Counterfeit Item Screening Using NLP Questions and Image Analysis
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Solution Overview
Problem
Detecting counterfeit items in online marketplaces is challenging due to the inability to physically inspect items and the manipulation of structured data by sellers, leading to ineffective conventional methods that fail to identify counterfeits until they are distributed on a large scale.
Innovation Solution
A system that generates questions from unstructured data using natural language processing and machine learning to identify item features, ranks these questions based on correlation with counterfeit items, and uses a neural network to analyze images for detection, adapting to new items and features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional structured data inspection methods are used, then the detection process is simple, but detection accuracy is low and counterfeits are not identified until large-scale distribution
Solution Approach 1:
The patent segments the detection process into multiple independent components: collecting unstructured data from various sources, processing data through NLP models, generating detection questions, analyzing seller responses, and making detection decisions. This segmentation allows each component to be optimized independently while achieving high overall detection accuracy.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing unstructured data about items before they are listed for sale. By proactively gathering information from multiple sources and pre-processing this data into structured formats, the system prepares detection evidence in advance, enabling accurate identification of counterfeit items before they enter the marketplace.
2Measurement precision
If physical inspection methods are used, then detection accuracy is high, but the method cannot be applied to online marketplaces where items cannot be physically inspected
Solution Approach 1:
The patent creates digital copies of physical inspection processes by collecting unstructured data (images, descriptions, specifications) from online sources and processing them through NLP models. This allows the system to perform virtual inspections that replicate the accuracy of physical inspection without requiring physical contact with the items, making the method adaptable to online marketplaces.
Solution Approach 2:
The system replaces mechanical physical inspection methods with computational analysis of unstructured data. Instead of manually examining physical items, the system uses NLP models, machine learning algorithms, and automated question-generation processes to analyze digital representations of items, achieving comparable detection accuracy in the online environment.
3Ease of operation
If sellers can manipulate structured data, then data collection is easy, but detection reliability decreases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring seller responses to generated questions and comparing them against patterns identified from unstructured data sources. When discrepancies or suspicious patterns are detected, the system adjusts its questioning strategy and analysis methods, creating a closed-loop system that improves detection reliability while maintaining ease of data collection.
Solution Approach 2:
The patent applies partial or excessive action by collecting more data than traditionally required - gathering unstructured data from multiple sources beyond standard structured fields. This excessive data collection approach ensures that even if sellers manipulate some data sources, the system has sufficient additional information to maintain high detection reliability.
4Measurement precision
If the system adapts to new items and features continuously, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic action by updating its NLP models and question-generation algorithms at scheduled intervals rather than continuously. This allows the system to adapt to new items and features over time, improving detection accuracy through periodic retraining and updates, while minimizing processing time losses by maintaining stable operation between update cycles.
Data Source
AI summary
A counterfeit item detection system detects counterfeit items during an item listing processes provided by an online marketplace. The system enhances the ability of the online marketplace to identify and reject potential counterfeit items. The system comprises a trained counterfeit item detection model that is configured to receive an image and identify whether the image includes a counterfeit item. The model is trained using a data set of training images. An image of the data set is taken from a video related to the time based on identifying that the context of text associated with the video relates to counterfeit items. The text can be determined from the video's audio, and the image is obtained at a time in the video where the text corresponds to a counterfeit item context.