Deep Learning Machine Vision for Locality Categorization
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Solution Overview
Problem
Current information handling systems lack the ability to efficiently categorize localities for comparative spending analyses, leading to inefficiencies in understanding customer technology needs and missed opportunities for technology improvements.
Innovation Solution
A system utilizing deep learning machine vision to analyze map images, assign locality profile scores, group similar localities, extract entities, and perform comparative spending analyses by ranking spending across product areas, thereby identifying technological needs and sales opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional information handling systems are used to categorize localities, then the system structure is simple, but the ability to efficiently categorize localities for comparative spending analyses is insufficient
Solution Approach 1:
The patent replaces traditional mechanical information processing systems with deep learning machine vision systems. The system uses neural networks to automatically analyze map images, extract entity classes, and generate locality profile scores, substituting manual or rule-based categorization methods with automated AI-driven analysis that significantly improves categorization efficiency
Solution Approach 2:
The patent introduces an intermediary processing layer that converts map images into structured locality profile data. The machine vision system acts as an intermediary between raw map images and spending analysis, extracting meaningful entity classes and generating standardized locality profiles that enable comparative analysis across different localities
2Measurement precision
If deep learning machine vision is implemented to analyze map images, then locality categorization accuracy is improved, but computational resources and system complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing map images and pre-extracting entity classes before the main spending analysis. The machine vision system pre-generates locality profile scores and groups localities in advance, so that when comparative spending analysis is needed, the computationally intensive image processing has already been completed, reducing real-time computational resource consumption
Solution Approach 2:
The patent segments the complex task of locality analysis into distinct components: map image processing, entity class extraction, locality profile score generation, and spending analysis. This segmentation allows each component to be optimized independently and processed in stages, reducing the computational burden on any single system component
Data Source
AI summary
A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.