On-Device Biometric Personalization via Neural Vector Encoding
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Solution Overview
Problem
Existing electronic devices struggle to provide personalized user experiences without requiring explicit user input or compromising user privacy, as they often need to enroll biometric signatures or send user data outside the device for processing.
Innovation Solution
A method and apparatus that utilize neural network models stored on the device to encode biometric information from sensors into vectors, allowing for personalized user interface configurations without external data transmission, enabling transparent and privacy-preserving user profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If biometric information is processed externally to provide personalized user experience, then personalization accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent introduces an intermediary mechanism by extracting and encoding biometric features into template vectors that serve as intermediaries between the user's biometric data and the personalization service. The actual biometric data never leaves the device, but the encoded template vectors enable personalized experiences by serving as privacy-preserving identifiers that can be matched against stored profiles.
Solution Approach 2:
The patent extracts only the essential biometric features needed for identification and encodes them into compact template vectors, separating the identification function from the raw biometric data. This extraction allows the device to perform personalization based on encoded features without processing or transmitting the actual biometric information, thus maintaining privacy while achieving personalization.
2Adaptability or versatility
If biometric enrollment is required for device personalization, then personalization accuracy is improved, but user convenience deteriorates
Solution Approach 1:
The patent performs preliminary encoding of biometric information into template vectors during an initial enrollment phase, storing these encoded templates locally. Subsequent personalization operations can then proceed by comparing newly captured biometric data against the pre-encoded templates, eliminating the need for repeated enrollment processes and enabling rapid, convenient personalization.
Solution Approach 2:
The device performs self-service by automatically capturing biometric data, encoding it into template vectors, and matching against stored profiles without requiring explicit user enrollment actions. The system autonomously personalizes the user interface by detecting user presence and applying appropriate profiles, making the personalization process transparent and convenient for users.
3Power
If sensor data is transmitted outside the device for processing, then processing capability is improved, but data security deteriorates
Solution Approach 1:
The patent uses encoded template vectors as intermediaries that enable secure local processing. Instead of transmitting raw sensor data externally, the device encodes biometric features into template vectors and performs matching operations locally using stored templates. This intermediary approach maintains data security by keeping sensitive information on-device while still enabling sophisticated biometric processing capabilities.
Data Source
AI summary
A method and apparatus for device personalization. A device is configured to receive first sensor data from one or more sensors, detect biometric information in the first sensor data, encode the biometric information as a first vector using one or more neural network models stored on the device, and configure a user interface of the device based at least in part on the first vector. For example, the profile information may include configurations, settings, preferences, or content to be displayed or rendered via the user interface. In some implementations, the first sensor data may comprise an image of a scene and the biometric information may comprise one or more facial features of a user in the scene.