Contextual Chat Message Generation Using Linguistic Profiles
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Solution Overview
Problem
Existing online communication systems in virtual environments face challenges in facilitating user communication across diverse demographics and communities, as users often employ unique jargon and abbreviations that may not be understood by others, leading to breakdowns in communication and cohesion.
Innovation Solution
A computer-implemented method generates user profiles based on behavior and linguistic patterns within online environments, creating a list of contextual chat messages that can be translated and tailored for communication between users, ensuring that messages are understood and culturally relevant, while maintaining a safe and cohesive community experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-selected chat messages are provided for users to choose from, then communication safety and civility are improved, but the system cannot adapt to evolving user jargon and slang
Solution Approach 1:
The chat message system transitions from static pre-selected messages to dynamic context-aware message generation. The system adapts message selection based on real-time analysis of user behavior patterns, conversation context, and community language evolution, allowing the message set to evolve organically with user demographics while maintaining safety filters.
Solution Approach 2:
The system enables users to contribute to the chat message vocabulary by allowing community members to propose and vote on new messages. This self-service mechanism allows the chat system to automatically incorporate emerging jargon and slang into the approved message set without requiring manual curation of every new term.
2Ease of operation
If manual predetermined chat messages are used, then communication control is improved, but communication breakdown occurs between users with different jargon
Solution Approach 1:
The system incorporates feedback loops where user interactions, message effectiveness, and communication outcomes are continuously monitored. This feedback informs dynamic adjustments to message recommendations and translations, improving the system's ability to bridge jargon gaps between different user communities while maintaining appropriate communication control.
Solution Approach 2:
The system acts as an intermediary by providing real-time message translation and adaptation between different user communities. When a user selects a chat message, the system analyzes the recipient's community language patterns and automatically translates or adapts the message to ensure understanding across different jargon dialects while preserving the original intent.
3Object-affected harmful factors
If chat messages are filtered to remove inappropriate content, then community safety is improved, but language evolution and community cohesion are hindered
Solution Approach 1:
The system performs preliminary analysis and categorization of language patterns before they become problematic. By monitoring emerging slang and jargon in advance, the system can pre-approve appropriate terms and phrases, allowing language evolution to proceed smoothly while maintaining safety standards. This proactive approach prevents rather than merely reacts to inappropriate content.
Data Source
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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.