Adaptive Virtual Avatar Modeling With Knowledge Graph Personalization
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Solution Overview
Problem
Existing self-awareness computer programs utilize static, generic avatars that fail to adapt to the changing nature of the end user, limiting the effectiveness of self-awareness interactions.
Innovation Solution
A method for virtual avatar generation and simulation that involves collecting multi-source characterization data from heterogeneous sources, transforming it into a knowledge graph, and parameterizing an avatar using a large language model (LLM) to create a personalized and adaptive avatar for scenario simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static, generic avatar is used in self-awareness computer programs, then the device complexity is reduced and ease of manufacture is improved, but the adaptability to the changing nature of the end user deteriorates
Solution Approach 1:
The patent transforms the static avatar into a dynamic system that continuously adapts to the end user's changing nature. The avatar is updated in real-time based on new data from heterogeneous sources, ensuring it remains current and relevant. This dynamic update mechanism resolves the contradiction by making the avatar adaptable without requiring complete system redesign, thus managing complexity through incremental updates rather than static complexity.
Solution Approach 2:
The patent creates a virtual copy (avatar) of the end user that mirrors their characteristics, behaviors, and patterns. This copy can be simulated and updated independently of the actual user, allowing the system to adapt to user changes without directly modifying the core system structure. The avatar serves as a flexible proxy that absorbs the adaptability requirements, reducing the complexity burden on the main system.
2Adaptability or versatility
If multi-source characterization data is collected and processed to create a personalized avatar, then the adaptability and personalization are improved, but the resource consumption and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring data from heterogeneous sources as it is collected. The system maintains organized characterizing data in advance, transforming raw data into structured formats suitable for avatar generation. This preliminary preparation reduces the processing time required when the avatar needs to be updated or generated, as the data is already prepared and organized rather than requiring intensive processing at the moment of need.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with artificial intelligence and machine learning techniques. The AI-driven avatar generation system can efficiently process and synthesize multi-source data without the linear processing bottlenecks of traditional systems. This substitution enables rapid analysis of diverse data sources and generation of personalized avatars, reducing processing time while maintaining high personalization quality.
3Adaptability or versatility
If a static model is trained with enormous consumption of resources, then the initial model accuracy is improved, but the model cannot adapt to the changing nature of the subject human
Solution Approach 1:
The patent implements periodic updates to the avatar model using newly collected characterizing data. Instead of requiring enormous resources for complete retraining, the system periodically incorporates new data to refresh and adapt the model. This periodic action allows the model to evolve with the user over time, maintaining adaptability while distributing resource consumption across multiple smaller update cycles rather than one large initial training event.
Solution Approach 2:
The system establishes a feedback loop where the avatar's performance and user interactions continuously inform subsequent updates. The avatar is simulated in various scenarios, and the results feed back into refining the model parameters. This feedback mechanism enables incremental adaptation to the user's changing nature, allowing the model to improve over time with minimal additional resources compared to the initial training, as each update builds on previous learning rather than starting from scratch.
Data Source
AI summary
Virtual avatar generation and simulation includes registering an end user in a self-awareness computer program and establishing a communicative coupling to different heterogeneous data sources, so that characterizing data of the end user is received from over each coupling to each heterogeneous data source. The characterizing data is transformed into a knowledge graph associated with the end user and an avatar is instantiated in the self-awareness computer program in correspondence to the end user. Thereafter, an avatar generation prompt is formulated requesting parameterization of the avatar including both a reference to the knowledge graph and also a reference to the avatar. The prompt is then transmitted to a large language model (LLM) so as to receive in response the requested parameterization. Finally, the avatar is parameterized with the received parameterization and simulated in the self-awareness computer program with respect to a scenario artifact defining a scenario for the end user.