XR Recommendation Environments for Personalized Future Planning
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Solution Overview
Problem
Existing recommendation systems struggle to generate personalized recommendations that account for user-specific criteria, changes in trends, and the impact of interactions with others, often failing to provide immersive and interactive experiences, particularly for future-oriented plans like retirement planning.
Innovation Solution
A recommendation system that integrates both non-XR and XR data sources to generate personalized recommendations, utilizing machine learning models to analyze user data from various sources, including XR systems, and provides immersive digital representations of future scenarios, allowing for continuous learning and adjustment based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used, then system simplicity is maintained, but personalization accuracy and user engagement deteriorate
Solution Approach 1:
The patent combines multiple data sources (XR data from virtual environments, non-XR data from traditional systems, behavioral data, and contextual data) into a unified recommendation system. This merging of diverse data streams enables comprehensive personalization accuracy while managing system complexity through integrated architecture.
Solution Approach 2:
The patent introduces XR (extended reality) data as an additional dimension beyond traditional recommendation systems. By incorporating data from virtual environments, avatars, and immersive experiences, the system expands the data space from conventional 2D interactions to multi-dimensional XR contexts, enhancing personalization capabilities.
2Measurement precision
If comprehensive data analysis is performed, then recommendation quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary data collection and processing by gathering XR data, user profiles, and contextual information in advance before generating recommendations. This preliminary action prepares the data infrastructure so that when recommendations are needed, the system can quickly retrieve and analyze pre-processed data, reducing real-time processing time while maintaining comprehensive analysis quality.
3Ease of operation
If traditional 2D interfaces are used, then implementation simplicity is maintained, but user engagement and immersion deteriorate
Solution Approach 1:
The patent transitions from traditional 2D interfaces to XR (extended reality) environments, adding spatial and immersive dimensions to the user interface. This enables users to interact with recommendations in virtual spaces, view digital representations of future scenarios, and engage with avatars, significantly enhancing user engagement and immersion while managing interface complexity through standardized XR frameworks.
Data Source
AI summary
A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including obtaining user data associated with a user, generating, using at least one machine learning (ML) model processing the user data, a virtual environment associated with an extended reality (XR) representation corresponding to a personalized recommendation for the user, and creating, based on the user data, a virtual avatar for the user reflecting a representation of the user within the virtual environment. The virtual avatar corresponds to a set of attributes representing visual features of the user.