CN114663106A - Method and computer system for identifying counterfeit personal consumer product - Google Patents

Abstract

Methods and computer systems for identifying counterfeit personal consumer product products are disclosed. A method for identifying counterfeit personal consumable products includes: in response to multiple scans of identification codes for a plurality of personal consumer product products, constructing a database containing a plurality of entries, each entry corresponding to a scan and each entry comprising: a representative of Identification code data of the scanned identification code, time data representing the time of the scan, and geographic location data representing the geographic location of the scan, wherein the identification code of the personal consumable product includes a unique code that is unique to a single a common code shared by personal consumer product products; and determining whether a personal consumer product product with the same identification code data is a counterfeit product based on the characteristics of the entry in the database with the same identification code data.

Description

Method and computer system for identifying counterfeit personal consumer product products

technical field

The present disclosure relates to methods, computer systems, computer program products, and storage media for identifying counterfeit personal consumer product products. The present disclosure also relates to personal consumer product products having counterfeit detection indicia.

Background technique

Counterfeiting of personal consumer products is an ongoing problem for manufacturers, distributors, retailers and end consumers. There is an ongoing need for counterfeit detection methods applicable to low-margin personal consumer product products.

SUMMARY OF THE INVENTION

It is an object of the present disclosure to provide a new method and computer system for identifying counterfeit personal consumer product products that are capable of identifying counterfeit products.

The present disclosure proposes a method for identifying counterfeit personal consumer product products, comprising: in response to multiple scans of identification codes for multiple personal consumer product products, constructing a database containing multiple entries, each entry corresponding to one scan And each entry includes: identification code data representing the scanned identification code, time data representing the time of the scan, and geographic location data representing the geographic location of the scan, wherein the identification code of the personal consumable product includes unique to a single personal consumable product. A unique code or a common code shared by a plurality of personal consumer product products; and, based on characteristics of entries in the database with the same identification code data, determining whether a personal consumer product with the same identification code data is a counterfeit product.

Other features and advantages of the present disclosure will become apparent from the following description with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure without limitation. In the figures, like numerals are used to refer to like items. In the figures, various components are not necessarily drawn to scale.

1 is a block diagram of an exemplary counterfeit product identification apparatus according to some embodiments of the present disclosure.

2 is a flowchart illustrating an exemplary counterfeit product identification method according to some embodiments of the present disclosure.

3 is a block diagram illustrating the configuration of an exemplary counterfeit product determination component according to some embodiments of the present disclosure.

4 is a flowchart illustrating an exemplary counterfeit product determination process in accordance with some embodiments of the present disclosure.

5A shows a histogram of the number of scans for the QR codes in the blacklist, FIG. 5B shows the distribution of the QR code reproduction time interval, and FIG. 5C shows the distribution of the QR code reproduction spatial distance.

6A-6B respectively illustrate examples of exemplary Datamatrix codes printed on packaging of personal consumer product products.

7A-7B respectively illustrate an example GUI of an example web-based application according to some embodiments of the present disclosure.

8 is a diagram illustrating a general hardware environment in which the present disclosure may be applied, according to some embodiments of the present disclosure.

Detailed ways

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the described exemplary embodiments. However, it will be apparent to those skilled in the art that the described embodiments may be practiced without some or all of these specific details. In the described example embodiments, well-known structures or process steps have not been described in detail to avoid unnecessarily obscuring the concepts of the present disclosure.

The steps of the methods presented in this disclosure are intended to be illustrative. In some embodiments, the method may be accomplished with and/or without one or more of the steps discussed. Furthermore, the order in which the steps of the method are illustrated and described is not intended to be limiting.

It is known that for genuine personal consumable products, each product has an identification code. The identification code of a personal consumable product includes a unique code that is unique to a single personal consumable product or a common code that is shared by multiple personal consumable product products. "Unique code" means: For a class of personal consumer product products manufactured by the same manufacturer, the products have different codes from each other. It should be understood that during the high-speed manufacturing process of products, there may be multiple products (eg, no more than 5 products) having exactly the same identification code. In this case, the multiple products share the same code. The identification code may include a product code or a QR (Quick Response) code. The identification code is usually printed on the packaging of the product. For example, an example of a product code printed on the packaging of a sanitary napkin product might be: "07/12/2024 8250 2702 17 03:029739". As another example, an example of a product code printed on the packaging of a shampoo product could be:

"B26136591 22:51P03/19

MRP179.49+ST30.51=210PKR”.

The product code may consist of letters, numbers and/or symbols, and the product code may represent the production date of the product, the workshop and production line of the product, the time of production of the product (ie time of day), the counter value assigned to the product, and so on. A QR code is known as a two-dimensional code, and in the present disclosure, the QR code may be encoded with the ID of the personal consumer product product. The ID of a personal consumable product may include the serial number of the product.

Also, in the case of genuine products, the identification code is printed on the product as it leaves the production line. The printing method of the identification code is usually different from the printing method of the patterns, characters, etc. on the product packaging. For example, in the case of printing patterns, characters, etc. on the package by inkjet printing, the identification code can be printed by laser printing.

