Package Counterfeit Detection Using Database Similarity Matching
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Solution Overview
Problem
Existing sorting facilities lack a systematic method to detect counterfeit or illegal products, allowing easy delivery of such items to customers without consequences.
Innovation Solution
A method and system using a counterfeit database and algorithm to compare package data with known counterfeit information, identifying matches or similarities, and determining the likelihood of a package being counterfeit or illegal, with further investigation and potential seizure if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a physical assistant manually inspects packages in sorting facilities, then some counterfeit packages may be detected, but the detection process is inefficient, time-consuming, and cannot systematically identify counterfeit products at scale
Solution Approach 1:
The patent replaces manual physical inspection with an automated image recognition system using cameras and machine learning algorithms. The system captures images of packages during normal sorting operations and automatically analyzes them for counterfeit indicators, eliminating the need for manual inspection while maintaining or improving detection reliability.
Solution Approach 2:
The system enables packages to be self-inspected through automated image capture and analysis during the sorting process. The machine learning model automatically identifies suspicious packages without human intervention, allowing the sorting facility to maintain high productivity while improving counterfeit detection capabilities.
2Productivity
If no detection system is implemented in sorting facilities, then package sorting remains fast and efficient, but counterfeit and illegal products are delivered to customers without consequences
Solution Approach 1:
The system performs preliminary inspection of packages during the sorting process before delivery to customers. By capturing and analyzing images at this intermediate stage, the system identifies counterfeit packages early in the logistics chain, allowing for interception before they reach customers while maintaining overall delivery speed.
Solution Approach 2:
The patent introduces an intermediary detection system between the sorting process and final delivery. The image recognition system acts as a mediator that automatically identifies suspicious packages and flags them for further inspection or seizure, preventing counterfeit products from reaching customers without disrupting normal logistics operations.
3Reliability
If manual inspection by physical assistants is used, then some counterfeit packages can be identified, but the method lacks systemization and cannot provide comprehensive data analysis across multiple packages
Solution Approach 1:
The patent creates a universal detection system that can analyze multiple types of data (package appearance, labels, barcodes, text) using a single integrated machine learning platform. The system learns from patterns across numerous packages and provides comprehensive data analysis, far exceeding the capabilities of manual inspection while maintaining manageable complexity through automated processing.
Data Source
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AI summary
The invention relates to a method for detecting a package containing a counterfeitproduct, preferably in an associated package sortation facility, wherein the method comprises the steps of providing at least one counterfeit database (110, 5111, 11) comprising information of previously found counterfeit packages, providing data of information of at least one package (101), preferably visible information, the information being at least one of text data and/or image data, uploading and storing the provided data, preferably text and/or image, to the counterfeit database (110, 111, 112), comparing the provided data of the 10package to data already stored in the counterfeit database and search for matches, detecting exact data matches or high similarity data matches, if any exact matches or high similarity matches; determine the possibility, preferably in %, of the package being a counterfeit package by using a counterfeit comparison algorithm.