Document Merge Model for Automatic Conflict Resolution
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Solution Overview
Problem
Current collaborative document platforms struggle to automatically resolve conflicts in textual content generated by multiple users, failing to capture user intent and overall document intent, and often require manual intervention to address structural conflicts without considering semantic or intent-based resolutions.
Innovation Solution
A system and method that employ a document merge model to detect conflicts and generate adaptive digital content by analyzing multi-user input data, determining conflicts, and suggesting resolutions with confidence scores, allowing for automatic incorporation or user confirmation of suggested merge resolutions, while retraining the model based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to resolve document conflicts, then conflict resolution accuracy is improved, but productivity and time consumption deteriorate
Solution Approach 1:
The system enables automatic conflict resolution through machine learning models that autonomously analyze document conflicts, generate resolutions, and apply them without requiring manual user intervention. The model learns from user feedback and continuously improves its resolution accuracy while maintaining high productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where user confirmations or rejections of suggested resolutions are used to retrain and improve the machine learning model. This feedback loop enables the system to learn from actual user decisions and progressively enhance conflict resolution accuracy while maintaining automated operation.
2Productivity
If automatic conflict resolution is implemented, then productivity is improved, but measurement precision and conflict resolution accuracy deteriorate
Solution Approach 1:
The system autonomously performs conflict analysis and resolution generation through machine learning models, eliminating the need for manual intervention and significantly improving productivity while maintaining acceptable accuracy through iterative learning.
Solution Approach 2:
User feedback on automatic resolutions is captured and used to retrain the model, enabling continuous improvement of resolution accuracy while the system operates automatically, thus resolving the accuracy-productivity tradeoff over time.
3Adaptability or versatility
If machine learning models are used for conflict resolution, then adaptability and user intent capture are improved, but device complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between multiple users and the document collaboration platform, capturing user intent and translating it into appropriate conflict resolutions. This intermediary layer handles the complexity internally while presenting simple automated resolutions to users.
Solution Approach 2:
The machine learning model serves multiple functions including conflict detection, resolution generation, user intent analysis, and continuous learning from feedback. This multi-functionality consolidates various complex operations into a single adaptable system component.
4Measurement precision
If iterative model retraining is performed, then measurement precision and resolution accuracy are improved over time, but use of energy and computational resources increase
Solution Approach 1:
The system performs model retraining periodically or triggered by specific conditions (such as accumulated feedback threshold) rather than continuously, reducing computational resource consumption while still maintaining and improving resolution accuracy over time through iterative learning.
Data Source
AI summary
Techniques for automatically resolving document conflicts for multiple client computing devices associated with multiple respective user profiles are discussed herein. Embodiments are configured to receive multi-user input data associated with a collaborative document, where the multi-user input data is generated by two or more client computing devices associated with two or more user profiles supported by a document collaboration platform. Embodiments can generate user input step events based on the multi-user input data, where each of the user input step events are associated with a respective user profile. Embodiments can determine one or more document conflicts associated with the collaborative document based on application of a document merge model to the document object. The document merge model generates document merge resolutions associated with a respective merge resolution confidence score and determines whether to automatically incorporate the document merge resolutions into the collaborative document based on the merge resolution confidence scores.