Content Editor Authorship Tokens for Mixed Human-AI Provenance

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Solution Overview

Problem

Traditional content editing software lacks the capability to accurately distinguish between human-authored and artificially-authored content, particularly in cases of mixed authorship, leading to inefficiencies and inaccuracies in tracking provenance and security vulnerabilities.

Innovation Solution

Implementing authorship tokens within content editing software to automatically label regions of content as human or artificial authored, using techniques such as detecting user input through human interfaces and monitoring edits to apply tokens, with options for storage and encryption to resist tampering.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional content editing software is used, then the software is simple and easy to operate, but it cannot accurately distinguish between human-authored and artificially-authored content

Engineering Contradiction:

Improveauthorship identification accuracyVSAvoidsoftware complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments content into distinct regions with authorship labels, dividing the content into human-authored portions and artificially-generated portions. This segmentation enables precise tracking of authorship origin for each region while maintaining a relatively simple overall system architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary mechanism (authorship labels and tracking system) that sits between the content creation process and the final content output. This intermediary captures and preserves authorship information without fundamentally altering the content editing functionality, thus improving measurement precision while limiting complexity increase.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If authorship tracking is implemented, then provenance tracking accuracy is improved, but computational resources and processing time increase

Engineering Contradiction:

Improveprovenance tracking accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies authorship labels at the moment content is created or imported, performing the tracking setup in advance rather than analyzing content retrospectively. This preliminary tagging approach ensures high provenance tracking accuracy while minimizing computational overhead during subsequent editing operations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically tracks and labels authorship information without requiring continuous manual intervention or complex real-time analysis. Once initial labels are applied, the system maintains provenance information through self-service mechanisms that consume minimal computational resources.

Inventive Principle:

Principle #25Self-service

3Productivity

If manual authorship labeling is used, then computational resources are reduced, but productivity and accuracy of authorship identification decrease

Engineering Contradiction:

Improveauthorship identification efficiencyVSAvoidautomation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system automatically detects and labels authorship information without requiring manual intervention. The editing software itself performs the authorship identification task, eliminating the need for separate manual labeling processes and significantly improving productivity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms that monitor editing actions and automatically update authorship labels based on detected patterns. This automated feedback loop maintains high identification accuracy and productivity while managing system complexity through rule-based decision making.

Inventive Principle:

Principle #23Feedback

4Reliability

If content regions are labeled with authorship tokens, then training data quality and security are improved, but device complexity and ease of operation worsen

Engineering Contradiction:

Improvesecurity and complianceVSAvoiduser interface simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent uses metadata tokens and labels that copy and store authorship information alongside the content. These lightweight data structures provide security and compliance information without significantly altering the user interface or complicating content creation workflows.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system adds authorship information in a separate metadata dimension rather than embedding it directly in the visible content structure. This allows security and compliance tracking to occur in parallel with content creation without interfering with user interface simplicity.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20260065394A1Content editing software via automatic and auditable authorship attribution

Publication Date: 2026.03.05 US BANK NATIONAL ASSOCIATION

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.