AI Chatbot Creation System Using Modular Templates
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Solution Overview
Problem
Conventional chatbot creation systems face challenges such as increased consumption, lack of standardized frameworks, and duplication of development efforts, leading to inefficiencies and reduced productivity despite intended improvements in productivity and human effort minimization.
Innovation Solution
An AI-based communication system that facilitates the creation and interaction with custom conversational entities through phases of design, build, analytics, and maintenance, utilizing natural language processing, machine learning, and data integration from various sources to provide efficient and customizable chatbot development and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chatbot creation systems are used with distinct customizations for each business function, then each chatbot can be tailored to specific requirements, but development efforts must be duplicated across domains leading to increased time and resource consumption
Solution Approach 1:
The patent implements a universal chatbot framework that can serve multiple business functions and domains through configurable templates and modular components. Instead of creating separate chatbots for each domain, the system allows a single chatbot engine to be adapted to different scenarios (customer service, sales, support, etc.) by configuring different knowledge bases, response templates, and interaction patterns, thereby eliminating redundant development efforts while maintaining domain-specific functionality
Solution Approach 2:
The chatbot creation system is divided into independent modular components including knowledge base modules, response template modules, interaction flow modules, and domain-specific configuration modules. This segmentation allows developers to create once and reuse across multiple domains by simply reconfiguring the modular components rather than rebuilding entire chatbot systems for each business function
2Adaptability or versatility
If conventional chatbot creation systems are used with distinct customizations for each business function, then each chatbot can be tailored to specific requirements, but large amounts of rework are required reducing efficiencies and overall productivity
Solution Approach 1:
The system provides pre-configured chatbot templates, response patterns, and knowledge base structures for common business functions such as customer service, sales, and technical support. These preliminary configurations include pre-defined interaction flows, greeting templates, and common query responses that can be immediately deployed or slightly customized for specific domains, eliminating the need to start from scratch for each new chatbot project and significantly reducing rework
3Quantity of substance
If chatbot consumption increases to meet growing consumer base demands, then service coverage is expanded, but pressure on existing customer care infrastructure and manpower increases leading to longer wait times and ticket resolution
Solution Approach 1:
The universal chatbot framework enables deployment of multiple specialized chatbots across different domains and customer care channels without proportionally increasing infrastructure requirements. A single centralized chatbot platform can handle diverse customer inquiries across sales, support, billing, and service domains simultaneously, scaling to meet growing consumer demands while maintaining efficient resource utilization and preventing increased wait times
Data Source
AI summary
A system for creating and managing an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a data access interface to receive instructions with configuration details from a requestor to create an artificial conversational entity, as well as data from a data source. The system may comprise a processor to generate the artificial conversational entity by: identifying a data source type associated with the data; performing data treatment on the received data based on the data source type in order to focus data on at least one targeted topic; determining and applying a compression technique to the received data; performing an intellective computing technique on the compressed data; performing a synoptic evaluation of the data; and generating an executable based on the synoptic evaluation. The executable may be associated with the artificial conversational entity to be presented to the user interacting with the artificial conversational entity.