Deep Learning Machine Vision for Product Positioning Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information handling systems lack effective methods to analyze geographic data from map images for product positioning and customer ecosystem insights, limiting their ability to enhance account retention, induce spending, identify whitespace accounts, and position products effectively.
Innovation Solution
A system utilizing deep learning machine vision to analyze map images, generating locality profile scores through neural network analyses, extracting entity classes, retrieving historical purchasing data, and determining product sequences likely to be purchased by entities based on similarity characteristics and entity profile weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional information handling systems are used to process geographic data, then basic data processing can be performed, but the ability to extract actionable insights from map images for product positioning is insufficient
Solution Approach 1:
The patent replaces traditional mechanical information processing methods with deep learning machine vision operations. Specifically, neural networks are used to automatically analyze map images, extract entity classes, and generate locality profile scores, substituting manual or rule-based processing with intelligent automated systems that can interpret visual geographic data effectively
Solution Approach 2:
The patent introduces an intermediary processing layer between raw map images and business insights. The machine vision system acts as a mediator that transforms complex image data into structured locality profile scores and entity classifications, which then feed into the product positioning recommendation engine, making the overall system more manageable and effective
2Loss of information
If deep learning machine vision operations are implemented to analyze map images, then actionable insights for product positioning can be obtained, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the complex analysis task into distinct components: map image processing, entity class extraction, locality profile score generation, and product sequence prediction. Each component is handled by specialized modules within the machine vision system, allowing for more efficient processing and easier maintenance of the overall system
Solution Approach 2:
The patent performs preliminary analysis by generating locality profile scores and extracting entity classes before conducting product positioning analysis. This preliminary processing organizes the raw geographic data into structured formats that facilitate more efficient downstream analysis and decision-making
Data Source
AI summary
At least one embodiment is directed to a computer-implemented method for using machine vision to categorize a locality to conduct product positioning analyses, the method including: generating locality profile scores for each locality of a plurality of localities using deep learning networks, where the locality profile score includes distributions of entity classes within the locality; extracting a set of entities having the same entity class from a group of localities; retrieving historical purchasing data for the entity set; and generating a sequence of products likely to be purchased by a target entity as a function of: the similarity of purchasing characteristics of the target entity with respect to other entities, product sequences found in product purchase of other entities, and entity profile weights extracted from the locality profile scores of other entities that have purchased one or more of the same products as the target entity.