Personalized Avatar Control Using Natural Language and Biometric Gating

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Solution Overview

Problem

Existing generative models (GMs) lack data privacy and security measures for generating personalized avatars and do not enable users to control these avatars in virtual environments effectively.

Innovation Solution

A system utilizing generative models to generate and control personalized avatars by processing user vision data and natural language instructions, incorporating biometric authorization, supervised fine-tuning, and reinforcement learning from human feedback to ensure authorized generation and realistic avatar control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If generative models generate personalized avatars of actual humans, then avatar personalization is improved, but data privacy and security deteriorate

Engineering Contradiction:

Improveavatar personalizationVSAvoiddata privacy and security

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system creates a virtual copy (avatar) of the user based on vision data, allowing the avatar to represent the user in virtual environments without exposing the user's actual personal information. The avatar is trained to mimic the user's facial expressions and movements, providing personalization while maintaining privacy through the copy principle.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The avatar serves as an intermediary between the user and the virtual environment. Instead of the user directly interacting in potentially unsafe environments, the avatar acts as a mediator that can be controlled through natural language instructions, protecting the user's data and physical presence while enabling personalized interaction.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If generative models output images in unique environments, then environmental versatility is improved, but control over avatar actions deteriorates

Engineering Contradiction:

Improveenvironmental versatilityVSAvoidcontrol over avatar actions

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system implements feedback loops where the model processes natural language instructions and adjusts the avatar's actions accordingly. The feedback mechanism allows the system to understand user intent and generate appropriate control signals for the avatar, enabling precise control while maintaining environmental versatility.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static image generation to dynamic video generation, allowing the avatar to perform sequences of actions in response to natural language instructions. This dynamic approach enables the avatar to adapt its behavior in real-time across different virtual environments while maintaining user control through language-based commands.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260045018A1System(s) and method(s) for utilizing generative model(s) to generate and/or control personalized avatar(s)

Publication Date: 2026.02.12 GOOGLE LLC

AI summary

Implementations are directed to utilizing generative model(s) (GM(s)) to generate and/or control personalized avatar(s). Processor(s) of a system can receive vision data that captures a user and generate a personalized avatar of the user (e.g., a virtual three-dimensional representation of the user) based on the vision data. Further, the processor(s) can receive natural language instructions for controlling the personalized avatar, process, using the GM(s), at least an indication of the personalized avatar and the natural language instructions, determine generative data that characterizes the personalized avatar of the user performing a sequence of actions defined by the natural language instructions, and cause the generative data to be rendered at a client device of the user or an additional client device of the user or an additional user. The generative data can include, for example, generative video data, generative audio data, etc.