Automated Mediation System Using NLP for Message Management
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Solution Overview
Problem
Conventional in-person mediation services are inconvenient and costly, and existing technologies lack efficient methods to manage inappropriate or unproductive messages in mediated conversations.
Innovation Solution
An Intelligent Mediation Message System (IMMS) using natural language processing (NLP) and machine learning to analyze messages, detect those exceeding a score threshold, and take remedial measures such as modification, delay, or rejection, allowing for convenient and affordable mediated conversations through user equipment like mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If in-person mediation services are used, then communication quality is maintained, but convenience and cost are worsened
Solution Approach 1:
The patent introduces an automated mediation system that acts as an intermediary between parties, using NLP and machine learning to facilitate communication without requiring physical presence of human mediators. This resolves the contradiction by providing convenience through remote automated mediation while maintaining communication quality through sophisticated language analysis capabilities.
Solution Approach 2:
The patent replaces the mechanical system of in-person mediation with an automated digital system using NLP and machine learning algorithms. This substitution eliminates the need for physical meetings while maintaining mediation effectiveness through automated analysis of communication patterns and content.
2Reliability
If human mediators constantly supervise conversations, then message appropriateness is ensured, but time and cost increase
Solution Approach 1:
The patent implements a self-service mediation system where the automated NLP-based system independently analyzes and manages messages without requiring constant human mediator intervention. The system automatically detects inappropriate content, scores messages, and manages communication flow, ensuring message appropriateness while eliminating time losses associated with human supervision.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously analyzes communication patterns, scores messages for appropriateness, and adjusts mediation strategies based on detected trends. This automated feedback loop ensures message appropriateness is maintained without requiring constant human oversight, reducing time losses.
3Object-generated harmful factors
If automated message analysis is implemented, then inappropriate messages are managed, but system complexity increases
Solution Approach 1:
The patent segments the complex task of message analysis into manageable components: NLP processing, machine learning classification, scoring mechanisms, and remediation strategies. This segmentation allows the system to handle inappropriate messages effectively while keeping individual system modules relatively simple and maintainable.
Data Source
AI summary
A system described herein may provide techniques for using machine learning and/or other techniques to monitor a conversation between two or more conversation participants through a messaging program. The system may utilize natural language processing (“NLP”) to determine the intent of phrases and/or words sent between mediation participants. The system may determine to take remedial measures, such as modifying, delaying, and/or rejecting a message from one of the participants when a score for the message exceeds a dynamic score threshold determined by the system based on one or more factors, such as the demographic information of the mediation participants, nature of the mediation, length of mediation, communications among mediation participants, and/or other factors.