The inventors of the present invention have discovered that counterfeiters of personal consumer product products replicate the identification code of the genuine product and print the same identification code on thousands of counterfeit products. In other words, the inventors of the present invention found that thousands of counterfeits use the same product code or QR code in the sales market. Also, to save costs, counterfeiters typically do not print identification codes using a different printing method than that used on product packaging.

In the present disclosure, a new personal consumable product with counterfeit detection marks is proposed, the product has a Datamatrix code for identifying the personal consumable product, the Datamatrix code is based on product production date, product production workshop and production line, product production generated by at least one of a time, a counter value assigned to the product, and an alphanumeric code identifying the product. Datamatrix codes are known two-dimensional codes, which may be codes defined by the ISO/IEC 16022 international standard. Figure 6A shows an example of a product code and a Datamatrix code printed on the packaging of a personal consumer product product. In FIG. 6A, "DDDDPPPPLL HH:MM:SS X abg" represents a product code. More specifically, "DDDD" can represent the product production date, "PPPPLL" can represent the product production workshop and production line, "HH:MM:SS" can represent the product production time (hours, minutes, and seconds), and "X" can represent The counter value assigned to the product, "abg" may represent an alphanumeric code identifying the product (preferably, the alphanumeric code may be unique to the product). Alphanumeric codes can have 3-7 digits. FIG. 6B shows a modification of the example of FIG. 6A . Where the Datamatrix code can identify the product, at least a part of the product code can be omitted, thereby enabling printing space to be saved. It should be understood that, here, the Datamatrix code may be a unique code unique to a product, or may be a common code shared by several products.

It should be understood that the machine readable codes printed on the personal consumer product products are not limited to the QR codes and Datamatrix codes mentioned above. Machine-readable codes printed on personal consumer product products may also include other suitable two-dimensional codes, such as circular codes and the like.

The following describes the counterfeit product identification method and device of the present disclosure by taking a shampoo product as an example. It should be understood, however, that personal consumable products are not limited to shampoo products. Personal consumer products can include: hair care products other than shampoos, such as conditioners, etc.; skin care products, such as body wash, skin lotion, etc.; laundry care products, such as washing powder, laundry liquid, fabric softener, laundry detergent, etc. Fragrance beads, etc.; hard surface care products, such as dishwashing detergents, floor cleaners, toilet cleaners, kitchen sink cleaners, etc.; air care products, such as air fresheners, fabric fresheners, etc.; scraper or razor products, such as razors , hair removal razors, etc.; toothbrush products with replaceable brush heads; oral care products, such as toothpaste, mouthwash, dental floss, toothpicks, etc.; feminine hygiene products, such as sanitary napkins, tampons, etc.; diaper products; health care products, such as Over-the-counter medicines, vitamin products, etc.; pest control products, such as household insecticides, household insect traps, etc.; etc.

In the following description, the case where the identification code of the personal consumer product product is the unique code will be described. It should be understood that the present disclosure is not limited thereto. Alternatively, the identification code for the personal consumable product may be a shared code.

In the following description, the QR code is described as an example of the unique code of the present disclosure. However, it should be understood that the present disclosure is not limited thereto. Alternatively, the unique code for the personal consumable product may comprise a product code or a Datamatrix code as previously described.

1 is a block diagram of an exemplary counterfeit product identification device 100 in accordance with some embodiments of the present disclosure.

The apparatus 100 may include a database construction component 110 for constructing, in response to multiple scans of QR codes for a plurality of shampoo products, a database containing a plurality of entries, each entry corresponding to one scan and each entry comprising: a representative The unique code data of the scanned QR code, the time data representing the scanning time, and the geographic location data representing the scanning geographic location; the feature analysis part 120, for determining based on the features of the entries in the database with the same unique code data Whether the shampoo product with the same unique code data is a counterfeit product; the blacklist generation part 130 is used to determine the shampoo product with the same unique code data is a counterfeit product, this same unique code Data is added to the counterfeit product blacklist, and unique code data that is artificially determined to indicate a counterfeit product is added to the counterfeit product blacklist; counterfeit product determination means 140, for the scanning of the QR code of the shampoo product to determine whether the QR code is included in the blacklist, and if the QR code is determined to be included in the blacklist, to determine that the shampoo product is a counterfeit product; and a notification component 150, for notifying that the shampoo product is a counterfeit product when it is determined that the shampoo product is a counterfeit product.

Although not shown in FIG. 1 , the apparatus 100 may further include a unique code data obtaining means for obtaining unique code data from a digital image of a QR code obtained via scanning. More specifically, the component may perform decoding processing on the digital image of the QR code to obtain the unique ID of the product (such as the serial number of the product) as the unique code data.

It should be understood that in the case where the unique code of the product is a Datamatrix code, the component can similarly perform a decoding process to obtain the unique ID of the product as the unique code data. Whereas in the case where the unique code of the product is the product code, the part may perform OCR (Optical Character Recognition) processing on the digital image of the product code to identify the specific product code as the unique code data.

The operation of the various components shown in FIG. 1 will be described in further detail below.

FIG. 2 is a flowchart illustrating an exemplary counterfeit product identification method 200 in accordance with some embodiments of the present disclosure. The method 200 may be performed by a server provided by the product manufacturer. Alternatively, the method 200 may be performed jointly by the electronic device performing the scan and the above-mentioned server.

