Chatbot Context Management Using Segmented Data Structures
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Solution Overview
Problem
Conventional methods for managing context information between a chatbot and a user device are complex, making it difficult for botbuilder managers to efficiently update, delete, or add new information, as they require loading and processing all existing data to find or modify specific values.
Innovation Solution
A server and method that utilize multiple data structure forms, such as Variable, Set, and List structures, to manage context information, allowing for easy access and manipulation of data by mapping key values to specific structures, enabling efficient addition, update, and deletion operations, and simplifying data management by allowing partial updates and deletions without loading all data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all existing context information is loaded from DB and processed to update context information, then the context information can be updated, but the processor becomes complicated and it becomes difficult to manage context information
Solution Approach 1:
The patent segments context information into different data structure forms (variable, list, set) and organizes them in a hierarchical manner. This segmentation allows the system to manage context information in smaller, more manageable units rather than loading and processing all context information at once, thereby reducing processor complexity while maintaining update reliability.
Solution Approach 2:
The patent introduces a new dimension of organization by classifying context information according to data structure forms (variable, list, set). This dimensional change enables the system to access and update specific types of context information efficiently without processing the entire context information set, thus reducing processor complexity while ensuring reliable updates.
2Reliability
If all existing context information is loaded and processed to find and modify specific values, then the context information can be updated, but it requires loading and processing all data which reduces efficiency
Solution Approach 1:
The patent extracts specific context information based on its data structure form (variable, list, set) and retrieves only the necessary portions for updating. This extraction principle allows the system to modify specific values without loading and processing all context information, thereby improving update efficiency while maintaining accuracy through targeted retrieval and validation.
Solution Approach 2:
By organizing context information into different data structure dimensions (variable, list, set), the system can efficiently query and update specific types of context information without processing the entire dataset. This dimensional organization enables selective access to relevant information, improving productivity while ensuring accurate updates through structured validation.
3Reliability
If conventional methods are used to manage context information, then all context information can be maintained, but it is difficult to add new information or delete old information selectively
Solution Approach 1:
The patent segments context information into distinct data structure forms (variable, list, set) with specific operational characteristics. This segmentation enables botbuilder managers to easily perform selective operations (add, delete, update) on specific types of context information without affecting the completeness of the overall context information system, thereby improving ease of operation while maintaining reliability.
Solution Approach 2:
The patent creates a universal management framework that handles multiple data structure forms (variable, list, set) through a unified interface. This multi-functional approach allows botbuilder managers to perform consistent operations (add, delete, update) across different types of context information, making the system easier to operate while ensuring the completeness and integrity of all context information through standardized validation.
Data Source
AI summary
A chatbot server that manages context information between a chatbot and a user device includes a receiving unit that receives, from a messenger server, a question message input for a service selected from multiple services by the user device and service account information corresponding to the selected service; a derivation unit that derives an answer to the question message by using the chatbot corresponding to the service account information; and a context information management unit that manages context information of a chat between the user device and the chatbot based on the question message and/or the answer.