Chat Message Generation Using Neural Network Analysis
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Solution Overview
Problem
Conventional chatbot systems are inefficient in providing personalized and relevant responses due to their inability to recognize user features, emotions, and relationships, leading to robotic interactions and slower response rates, especially when encountering new topics or emerging conversations.
Innovation Solution
A computer-implemented system that automatically generates messages using a repository, parser, analyzer, searching module, and message generator, which stores and analyzes chat session data to extract behavioral and relevant information, and uses an artificial neural network to create natural language inputs, along with a scheduler and segregator to manage chat sessions and emotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chatbot systems use keyword and pattern matching to generate responses, then the system structure remains simple, but the responses become robotic and lack personalization
Solution Approach 1:
The system segments chat session information into distinct components stored in the repository: contents of chats, historical timestamp information, personal information corresponding to each recipient, and relationship details. This segmentation allows the analyzer to process specific aspects (behavioral data, emotions, relationships) independently while maintaining overall system manageability.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary component between the repository/parser/analyzer and the message generation output. The ANN processes parsed data and extracted behavioral data to generate natural language inputs, acting as a mediator that transforms structured data into personalized responses without requiring complex rule-based systems.
2Productivity
If manual recognition of message features is used in conventional chatbot systems, then the system requires fewer computational resources, but the response rate becomes slower
Solution Approach 1:
The system performs preliminary action by storing chat session information, personal information, and relationship details in the repository during and after interactions. The parser continuously parses this stored information to generate parsed data, and the analyzer continuously extracts behavioral data. This preliminary processing ensures that when a response is needed, all necessary data is already prepared and readily available, enabling fast response generation without real-time manual analysis.
3Loss of information
If conventional chatbot systems do not store historical chat data, then the system complexity remains low, but the ability to provide relevant personalized responses is lost
Solution Approach 1:
The repository serves multiple functions: it stores contents of chats for conversation continuity, historical timestamp information for timing analysis, personal information corresponding to each recipient for personalization, and relationship details for contextual understanding. This multi-functional storage system enables the analyzer to extract various types of behavioral data (emotions, interests, relationships) from a single centralized repository, reducing the need for separate data collection systems.
Data Source
AI summary
The present disclosure relates to a computer implemented system and method for automatically generating messages. A repository (102) stores information related to chat sessions corresponding to a set of recipients, the information including contents of chats, historical timestamp information, personal information corresponding to each of the recipients, and relationship details of each recipient with a user. A parser (104) parses information to generate parsed data including verbs, nouns and common phrases. An analyzer (106) analyzes the stored information to extract behavioral data of the user. A searching module (108) searches and extracts relevant data from the web based on the parsed data. A message generator (110) generates messages corresponding to each recipient.