In response to scanning of a QR code of a shampoo product using an electronic device such as a consumer, retailer, distributor, etc., corresponding data is sent from the electronic device to the server.

The method 200 begins at step S210, where the database building component 110 builds the database as previously described. In response to a scan of the QR code of the shampoo product using the electronic device, the database building component 110 generates a new entry that may include: unique code data representing the scanned QR code, time data representing the time of the scan, and Geolocation data representing the scanned geographic location. The scan time data may contain the year, month, and day of the scan operation, and the scan time data may also contain the time of day when the scan operation occurred. The geographic location data may include GPS data of the electronic device scanning the QR code or the IP address of the electronic device scanning the QR code, where the IP address can be converted into GPS data. It will be appreciated that in response to multiple scans of the QR codes for multiple shampoo products, the database will contain multiple corresponding entries.

In the present disclosure, the electronic device that scans the QR code may be a portable electronic device, such as a smart phone, a tablet computer, and the like. Scanning can be accomplished using an application installed on the electronic device, such as WeChat.

In some embodiments of the present disclosure, several exemplary entries of the constructed database are shown in Table 1 below.

category brand serial number Electronic Device ID Scan time Electronic device IP address hair Head & Shoulders 89964307767522 orKdM5bJAUPROQKbOZ3eYcxnBnM8 01/04/2020 00:33 14.212.241.189 hair Head & Shoulders 71500467084131 orKdM5aGXiOcXnOjqnJlazgzPy1g 01/04/2020 00:00 106.17.18.101 hair Head & Shoulders 83536603511077 orKdM5Xugtm2i_T9lnfkg_JescOw 01/04/2020 00:06 106.61.105.220 hair Head & Shoulders 91616142304535 orKdM5ariiggNm4CJlrOIBYLOk-M 01/04/2020 00:14 182.139.78.136 hair Head & Shoulders 24045835405688 orKdM5UpDGRh8qHPp5ieBnbYY0qo 01/04/2020 00:24 112.96.71.104

Table 1

As can be seen from Table 1, each entry of the database may include: product category, product brand, serial number of the product, ID of the electronic device that scanned the QR code (this ID uniquely identifies the electronic device), scan time, and electronic The IP address of the device. Here, the serial number of the product corresponds to the unique code data, the scan time corresponds to the time data, and the IP address of the electronic device corresponds to the geographic location data. It should be understood that the category, brand, and electronic device ID data are not required here.

It should be understood that the IP address of the electronic device changes with the access point at which the electronic device accesses the communication network, so the IP address of the electronic device can well reflect the geographic location where the scanning operation occurs. Various services are known that can be used to convert IP addresses into GPS data, such as latitude and longitude coordinates. Table 2 below lists several known services that can be used to translate IP addresses into GPS data.

Table 2

It will be appreciated that it may be advantageous for each entry of the database to include the ID of the electronic device that scanned the QR code. In the case where there are a plurality of items with the same unique code data and the same electronic device ID, only one of the plurality of items may be considered in the counterfeit product determination in step S220 described later. In this way, repeated scanning of the same QR code by the same electronic device can be filtered out.

Next, the method 200 proceeds to step S220, where the feature analysis component 120 determines whether the shampoo product with the same unique code data is a counterfeit product based on the features of the entry in the database with the same unique code data . In other words, the feature analysis component 120 determines the probability that a shampoo product with this same unique code data is a counterfeit product. It is easy to understand that in the case of a high probability, it can be determined that the corresponding shampoo product is a counterfeit product.

In the case of genuine products, each product has its own unique QR code, so the number of scans of this QR code will be small. In contrast, in the case of fakes, thousands of fakes have the same QR code, so the number of scans of this QR code will be large. Even if only about half of the fake QR codes on the market are scanned, the number of times this QR code is scanned can be large.

Furthermore, in terms of genuine products, the lifespan (or existence time) of an individual product on the market is usually short. On the contrary, in the case of counterfeit goods, since counterfeit goods are continuously produced and sold, the lifespan of counterfeit goods in the market will be long.

Furthermore, with respect to genuine products, the spatial movement range of a single product is usually small. Conversely, in the case of counterfeit goods, since counterfeit goods with the same QR code may be sold nationwide, the spatial distribution of products may be wide when considering counterfeit goods with the same QR code.

Based on the above, the feature analysis component 120 can determine the probability that the considered QR code indicates a counterfeit product based on the features of the entries in the database with the same unique code data regarding the number of scans, the scan time span and/or the scan space span . If the determined probability is high, it is determined that the product with the QR code under consideration is a counterfeit product. Hereinafter, the operation of the feature analysis part 120 will be described with reference to FIGS. 3 , 4 , and 5A-5C.

In other words, the feature analysis component 120 may determine whether the product is a counterfeit product based on the statistical features of the entries in the database having the same unique code data. Here, the statistical features may include features related to the distribution of the number of scans, the scan time span, and/or the scan space span. Further, the feature analysis component 120 may determine whether the product is a counterfeit product based on multivariate-based statistical features of entries in the database having the same unique code data. Here, the multivariate-based statistical features may include features regarding the distribution of at least two of the number of scans, the scan time span, and the scan space span. It should be understood that, compared to considering the statistical characteristics of a single variable, when the statistical characteristics of multiple variables are comprehensively considered, a more reliable determination result can be obtained.

