Endpoint Identity Correction via Local Machine Learning
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Solution Overview
Problem
Users in digital and artificial reality environments often face challenges in presenting their authentic identity due to mismatches between their physical appearance and internal self-image, leading to dysmorphia, dysphoria, and negative social ramifications, with existing technologies failing to provide seamless and consistent identity correction across multiple platforms.
Innovation Solution
A system and method that utilize machine learning models to correct audio, video, and static images in real-time, allowing users to modify their presentation to match their authentic identity, with capabilities to update as the user transitions or recovers from injuries, and maintain consistent presentation across platforms without sending uncorrected data to servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to correct user data in real-time on endpoint devices, then user privacy and data security are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system downloads a trained machine learning model from a server to the endpoint device, creating a local copy that can operate independently. This allows the complex computational work to be performed locally without requiring continuous server connectivity, thereby improving data security while managing device complexity through selective model deployment
Solution Approach 2:
The machine learning model is pre-trained on a server using large datasets before being deployed to endpoint devices. This preliminary training action transfers the computational burden of model development to the server, allowing the endpoint device to perform only inference operations, thus balancing data security requirements with device complexity constraints
2Measurement precision
If machine learning models are updated with user-specific data, then correction accuracy is improved, but user privacy risks increase
Solution Approach 1:
The system uses an encrypted communication channel as an intermediary between the endpoint device and server. User data is encrypted before transmission, allowing the model to be updated with user-specific information for improved accuracy while maintaining privacy through cryptographic protection during data transfer
Solution Approach 2:
The system processes and stores user-specific data locally on the endpoint device rather than centrally on the server. This localized data handling improves correction accuracy by enabling personalized model updates while reducing privacy risks by minimizing centralized data storage and transmission requirements
3Ease of operation
If real-time correction is performed on endpoint devices, then user experience and authenticity are improved, but processing speed and energy consumption increase
Solution Approach 1:
The system applies real-time correction selectively to specific video frames or regions rather than processing every frame completely. This partial processing approach maintains user experience by providing timely corrections while reducing energy consumption by avoiding redundant processing of already-corrected or less-important frames
Solution Approach 2:
The machine learning model is segmented into different components that can be executed with varying levels of priority. Critical correction functions are performed in real-time with higher energy allocation, while less critical enhancements are processed with lower priority, balancing user experience requirements against energy consumption constraints
Data Source
AI summary
A computer-implemented method for correcting data to match user identity may include (i) receiving user input specifying an aspect of physical presentation of the user that does not match an authentic identity of the user, where the authentic identity of the user includes a realistic version of the user that reflects an internal self-image of the user, (ii) capturing, via a sensor, data of the user that includes the aspect of the physical presentation of the user, (iii) correcting the captured data of the user to portray a corrected version of the aspect that matches the authentic identity of the user, and (iv) storing the corrected data of the user that matches the authentic identity of the user instead of uncorrected data of the user that includes the aspect that does not match the authentic identity of the user. Various other methods, systems, and computer-readable media are also disclosed.