Online Profile Veracity Monitoring for Scalable Fraud Detection

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Solution Overview

Problem

Fraudulent profiles on online platforms pose significant risks, including identity theft and financial loss, due to opportunistic and dishonest user behavior, necessitating effective veracity monitoring to limit exposure to duped profiles.

Innovation Solution

A system comprising non-transitory electronic storage, hardware processors, and machine-readable instructions to detect, analyze, and verify profiles for notable similarities with entities, using detection, profile information, veracity, and indication components to update and transmit veracity information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual verification of profiles is performed, then accuracy of profile verification is improved, but productivity and scalability deteriorate

Engineering Contradiction:

Improveaccuracy of profile verificationVSAvoidscalability of verification process

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual verification processes with automated machine learning models and algorithms that analyze profile data, detect fraud patterns, and verify identities without human intervention. This substitution maintains verification accuracy while enabling scalable processing of large profile collections across multiple platforms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces intermediary components including data collection modules, processing pipelines, and analysis engines that mediate between raw profile data and verification outcomes. These intermediaries automate the verification workflow, maintaining precision through structured data processing while improving productivity through systematic automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive profile data is collected from multiple platforms, then measurement precision of veracity is improved, but device complexity and data management difficulty increase

Engineering Contradiction:

Improveveracity assessment accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements a universal verification system that operates across multiple third-party platforms with a single integrated architecture. The machine learning models and data processing pipelines are designed to handle diverse profile data formats and platform-specific characteristics, achieving comprehensive veracity assessment without proportionally increasing system complexity through standardized multi-functional components.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If real-time monitoring of profiles is implemented, then reliability of fraud detection is improved, but use of energy and computational resources increases

Engineering Contradiction:

Improvefraud detection reliabilityVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system implements periodic monitoring and batch processing mechanisms that update profile veracity assessments at optimized intervals rather than continuously. Machine learning models process profile data in batches and trigger alerts only when fraud patterns are detected, maintaining high detection reliability while reducing computational resource consumption through time-efficient periodic evaluation.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS20260064847A1Systems and methods to monitor veracity of profiles of third-party, online platforms

Publication Date: 2026.03.05 NOTCOMMON CORP

AI summary

Systems and methods to monitor veracity of profiles of a third-party, online platforms. Exemplary implementations may: store veracity information pertaining to profiles within third-party platforms; monitor the third-party platforms to detect profiles created within the third-party; identify collections of profiles that correspond with the detected profiles; obtained profile information for the detected profiles and the individual profiles of the corresponding collections of profiles; compare profile information for the detected profiles with profile information for the individual profiles of the corresponding collections of profiles; determine, based on the comparison, sets of values of veracity parameters for the detected profiles; determine whether the detected profiles are associated with the individual entities; responsive to the determination of the detected profiles being associated with the individual entities, update the veracity information; responsive to the determination of the detected profiles not being associated with the individual entities, generate and transmit indications to the entities.