Multisource Augmented Reality Model for Cross-Store Product Compatibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AR-based shopping systems are limited in their ability to augment items from multiple stores, perform compatibility analysis, and facilitate collaborative promotional activities across different stores, leading to inefficient and inaccurate procurement processes that are not scalable.
Innovation Solution
A multisource augmented reality model (MARM) system that includes a processor, augmented reality data generator, updater, and monitor to analyze user queries, gather data from multiple sources, and generate interactive AR interfaces for recommending products and creating sales packages based on compatibility and user filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If single store AR applications are used to promote store products, then store-specific product augmentation is achieved, but multi-store collaboration and item compatibility analysis are not possible
Solution Approach 1:
The patent combines multiple store catalogs and AR applications into a unified multi-store AR platform. This merging enables users to access and compare products from different stores simultaneously, facilitating collaboration between stores and comprehensive compatibility analysis across multiple sources.
Solution Approach 2:
The system creates a universal AR platform that serves multiple functions: product augmentation, compatibility analysis, multi-store collaboration, and promotional activity coordination. This multi-functional approach allows a single system to handle diverse requirements that previously needed separate applications.
2Productivity
If single source item augmentation is used, then simple AR display is achieved, but procurement efficiency and accuracy are constrained
Solution Approach 1:
The system segments the procurement process into multiple analytical components: compatibility analysis, price comparison, feature evaluation, and promotional assessment. Each segment processes specific aspects of multi-store data independently, then integrates results to provide comprehensive procurement recommendations with high accuracy.
Solution Approach 2:
The system implements feedback mechanisms where compatibility analysis results from multiple stores continuously refine and improve procurement recommendations. User interactions and comparison data feed back into the system to enhance future compatibility assessments and optimize procurement efficiency.
3Adaptability or versatility
If individual store AR applications are created, then store-specific promotional activities are possible, but collaborative promotional activities across stores cannot be implemented
Solution Approach 1:
The patent introduces a central coordination layer that acts as an intermediary between individual store AR applications. This mediator manages data exchange, synchronizes promotional activities, and coordinates collaborations between stores, reducing the complexity that would otherwise arise from direct peer-to-peer integration.
4Measurement precision
If real-time multi-store data analysis is implemented, then accurate compatibility analysis is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring data from multiple store catalogs before actual compatibility analysis. Data is normalized, categorized, and prepared in advance, reducing the complexity of real-time processing while maintaining high precision in compatibility assessments.
Data Source
AI summary
Examples of a multisource augmented reality model are defined. In an example, the system receives a query from a user. The system obtains representative data corresponding to an environment associated with the query and identifies at least one context therein. The system obtains product parameter data and identifies a parameter set therein to process the query. The system implements an artificial intelligence component to sort the product parameter data, the representative data, and the context for identifying pertinent data domains associated with the query. The system may establish a product augmented reality model corresponding to the product by performing a first cognitive learning operation on a domain from the updated pertinent data domains and the identified parameter set. The system may a list of related products for guided selling facilitating a shopping decision of the user. The system may generate an augmented reality result for the user.