AR Headset Online Learning Using Calibration Frame Embeddings

Solution Overview

Problem

Existing artificial reality systems face challenges in user customization due to resource constraints and user variation, making it difficult to train machine learning models efficiently for multiple users.

Innovation Solution

A headset and console system uses locally stored embeddings of calibration frames to train machine learning models, predicting differences between target and stored frames, reducing processing power and training time for personalized user customization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine learning models are trained for each user to achieve user customization, then user personalization quality is improved, but processing power requirements and training time increase significantly

Engineering Contradiction:

Improveuser customization qualityVSAvoidprocessing power

Core Design Contradiction:

Adaptability or versatilityVSPower

Solution Approach 1:

The patent segments the model training process into two distinct phases: (1) training a general model on aggregated data from multiple users, and (2) fine-tuning this pre-trained model with individual user data. This segmentation allows the system to benefit from both population-level patterns and user-specific characteristics without requiring full retraining for each user, thereby reducing processing power requirements while maintaining customization quality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary action by pre-training the machine learning model on aggregated data from multiple users before deploying it for individual user customization. This pre-trained model serves as a foundation that already contains general patterns and knowledge, reducing the computational burden and time required for subsequent user-specific fine-tuning while still achieving high-quality personalization.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If machine learning models are trained for each user to achieve user customization, then user personalization quality is improved, but training time increases significantly

Engineering Contradiction:

Improveuser customization qualityVSAvoidtraining time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent segments the model training process into two distinct phases: (1) training a general model on aggregated data from multiple users, and (2) fine-tuning this pre-trained model with individual user data. This segmentation allows the system to benefit from both population-level patterns and user-specific characteristics without requiring full retraining for each user, thereby reducing processing power requirements while maintaining customization quality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies preliminary action by pre-training the machine learning model on aggregated data from multiple users before deploying it for individual user customization. This pre-trained model serves as a foundation that already contains general patterns and knowledge, reducing the computational burden and time required for subsequent user-specific fine-tuning while still achieving high-quality personalization.

Inventive Principle:

Principle #10Preliminary action

3Power

If standard machine learning models are used without user customization, then processing resources are conserved, but user variation causes poor model performance

Engineering Contradiction:

Improveprocessing powerVSAvoidmodel performance

Core Design Contradiction:

PowerVSReliability

Solution Approach 1:

The patent applies local quality by adapting the general model to local user-specific characteristics through fine-tuning with individual user data. This allows the model to maintain high performance for each user while still leveraging the efficiency benefits of a shared pre-trained foundation, achieving both resource conservation and reliable user-specific performance.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent implements universality by creating a single pre-trained model that serves all users with common patterns, which then gets adapted to individual users through fine-tuning. This multi-functional approach allows one base model to serve multiple users effectively, reducing the need for completely separate models for each user while still accounting for user variation.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12475696B2Personalized online learning for artificial reality applications

Publication Date: 2025.11.18 META PLATFORMS TECHNOLOGIES LLC

AI summary

A console and headset system locally trains machine learning models to perform customized online learning tasks. To customize the online learning models for specific users of the system without using outside resources, the system trains the models to compare a target frame to stored calibration frames, rather than directly inferring information about a target frame. During deployment, an embedding is generated for the target frame. A sample embedding that is closest to the target embedding is selected from a group of embeddings of calibration frames. The information about the selected embedding and target embedding and ground truths for the calibration frame are provided as inputs to one of the trained models. The model predicts a difference between the target frame and the calibration frame, which can be used to determine information about the target frame.