Next, the method 200 proceeds to step S230, where the blacklist generating component 130 adds the unique code data determined at step S220 as indicating a counterfeit product to the counterfeit product blacklist. That is, the counterfeit product blacklist includes unique code data determined to indicate counterfeit products at step S220. In other words, the counterfeit product blacklist includes unique code data copied and used by counterfeit parties.

In addition, the blacklist generating section 130 also adds unique code data that is artificially determined to indicate a counterfeit product to the counterfeit product blacklist. As mentioned before, on the packaging of genuine products, the printing method of the QR code is different from the printing method of the pattern, text, etc. on the product packaging. On the packaging of the fake, the printing method of the two is the same. This enables distributors, retailers, etc. of the product to visually identify fakes. Alternatively, a product's distributor, retailer, etc. can identify a counterfeit by testing the product's chemical composition. In the event that a counterfeit product is identified, the distributor, retailer, etc. of the product can photograph the QR code of the product, upload a digital image of the QR code, and explain that the QR code indicates a counterfeit product. Thus, the blacklist generating part 130 can add unique code data representing this QR code to the blacklist. Alternatively, where the unique code is a product code, a distributor, retailer, etc. of the product may photograph the product code, upload a digital image of the product code, and explain that the product code indicates a counterfeit product. Alternatively, the distributor, retailer, etc. of the product may manually enter a product code indicating a counterfeit product and state that this product code indicates a counterfeit product. Thus, the blacklist generating part 130 can add the corresponding product codes to the blacklist. Still alternatively, where the unique code is a Datamatrix code, the Datamatrix code may be processed in a similar manner to that of the QR code.

In addition, the blacklist generating unit 130 periodically updates the generated blacklist. Specifically, as entries in the database increase, component 130 may add new unique code data to the blacklist. Alternatively, if a certain QR code is not scanned for a long time, the component 130 may delete the corresponding unique code data from the blacklist.

Further, the blacklist generation part 130 transmits the generated blacklist to the counterfeit product determination part 140 . It should be understood that the aforementioned steps S210-S230 correspond to the steps of generating the blacklist. Next, the step S240 of using the blacklist will be described. It should also be understood that the steps of generating the blacklist and using the blacklist may be parallel steps. Furthermore, an exemplary use example of the blacklist will be described below with reference to FIGS. 7A and 7B .

At step S240, the counterfeit product determination part 140 identifies counterfeit products using the blacklist generated in step S230. Specifically, in response to the scanning of the QR code of the shampoo product, the counterfeit product determination component 140 determines whether the QR code (ie, the corresponding unique code data) is included in the blacklist. If so, the counterfeit product determination part 140 determines that the product with this QR code is a counterfeit product. If not, the counterfeit product determination part 140 determines that the product with this QR code is genuine.

In the event that the shampoo product is determined to be a counterfeit product at step S220, or the shampoo product is determined to be a counterfeit product at step S240, the method 200 proceeds to step S250, where the notification component 150 sends the corresponding execution The scanned electronic device notifies that the scanned shampoo product is a counterfeit product. And, in other cases, the notification component 150 may notify the electronic device that the scanned shampoo product is genuine. That is, the notification part 150 notifies distributors, retailers, consumers, etc. who perform the scanning whether the scanned shampoo product is genuine or counterfeit. Such notification may be performed in various known manners, such as sending a text message, sending a voice message, and the like.

Hereinafter, the operation of the feature analysis part 120 will be described with reference to FIGS. 3 , 4 , and 5A-5C.

FIG. 3 is a block diagram illustrating the construction of an exemplary feature analysis component 120 in accordance with some embodiments of the present disclosure.

The feature analysis part 120 may include: a scan number analysis part 122 for determining whether the number of entries in the database with the same unique code data is greater than a preset number; a time span analysis part 124 for time data in the entry of the data, determining whether at least one scan time interval is greater than a preset time interval, the scan time interval indicating the time interval between the scan times of any two scans; The geographic location data in the entries with the same unique code data determines whether at least one scan spatial distance is greater than a preset spatial distance, the scan spatial distance indicating the spatial distance between the geographic locations of any two scans.

Although not shown, the component 120 may also include a comprehensive analysis component for determining whether the QR code under consideration indicates a counterfeit product based on a determination result of at least one of the component 122 , the component 124 , and the component 126 .

The operation of the various components shown in FIG. 3 will be described in further detail below.

FIG. 4 is a flow diagram illustrating an exemplary feature analysis process 220 in accordance with some embodiments of the present disclosure.

First, at step S222, the scan number analysis part 122 determines whether the scan number for the same QR code is greater than a preset number. And, the determination result at step S222 is sent to comprehensive analysis step S228 described later. Figure 5A shows a histogram of the number of scans for QR codes in the blacklist. The horizontal axis in FIG. 5A represents the number of scans for the same QR code, and the vertical axis represents the total count of each scan number. By comparing with the histogram of the number of scans of QR codes for genuine products, it is found that it is rare to scan the same genuine product 20 times or more. Therefore, at step S228, in the case where the number of scans for the same QR code is greater than, for example, 20, it can be determined that this QR code indicates a counterfeit product.

