Event-Tracked Authorship Tokens for Human and AI Content Regions
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Solution Overview
Problem
Existing content creation tools struggle to accurately distinguish between human-authored and artificially generated content, particularly in collaborative environments where both human and artificial intelligence contribute, leading to difficulties in copyright attribution, compliance with content policies, and maintaining content quality.
Innovation Solution
Implementing authorship tokens within content editors to automatically label regions as human-authored or artificially generated, using edits and other criteria to determine authorship, and storing these tokens within or separate from the content file to ensure accurate attribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content creation tools use generative artificial intelligence to produce content, then productivity and content quality are improved, but the ability to accurately distinguish human-authored from artificially generated content deteriorates
Solution Approach 1:
The patent segments content into distinct regions with different authorship attributes. Each region is tagged with metadata indicating whether it was human-authored or AI-generated, allowing precise tracking of content provenance while maintaining the ability to generate high-quality AI content efficiently.
Solution Approach 2:
The patent introduces an intermediary authorship attribution system that sits between the content generation process and the final content output. This system tracks and labels content regions based on their creation source, enabling accurate authorship distinction without interfering with the productivity benefits of AI generation.
2Measurement precision
If content tools automatically label content regions with authorship tokens, then authorship attribution precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the authorship attribution functionality directly into the existing content editor interface. By integrating authorship tracking with the familiar text editing environment and using standard metadata formats, the system achieves precise authorship labeling without proportionally increasing perceived complexity for users.
Solution Approach 2:
The content editor is designed to perform multiple functions simultaneously: text editing, authorship tracking, and content management. This multi-functionality allows the system to provide precise authorship attribution without requiring separate dedicated tools, thereby limiting the increase in overall system complexity.
3Reliability
If content editors track and store authorship information for each region, then copyright compliance and policy adherence are improved, but information storage requirements increase
Solution Approach 1:
The patent applies local quality by storing authorship metadata only where needed - specifically at the region level where authorship transitions occur. Rather than uniformly tagging every content element, the system strategically places authorship markers at boundaries between human-authored and AI-generated regions, reducing overall metadata storage while maintaining copyright compliance reliability.
Data Source
AI summary
A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.