Language Neutral Virtual Assistant with Dynamic Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtual assistants lack the ability to effectively handle queries in multiple languages, leading to inefficient communication between users and enterprises, as they often require language-specific development and training, limiting their language neutrality.
Innovation Solution
A computerized method implementing a language-neutral virtual assistant using a language detector with trained classifiers, normalization, translation, and an AI personal assistant engine that conducts conversations and provides responses across languages, enabling dynamic language switching and continuous learning through supervised and assisted learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual assistants are developed with language-specific training, then response accuracy in specific language is improved, but device complexity and development cost increase
Solution Approach 1:
The patent implements a universal virtual assistant that can handle multiple languages through a single system architecture. The AI engine is designed to process inputs in various languages and generate responses in the same language, eliminating the need for separate language-specific assistants. This multi-functional approach maintains high response accuracy across different languages while reducing development complexity compared to creating multiple dedicated assistants.
Solution Approach 2:
The system dynamically changes language parameters by detecting the input language and adjusting processing parameters accordingly. The AI engine modifies its response generation parameters based on the detected language, allowing it to maintain language-specific accuracy without requiring separate models for each language. This parameter adaptation enables a single assistant to handle multiple languages effectively.
2Adaptability or versatility
If multiple language-specific virtual assistants are created, then language coverage is improved, but ease of operation and system simplicity deteriorate
Solution Approach 1:
The patent creates a single universal virtual assistant that can operate across multiple languages, replacing the need for multiple language-specific assistants. Users interact with a single interface that automatically adapts to their language, simplifying the user experience while maintaining broad language coverage. The system handles language detection and response generation in the user's preferred language without requiring users to switch between different assistants.
3Measurement precision
If language-specific training data is used, then response accuracy in that language is improved, but loss of time for training and deployment increases
Solution Approach 1:
The system uses parameter changes to adapt to different languages without requiring complete retraining. The AI engine employs language detection to identify the input language and then adjusts its processing parameters accordingly. This allows the system to maintain high response accuracy across multiple languages while avoiding the time-consuming process of training separate models for each language, as the same base model adapts its parameters dynamically.
Data Source
AI summary
In one aspect, a computerized method useful for implementing a language neutral virtual assistant including the step of providing a language detector. The language detector comprises one or more trained language classifiers. With language detector identifying a language of an incoming message from a user to an artificially intelligent (AI) personal assistant. The method includes the step of receiving an incoming message to the AI personal assistant. The method includes the step of normalizing the incoming message, wherein the normalizing the incoming message comprises a set of spelling corrections and a set of grammar corrections. The method includes the step of translating the incoming message to a specified language with a specified encoding process and a specified decoding process. The method includes the step of providing an AI personal assistant engine that comprise an artificial intelligence which conducts a conversation via auditory or textual methods. The AI personal assistant engine provides outputs a response translator. The method includes the step of providing a response translator that uses the AI personal assistant engine output to provide a response to the user.