Cross-Platform Preference Correlation for Context-Aware Matching

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Solution Overview

Problem

Existing online consultancy systems struggle to efficiently track and match client preferences across multiple platforms due to the vast amount of data and changing preferences, requiring costly supervised training and lacking context in search results.

Innovation Solution

A software system and method that automatically correlates subject matter items and provider data across platforms using natural language processing and data scraping, tracking client preferences and sentiment to generate targeted lists based on third-party reviews and consultant workflows.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If conventional extraction techniques use regression analysis and low-dimensional classification schemes, then training can be performed on smaller datasets, but the training time becomes very costly and requires large amounts of data to achieve accurate natural language processing

Engineering Contradiction:

Improveamount of training dataVSAvoidtraining time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The patent transforms the classification approach from low-dimensional to high-dimensional by using embedded transformers, fundamentally changing the parameter space in which data is processed. This allows the system to achieve accurate NLP with less training data while reducing training time through unsupervised pre-training

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs unsupervised pre-training on large corpora before actual use, preparing the model in advance with general language knowledge. This preliminary action enables the model to require less supervised training data for specific tasks, thereby reducing both data requirements and training time for deployment

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If BERT and successor models use high-dimensional classification schemes like embedded transformers with unsupervised training, then training time is greatly reduced and accuracy is increased, but the system complexity increases

Engineering Contradiction:

Improveaccuracy of extracted meaningVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs self-service through unsupervised pre-training, automatically learning language patterns and knowledge from large corpora without human intervention. This self-training mechanism achieves high accuracy while managing complexity by eliminating the need for extensive manual annotation and supervised training processes

Inventive Principle:

Principle #25Self-service

3Loss of information

If online consultancy systems track vast amounts of data across multiple platforms, then client preference matching improves, but the systems struggle with efficiency due to data volume and changing preferences

Engineering Contradiction:

Improveclient preference tracking accuracyVSAvoidconsultancy service efficiency

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The system extracts only the most relevant features and preferences from vast amounts of multi-platform data using advanced NLP techniques. By selecting and focusing on key information rather than processing all data equally, the system maintains accurate preference tracking while improving processing efficiency

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system dynamically adapts to changing client preferences by continuously updating its understanding through unsupervised learning and real-time data processing. This dynamic approach allows the system to remain efficient while accurately tracking evolving preferences across multiple platforms

Inventive Principle:

Principle #15Dynamics

4Loss of information

If consultants manually track expanding data across platforms and changing client preferences, then comprehensive information is available, but the effort becomes unsustainable and costly

Engineering Contradiction:

Improvecompleteness of client informationVSAvoidconsultant tracking time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent replaces the mechanical manual tracking process with automated computational systems using unsupervised learning and natural language processing. This substitution maintains comprehensive information collection while eliminating the time-consuming manual effort, making the system sustainable and scalable

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12602726B2Software systems and methods to automatically correlate subject matter items and provider data across multiple platforms

Publication Date: 2026.04.14 DILIGENCE FUND DISTRIBUTORS INC

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.