Virtual Agent Bot Semantic Skill Set Selection
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Solution Overview
Problem
Existing agent bots are limited to a single pre-stored skill set, requiring manual updates and lacking the ability to provide relevant responses to diverse user queries, as they cannot perform semantic analysis across multiple skill sets.
Innovation Solution
A virtual agent bot system that receives user inputs, analyzes them to determine a semantic skill set among multiple candidate skill sets, and provides a semantic topic response, enabling relevant and logically related answers by invoking the appropriate skill set for each query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing agent bots use a single pre-stored skill set, then the system complexity is reduced and ease of operation is improved, but the adaptability to diverse user queries deteriorates and response relevance is reduced
Solution Approach 1:
The virtual agent bot is designed to perform multiple functions by selecting from a plurality of different candidate skill sets based on semantic analysis of user input. The system can adapt its behavior and knowledge base dynamically, allowing a single bot to serve multiple domains (e.g., weather, news, sports) without requiring separate bot instances for each skill set.
Solution Approach 2:
The system dynamically selects which skill set to invoke based on real-time semantic analysis of the user's input query. Rather than being static and limited to one skill set, the bot's capabilities are dynamic and adapt to the specific needs of each user interaction, improving both adaptability and response relevance.
2Device complexity
If existing agent bots are limited to a single skill set, then the device complexity is reduced, but the ability to provide relevant responses to diverse queries deteriorates
Solution Approach 1:
The skill sets are segmented into distinct, modular units that can be independently selected based on user input. Each skill set represents a separate knowledge domain that can be activated independently, allowing the system to maintain lower complexity while providing diverse and relevant responses by only loading or activating the necessary skill set for each interaction.
Solution Approach 2:
A semantic analysis component acts as an intermediary between the user input and the skill set selection mechanism. This intermediary analyzes the semantic content of queries and determines which skill set is most appropriate, enabling the system to provide relevant responses without requiring complex hard-coded routing logic.
3Device complexity
If manual updates are required for skill sets, then the system structure is simplified, but the productivity and ability to respond to new queries deteriorates
Solution Approach 1:
The system enables self-service by automatically selecting appropriate skill sets based on semantic analysis of user input, eliminating the need for manual configuration or updates for each query type. The bot serves itself by dynamically adapting its knowledge base to match user needs.
Solution Approach 2:
The system changes parameters dynamically by selecting different skill sets based on the semantic content of user input. Rather than requiring manual updates to add new capabilities, the system adjusts its operational parameters (which skill set is active) based on the specific query, improving productivity and response capability.
Data Source
AI summary
A computer-implemented method of executing a virtual agent bot includes receiving, via a computer server, at least one input query from a user, and analyzing the at least one input query to extract at least one input term. The method further comprises determining a semantic skill set of the virtual agent bot among a plurality of different candidate skill sets based at least in part on the at least one input term; and invoking the virtual agent bot to provide a semantic topic response corresponding to the semantic skill set to provide an answer to the at least one input query.