Generative AI Real-Time Assistance System
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Solution Overview
Problem
Existing real-time assistance tools face challenges such as technical glitches, user resistance, privacy concerns, scalability issues, and lack of personalization, failing to provide effective step-by-step instructions or multi-modal guidance to users in resolving their queries.
Innovation Solution
A method and system utilizing a generative Artificial Intelligence (AI) model that processes user queries, including multi-modal inputs, in real-time to determine the type of query and generate instructions for assistance, using on-screen controls, guided instructions, textual, and audio cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing real-time assistance tools are used, then immediate solutions can be provided to consumer concerns, but technical glitches, downtime, and scalability issues occur
Solution Approach 1:
The patent introduces an AI model as an intermediary between the user and the troubleshooting system. The AI model processes user queries and generates step-by-step instructions, acting as a mediator that eliminates direct technical system failures while maintaining rapid response. The AI model handles the complexity of real-time processing, isolating the user from technical glitches and downtime issues.
Solution Approach 2:
The system enables users to resolve their own issues through AI-generated step-by-step instructions without requiring continuous technical support infrastructure. The AI model empowers users to independently troubleshoot and solve problems, reducing dependency on fragile real-time assistance systems while maintaining fast resolution times.
2Productivity
If existing real-time assistance tools are used, then consumer concerns can be addressed rapidly, but lack of personalization and effective step-by-step guidance occurs
Solution Approach 1:
The AI model provides personalized assistance by tailoring step-by-step instructions to each user's specific problem and context. Rather than offering generic solutions, the system adapts its guidance to the individual user's situation, device type, error messages, and troubleshooting progress, delivering localized quality assistance for each interaction.
Solution Approach 2:
The troubleshooting instructions are dynamically generated and adapted based on user feedback and progress. The AI model adjusts the complexity, detail, and approach of instructions in real-time based on the user's understanding and problem resolution status, making the system adaptable to each interaction rather than static and one-size-fits-all.
3Loss of time
If existing real-time assistance tools are used, then immediate support can be provided, but user resistance and privacy concerns arise
Solution Approach 1:
The AI model serves as a privacy-protecting intermediary that processes user information without requiring direct human intervention. Users can share sensitive device data and error information with the AI system without concerns about human privacy violations, as the AI processes data according to predefined policies and provides immediate assistance without human privacy risks.
4Productivity
If existing real-time assistance tools are used, then consumer support can be enhanced, but scalability issues occur
Solution Approach 1:
The AI-powered system enables unlimited parallel support interactions without requiring proportional increases in human support staff. Each user receives personalized, step-by-step guidance from the AI model independently, allowing the system to scale to any number of concurrent users without increasing operational complexity or infrastructure requirements.
Data Source
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AI summary
This disclosure relates to a method and a system for providing real-time assistance to a user using a generative AI model. The method includes receiving by the generative AI model, a user query corresponding to an activity. The user query includes one or more multi-modal inputs. The generative AI model is pretrained based on a set of predefined policies associated with an entity. The method further includes processing in real-time, by the generative AI model, the user query to determine a type of the user query based on the activity. The method further includes providing, by the generative AI model, assistance to the user in real-time by generating instructions in response to the processing of the user query.