It can be understood that, in general, in the case of genuine products, it is not common to scan the same genuine product 20 times. It is extremely rare to scan the same genuine item 100 times. Conversely, in the case of fakes, since thousands of fakes have the same QR code, it would be common to scan the same QR code dozens of times.

At step S224, the time span analysis part 124 determines whether at least one scan time interval is greater than a preset time interval among the multiple scans for the same QR code. For example, component 124 may determine whether there is a scan time interval greater than a predetermined time interval. As another example, component 124 may determine whether two or more scan time intervals are greater than a preset time interval. And, the determination result at step S224 is sent to the comprehensive analysis step S228. FIG. 5B shows the distribution of QR code reproduction time intervals (ie, scan time intervals). The horizontal axis in FIG. 5B represents the scan time interval as an independent variable, which is in days, and the vertical axis represents the probability density function for the independent variable. In FIG. 5B , the solid line represents the distribution of the reproduction time intervals of the QR codes that are in the blacklist, and the dotted line represents the distribution of the reproduction time intervals of the QR codes that are not in the blacklist. By comparing the distribution represented by the solid line and the distribution represented by the dashed line, it can be seen that the scanning time interval for genuine products is mostly concentrated in 0-25 days, while the scanning time interval for fake products is mostly concentrated in 50-150 days. Therefore, at step S228, if there is at least one scan time interval greater than eg 25 days or 50 days with respect to the scan time interval of the same QR code, it can be determined that this QR code indicates a counterfeit product. It should be understood that the more scanning time intervals that are greater than the preset time interval, the more reliable the determination result.

It can be understood that, in general, in the case of genuine products, since the market life of a single genuine product is not long, the scanning time interval for a single genuine product will not be large. On the contrary, in the case of fakes, since thousands of fakes with the same QR code are continuously sold in the market, the scanning time interval for the fakes tends to be large compared to the real ones.

At step S226, the spatial span analysis component 126 determines whether at least one scan spatial distance is greater than a preset spatial distance among multiple scans of the same QR code. For example, component 126 may determine whether a scan spatial distance is greater than a predetermined spatial distance. As another example, component 124 may determine whether two or more scan spatial distances are greater than a predetermined spatial distance. And, the determination result at step S226 is sent to the comprehensive analysis step S228. FIG. 5C shows the distribution of the QR code reproduction spatial distance (ie, the scanning spatial distance). The horizontal axis in Figure 5C represents the scan space distance as an independent variable, which is in kilometers, and the vertical axis represents the probability density function for the independent variable. In FIG. 5C , the solid line represents the distribution of the reproduction space distances of the QR codes in the blacklist, and the dotted line represents the distribution of the reproduction space distances of the QR codes not in the blacklist. By comparing the distribution represented by the solid line and the distribution represented by the dotted line, it can be seen that the scanning spatial distance of the genuine product is mostly concentrated in 0-200 km, while the scanning spatial distance of the fake product is mostly concentrated in 200-2000 km. Therefore, at step S228, if there is at least one scan spatial distance greater than, for example, 200 kilometers with respect to the scan spatial distance of the same QR code, it can be determined that this QR code indicates a counterfeit product. It should be understood that the more the scanning spatial distance is greater than the preset spatial distance, the more reliable the determination result is.

It will be appreciated that, in general, in the case of genuine articles, the geographic location where a single genuine article is scanned usually does not vary significantly. Conversely, in the case of fakes, since thousands of fakes with the same QR code are widely sold across the country, the scanning spatial distance for fakes tends to be large compared to genuine ones.

Next, at step S228, the comprehensive analysis part determines whether the QR code under consideration indicates a counterfeit product based on at least one of the determination results obtained at steps S222, S224 and S226. For example, it may be determined that the QR code under consideration indicates a counterfeit product if any of the determination results obtained at steps S222, S224, and S226 is a positive determination. For another example, it may be determined that the QR code under consideration indicates a counterfeit product if any two of the determination results obtained at steps S222, S224 and S226 are positive determinations. For another example, it may be determined that the QR code under consideration indicates a counterfeit product if all of the determination results obtained at steps S222, S224 and S226 are positive determinations.

It should be understood that the aforementioned preset number, preset time threshold and preset distance threshold are values preset according to experience. These preset values vary between different categories of individual consumer products. For example, in the case of shampoo products, the lifespan in the market is usually short, such as a few months, and in the case of scraper products, the lifespan in the market is usually long, such as several years, so for this The preset time thresholds for the two types of products may be different. In addition, when two or more determination results are considered, a lower preset value can be employed than when a single determination result is considered.

In some embodiments of the present disclosure, feature analysis component 120 may determine whether a personal consumable product with the same unique code data is a counterfeit product based on temporal data and geographic location data in entries with the same unique code data. More specifically, the component 120 may determine whether the product is a counterfeit product based on the ratio of the scan time interval to the scan spatial distance between any two scans. For example, if it is determined that two scans of a QR code occur at almost the same time, but the geographic locations of the two scans are far apart in space, such as across provinces/cities, then it can be determined that the QR code indicates a counterfeit product .

