Targeted Client Support via Operational Data Analysis
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Solution Overview
Problem
Users of computing devices often face operational problems such as registry errors, memory issues, and hardware failures, which they cannot diagnose or resolve on their own, leading to inefficient and costly support processes for both users and providers, with repeated troubleshooting sessions being common.
Innovation Solution
A rules and action-based client support system that collects operational data from multiple users, analyzes it to produce targeted support instructions, and automatically provides these instructions to affected users, reducing the need for extensive user intervention and repetitive troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional phone-based technical support is used, then users can receive direct assistance from support personnel, but the process becomes lengthy and time-consuming for both users and support providers
Solution Approach 1:
The system performs preliminary actions by automatically collecting operational data from clients and generating support instructions before users need assistance. The instructions are prepared in advance and can be immediately provided to users, eliminating the need for lengthy back-and-forth troubleshooting conversations.
Solution Approach 2:
The system enables self-service by automatically analyzing operational data and generating support instructions without requiring continuous human intervention. Users receive pre-prepared instructions that they can follow independently, reducing both user and support personnel time investment.
2Reliability
If traditional repetitive troubleshooting sessions are conducted for each client, then individual problems can be addressed, but support costs increase significantly for large numbers of users
Solution Approach 1:
The system creates universal support instructions that can be applied to multiple clients experiencing the same operational problem. By analyzing operational data patterns across the client base, the system generates reusable instruction sets that address common issues efficiently, reducing per-client support costs while maintaining effective problem resolution.
3Reliability
If users directly interact with support personnel for troubleshooting, then problems can be diagnosed and solved, but the process is frustrating and inefficient for both parties
Solution Approach 1:
The system empowers users to self-diagnose and self-resolve problems by providing them with customized support instructions generated from their operational data. Users no longer need to navigate complex interactive troubleshooting sessions with support personnel, making the process simpler and more user-friendly while maintaining effective problem solving.
4Loss of energy
If operational problems are left unattended due to traditional support limitations, then support resources are preserved, but device performance deteriorates and provider reputation suffers
Solution Approach 1:
The system implements continuous feedback by automatically collecting operational data from clients, analyzing it to identify performance issues, and providing support instructions to address problems. This closed-loop feedback mechanism ensures problems are detected and addressed proactively, maintaining device performance without requiring extensive human support resources.
Data Source
AI summary
Targeted rules and action based support techniques are described, in which, operational data collected from a plurality of clients is used to generate support instructions for troubleshooting operational problems of the clients. Clients are provided targeted access to support instructions based upon information included in the support instructions which matches the support instructions to the clients. In an implementation, clients may be placed in one or more groups based on the analysis of the operational data and may receive support instructions corresponding to the group automatically and without user intervention.