-time comprehensive analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts only the most relevant features from comprehensive historical data using neural network analysis. Instead of processing all available data, the system identifies and extracts key characteristics such as entity types, locality economic indicators, and campaign performance metrics that are most predictive of lead quality.

Inventive Principle:

Principle #2Taking out (Extraction)

3Measurement precision

If neural network analyses are applied to map images and entity images, then locality profile precision and economic categorization accuracy are improved, but use of energy and device complexity increase

Engineering Contradiction:

Improvelocality profile accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by using neural networks to analyze only the most critical aspects of map images and street view images. Rather than performing exhaustive analysis of all image features, the system focuses on extracting key locality characteristics and economic indicators that are sufficient for accurate lead scoring.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11430002B2System and method using deep learning machine vision to conduct comparative campaign analyses

Publication Date: 2022.08.30 DELL PROD LP

AI summary

At least one embodiment of the disclosed system is directed to computer-implemented method for using machine vision to categorize a locality to conduct lead mining analyses. Embodiments of the method may include: generating locality profile scores and economic categorization for each locality of a plurality of localities, the locality profile score for each locality being derived through neural network analyses of map images of the locality, the economic categorization being derived through neural network analyses of images of entities within the locality; and generating a lead score for each entity in the locality group as a function of the locality profile score for the locality in which the entity is located, the economic categorization of the locality in which the entity is located, and campaign vehicles used in the locality in which the entity is located.