In some embodiments of the present disclosure, a personal consumable product may have an outer identification code printed on the outer packaging of the product and an inner identification code printed on the inner packaging of the product. The external identification code and the internal identification code are different identification codes (different in form or content). And, in response to multiple scans of the internal identification codes of the plurality of personal consumer product products, the database as previously described is constructed. In this way, the difficulty of duplicating the identification code by the counterfeiter can be improved.

Next, an exemplary use example of the blacklist is described with reference to FIGS. 7A and 7B . More specifically, an exemplary web-based application according to some embodiments of the present disclosure is described with reference to FIGS. 7A and 7B . This web-based application can be implemented by a server provided by the product manufacturer. The functionality of this application is described below from the perspective of a user of this application. Here, users of the web-based application may be consumers, retailers, dealers, investigators, law enforcement officials, and the like. In this example, a user uses a web-based application to query whether a product is a counterfeit product, and this query can include the following 8 steps. In this example, the sanitary napkin product is described as an example of a personal consumable product, and the product code is described as an example of an identification code.

Step 1. The user connects to the web-based application by using an electronic device (such as a desktop computer, a laptop computer, a smart phone, etc.). Specifically, the user connects to the corresponding Web page by accessing a preset web address.

Step 2. Optional login step. Users can choose real-name access or anonymous access to Web pages.

Step 3. The user confirms the license option. For example, a user can grant an application access to its GPS data or IP address, grant an application access to the camera, and so on.

Step 4. The user is directed to scan the bar code of the sanitary napkin product. The value of the barcode, scan time, GPS data, and optional electronic device ID are retrieved and recorded. Here, the barcode of the product can indicate the manufacturer and category of the product.

Step 5. The web-based application displays the fake product blacklist to the user via the web page. Counterfeit product blacklists include product codes that have been copied and used by counterfeit parties. The application can display the blacklist via the GUI as shown in Figure 7A. The table in FIG. 7A corresponds to an exemplary blacklist that includes barcodes, product codes copied by counterfeiters, and images of the copied product codes. Here, barcodes are used to manage the copied product codes by category. As described later, the rectangular box at the top of Figure 7A is used to provide a smart lookup function.

Step 6. The user enters the product code "07/12/2024 8250 2702 17 03:029739" on the sanitary napkin product digit by digit into the rectangular box. Symbols and spaces can be ignored as you type. Mistyped digits can be deleted as you type. Accordingly, product codes in the blacklist that match the digits that have been entered are displayed in the table in Figure 7A. As shown in Figure 7A, where the user has typed "07", the user finds the product code for the product, as shown in the first row of the table in Figure 7A.

Step 7. The user selects (eg clicks) a product code as shown in the first row of the table in Figure 7A, and then a GUI as shown in Figure 7B is presented to the user for the user to confirm whether the selected product code is Code printed on the product.

Step 8. If the user selects "Yes" in the GUI as shown in FIG. 7B, the web-based application can notify the user via the web page that the queried product is a counterfeit product. If the user selects "No" in the GUI as shown in FIG. 7B, the web-based application returns to step 3 above. If the user selects "re-enter product code" in the GUI as shown in Figure 7B, the web-based application returns to step 5 above. If the user selects "Undo" in the GUI as shown in FIG. 7B, the web-based application ends the display of the GUI as shown in FIG. 7B.

It should be understood that the examples described above with reference to FIGS. 7A and 7B are merely exemplary, and the present disclosure is not limited thereto. For example, in the aforementioned step 6, instead of entering the product code by the user, the user can scan the QR code or the Datamatrix code on the product package, so as to obtain the ID of the product through decoding. Alternatively, the user can photograph the product code on the product package, so as to obtain the product code through OCR technology. In addition, the web-based application can display the distribution of scanning activity of interest to the user via a web page. For example, a web-based application can mark on a map the location of scanning activity for the same identification code during a time period of interest. Alternatively, a web-based application can map the geographic distribution of products identified as counterfeit.

It will be appreciated that the above describes the situation where the identification code of the personal consumer product product is a unique code. However, the present disclosure is not limited thereto. Alternatively, the identification code of the personal consumable product may be a common code shared by a plurality (or group of) personal consumable products. For example, as mentioned earlier, during the high-speed manufacturing of products, a group of products may have exactly the same identification code. For another example, when a part of the product code "DDDDPPPPLL HH:MM:SS" as shown in FIG. 6A is used as the identification code, a group of products offline at the same time can share the same identification code. This set of products can be, for example, more than 2 and less than 20 products. Alternatively, the set of products may be greater than 2 and less than 10 products, for example. Still alternatively, the set of products may be greater than 2 and less than 5 products, for example. Even though one identification code is shared by a group of products as described above, the method and apparatus according to the present disclosure can still identify counterfeit products.

