Chatbot Module Context Preservation via Name-Based Invocation
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Solution Overview
Problem
Current chatbot technologies lose context and state when transitioning between modules, requiring users to repeat information and limiting the seamless interaction between different skills or modules.
Innovation Solution
A system and method that calculates scores for module relevance, associates modules with names, and allows users to interact with modules by mentioning their names, while saving and resuming module states to maintain context and enable dynamic module selection based on user input and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If chatbot modules are activated by name as independent skills, then users can invoke specific modules directly, but context and state are lost when transitioning between modules
Solution Approach 1:
The patent merges the invocation mechanism with context preservation by integrating state management into the module activation process. When a module is invoked by name, the system automatically transfers relevant context and state from the previous module, combining the benefits of direct invocation with seamless conversation continuity.
Solution Approach 2:
The patent introduces a mediator component that manages context and state transfer between modules. This intermediary layer captures the conversation state from the active module and passes it to the invoked module, ensuring that context is preserved during transitions without requiring users to repeat information.
2Adaptability or versatility
If multiple modules are introduced to users with names, then users can interact with specific functionalities, but users must know the exact names of skills to invoke them
Solution Approach 1:
The patent implements self-service through automatic module discovery and introduction. When a user expresses an intent, the system automatically identifies the relevant module, retrieves its name and description, and introduces it to the user. This allows users to interact with module functionalities without needing to memorize or know the exact names of skills in advance.
Solution Approach 2:
The patent performs preliminary actions by pre-introducing modules to users before invocation. The system proactively provides module names and functionality descriptions when they become relevant, preparing users for subsequent interactions and eliminating the need for users to have prior knowledge of skill names.
3Adaptability or versatility
If chatbot modules are treated as independent applications, then each module can handle specific tasks, but seamless interaction between modules is limited
Solution Approach 1:
The patent ensures continuity of useful action by maintaining conversation state and context across module transitions. When modules are invoked sequentially or concurrently, the system preserves the flow of information and user intent, allowing seamless interaction that maintains productivity despite module independence.
Solution Approach 2:
The patent introduces dynamics by enabling flexible module activation and deactivation based on conversation context. Modules can be dynamically invoked, paused, and resumed while maintaining their state, allowing the chatbot to adapt its structure and behavior in real-time based on user needs and conversation flow.
Data Source
AI summary
A method comprising operating a computerized chatbot to: calculate first and second scores representing a relevance of input received from a user to functionalities provided by respective first and second modules, respectively, of the chatbot; associate the first and second modules with respective first and second names; introducing the modules to the user using their associated names; selecting a module to interact with the user based on at least one of: a name mentioned by the user and a score and switching between the first and second modules based on the names.