Digital Human Session Context Transfer Across Destinations
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Solution Overview
Problem
Digital humans struggle to maintain user engagement due to reduced physical interactions, making it difficult to assess user interests and adapt responses effectively in remote environments.
Innovation Solution
The technique involves storing session context information and reinstantiating a digital human with this context when navigating to a different destination, using persistent storage and machine learning algorithms to enhance user interaction and response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital humans operate in remote environments without physical interactions, then user engagement is reduced, but maintaining interaction continuity across different destinations becomes difficult
Solution Approach 1:
The system performs preliminary actions by storing session context information (including user preferences, conversation history, and interaction state) before navigation occurs. When the user navigates to a different destination, the digital human retrieves this pre-stored context to maintain interaction continuity, thus resolving the contradiction between reduced engagement in remote environments and the need for continuous interaction across destinations.
Solution Approach 2:
The patent introduces session context information as an intermediary element that mediates between the user and the digital human across different destinations. This intermediary carries essential interaction data, allowing the digital human to maintain engagement reliability even when physical interactions are reduced and destinations change.
2Adaptability or versatility
If digital humans store and retrieve session context information across destinations, then interaction continuity is maintained, but system complexity increases
Solution Approach 1:
The system extracts only the essential session context information (user preferences, conversation history, interaction state) needed for maintaining interaction continuity, rather than storing complete system states. This extraction approach maintains adaptability across destinations while minimizing the increase in system complexity by focusing only on critical data elements.
Solution Approach 2:
The patent creates simplified copies of the interaction state in the form of session context information that can be easily stored, transmitted, and retrieved across different destinations. These copies contain only the necessary elements to maintain interaction continuity, avoiding the complexity of replicating the entire system state while still achieving the desired adaptability.
3Reliability
If digital humans maintain detailed session context, then user engagement improves, but information storage requirements increase
Solution Approach 1:
The system applies local quality by storing different types of session context information with varying levels of detail based on their importance. Critical engagement-related data (user preferences, conversation intent) is stored with high fidelity, while less critical data is stored with lower detail. This approach improves user engagement through maintained context while optimizing information storage requirements by not uniformly storing all data at maximum detail.
Data Source
AI summary
Techniques are provided for reinstantiating digital humans with stored session context in response to navigation to a different destination. One method comprises obtaining information characterizing a user interacting with a digital human instantiated in a first destination (e.g., a webpage or a virtual environment); and, in response to a navigation from the first destination to a different destination: automatically storing session context information in a persistent session context storage based on the obtained information; and reinstantiating the digital human in the different destination with the stored session context information. A query, based on a user input, may be provided to an information retrieval system that generates query results; and a prompt for a language model may be determined based on the query results, wherein the language model generates a response comprising a first content portion comprising a first content type and a second content portion comprising a second content type.