Real-Time AI Conversation Assistance System
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Solution Overview
Problem
Existing virtual assistants are limited in providing real-time conversation assistance as they need to be activated manually and lack perception, making them inadequate for interjecting assistance during normal conversations based on speech patterns or styles.
Innovation Solution
An AI-based system that analyzes conversation streams in real-time using a content analyzing model to identify assistance requirements and contexts, generating assistive conversation streams based on user profiles and secondary contexts, and rendering them contemporaneously to users during conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If virtual assistants are manually activated, then they can provide automated responses and generate actions, but they cannot interject in real-time conversations perceptively
Solution Approach 1:
The system performs preliminary analysis of conversation streams to identify assistance requirements and contexts before generating responses. The content analyzing model continuously monitors conversations, detects user needs, and prepares assistive models in advance, enabling the virtual assistant to interject perceptively during real-time conversations without manual activation.
2Productivity
If virtual assistants provide automated assistance, then they can execute tasks, but they lack the ability to detect and respond to speech patterns and styles
Solution Approach 1:
The system employs feedback mechanisms where the content analyzing model continuously analyzes conversation streams, identifies user speech patterns and styles, and adjusts the generation of assistive conversation streams accordingly. This feedback loop enables the virtual assistant to detect speech patterns and provide tailored automated assistance that adapts to user communication styles.
3Speed
If virtual assistants interject in real-time conversations, then they can provide timely assistance, but they require complex analysis models to identify assistance requirements and contexts
Solution Approach 1:
The system segments the complex task of real-time conversation analysis into distinct components: content analyzing model for identifying assistance requirements, context analyzing model for determining conversation contexts, and generation model for creating assistive responses. This segmentation allows each model to specialize in specific functions, reducing overall system complexity while maintaining real-time performance.
Data Source
AI summary
The disclosure relates to system and method for providing Artificial Intelligence (AI) based automated conversation assistance. The method includes analyzing, using a content analyzing model, at least one conversation stream captured during a real-time conversation between a plurality of users. The method includes identifying an assistance requirement of at least one first user of the at least one user and at least one primary context associated with the at least one conversation stream using the content analyzing model. Further, the method includes identifying at least one intelligent assistive model based on the identified assistance requirement using an AI model. Using the at least one intelligent assistive model, the method generates at least one assistive conversation stream. Contemporaneous to the at least one conversation stream being captured, the method renders the at least one assistive conversation stream to the at least one first user in real-time.