Automated Catalog Version Rollback via Machine Learning
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Solution Overview
Problem
Current systems lack an efficient self-service mechanism for entities to recall previous versions of their catalogs, requiring manual intervention by customer service, which is time-consuming and inefficient.
Innovation Solution
Implementing a machine learning mechanism that trains on previous rollback requests to determine when a modification warrants a rollback, allowing automated recall to a previous version of the catalog, enabling entities to self-manage catalog versions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention by customer service is used to recall previous catalog versions, then the system can handle rollback requests, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables entities to autonomously initiate and complete catalog version rollback operations through self-service interfaces. The machine learning mechanism automatically analyzes rollback requests, determines appropriate previous versions, and executes the rollback without requiring customer service intervention, thus eliminating time loss while maintaining reliability.
Solution Approach 2:
The patent replaces the manual mechanical process of customer service intervention with an automated machine learning system. The ML mechanism processes rollback requests, evaluates catalog states, and executes version recalls algorithmically, substituting human-operated procedures with automated computational processes that operate faster and without manual time investment.
2Productivity
If automated machine learning mechanism is implemented for rollback determination, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The machine learning mechanism serves as an intermediary layer between the entity's rollback request and the catalog database. It processes requests, determines appropriate previous versions based on trained criteria, and coordinates the rollback execution, thereby automating operations without requiring proportional increases in overall system complexity.
Solution Approach 2:
The system utilizes parameter changes in the catalog data structure to implement version control and rollback capabilities. By tracking version parameters, timestamps, and state descriptors, the ML mechanism can efficiently determine and execute rollbacks without fundamentally restructuring the entire system, thus improving productivity with manageable complexity increases.
3Measurement precision
If machine learning mechanism trains on previous rollback requests, then automated rollback accuracy improves, but data processing requirements increase
Solution Approach 1:
The machine learning mechanism performs preliminary training on historical rollback request data before actual rollback operations. By pre-processing and learning from past patterns, the system establishes decision models that enable accurate automated rollback determination without requiring intensive data processing during actual rollback events, thus improving accuracy while managing data processing requirements.
Data Source
AI summary
Techniques for effectuating a rollback to a previous version of a database (e.g., a catalog or inventoy database) associated with an entity are described. A payment processing service may receive instructions to modify a database. The payment processing service may modify the database based at least partly on the instructions and add an entry to a modification log. Each entry in the modification log may correspond to a previous version of the database and the entry may correspond to a new version of the database after the modification to the database. The payment processing service may determine that the modification to the database is likely to warrant a rollback and may effectuate the rollback by determining a previous entry of the modification log that precedes the entry and adding a new entry to the modification log that corresponds to the previous version of the database associated with the previous entry.