Contextual Chat Filtering Using Machine Learning Models
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Solution Overview
Problem
Conventional approaches to addressing toxic behaviors and language in online gaming and chat platforms are inadequate, as they fail to differentiate between offensive and non-offensive language usage in different contexts, leading to over-censorship and a poor user experience.
Innovation Solution
A server system equipped with a user chat filtering program that utilizes a plurality of trained machine learning models and a filter decision service to determine filter actions for user chat data. This system processes user chat data to differentiate between offensive and non-offensive language usage based on contextual information, intent, and sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches flag known bad or abusive words, then filtering of offensive language is achieved, but over-censorship occurs and user experience deteriorates
Solution Approach 1:
The system changes the parameter of language evaluation from simple keyword matching to contextual analysis by processing surrounding words and sentence structure. This allows the same word to be evaluated differently based on its context, reducing false positives while maintaining filtering accuracy for genuinely offensive language.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with an AI-based natural language processing system. This substitution enables the system to understand semantic meaning, intent, and context, thereby distinguishing between offensive and non-offensive usage of the same words without requiring manual rule updates.
2Ease of operation
If contextual analysis is implemented to differentiate offensive from non-offensive language, then user experience is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary AI processing layer between the user input and the filtering decision. This intermediary analyzes the contextual meaning, sentiment, and intent of the language before determining whether to apply filtering, thereby managing complexity through modular architecture rather than monolithic rule sets.
Solution Approach 2:
The language filtering system is segmented into multiple independent components: natural language processing module, sentiment analysis module, contextual evaluation module, and filtering decision module. Each component handles a specific aspect of the analysis, making the overall system more manageable and maintainable despite its complexity.
3Measurement precision
If AI-based contextual analysis is used, then filtering precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by pre-computing contextual features, sentiment indicators, and language patterns during the analysis phase. This preliminary action prepares the data in advance, allowing the final filtering decision to be made quickly without re-analyzing the entire context, thereby reducing actual processing time while maintaining precision.
Data Source
AI summary
A server system is provided that includes one or more processors configured to execute a platform for an online multi-user chat service that communicates with a plurality of client devices of users of the online multi-user chat service that exchanges user chat data between the plurality of client devices. The one or more processors are configured to execute a user chat filtering program that performs filter actions for user chat data exchanged on the platform for the online multi-user chat service. The user chat filtering program includes a plurality of trained machine learning models and a filter decision service that determines a filter action to be performed for target portions of user chat data based on output of the plurality of trained machine learning models for those target portions of user chat data.