Conversational Agent Model Dynamic Configuration
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Solution Overview
Problem
Conventional cognitive computing models for client interaction are static and require continuous administrator intervention, lacking dynamic configuration and update capabilities to address changing client needs and new information sources.
Innovation Solution
A conversational agent learning model that dynamically constructs and configures itself by retrieving information from multiple repositories and external data sources, including social networks, to adapt to client interactions, new trends, and technological changes, allowing for autonomous updates and refinement without continuous human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static knowledge base is used in conventional cognitive computing models, then the model structure is simple and easy to maintain, but the model cannot adapt to changing client needs and requires continuous administrator intervention
Solution Approach 1:
The patent implements dynamic construction of the cognitive computing model by automatically retrieving information from multiple sources (corpus, knowledge base, external data sources) and updating the knowledge base in real-time based on client interactions and new information, transforming the static model into a dynamic adaptive system
Solution Approach 2:
The system performs self-service by automatically identifying deficiencies through client interactions, retrieving relevant information from multiple sources, and updating the knowledge base without continuous administrator intervention, enabling autonomous adaptation to changing needs
2Reliability
If continuous administrator intervention is implemented to monitor and update the knowledge base, then the model maintains high reliability, but the operational complexity and time consumption increase significantly
Solution Approach 1:
The patent implements feedback mechanisms where client interactions with the conversational agent are continuously monitored, deficiencies are automatically identified, and the knowledge base is updated based on this feedback, creating a closed-loop system that maintains reliability autonomously
Solution Approach 2:
The system enables continuous automatic updating of the knowledge base through ongoing client interactions and information retrieval from multiple sources, eliminating gaps in monitoring and ensuring continuous improvement without administrator intervention
3Adaptability or versatility
If a static knowledge base is used, then the system is easier to manage, but it lacks the capability to incorporate new information from social networks and external sources
Solution Approach 1:
The patent implements multi-functionality by enabling the knowledge base to automatically retrieve and integrate information from multiple diverse sources including corpus, knowledge base, social networks, and external data sources, making the system universally adaptable to various information sources
Solution Approach 2:
The system performs preliminary actions by proactively retrieving information from multiple sources and updating the knowledge base before client needs arise, rather than waiting for manual updates, ensuring the model is always prepared with current information
Data Source
AI summary
An approach is described with respect to dynamically constructing and configuring a conversational agent learning model. Various aspects of the conversational agent learning model may be constructed and updated without continuous intervention of a domain administrator. A method pertaining to such approach may include retrieving a corpus of information. The corpus of information may include records from a set of repositories and external data, including data from social networks or applications. The method further may include configuring the conversational agent learning model based upon the retrieved corpus of information. The method further may include deploying the conversational agent learning model by facilitating interaction between the conversational agent learning model and a plurality of clients. The method further may include updating the conversational agent learning model to address any modification to the corpus of information.