WO2022140729A1

WO2022140729A1 - Method and computer system for identifying counterfeit personal consumer product

Info

Publication number: WO2022140729A1
Application number: PCT/US2021/072797
Authority: WO
Inventors: Jonathan Richard Stonehouse
Original assignee: The Procter & Gamble Company
Priority date: 2020-12-23
Filing date: 2021-12-08
Publication date: 2022-06-30
Also published as: CN116583858A; CN114663106A

Abstract

A method and computer system for identifying counterfeit personal consumer product is disclosed. A method for identifying counterfeit personal consumer product comprising: in response to a plurality of scans of identification codes of a plurality of personal consumer product, building a database containing a plurality of entries, each entry corresponding to one scan and each entry comprising: identification code data representative of the scanned identification code, time data representative of the time of the scan, and geographic location data representative of the geographic location of the scan, wherein the identification code of the personal consumer product comprises a unique code that is unique to a single personal consumer product or a common code that is common to a plurality of personal consumer products; and determining whether the personal consumer product having the same identification code data is a counterfeit product based on characteristics of entries in the database having the same identification code data.

Description

METHOD AND COMPUTER SYSTEM FOR IDENTIFYING COUNTERFEIT PERSONAL CONSUMER PRODUCT

FIELD OF THE INVENTION

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

BACKGROUND OF THE INVENTION

Counterfeiting of individual consumer products is a continuing problem for manufacturers, distributors, retailers, and end consumers. There is a continuing need for counterfeit detection methods suitable for low-margin personal consumer 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, which can identify counterfeit product.

The present disclosure proposes a method for identifying counterfeit personal consumer product, comprising: in response to a plurality of scans of identification codes of a plurality of personal consumer product, building a database containing a plurality of entries, each entry corresponding to one scan and each entry comprising: identification code data representative of the scanned identification code, time data representative of the time of the scan, and geographic location data representative of the geographic location of the scan, wherein the identification code of the personal consumer product comprises a unique code that is unique to a single personal consumer product or a common code that is common to a plurality of personal consumer products; and determining whether the personal consumer product having the same identification code data is a counterfeit product based on characteristics of entries in the database having the same identification code data.

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

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain, without limitation, the principles of the disclosure. In the drawings, like numbering is used to indicate like items. In the drawings, the various elements are not necessarily drawn to scale.

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

Fig. 2 is a flow chart illustrating an example counterfeit product identification method, according to some embodiments of the present disclosure.

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

Fig. 4 is a flow chart illustrating an exemplary counterfeit product determination process, according to some embodiments of the present disclosure.

Fig. 5 A shows a histogram of the number of scans for a QR code in a blacklist.

Fig. 5B shows a distribution of time intervals at which the QR code is reproduced.

Fig. 5C shows a distribution of spatial distances at which the QR code is reproduced.

Figs. 6A-6B each illustrate an example of an exemplary Datamatrix code printed on packaging for a personal consumer product.

Figs. 7A-7B respectively illustrate example GUIs of example Web-based applications in accordance with some embodiments of the present disclosure.

Fig. 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 DESCRIPTION OF THE INVENTION

In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the described example embodiments. It will be apparent, however, to one skilled in the art, that the described embodiments may be practiced without some or all of these specific details. In the described exemplary embodiments, well-known structures or processing steps have not been described in detail in order 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 one or more additional steps not described and/or without one or more of the steps discussed. Further, the order in which the steps of the method are illustrated and described is not intended to be limiting.

It is known that for personal consumer product products that are genuine, each product has an identification code. The identification code of the personal consumer product comprises a unique code that is unique to a single personal consumer product or a common code that is common to a plurality of personal consumer product. The meaning of "unique code" is: for a class of personal consumer product manufactured by the same manufacturer, the products have codes that are different from one another. It should be understood that during high speed manufacturing of products, it is possible that multiple products (e.g., no more than 5 products) have identical identification codes. In this case, the plurality of products share the same code. The identification code may include a product code or a QR (quick response) code. The identification code is typically printed on the packaging of the product. Examples of product codes printed on the packaging of sanitary napkin products may be: "07/12/2024825027021703:029739". As another example, an example of the product code printed on the package of the shampoo product may be:

"B2613659122: 51P 03/19

MRP179.49+ ST30.51=210 PKR ".

