AR Shopping Interface With Personalized Route Recommendations
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Solution Overview
Problem
In-store shopping experiences often lack personalized promotions and location-specific recommendations, leading to inefficiencies and sub-optimal experiences for individuals.
Innovation Solution
A computing platform that utilizes historical shopping information to provide personalized shopping recommendations and gamification elements, including virtual coins and augmented reality interfaces, to enhance the shopping experience based on user preferences and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalized promotions and location-specific recommendations are provided using historical shopping information and machine learning models, then user engagement and shopping experience are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces a computing platform as an intermediary between the user device and the shopping environment. This platform hosts the machine learning models and processes historical shopping information to generate personalized recommendations, thereby reducing the computational burden on individual user devices while still delivering adaptive shopping experiences through the mobile application interface.
Solution Approach 2:
The system performs preliminary processing of historical shopping information and training of machine learning models in advance, before the actual shopping experience. By pre-processing data and pre-computing recommendation algorithms on the computing platform, the system reduces real-time computational requirements during the shopping trip, balancing personalization capability with device complexity.
2Productivity
If real-time location tracking and dynamic recommendation updates are implemented, then shopping efficiency and user experience are improved, but energy consumption and processing requirements increase
Solution Approach 1:
The system implements periodic updates of location tracking and recommendation refreshes rather than continuous real-time processing. The mobile application receives location updates at intervals and dynamically refreshes recommendations based on significant location changes or user interactions, reducing energy consumption while maintaining shopping efficiency through timely updates.
Solution Approach 2:
The recommendation system dynamically adjusts its operation based on user behavior and location context. Instead of continuously tracking and updating all parameters, the system activates intensive processing only when triggered by specific events such as entering new store zones, pausing shopping activities, or explicit user requests, thereby optimizing energy usage while preserving productivity.
3Adaptability or versatility
If gamification elements and augmented reality interfaces are added to enhance engagement, then user interest and shopping experience are improved, but interface complexity and development requirements increase
Solution Approach 1:
The patent merges gamification elements and augmented reality interfaces with the core recommendation functionality into a unified mobile application. By integrating virtual coins, rewards, and AR features with the personalized recommendation engine, the system delivers enhanced engagement capabilities without requiring separate complex systems, thereby managing interface complexity through consolidation.
Solution Approach 2:
The mobile application serves multiple functions simultaneously: it provides personalized recommendations, tracks location, manages gamification rewards, and displays augmented reality information. This multi-functional design allows the system to deliver diverse engagement capabilities through a single interface, reducing overall system complexity compared to having separate applications for each function.
Data Source
AI summary
A computing platform may receive, from a user device, historical shopping information indicating previously purchased items and/or previous routes within shopping environments for a first user of the user device. The computing platform may input, into a shopping gamification model, the historical shopping information, which may output shopping recommendation information indicating one or more of: recommended items or recommended routes within a first shopping environment. The computing platform may send, to the user device, a shopping gamification interface that includes the shopping recommendation information and one or more commands directing the user device to display the shopping gamification interface. The computing platform may receive, from the user device, user feedback information indicating acceptance or rejection of the shopping recommendation information by the first user. The computing platform may update, based on the user feedback information, the shopping gamification model.