Virtual Advisor Avatars for Cross-Domain, Cross-Linguistic Servicing
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Solution Overview
Problem
Existing systems fail to provide effective user servicing due to advisors' inability to understand user needs, communicate in preferred languages, or converse across different domains, leading to inefficient communication and resource utilization.
Innovation Solution
A system that allows users to interact with virtual advisor avatars in a metaverse session, determining preferred languages and domains, accessing knowledge servers for responses, or connecting with physical advisors, and translating queries to ensure efficient communication and knowledge updates without physical advisors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a physical advisor is used to service users, then the user can receive personalized assistance, but the system requires more computing resources and cannot serve multiple languages/domains efficiently
Solution Approach 1:
The patent creates virtual advisor avatars that are digital copies of physical advisors, enabling the system to provide personalized assistance without requiring physical presence. These virtual avatars can serve multiple users simultaneously across different domains and languages, dramatically improving productivity while reducing computing resource usage compared to deploying actual physical advisors for each interaction.
Solution Approach 2:
The virtual advisor system is designed to handle multiple languages, domains, and user types simultaneously through a single unified platform. The knowledge server stores multi-lingual, multi-domain knowledge that can be accessed by any virtual advisor avatar, making the system universally applicable across diverse servicing scenarios without requiring separate systems for each language or domain.
2Adaptability or versatility
If advisors are trained in multiple languages and domains, then they can serve more users, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex knowledge base into distinct language-specific and domain-specific modules stored in the knowledge server. Each virtual advisor avatar can be configured with specific language and domain combinations, allowing the system to achieve broad adaptability without requiring each advisor to master all languages and domains. This modular segmentation reduces system complexity by organizing knowledge into reusable, independent components.
Solution Approach 2:
The knowledge server acts as an intermediary between users and virtual advisors, translating user queries into appropriate domain-specific responses. When a user interacts with a virtual advisor, the system queries the knowledge server which contains pre-organized multi-lingual, multi-domain knowledge. This intermediary layer handles the complexity of language translation and domain matching, allowing virtual advisors to provide accurate responses without directly managing the complexity of multiple languages and domains.
3Loss of energy
If the system uses virtual advisor avatars instead of physical advisors, then computing resources are reduced, but the ability to handle complex cross-linguistic and cross-domain queries may be limited
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with virtual advisors are continuously analyzed and used to update the knowledge server. When users rate the quality of responses or when interaction patterns indicate knowledge gaps, the system automatically updates the knowledge base to improve future responses. This continuous feedback loop ensures that virtual advisors maintain high reliability in resolving complex cross-linguistic and cross-domain queries while keeping computing resource usage low.
Solution Approach 2:
The knowledge server is pre-populated with comprehensive multi-lingual and multi-domain knowledge before the system goes live. This preliminary action ensures that virtual advisors have access to accurate, pre-processed information for handling complex queries from the outset, reducing the need for real-time computation and improving response reliability. The system performs knowledge curation and organization in advance, allowing virtual advisors to provide accurate responses without requiring complex real-time processing.
Data Source
AI summary
A method includes generating a metaverse session, a first avatar of a first user, and a virtual advisor avatar. The first user is allowed access to the metaverse session using the first avatar. A voice query of the first user is received and analyzed to determine a preferred language/dialect of the first user, and an issue and a domain of the voice query. In response to determining that a language/knowledge server includes a response corresponding to the issue and the domain, the response is retrieved from the language/knowledge server. In response to determining that a language/dialect of the response matches the preferred language/dialect of the first user, a first voice response is generated from the retrieved response. The first voice response is in the preferred language/dialect of the first user. The first voice response is communicated to the first user using the virtual advisor avatar.