The product code may be composed of letters, numbers and/or symbols, and the product code may indicate a production date of the product, a production plant and production line of the product, a production time of the product (i.e., time of day), a counter value assigned to the product, and the like. The QR code is a known two-dimensional code, and in the present disclosure, the QR code may be encoded with an ID of a personal consumer product. The ID of the personal consumer product may include a serial number of the product.

Furthermore, in the case of genuine products, the identification code is printed on the product as it leaves the production line. The identification code is usually printed in a different manner than the pattern, text, etc. on the packaging of the product. For example, when a pattern, a character, or the like on the package is printed by an inkjet printing method, the identification code may be printed by a laser printing method.

The inventors of the present invention have discovered that counterfeiters of personal consumer product duplicate the identification code of a genuine product and print the same identification code onto 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 on the sales market. Also, in order to save costs, counterfeiters typically do not print the identification code using a different printing than that used on the product packaging.

In the present disclosure, a new personal consumer product having a forgery detection mark is proposed, the product having a Datamatrix code identifying the personal consumer product, the Datamatrix code being generated according to at least one of a product production date, a product production plant and line, a product production time, a counter value assigned to the product, and an alphanumeric code identifying the product. The Datamatrix code is a known two-dimensional code, which may be a code defined by the ISO/IEC 16022 international standard. Fig. 6A illustrates an example of a product code and Datamatrix code printed on packaging of a personal consumer product. In fig. 6A, "DDDDPPPPLL HH: MM: SS X abg" indicates a product code. More specifically, "DDDD" may indicate a product production date, "ppll" may indicate a product production plant and a production line, "HH: MM: SS" may indicate a product production time (hours, minutes, and seconds), "X" may indicate a counter value assigned to the product, and "abg" may indicate an alphanumeric code (preferably, the alphanumeric code may be unique to the product) that identifies the product. The alphanumeric code may have 3-7 digits. Fig. 6B shows a modification of the example of fig. 6A. In the case where the Datamatrix code can identify a product, at least a part of the product code may be omitted, thereby enabling a saving in printing space. It should be understood here that 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 code printed on the personal consumer product is not limited to the above-mentioned QR code and Datamatrix code. The machine- readable code printed on the personal consumer product may also include other suitable two- dimensional codes, such as circular codes and the like.

Hereinafter, a method and an apparatus for identifying counterfeit products according to the present disclosure will be described by taking shampoo products as an example. However, it should be understood that the personal consumer product is not limited to shampoo products. Personal consumer product may include: hair care products other than shampoos, such as conditioners and the like; skin care products such as body wash, skin care lotion, and the like; laundry care products such as washing powders, laundry detergents, fabric softeners, laundry beads, and the like; hard surface care products such as dishwashing detergents, floor cleaners, toilet cleaners, kitchen sink cleaners, and the like; air care products such as air fresheners, fabric fresheners, and the like; a scraper or razor product such as a shaver, hair removal razor, or the like; a replaceable head toothbrush product; oral care products such as toothpaste, mouthwash, dental floss, toothpicks, and the like; feminine hygiene products such as sanitary napkins, tampons, and the like; a diaper product; health products such as over-the-counter medications, vitamin products, etc.; pesticidal products such as household insecticides, household insect traps, etc.; and so on.

In the following description, a case where the identification code of the personal consumer product is a unique code will be described. It is to be understood that the present disclosure is not so limited. Alternatively, the identification code of the personal consumer product may be a common code.

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

Fig. 1 is a block diagram of an example counterfeit product identification apparatus 100, in accordance with some embodiments of the present disclosure.

