Virtual Speech Assistant Domain Routing for Personalized Commands
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Solution Overview
Problem
Existing virtual speech assistants struggle to accurately process user commands across multiple domains while accounting for individual user preferences and contexts, leading to resource-intensive training and potential overfitting, and fail to handle similar commands with different meanings effectively.
Innovation Solution
A method involving a server that processes user requests through domain models and skills, using Matching-Score-Analyzers to assess user context and preferences, selecting the most suitable domain model based on Domain-Matching-Scores, and employing a feedback loop to ensure accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a global dispatcher model is trained to recognize all possible user commands across multiple domains, then command recognition coverage is improved, but training resources and computational complexity increase significantly
Solution Approach 1:
The system segments the command recognition task by introducing domain-specific sub-models that specialize in particular domains (e.g., weather, news, sports). Each sub-model is trained independently on domain-specific data, avoiding the need to train one massive global model on all domains. This segmentation reduces overall training complexity while maintaining comprehensive command recognition coverage across multiple domains.
Solution Approach 2:
The system introduces a domain classifier as an intermediary component between the general dispatcher model and domain-specific sub-models. The domain classifier first identifies which domain a command belongs to, then routes it to the appropriate specialized sub-model. This intermediary approach allows the system to handle diverse commands efficiently without requiring every model to know every command, reducing overall computational complexity.
2Measurement precision
If the dispatcher model is trained to differentiate between similar commands with different meanings, then command accuracy is improved, but the model becomes prone to overfitting
Solution Approach 1:
The system segments similar commands with different meanings into separate domain-specific sub-models. Each sub-model is trained only on its specific domain's similar commands, allowing it to learn fine-grained distinctions without the complexity of all possible variations across domains. This reduces overfitting risk while maintaining high accuracy for domain-specific command differentiation.
Solution Approach 2:
The system applies local quality by making each domain sub-model specialized in its specific domain characteristics. Instead of one general model trying to handle all variations, each sub-model develops specialized knowledge for its domain's similar commands (e.g., weather-specific similar commands vs. news-specific similar commands). This localized specialization improves accuracy for each domain while reducing overall overfitting through distributed specialization.
3Adaptability or versatility
If the dispatcher model is updated to include new user entities or preferences, then user personalization is improved, but retraining and deployment time increase
Solution Approach 1:
The system segments user personalization into domain-specific user profiles stored locally with each domain sub-model. When user preferences or entities change in a specific domain, only the corresponding domain sub-model and its local user profile need to be updated, not the entire global dispatcher model. This segmentation enables rapid, localized updates without system-wide retraining, reducing deployment time while maintaining personalized user experiences.
Solution Approach 2:
The system implements self-service by allowing domain sub-models to independently manage their own user profiles and preferences. Each domain model can locally adapt to user preferences within its domain without requiring centralized retraining. This autonomous adaptation capability enables real-time personalization updates with minimal computational overhead and no full-system retraining required.
Data Source
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AI summary
The invention relates to techniques to provide a customized response for users communicating with a virtual speech assistant, wherein the virtual assistant is configured to receive a request from the user and to forward the request via a communication network to a server that is configured to process the request by means of skills of at least two domain models and to deliver the response back to the virtual assistant, wherein the method comprises the following steps: • starting a selection process to select a suitable domain model regarding the text request, wherein the selection process comprises the following steps: o provisioning of a Matching-Score-Analyzer by each of the domain models, wherein the Matching-Score-Analyzers differ from each other; o using the text request as input for the Matching-Score-Analyzers and calculating a respective Domain-Matching-Score for the different domains; o comparing the resulting Domain-Matching-Scores and selecting the domain model that is associated to the highest Domain-Matching-Scores as the selected domain model to process the text request; • processing the text request with the selected domain model and transmitting the response to the virtual assistant.