AI Editor Recommender for Workload Balancing
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Solution Overview
Problem
The existing methods for assigning editors to scientific or academic journal submissions are inefficient, as they do not effectively leverage machine learning to recommend and balance workloads across different editors with varying hierarchies and structures.
Innovation Solution
A method and system that convert structured text documents into vectors using natural language processing, train machine learning models to associate these vectors with editor information, and distribute new submissions to appropriate editor teams based on confidence scores, ensuring balanced workload and hierarchical or non-hierarchical team assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual editor selection methods are used, then editor expertise and knowledge can be applied, but the process is inefficient and cannot effectively balance workloads across editors
Solution Approach 1:
The patent replaces manual editor selection with an automated machine learning system. The ML model processes document vectors and editor profiles to automatically recommend and assign editors, eliminating the inefficient manual process while maintaining expertise matching through learned patterns from historical data.
Solution Approach 2:
The system enables self-service editor assignment by allowing the document submission system to automatically receive and process submissions, convert them to vectors, query the ML model for editor recommendations, and distribute documents to appropriate editors without human intervention in the selection process.
2Ease of operation
If traditional editor assignment methods are used, then simple distribution is possible, but workload balancing across editors with varying hierarchies and structures cannot be achieved
Solution Approach 1:
The patent transforms the editor assignment problem by changing parameters from simple document-meta data matching to multi-dimensional vector representations that capture document characteristics, editor expertise, workload status, and hierarchical relationships. The ML model learns optimal parameter combinations for balanced and effective assignment.
Solution Approach 2:
The system segments the editor population into different teams and hierarchies (executive editors, associate editors, section editors) and processes assignments for each segment separately through the ML model, allowing tailored workload balancing strategies for different editor groups while maintaining overall system coherence.
3Measurement precision
If machine learning models are trained on structured text documents, then accurate editor recommendations can be generated, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on historical document-editor assignment data before deployment. This offline training phase establishes the complex relationships and patterns that enable accurate recommendations, separating the complexity of model development from the simplicity of online assignment operations.
Solution Approach 2:
The system introduces vector representations as an intermediary between raw document text and editor assignment decisions. This intermediate layer simplifies the interface between the ML model and the assignment process, allowing accurate recommendations without exposing the full complexity of the underlying model to the operational system.
Data Source
AI summary
A method is disclosed, involving converting at least one structured text document stored in a database into one or more vectors, training a machine learning model to associate the at least one vector with the editors for that structured text document, training a machine learning model to associate the at least one vector with the editors for that structured text document, receiving a second structured text document and converting said second structured text document into one or more vectors, then processing the one or more vectors of the unpublished structured text documents through the trained machine learning model to identify appropriate editor teams, before finally sending the unpublished structured text documents to a computer device associated with an editor.