Message Tone Monitoring via Language Analysis
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Solution Overview
Problem
Existing systems fail to monitor and compare the wide range of styles and content in all messages sent within an organization, limiting their ability to identify employees whose communication style is consistently different from the norm.
Innovation Solution
A method and system that receive, analyze, and store messages to calculate language content measures, comparing them across recipients and users to identify deviations from the norm, using lexical and grammatical analysis to generate sentiment and tone indicators, and alert administrators to potential issues like workplace bullying or poor customer service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive monitoring of all messages is implemented, then the ability to identify communication style deviations is improved, but the system complexity and resource requirements increase
Solution Approach 1:
The patent introduces an intermediary system comprising a language processor and analysis engine that mediates between the message traffic and the monitoring objectives. This intermediary layer applies natural language processing techniques to extract communication style metrics without requiring direct intervention in the message routing infrastructure, thereby reducing system complexity while maintaining monitoring precision.
Solution Approach 2:
The patent replaces manual communication monitoring and analysis with automated computational systems. Natural language processing algorithms and sentiment analysis engines substitute for human reviewers, enabling comprehensive monitoring of all messages without proportionally increasing operational complexity. The system automatically calculates communication style scores and identifies deviations from organizational norms.
2Measurement precision
If detailed language content analysis is applied to all messages, then the identification of tone and style variations is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing analysis on specific language features and communication style indicators rather than examining every aspect of each message in equal detail. The system identifies and weights key linguistic markers that are most indicative of communication style deviations, applying intensive analysis only to these relevant features while using lighter processing for routine messages, thereby reducing overall processing time while maintaining accuracy in identifying problematic communications.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on message characteristics, sender history, and organizational context. The system modifies the depth and type of language analysis applied to different messages, using shallow processing for routine communications and deeper analysis only when deviation from communication norms is detected. This adaptive parameter adjustment reduces average processing time while maintaining high detection accuracy for problematic messages.
3Productivity
If sentiment analysis is used to determine positive or negative tone, then the identification of communication issues is improved, but the nuance and context understanding may be lost
Solution Approach 1:
The patent segments communication tone analysis into multiple independent dimensions rather than relying on a single sentiment score. The system separately evaluates formality, politeness, assertiveness, empathy, and other communication style attributes. This segmentation allows the system to maintain high productivity in identifying communication issues while preserving nuanced understanding of different tone characteristics, as each dimension captures specific aspects of communication that simple positive/negative sentiment would conflate.
Solution Approach 2:
The patent creates a composite communication style metric by combining multiple analysis layers including sentiment analysis, linguistic feature extraction, and contextual information. Rather than relying on a single sentiment score, the system integrates results from multiple analysis methods to form a comprehensive communication style profile. This composite approach maintains efficiency while preserving nuance, as the combination of different analysis techniques compensates for the limitations of any single method.
Data Source
AI summary
A method of controlling a system of monitoring messages in a network is described. A message sent by a user of the network to one or more recipients is received and a weight is applied to any text from any previous message that appears in the message. A measure of language content used in the message is formed and stored in one or more data stores. Information identifying the sender of the message is also stored. The stored measure of the language content and the stored information identifying the sender is reported to an administrator of the system.