Virtual Assistant Aggregating Multiple Response Modules
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Solution Overview
Problem
Conventional chatbots and IVR systems are limited by static databases, leading to incomplete responses and the need for human intervention when users ask questions outside the database's scope, resulting in prolonged and costly interactions.
Innovation Solution
A method and apparatus for aggregating user sessions by initiating multiple response modules associated with different network entities, converting user utterances into textual strings, and sending queries to natural language processing systems for combined responses, enabling semantically suitable answers across multiple networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static database is used for chatbot responses, then the system is simple and easy to maintain, but the chatbot cannot answer questions outside the database scope
Solution Approach 1:
The system segments the response generation function into multiple independent response modules, each associated with different network entities. Each module can process specific types of queries independently, allowing the system to handle diverse questions without requiring a monolithic complex architecture. The modular design enables flexible combination of multiple simple modules to achieve complex query handling capabilities.
Solution Approach 2:
The virtual assistant system provides universal functionality by aggregating multiple response modules from different network entities. Instead of building a single specialized system, the architecture allows one virtual assistant to perform multiple functions by dynamically selecting and combining appropriate response modules, thereby achieving versatility without proportionally increasing overall system complexity.
2Adaptability or versatility
If multiple response modules from different network entities are aggregated, then comprehensive responses can be provided, but the system complexity increases
Solution Approach 1:
The virtual assistant acts as an intermediary layer between the user and multiple response modules from different network entities. It receives user queries, selects appropriate response modules, coordinates their execution, and aggregates their responses. This intermediary approach simplifies the user's interaction by providing a single interface while managing the complexity of coordinating multiple underlying systems.
Solution Approach 2:
The system merges responses from multiple independent response modules into a unified answer. By combining the outputs of different modules in a coordinated manner, the system achieves comprehensive response capability while managing complexity through structured integration. The merging process ensures that responses from various sources are synthesized into a coherent whole rather than presenting fragmented information.
3Reliability
If human intervention is used for complex queries, then accurate answers can be provided, but interaction time and cost increase
Solution Approach 1:
The system enables self-service by allowing the virtual assistant to autonomously select and coordinate response modules to answer complex queries without human intervention. The automated aggregation process handles queries that would traditionally require human operators, reducing interaction time while maintaining accuracy through intelligent response synthesis from multiple modules.
Solution Approach 2:
The system incorporates feedback mechanisms where the virtual assistant evaluates query complexity and dynamically adjusts the selection of response modules based on previous interactions and query patterns. This feedback-driven approach improves answer accuracy by learning from past performance while reducing unnecessary human intervention and associated time costs.
Data Source
AI summary
Methods, apparatuses, and computer program products are described for aggregating user sessions for conversational exchanges using a virtual assistant. A user device can receive conversational inputs, convert the conversational inputs into textual strings, associate, based upon semantic analysis of different portions of the textual strings, a first network and a second network, and initiate, respectively, a first and second user session with a first response module of the first network and a second response module of the second network. The portions of textual strings can be transmitted to the first and second response modules via, respectively, the first and second user sessions. Once response fragments are received from the first and second response modules, the response fragments can be combined in a semantically suitable order to form a generated response.