AR Surface Item Placement Layout Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
E-commerce platforms lack the ability to provide personalized recommendations for item placement on physical surfaces, failing to consider the physical dimensions and layout of existing surfaces, leading to inefficient space utilization.
Innovation Solution
A computer-implemented method and system that receives surface data from a customer device, selects merchant products based on dimensional data, determines the quantity and size of items to fit the surface, and generates an illustrative layout using 3D feature data, incorporating machine learning algorithms and layout rules for optimal placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional e-commerce recommendation systems are used, then recommendations are provided based on user characterization, but the physical surface dimensions and layout are not considered
Solution Approach 1:
The system captures images of the physical surface beforehand and performs computer vision analysis to detect surface dimensions, boundaries, and existing items before generating recommendations. This preliminary action ensures surface information is available for subsequent recommendation generation without requiring real-time measurements during the recommendation process
Solution Approach 2:
A computer vision system acts as an intermediary between the physical surface and the recommendation engine. The vision system extracts dimensional and spatial information from surface images, converting physical characteristics into digital data that the recommendation system can process and use for generating personalized item placement recommendations
2Productivity
If item recommendations are generated without considering surface constraints, then selection flexibility is maintained, but space utilization efficiency deteriorates
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: a computer vision module for surface analysis, a constraint extraction module for identifying surface boundaries and dimensions, a recommendation generation module for suggesting items, and an illustrative layout module for visualizing placements. This segmentation allows each module to specialize in specific tasks while working together to achieve efficient space utilization
Solution Approach 2:
The system transitions from traditional one-dimensional recommendation lists to two-dimensional spatial layouts by generating illustrative visualizations that show how recommended items fit on the physical surface. This dimensional change provides users with intuitive spatial understanding of item placements and improves space utilization efficiency
3Measurement precision
If surface data processing is implemented, then accurate item placement recommendations are achieved, but computational requirements increase
Solution Approach 1:
The computer vision system processes surface images locally on the user's device or edge server, enabling self-service processing without requiring continuous cloud computation. The system automatically detects surface characteristics, extracts dimensional information, and prepares data for recommendations without manual intervention or heavy centralized processing
Solution Approach 2:
The system performs partial processing by focusing computational resources on detecting only the most relevant surface features (boundaries, dimensions, existing items) rather than analyzing every pixel or characteristic. This selective processing achieves sufficient measurement accuracy while reducing overall computational energy consumption
Data Source
AI summary
Computer-implemented methods and systems including receiving feature data identifying a physical surface determined using augmented reality software and an imaging device operated by a computing device in a physical environment comprising the physical surface; determining a positioning of at least one surface item upon the physical surface based on surface dimensions determined from the feature data; and causing an augmented reality interface to be displayed by the augmented reality software on the computing device, the augmented reality interface comprising an illustrative layout of the physical surface generated using the feature data and showing the determined positioning of the at least one surface item upon the physical surface in the illustrative layout while imaging the physical surface in the physical environment.