Relevance Information Extraction for Instant Messaging Users
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Solution Overview
Problem
In instant messaging services, users who are non-friends face difficulties in obtaining detailed information about each other, as conventional applications only provide limited information such as profile images and names, making it hard to identify and connect with relevant users.
Innovation Solution
A method and system for providing relevance information between users, which involves collecting message information from group chat rooms, extracting conversations, and displaying relevant information, including correlation scores, mention messages, and statistical data, to facilitate user connections and friend recommendations based on interaction history and profile analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional instant messaging applications provide only limited information (profile images and names) for non-friend users, then user privacy is protected, but users cannot easily identify and connect with relevant non-friend users
Solution Approach 1:
The patent introduces group chat room information as an intermediary element to bridge the gap between privacy protection and information availability. By displaying group chat room names and interaction history where the current user and target non-friend user both participate, the system provides indirect information about the non-friend user without exposing their private data directly. This intermediary information helps users identify relevant non-friends while maintaining privacy boundaries.
2Loss of information
If the system collects and processes message information from group chat rooms to extract conversation data, then relevance information between users can be provided, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant information from group chat room messages, specifically focusing on conversation pairs between the current user and target non-friend user, as well as mention messages. Rather than processing and storing all message data, the system selectively extracts interaction-relevant information such as conversation content, mention instances, and calculates correlation scores only for relevant user pairs. This extraction approach provides comprehensive relevance information while avoiding the complexity of processing entire chat histories.
3Measurement precision
If the system displays detailed conversation history and interaction information between users, then user connection accuracy improves, but information overload and display clutter increase
Solution Approach 1:
The patent segments the relevance information into distinct, organized components: group chat room information (showing common groups), conversation information (extracted direct interactions), and mention information (instances where users mentioned each other). Each segment is displayed in a separate section with clear visual differentiation. This segmentation allows users to assess user relevance through multiple organized dimensions without presenting a cluttered wall of text, maintaining measurement precision while managing display space efficiently.
4Measurement precision
If the system calculates correlation scores using language models to identify relevant conversations, then conversation relevance accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by calculating correlation scores only for conversation pairs that meet specific relevance criteria: messages where the current user and target non-friend user interact directly, or where one user mentions the other. The language model processes only these selectively identified conversation pairs rather than all possible message combinations. This partial processing approach maintains high accuracy in scoring relevant conversations while significantly reducing overall processing time and computational resource requirements compared to analyzing all messages in group chat rooms.
Data Source
AI summary
A method for providing relevance information between users performed by one or more processors of a user terminal including receiving, from a user, a request for the relevance information between a first user account and a second user account of an instant messaging application from a user of the user terminal, collecting message information in a group chat room that includes the first user account, the second user account, and one or more additional user accounts, extracting a conversation between the first user account and the second user account from the collected message information, and displaying the extracted conversation between the first user account and the second user account along with information on the group chat room on a display of the user terminal, in which the first user account is associated with the user terminal, may be provided.