The foregoing describes methods and apparatus for identifying counterfeit personal consumer product products in accordance with the present disclosure. By means of data analysis, the method and apparatus of the present disclosure can reliably identify counterfeit products. In addition, based on the constructed database, it is possible to determine the area and time period in which counterfeit products exist, which may be meaningful for product manufacturers to combat counterfeiting.

hardware implementation

8 is a diagram illustrating a general hardware environment in which the present disclosure may be applied, according to some embodiments of the present disclosure.

8, a computing device 800 will now be described as an example of a hardware device in which aspects of the present disclosure may be applied. The computer system of the present disclosure may be implemented by computing device 800, for example. Computing device 800 may be any machine configured to perform processing and/or computation, which may be, but is not limited to, a workstation, server, desktop computer, laptop computer, tablet computer, personal digital assistant, smart phone, portable camera, or any of these. combination. The aforementioned servers may be implemented in whole or at least in part by computing device 800 or a similar device or system.

Computing device 800 may include elements capable of connecting to or in communication with bus 802 via one or more interfaces. For example, computing device 800 may include a bus 802 , one or more processors 804 , one or more input devices 806 , and one or more output devices 808 . The one or more processors 804 may be any type of processor, and may include, but are not limited to, one or more general-purpose processors and/or one or more special-purpose processors (such as special-purpose processing chips). Input device 806 may be any type of device capable of inputting information to a computing device, and may include, but is not limited to, a mouse, keyboard, touch screen, microphone, and/or remote control. Output device 808 may be any type of device capable of presenting information, and may include, but is not limited to, displays, speakers, video/audio output terminals, and/or printers. Computing device 800 may also include or be connected to non-transitory storage device 810, which may be any storage device that is non-transitory and that may implement a data repository, and may include, but is not limited to disk drive, optical storage device, solid state storage device, floppy disk, flexible disk, hard disk, magnetic tape or any other magnetic medium, compact disk or any other optical medium, ROM (read only memory), RAM (random access memory), high speed Cache memory and/or any other memory chip or cartridge, and/or any other medium from which a computer can read data, instructions and/or code. The non-transitory storage device 810 may be removable from the interface. The non-transitory storage device 810 may have data/instructions/code for implementing the methods and steps described above. Computing device 800 may also include communication device 812 . Communication device 812 may be any type of device or system capable of communicating with external devices and/or with a network, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication equipment, and/or devices such as Bluetooth ^™ , 802.11 Chipsets for devices, WiFi devices, WiMax devices, cellular communication facilities, etc.

Bus 802 may include, but is not limited to, Industry Standard Architecture (ISA) bus, Microchannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) Local Bus, and Peripheral Component Interconnect (PCI) bus.

Computing device 800 may also include working memory 814, which may be any type of working memory that may store instructions and/or data useful for the operation of processor 804, and may include, but is not limited to, random access memory and/or read-only memory device.

Software elements may reside in working memory 814, including, but not limited to, operating system 816, one or more application programs 818, drivers, and/or other data and code. Instructions for performing the above-described methods and steps may be included in one or more application programs 818, and the components of the above-described apparatus 100 or 300 may be implemented by the processor 804 reading and executing the instructions of the one or more application programs 818 . More specifically, the database construction component 110 may be implemented, for example, by the processor 804 when executing the application program 818 having the instruction to perform step S210. The feature analysis component 120 may be implemented, for example, by the processor 804 when executing the application program 818 having the instructions to perform step S220. Similarly, the blacklist generation component 130, the counterfeit product determination component 140, and the notification component 150 may be implemented, for example, by the processor 804 when executing the application 818 having instructions to perform steps S230, S240, S250, respectively. Furthermore, the scan number analysis component 122, the time span analysis component 124, and the space span analysis component 126 may be implemented, for example, by the processor 804 when executing the application program 818 having instructions to perform steps S222, S224, S226, respectively. The executable or source code of the instructions for the software elements may be stored in a non-transitory computer-readable storage medium, such as the storage device(s) 810 described above, and may be compiled and/or installed, possibly It is read into the working memory 814 . The executable code or source code of the instructions for the software elements may also be downloaded from a remote location.

From the above embodiments, those skilled in the art can clearly understand that the present disclosure can be implemented by software and necessary hardware, or can be implemented by hardware, firmware, and the like. With this understanding, embodiments of the present disclosure may be implemented in part in software. Computer software may be stored in a computer-readable storage medium, such as a floppy disk, hard disk, optical disk, or flash memory. Computer software includes a series of instructions that cause a computer (eg, a personal computer, a service station, or a network terminal) to perform methods, or portions thereof, according to various embodiments of the present disclosure.

Having thus described the present disclosure, it will be apparent that the present disclosure may be varied in many ways. Such changes are not to be regarded as a departure from the spirit and scope of this disclosure, but all such modifications obvious to those skilled in the art are intended to be included within the scope of the following claims.

