Chat Room Prioritization via Machine Learning Ranking

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Users face frustration in online chat rooms due to the difficulty in selecting the most interesting chat rooms, as existing systems lack effective methods to prioritize or rank chat rooms based on user preferences and content relevance.

Innovation Solution

A system and method that utilize a Machine Learning (ML) model to analyze chat room content, parse it into instances based on user interactions, and incorporate user feedback to personalize the ranking, considering features like user mentions, post frequency, and keyword relevance, allowing users to customize the model for improved accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If users manually browse and select chat rooms without assistance, then they have full control over their choices, but they spend unnecessary time determining which chat room to participate in and may choose uninteresting chat rooms

Engineering Contradiction:

Improvetime to determine which chat room to participateVSAvoiduser control over chat room selection

Core Design Contradiction:

Loss of timeVSEase of operation

Solution Approach 1:

The system performs automatic chat room prioritization and ranking without requiring user intervention. The machine learning model autonomously analyzes chat room content, user preferences, and interaction patterns to generate prioritized recommendations, eliminating the need for users to manually browse and evaluate multiple chat rooms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of user browsing and selection is replaced with an automated intelligent system. The machine learning model processes chat room data, user profiles, and interaction history to automatically rank chat rooms, substituting human cognitive effort with computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If the system provides basic summary information about chat rooms, then users have some data to make decisions, but this information is insufficient to accurately determine user interest and leads to user frustration

Engineering Contradiction:

Improvequality and relevance of chat room informationVSAvoidcomplexity of the ranking system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The chat room information is segmented into multiple distinct features including content keywords, user mentions, post frequency, participant activity levels, and topic categories. This segmentation allows the system to analyze and weight different aspects of chat room quality independently, providing comprehensive information for accurate ranking.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system transforms raw chat room data into meaningful ranked parameters by applying machine learning algorithms. User profiles and chat room characteristics are converted into priority scores that reflect user interest, transforming unstructured information into actionable ranking metrics.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If the system uses a simple ranking method, then the system remains easy to understand and implement, but it cannot accurately prioritize chat rooms based on user preferences and content relevance

Engineering Contradiction:

Improveaccuracy of chat room prioritizationVSAvoidcomplexity of the machine learning model

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system incorporates user feedback mechanisms where user interactions with chat rooms (views, joins, messages sent) are fed back into the machine learning model. This feedback loop continuously refines the ranking algorithm, improving measurement precision by learning from actual user behavior patterns and adjusting priorities accordingly.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of user profiles and chat room characteristics before generating rankings. By pre-processing and storing relevant features such as user interests, chat room topics, and historical interaction data, the system prepares the necessary information in advance to enable accurate and efficient prioritization when users access the system.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11206236B2Systems and methods to prioritize chat rooms using machine learning

Publication Date: 2021.12.21 CISCO TECHNOLOGY INC

AI summary

Chat room content classification, in an online communication environment, where higher applicable chat rooms are prioritized for a user, is provided. First, an chat room service receives chat room content for at least a first chat room and a second chat room. A chat room analyzer can then analyze a characteristic(s) associated with the first chat room and/or the second chat room. Based on the characteristic, the chat room determines that the first chat room is more applicable to the user. Then, a user interface may be presented to the user where the first chat room is prioritized (or ranked) over the second chat room.