Contextual Chat Linguistic Profile Translation
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Solution Overview
Problem
Existing online communication systems in multiplayer games and social environments face challenges in promoting civility and community cohesion due to language barriers and the need for manual filtering of chat messages, which limits user experience and understanding between users with different linguistic profiles.
Innovation Solution
A computer-implemented method that generates user profiles based on behavior and usage patterns, creating linguistic profiles to facilitate contextual chat by translating and tailoring chat messages between users with different profiles, ensuring safe and relevant communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual filtering and pre-selected word choices are used to promote civility, then safety and appropriateness of communication is improved, but user experience and communication freedom deteriorates
Solution Approach 1:
The system automatically generates linguistic profiles and filters messages without requiring manual configuration by safety personnel. The chat filtering system serves itself by learning from user behavior patterns and automatically adapting to new slang and abbreviations, eliminating the need for continuous manual updates while maintaining safety standards.
Solution Approach 2:
The system dynamically adjusts filtering parameters based on user profiles and contextual information. Instead of using fixed filter rules, the system modifies its behavior thresholds and filtering sensitivity according to the specific user interaction context, allowing for more nuanced and accurate message evaluation that preserves user experience while maintaining safety.
2Reliability
If manual predetermined words and phrases are provided for user selection, then communication safety is improved, but adaptability to new slang and abbreviations deteriorates
Solution Approach 1:
The system continuously monitors chat messages and user behavior patterns, using this feedback to automatically update linguistic profiles and expand its vocabulary of recognized slang and abbreviations. This closed-loop learning process allows the system to adapt to emerging language trends while maintaining safety standards, as new terms are learned and evaluated against safety criteria automatically.
Solution Approach 2:
The system pre-generates multiple possible interpretations and translations for ambiguous messages before final filtering decisions are made. By preparing multiple candidate meanings and safety evaluations in advance, the system can quickly adapt to new slang and abbreviations without compromising safety, as the preliminary analysis already considers various contextual possibilities.
3Adaptability or versatility
If users from different communities with different jargon communicate, then community diversity is improved, but understanding and communication effectiveness deteriorates
Solution Approach 1:
The system introduces a linguistic profile translation layer that acts as an intermediary between users from different communities. This intermediary automatically translates slang, abbreviations, and jargon from one community's linguistic profile into another's equivalent terms, preserving the original meaning while ensuring mutual understanding. Users can communicate across community boundaries without losing the nuance or intent of their messages.
4Productivity
If automated profile generation is used to facilitate communication, then communication effectiveness is improved, but system complexity increases
Solution Approach 1:
The linguistic profile system serves multiple functions simultaneously: it filters inappropriate messages, translates between different communities' jargon, identifies user behavior patterns, and adapts to new slang. By consolidating these diverse functions into a single multi-functional framework, the system achieves high communication effectiveness without proportionally increasing complexity, as the same core mechanisms handle multiple tasks.
Data Source
AI summary
Techniques are disclosed for providing an enhanced contextual chat feature in online environments. The contextual chat feature may be used to present users with a list of expressions that may be sent to other users within an online environment (or to users in other online environments). The list of messages may be derived from a linguistic profile which itself may change as the use of language in an online environment (or by a particular user group) evolves, over time. In cases where a user sends a contextual chat message to another user in the same online environment, messages may be sent without being altered. However, when a user selects a contextual chat message from the list to send to a user in another online environment, the message may be translated based on a linguistic profile associated with users in the second environment.