Adaptive Content Generation for Multi-Profile Collaborative Documents

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Solution Overview

Problem

Existing digital content generation models for collaborative documents fail to consider multiple user profiles with unique authoring styles collaborating simultaneously, leading to inefficient and computationally heavy processes.

Innovation Solution

A document collaboration platform generates adaptive digital content by analyzing temporally sequenced input from multiple user profiles, using machine-learning models to reflect individual and group authoring styles, reducing computational load and improving efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing digital content generation models process multiple user profiles with unique authoring styles simultaneously, then the content generation becomes more comprehensive and adaptive, but the computational load increases significantly

Engineering Contradiction:

Improveadaptability to multiple user profilesVSAvoidcomputational load

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system segments the content generation process by creating separate processing pipelines for each user profile. Each profile's authoring style is analyzed and applied independently to relevant content sections, allowing parallel processing that reduces overall computational load while maintaining adaptability to multiple profiles

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis of each user profile's authoring style in advance, storing these style profiles for later reuse. This preliminary action allows the system to quickly apply pre-analyzed styles during content generation without repeatedly processing the same style information, significantly reducing computational load during actual content creation

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If the system generates adaptive digital content for collaborative documents with multiple user profiles, then the content quality and relevance improve, but the processing time increases

Engineering Contradiction:

Improvecontent qualityVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system pre-analyzes and stores authoring style profiles for each user before content generation. This preliminary action enables rapid style application during content creation, maintaining high content quality while minimizing processing time during actual collaborative work

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies different user profile styles to specific sections or portions of content rather than uniformly processing entire documents. This local quality approach allows the system to maintain high adaptability and content quality for relevant sections while reducing unnecessary processing elsewhere, thereby decreasing overall processing time

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If the system analyzes temporally sequenced input from multiple user profiles, then the generated content better reflects individual and group styles, but the device complexity increases

Engineering Contradiction:

Improvestyle reflection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the complex analysis task by creating separate analysis modules for individual user profiles and another for group dynamics. Each module processes specific aspects independently, making the overall complex system more manageable and maintainable while still achieving comprehensive style reflection

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary components such as style profile storage and temporal sequencing buffers that mediate between raw user inputs and the content generation model. These intermediaries simplify the overall system architecture by pre-processing and organizing data, reducing the complexity of the main generation system while improving style reflection accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250392631A1Automatic adaptive digital content generation for collaborative documents using machine-learning-based digital content processing techniques

Publication Date: 2025.12.25 ATLASSIAN PTY LTD

AI summary

Techniques for simultaneously generating adaptive digital content for multiple client computing devices associated with multiple respective user profiles are discussed herein. Embodiments are configured to receive user input data associated with a collaborative document, where the user input data is associated with a user profile stored in a document collaboration platform. Embodiments can generate temporally correlated user input step events based on the user input data, and render, on interactive user interfaces associated with the collaborative document, temporally sequenced digital content generated based on the temporally correlated user input step events. Embodiments can generate suggested adaptive digital content based on model output generated by an adaptive digital content processing model comprised in a document assistance system, where the model output is generated based on the portion of the temporally sequenced digital content, and render, on interactive user interfaces associated with the collaborative document, the suggested adaptive digital content.