Intent-Based AR Virtual Assistant for Automated Troubleshooting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges with product malfunctions and operational confusion, and existing customer support methods are inefficient in providing tailored assistance for specific product issues, leading to a need for enhanced technical support solutions.
Innovation Solution
An intent-based virtual assistant utilizing augmented reality (AR) and artificial intelligence/machine learning (AI/ML) to detect user intents and provide personalized actions, such as troubleshooting steps, by overlaying virtual assistants on real-world views to guide users through specific actions related to their devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional customer support methods are used, then human agents can provide assistance, but the support process becomes inefficient and requires significant human intervention for routine issues
Solution Approach 1:
The system enables customers to independently diagnose and resolve technical issues through automated intent detection and recommendation generation. The virtual assistant autonomously analyzes customer descriptions, identifies potential issues, and provides troubleshooting steps without requiring human agent intervention, allowing the system to serve itself for routine support tasks.
Solution Approach 2:
An AI-powered virtual assistant acts as an intermediary between customers and human support agents. This intermediary automatically processes customer inquiries, detects intents, generates recommendations, and only escalates complex cases to human agents, thereby reducing the burden on human intervention while maintaining support quality.
2Adaptability or versatility
If generic support responses are provided, then support coverage is broad, but the assistance lacks personalization for specific product issues
Solution Approach 1:
The system segments support interactions by detecting specific customer intents and routing them to specialized knowledge bases. By dividing the support process into intent detection, issue classification, and targeted recommendation generation, the system delivers personalized assistance tailored to each customer's specific product issue rather than providing generic responses.
Solution Approach 2:
The virtual assistant applies local quality by providing customized support recommendations based on the specific product, issue type, and customer context. Each customer receives tailored troubleshooting steps and solutions relevant to their particular problem, rather than uniform generic responses, thereby maintaining high adaptability without losing issue specificity.
3Measurement precision
If manual troubleshooting processes are used, then detailed diagnostic information can be gathered, but the process consumes excessive time for customers
Solution Approach 1:
The system performs preliminary diagnostic actions by automatically analyzing customer descriptions and detecting intents before full troubleshooting begins. This preliminary intent detection and issue classification enables the system to quickly narrow down potential problems and provide targeted recommendations, achieving detailed diagnostic accuracy without requiring customers to undergo lengthy manual troubleshooting processes.
Data Source
AI summary
A system described herein may maintain one or more models that associate respective objects and triggers with sets of actions. The system may receive visual information from a User Equipment (“UE”), may determine that the visual information received from the UE depicts the particular object, and may identify that the one or more triggers are met. The system may provide, to the UE, based on identifying that the visual information received from the UE depicts the particular object, and further based on identifying that the one or more triggers are met, an augmented reality (“AR”) virtual assistant that performs the particular set of actions with respect to the particular object.