Cross-Platform Data Correlation for Preference-Aware Item Matching
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Solution Overview
Problem
Existing data matching systems for online consultancy services require large datasets for supervised training, making them costly and time-consuming, and struggle to track the vast amount of information across multiple platforms and changing client preferences effectively.
Innovation Solution
A software system and method that automatically correlates subject matter items and provider data across platforms using unsupervised training with modern natural language processing techniques, such as BERT, to generate targeted lists of relevant items based on client preferences and third-party reviews, while tracking preference changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional supervised training techniques are used for data matching, then extraction accuracy can be achieved, but training time and cost increase significantly due to large datasets required
Solution Approach 1:
The patent changes the training approach from supervised to unsupervised learning, fundamentally altering the parameter space of the training process. This allows the system to achieve accurate extraction without requiring large labeled datasets, thereby reducing training time and cost while maintaining extraction accuracy.
Solution Approach 2:
The patent replaces conventional supervised learning mechanisms with modern unsupervised learning techniques including BERT and other transformer models. This substitution eliminates the need for manual labeling and reduces training time significantly, as the models can learn from unlabeled data through self-supervised pre-training.
2Measurement precision
If conventional supervised training techniques are used for data matching, then extraction accuracy can be achieved, but training cost increases due to large datasets required
Solution Approach 1:
The patent changes the training approach from supervised to unsupervised learning, fundamentally altering the parameter space of the training process. This allows the system to achieve accurate extraction without requiring large labeled datasets, thereby reducing training time and cost while maintaining extraction accuracy.
Solution Approach 2:
The patent replaces conventional supervised learning mechanisms with modern unsupervised learning techniques including BERT and other transformer models. This substitution eliminates the need for manual labeling and reduces training time significantly, as the models can learn from unlabeled data through self-supervised pre-training.
3Quantity of substance
If data scraping technology is used to track information across multiple platforms, then comprehensive data collection is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal data collection framework that can scrape and process data from multiple platforms simultaneously using standardized techniques. The system uses a single unified architecture to handle diverse data sources, reducing overall system complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent segments the data collection process into modular components, with separate scrapers for different platforms that can operate independently. This segmentation allows the system to handle complex multi-platform data collection through standardized, reusable modules, reducing overall system complexity.
4Productivity
If traditional NLP techniques are used for preference analysis, then processing speed is maintained, but accuracy of understanding client preferences decreases
Solution Approach 1:
The patent replaces traditional NLP techniques with modern transformer-based models like BERT that have been pre-trained on extensive corpora. These models achieve superior accuracy in understanding client preferences through contextualized word representations and attention mechanisms, while maintaining processing speed through efficient parallel computation capabilities.
Data Source
AI summary
Computer systems and software methods configured to automatically correlate subject matter items and provider data across multiple platforms. These platforms can include newsfeeds, websites, social websites, apps and networks, internet and social network posts, online reviews, online queries, and the like. The system automatically generates a targeted list of relevant subject matter items, associated with entity-provided workflow steps, to be matched with enhanced preferences, and generating a list of options to be presented to the subject matter users or clients. Subject matter items can be listed in order based on third-party reviews, if any, and the best fit for client preferences, with or without associated providers. Subject matter item providers who interact with the system operator can ensure that their goods and services are included within the system.