AI Narrative Refinement System for Truthful Communication
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Solution Overview
Problem
Existing communication methods often result in non-truthful speaking styles that disempower individuals, leading to ineffective goal-directed behavior and invalidation of self and others, as people habitually use language that contradicts their intentions and emotions, making it difficult to achieve valued goals and maintain relationships.
Innovation Solution
A software application and system that provides continuous feedback to users, modifying their written and spoken language to promote truthful communication by suggesting corrections and inserting empowering phrases, based on principles from Acceptance and Commitment Therapy, Gestalt Therapy, and Mindfulness, to help users distance themselves from thoughts and emotions, and focus on present actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous feedback mechanisms are implemented to modify user communication, then communication truthfulness and effectiveness are improved, but device complexity and processing requirements increase
Solution Approach 1:
The feedback system is divided into separate functional modules: a linguistic analysis module that processes user input, a feedback generation module that creates suggestions, and a delivery module that presents corrections to the user. This segmentation allows each component to be optimized independently and reduces overall system complexity.
Solution Approach 2:
The system introduces an intermediary processing layer between the user's intended message and the actual communication output. This intermediary analyzes the linguistic content and provides structured feedback based on predefined communication principles, acting as a mediator that guides users toward more effective expression without requiring complex real-time intervention.
2Productivity
If linguistic analysis and feedback mechanisms are added to modify communication, then communication effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes and categorizes linguistic patterns during system initialization, creating lookup tables and rule sets for common communication issues. When analyzing user input, the system first checks against these pre-prepared categories before performing deeper analysis, significantly reducing real-time processing requirements.
Solution Approach 2:
The feedback mechanism applies analysis at varying depths based on the detected severity and type of communication issue. For minor issues, only partial analysis is performed using simplified rules, while more significant problems receive comprehensive analysis. This selective approach balances processing thoroughness with time efficiency.
Data Source
AI summary
A method for processing a narrative generated by an artificial Intelligence based natural language generator, to assess relationships between words and phrases in the generated narrative, where necessary, to replace particular words and phrases and more clearly convey a desired intended semantic content of the generated narrative, and/or generate learning data for use by the natural language generator to improve its text generating operation. Based on an input received by the AI based natural language generator, the method generates a narrative, processing the narrative by implementing one or more mechanisms to provide at least one cue in the narrative, in accordance with a plurality of rules to identify semantic content and based on a cue, and the identified semantic content of the narrative, determining how relationships between the words and phrases comprising the narrative could be altered to more clearly convey the semantic content, to realize a directive and communicating the directive.