Query Router for Software Support Systems
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Solution Overview
Problem
Users of software applications face difficulties in finding answers to their queries due to the vast amount of information in searchable knowledge bases and community knowledge-based support systems, and real-time human interaction is often inefficiently utilized, leading to increased costs and user dissatisfaction.
Innovation Solution
A method that selects the appropriate support system for processing user requests based on the content and context of the query, using a query processor to parse the request, identify characteristics, and route it to either a virtual agent, incremental searcher, or live support system, reserving real-time human interaction for complex or high-risk queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If searchable knowledge bases and community knowledge-based support systems are used to provide comprehensive support information, then the breadth of information available to users is improved, but the time required for users to find answers increases
Solution Approach 1:
The patent introduces a query router as an intermediary component that sits between the user's support request and the multiple support systems. The query router analyzes the query characteristics and automatically routes the request to the most appropriate support system (searchable knowledge base, community knowledge base, virtual agent, or live support), eliminating the need for users to manually search through all available resources and significantly reducing time to find answers while preserving access to comprehensive information.
Solution Approach 2:
The system dynamically selects and routes queries to different support systems based on real-time analysis of query characteristics such as query type, user preferences, and system workload. This dynamic routing mechanism adapts the support delivery pathway for each individual query, optimizing the balance between providing comprehensive information and minimizing search time.
2Reliability
If live, interactive support systems with human agents are used to provide real-time support, then user satisfaction and response quality are improved, but support costs increase
Solution Approach 1:
The query router acts as an intelligent intermediary that filters and directs only the most appropriate queries to expensive human agent resources. By analyzing query characteristics and routing simpler queries to automated systems (virtual agents, searchable knowledge bases), the system ensures that human agents focus only on complex queries requiring their expertise, thereby maintaining high support quality while significantly reducing overall support costs.
Solution Approach 2:
The system enables self-service by routing appropriate queries to automated support systems such as virtual agents and searchable knowledge bases, which can handle routine inquiries without human intervention. This allows users to obtain support for common issues through automated systems, reducing the need for expensive human agent involvement while maintaining reliable support for queries that truly require human expertise.
3Loss of energy
If virtual chat systems are used to automate support responses, then support costs are reduced, but the ability to handle complex queries effectively decreases
Solution Approach 1:
The support system is segmented into multiple specialized components: searchable knowledge bases for factual information, community knowledge bases for peer support, virtual agents for routine automated responses, and live human agents for complex queries. The query router segments the incoming query stream and directs each query to the most appropriate segment based on its characteristics, allowing virtual agents to handle routine queries cost-effectively while complex queries are routed to human agents who can handle them effectively.
Solution Approach 2:
The system dynamically determines the appropriate support channel for each query based on real-time analysis of query characteristics, user preferences, and system state. This dynamic routing allows the system to flexibly allocate resources, using cost-effective virtual agents for simple queries while maintaining the capability to handle complex queries through human agents when needed, thus balancing cost reduction with effective query handling.
Data Source
AI summary
The present disclosure relates to processing support requests, or support queries, in a software application including automated and human interaction-based support systems. An example method generally includes receiving a support request from a client device. A query processor parses the support request to identify one or more characteristics of the support request and identifies a support system to process the support request based at least in part on the identified one or more characteristics of the support request. The query processor instantiates a support session with the identified support system to process the support request.