Automated Assistant Multilingual Response Rendering
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Solution Overview
Problem
Users interacting with automated assistants face challenges when submitting queries in a second language, particularly when their proficiency in that language is low, leading to poorly formed queries and prolonged or failed interactions.
Innovation Solution
The system processes audio data from users to generate responses in both the user's primary language and a secondary language of interest, rendering multilingual content based on verification data, and adjusting content rendering based on user proficiency in the secondary language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the user submits queries in a second language with low proficiency, then the user can interact with the automated assistant in the second language, but the queries may be poorly formed leading to prolonged or failed interactions
Solution Approach 1:
The patent introduces an automated assistant that acts as an intermediary between the user and the information system. The assistant receives user queries in natural language (including second language queries with low proficiency), interprets the intent, and formulates proper information retrieval queries. This mediator bridges the gap between imperfect user input and the requirements of the information system, resolving the contradiction between language accessibility and query quality.
Solution Approach 2:
The automated assistant performs self-service by automatically analyzing user intent, generating appropriate queries, and executing information retrieval tasks without requiring the user to manually construct complex queries. The system serves itself by handling the query formulation process, eliminating the need for users to have high proficiency in the second language while maintaining reliable information retrieval.
2Reliability
If the automated assistant provides responses only in the user's primary language, then the response quality is high, but the user cannot receive multilingual responses despite interest in learning a second language
Solution Approach 1:
The automated assistant is designed with multi-functionality to handle multiple languages. It can receive queries in one language and provide responses in either the same language or a different language based on user preferences. This universal capability allows the system to maintain high response quality while adapting to multilingual needs, resolving the contradiction between response quality and language flexibility.
Solution Approach 2:
The system changes the language parameter of responses based on user preferences and learning goals. The automated assistant can dynamically adjust the output language while maintaining the quality and accuracy of the information provided. This parameter adjustment allows users to receive high-quality responses in their target language, facilitating language learning while preserving response reliability.
3Measurement precision
If the user submits multiple requests to obtain accurate information, then the information retrieval accuracy improves, but the computing resources are consumed
Solution Approach 1:
The automated assistant performs preliminary actions by analyzing user intent and formulating optimized queries before executing information retrieval. It pre-processes the user's natural language input to create efficient search queries that maximize retrieval accuracy in a single operation. This preliminary preparation reduces the need for multiple retry requests, thereby conserving computing resources while maintaining high information accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the automated assistant learns from interaction patterns and improves query formulation over time. By analyzing successful query patterns and user preferences, the system refines its information retrieval strategies to achieve high accuracy with fewer requests, reducing overall computing resource consumption while maintaining measurement precision.
Data Source
AI summary
Implementations relate to determining multilingual content to render at an interface in response to a user submitted query. Those implementations further relate to determining a first language response and a second language response to a query that is submitted to an automated assistant. Some of those implementations relate to determining multilingual content that includes a response to the query in both the first and second languages. Other implementations relate to determining multilingual content that includes a query suggestion in the first language and a query suggestion in a second language. Some of those implementations relate to pre-fetching results for the query suggestions prior to rendering the multilingual content.