Encoder-Based Data Assignment for Custom Category Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data handling and categorization processes face challenges due to varying third-party software platforms with unique data schemas, lacking adequate contextual information, and difficulty in accommodating user-friendly and scalable data categories, leading to inefficiencies in accurate data instance assignment.
Innovation Solution
A computing server trains a machine-learned encoder model using custom-defined categories to generate embeddings, separating positive and negative data instances, and assigns categories to target data instances for import into third-party platforms, reducing resource consumption and enhancing scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional data handling processes are used with third-party software platforms, then data can be processed, but processing resource consumption and network bandwidth usage are high
Solution Approach 1:
The system performs preliminary actions by training the machine-learned encoder model in advance with historical data and category definitions. The model is pre-trained to understand data patterns and category relationships before actual data assignment occurs, enabling efficient processing without requiring extensive computational resources during runtime operations.
Solution Approach 2:
The machine-learned encoder model acts as an intermediary between raw data instances and category assignments. Instead of directly processing and comparing data against category definitions, the system uses the trained model to generate embeddings and determine categories, significantly reducing processing resource consumption and network bandwidth usage while maintaining high accuracy.
2Measurement precision
If custom-defined categories are implemented for different organizations, then data categorization accuracy improves, but system complexity increases
Solution Approach 1:
The machine-learned encoder model provides a universal solution that handles diverse custom-defined categories across different organizations and third-party platforms. The model is trained to adapt to various category schemas and data structures, enabling accurate categorization without requiring separate complex systems for each organization or platform.
Solution Approach 2:
The system manages complexity by changing parameters within the machine learning model rather than modifying the overall system architecture. Custom categories are accommodated by adjusting the training data, category definitions, and model parameters, allowing the same core system to handle different organizational requirements without increasing structural complexity.
3Measurement precision
If adequate contextual information is added to data instances, then categorization accuracy improves, but data processing time increases
Solution Approach 1:
The system extracts and processes contextual information in advance during the model training phase. The encoder model learns to recognize and utilize relevant contextual features from training data, so that during actual categorization operations, the model can quickly process data instances without requiring extensive real-time analysis of contextual information.
Solution Approach 2:
The system replaces manual or rule-based contextual analysis with a machine-learned encoder model. Instead of using complex mechanical processes to analyze and interpret contextual information, the neural network model automatically learns patterns and relationships in contextual data, significantly reducing processing time while maintaining or improving categorization accuracy.
Data Source
AI summary
A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.