Fraudulent Content Detection Engine for Online Marketplace Data Harvesting
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Solution Overview
Problem
The abundance of digital content across networked environments makes it challenging to identify and remove fraudulent content effectively, as existing technologies lack efficient methods for distinguishing legitimate from fraudulent item identifiers, leading to widespread availability of counterfeit products.
Innovation Solution
A system and method that utilize a fraudulent content detection engine, comprising a harvesting engine, extraction engine, tagging engine, and analysis engine, to search, parse, and analyze item identifiers like GTINs and brand names across networked environments, determining legitimacy and initiating removal of fraudulent content through automated takedown notices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated systems search and harvest content from networked environments, then the quantity of data collected increases, but the complexity of the system increases
Solution Approach 1:
The system divides the content harvesting process into separate functional modules: a search engine component that queries content sources, a harvesting component that collects search results, and an analysis component that processes the data. This segmentation allows each component to handle specific tasks independently, managing overall system complexity while maximizing data collection capacity.
Solution Approach 2:
The patent introduces intermediary components that act as mediators between the search engine and the analysis system. These intermediaries process and filter search results before they reach the analysis stage, reducing the complexity of data processing while maintaining high-volume data collection capability.
2Measurement precision
If the system analyzes item identifiers to distinguish legitimate from fraudulent content, then the accuracy of fraud detection improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary analysis of item identifiers by extracting and validating GTINs and brand names before final fraud determination. This preliminary processing identifies obvious fraudulent patterns early in the analysis pipeline, reducing the time required for comprehensive analysis while maintaining high detection accuracy through multi-stage verification.
Solution Approach 2:
The analysis system operates continuously, processing item identifiers in a streamlined pipeline that maintains constant throughput. The system keeps the analysis process continuous by immediately processing search results as they are harvested, eliminating idle time and maintaining high detection accuracy without significant time delays.
3Object-affected harmful factors
If the system removes fraudulent content from networked environments, then the availability of counterfeit goods decreases, but the difficulty of implementing removal increases
Solution Approach 1:
The system implements feedback mechanisms where the analysis results feed back into the search and harvesting processes. When fraudulent content is identified, the system uses this feedback to refine future search queries and harvesting operations, making the removal process more targeted and easier to implement while effectively reducing counterfeit goods availability.
Data Source
AI summary
Exemplary embodiments of the present disclosure relate to systems, methods, and non-transitory computer-readable media for harvesting, parsing, and analyzing item identifiers in networked content to identify fraudulent content.