Claims (26)

1. A method for identifying counterfeit personal consumer product products, comprising: In response to multiple scans of the identification codes of the plurality of personal consumer product products, constructing a database containing a plurality of entries, each entry corresponding to a scan and each entry including: identification code data representing the scanned identification codes, representing the scans Temporal data of time, and geographic location data representing the geographic location of the scan, where the identification code of a personal consumable product includes a unique code that is unique to a single personal consumable product or a common code shared by multiple personal consumable product products, and Based on the characteristics of the entry in the database with the same identification code data, it is determined whether the personal consumer product product with this same identification code data is a counterfeit product. 2. The method of claim 1, wherein determining whether a personal consumer product product having the same identification code data is a counterfeit product is based on a result of at least one of the following determinations: determining whether the number of entries in the database with the same identification code data is greater than a preset number, determining whether at least one scan time interval is greater than a preset time interval based on the time data in the entries having the same identification code data, the scan time interval indicating the time interval between the scan times of any two scans, and Based on the geographic location data in the entries having the same identification code data, it is determined whether at least one scan spatial distance is greater than a preset spatial distance, the scan spatial distance indicating the spatial distance between the geographic locations of any two scans. 3. The method of claim 2, wherein at least one of the preset number, the preset time interval, and the preset spatial distance varies with the category of the personal consumer product product. 4. The method of claim 1, wherein determining whether a personal consumer product product having the same identification code data is a counterfeit product is based on temporal data and geographic location data in an entry having the same identification code data. 5. The method of any one of claims 1 to 4, further comprising: In the event that the personal consumer product product having this same identification code data is determined to be a counterfeit product, this same identification code data is added to the list of counterfeit products. 6. The method of claim 5, further comprising: Identification code data artificially determined to indicate a counterfeit product is added to the counterfeit product list. 7. The method of claim 6, further comprising: A list of counterfeit products is published on the website for inquiry. 8. The method of claim 6, further comprising: This is determined in response to a scan of an identification code of a personal consumable product that is different from the plurality of personal consumable products, or in response to manual entry via a web page of an identification code of a personal consumable product that is different from the plurality of personal consumable products whether the identification code is included in the list of counterfeit products, and Where it is determined that the identification code is included in the counterfeit product list, it is determined that the personal consumable product is a counterfeit product. 9. The method of claim 1 or 8, further comprising: in the event that the personal consumer product product is determined to be a counterfeit product, notifying that the personal consumer product product is a counterfeit product. 10. The method of any one of claims 1 to 4, wherein the identification code comprises a product code or a machine readable code. 11. The method of any one of claims 1 to 4, wherein the identification code comprises a machine readable code, wherein the machine readable code is generated from a product code of a personal consumer product product. 12. The method of claim 11 , wherein the machine readable code is based on a product manufacturing date, a product manufacturing plant identification, a product manufacturing line identification, a product manufacturing time, a counter value assigned to the product, and a letter identifying the product generated by at least one of the digital codes. 13. The method of any one of claims 1 to 4, wherein the shared code comprises a shared code shared by greater than 2 and less than 20 personal consumer product products. 14. The method of any one of claims 1 to 4, wherein the geographic location data comprises GPS data of the electronic device scanning the identification code or an IP address of the electronic device scanning the identification code, wherein the IP address Can be converted into GPS data. 15. The method of any one of claims 1 to 4, wherein a digital image of the identification code is obtained by scanning, and the identification code data is obtained from the digital image by: where the identification code is a product code, perform OCR processing on the digital image to identify the product code in the digital image as identification code data, or In the case where the identification code is a machine-readable code, decoding processing is performed on the machine-readable code in the digital image to obtain a product ID as identification code data. 16. The method of any one of claims 1 to 4, wherein each entry of the database further comprises: an electronic device identification representing the electronic device performing the scan, and wherein the identification code data is the same in the presence of the electronic device In the case of multiple entries that identify the same, only one of the multiple entries is considered in the counterfeit product determination. 17. The method of any one of claims 1 to 4, wherein the personal consumer product product has an outer identification code printed on the outer packaging of the product and an inner identification code printed on the inner packaging of the product, and wherein the response to The database is constructed from multiple scans of the internal identification codes of multiple personal consumer product products. 18. The method of any one of claims 1 to 4, wherein the characteristics of entries in the database having the same identification code data comprise statistical characteristics of the entries. 19. The method of any one of claims 1 to 4, wherein the characteristics of entries in the database having the same identification code data include multivariate analysis-based statistical characteristics of the entries. 20. The method of any one of claims 1 to 4, wherein the personal consumer product product is selected from the group consisting of hair care products, skin care products, laundry care products, hard surface care products, air care products , scraper or razor products, toothbrush products with replaceable brush heads, oral care products, feminine hygiene products, diaper products, health products, and pest control products. 21. A personal consumer product product with a counterfeit detection mark, characterized in that it comprises: Machine-readable codes that identify personal consumer product products, The machine readable code is generated based on at least one of a product production date, a product production workshop identification, a product production line identification, a product production time, a counter value assigned to the product, and an alphanumeric code identifying the product. 22. The personal consumer product product of claim 21, wherein the machine-readable code comprises a Datamatrix code. 23. A computer system, characterized in that, comprising: at least one processor; and At least one storage device storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of any of claims 1-20. 24. A computer program product comprising instructions which, when executed by a processor, cause the method of any of claims 1-20 to be performed. 25. The computer program product of claim 24, wherein the computer program product comprises a web-based application. 26. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, cause the method of any of claims 1-20 to be performed.

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