Augmented Reality Item Recommendation System
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Solution Overview
Problem
Existing augmented reality systems for item recommendations lack integration of real-world data and contextual information, relying primarily on online shopping experiences and failing to effectively utilize real-world data of interest to users.
Innovation Solution
A method utilizing communication devices to capture and process audiovisual data in real-world environments, combining tagging of items with contextual information to generate recommendations, which are then communicated to users through augmented reality interfaces, leveraging machine learning and computer vision for item recognition and contextual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general advertisement strategies are used to alert users of coupons and deals, then users are notified of available items, but users may ignore or give only cursory review due to the need to browse through many items
Solution Approach 1:
The system enables users to actively capture audiovisual data of items in their environment and automatically generates personalized recommendations based on their captured data and profile, rather than passively receiving generic advertisements. This self-service approach ensures recommendations are highly relevant to the user's immediate context and interests.
Solution Approach 2:
The system performs preliminary analysis of user profiles, captured audiovisual data, and item databases before generating recommendations. By pre-processing and matching user preferences with item characteristics in advance, the system delivers immediately relevant recommendations without requiring users to browse through numerous unrelated items.
2Adaptability or versatility
If targeted advertisements are provided based on online shopping experiences, then recommendations are more personalized, but real-world data that may be of interest to users is not utilized
Solution Approach 1:
The system merges online user profile data with offline captured audiovisual data from the user's real-world environment. By combining these previously separate data sources, the system creates a comprehensive view of user preferences and context, enabling recommendations that are both personalized and grounded in real-world observations.
Solution Approach 2:
The system replaces traditional manual online browsing and selection mechanisms with automated audiovisual capture and analysis. Users simply capture items in their environment using their device's camera, and the system automatically processes the visual data, matches it with user preferences, and generates recommendations, eliminating the need for extensive online searching.
3Adaptability or versatility
If audiovisual data capture and processing is implemented for real-world item recognition, then personalized recommendations can be generated, but system complexity increases
Solution Approach 1:
The system introduces an intermediary service layer that handles the complex audiovisual data processing, item recognition, and recommendation generation. This service acts as a mediator between the user's simple capture action and the complex analysis required, abstracting away the computational complexity from the user interface while enabling sophisticated real-world data processing capabilities.
Data Source
AI summary
There are provided systems and methods for contextual data in augmented reality processing for item recommendations. A first user may provide a recommendation to a second user of an item viewed by the first user through a communication device. The first user may tag the item, for example, through voice input or visual cues while viewing the item at a physical merchant location, or through menu selections within an interface displaying a browsing window of the first user. The first user may provide contextual data that may be used to determine that the first user is recommending the item to the second user, such as voice data that includes an identifier for the second user or visual cues within audiovisual content captured of the item at the physical merchant location. During online browsing, the first user may provide the contextual data through actions, menus selections, and data entry.