Attribute Prediction Machine Learning Model Training
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Solution Overview
Problem
Current computer-based systems struggle with accurately predicting variations in product attributes over time due to the lack of integration of multiple dataset types for machine learning, leading to inaccurate predictions.
Innovation Solution
A method involving a processor that collects product information and historical event data, generates an event-dependent training dataset, trains an attribute prediction machine learning model, and applies it to predict future attribute changes, using additional event information to display estimates on a computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple distinct dataset types are integrated for machine learning prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple distinct dataset types (product information datasets and historical event datasets) into a unified machine learning model training framework. The system integrates these diverse datasets through a common processing architecture that handles different data types consistently, enabling the model to leverage both product attributes and external events for improved prediction accuracy while managing complexity through standardized integration procedures.
Solution Approach 2:
The machine learning system is designed with universal capabilities to handle multiple dataset types through a single unified model architecture. The system can process both product information datasets containing attribute data and historical event datasets containing temporal event information, using a common training and prediction framework that adapts to different data types without requiring separate specialized models for each dataset type.
2Reliability
If historical event information is collected and integrated into training datasets, then prediction reliability is improved, but data collection and processing complexity increases
Solution Approach 1:
The system performs preliminary data collection and preprocessing of historical event information during the training phase. Historical events are aggregated, cleaned, and formatted into the training datasets in advance, so that when predictions are made, the event data is already prepared and ready for immediate use. This preliminary action reduces the complexity of real-time data collection and processing during prediction operations.
Data Source
AI summary
In order to facilitate machine learning for prediction using distinct dataset types, systems and methods include collecting content information from archived websites databases. Collecting historical event information from online sources, where the historical event information is associated with a plurality of historical events. Generating event-dependent products training datasets based on the content information and the historical event information, where the event-dependent content training datasets defines for content historical events that are associated with attributes of the content, attribute change of the content, or both. Training an attribute prediction machine learning model based on the event-dependent content training datasets. Applying the trained attribute prediction machine learning model to additional event information to predict, for content, a future attribute estimate, a future attribute change estimate, or both. Causing to display an indication representative of the future attribute estimate, the future attribute change estimate, or both.