The apparatus 100 may comprise: a database construction section 110 for constructing a database containing a plurality of entries, each entry corresponding to one scan and each entry including, in response to a plurality of scans of QR codes of a plurality of shampoo products: unique code data representing the scanned QR code, time data representing the time of scanning, and geographic location data representing the geographic location of scanning; a characteristic analysis part 120 for determining whether the shampoo product having the same unique code data is a counterfeit product based on characteristics of the items having the same unique code data in the database ; a blacklist generating means 130 for, in a case where it is determined that the shampoo product having the same unique code data is a counterfeit product, adding the same unique code data to a counterfeit product blacklist, and adding unique code data artificially determined to indicate a counterfeit product to the counterfeit product blacklist; a counterfeit product determination section 140 for determining whether or not the QR code is contained in the blacklist in response to scanning of the QR code for the shampoo product different from the plurality of shampoo products, and determining that the shampoo product is a counterfeit product in a case where it is determined that the QR code is contained in the blacklist; and a notifying part 150 for notifying that the shampoo product is a counterfeit product in case that it is determined that the shampoo product is a counterfeit product.

Although not shown in fig. 1, the apparatus 100 may further comprise unique code data retrieving means for retrieving unique code data from a digital image of the QR code retrieved via scanning. More specifically, the component may perform a decoding process on the digital image of the QR code to take a unique ID of the product (such as a serial number of the product) as unique code data.

It should be understood that in the case where the unique code of the product is a Datamatrix code, the component may similarly perform a decoding process to retrieve the unique ID of the product as unique code data. And in case that the unique code of the product is a product code, the component may perform OCR (optical character recognition) processing on the digital image of the product code to recognize a specific product code as unique code data.

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

Fig. 2 is a flow chart illustrating an example 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 a product manufacturer. Alternatively, the method 200 may be performed by both the electronic device performing the scan and the server described above.

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

The method 200 begins at step S210, the database construction component 110 constructs a database as previously described. In response to one scan of the QR code of the shampoo product using the electronic device, the database construction component 110 generates a new entry, which may include: unique code data representing the scanned QR code, time data representing the time of the scan, and geographic location data representing the geographic location of the scan. The scan time data may include the year, month, and day the scan operation occurred, and the scan time data may further include the time of day the scan operation occurred. The geographic location data may include GPS data of the electronic device scanning the QR code or an IP address of the electronic device scanning the QR code, where the IP address can be converted to GPS data. It should be understood that in response to multiple scans of 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 smartphone, a tablet computer, or the like. The scanning may be implemented using an application installed on the electronic device, such as a WeChat.

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

As can be seen from table 1, each entry of the database may include: a product category, a product brand, a serial number of the product, an ID of the electronic device that scanned the QR code (this ID uniquely identifies the electronic device), a scan time, and an IP address of the electronic 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 geographical location data. It should be understood that category, brand, electronic device ID data is not required here.

It should be appreciated that the IP address of the electronic device changes as the access point of the electronic device to the communication network changes, and thus the IP address of the electronic device is well reflective of the geographic location where the scanning operation occurred. 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 may be used to convert IP addresses to 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 scanning the QR code. In the case where there are a plurality of entries whose unique code data are the same and whose electronic device IDs are the same, only one entry of the plurality of entries may be considered in the counterfeit product determination in step S220 described later. In this way, repeated scans of the same electronic device for the same QR code can be filtered out.

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

As far as the genuine product is concerned, each product has its own unique QR code, and thus the number of times of scanning this QR code may be small. In contrast, in the case of a counterfeit, thousands of the counterfeit have the same QR code, and thus the number of times of scanning this QR code may be large. Even if only QR codes of about half of counterfeits on the market are scanned, the number of times of scanning the QR codes may be large.

Furthermore, the lifetime (or lifespan) of a single product on the market is often short in terms of genuine products. In contrast, in the case of a counterfeit, since the counterfeit is continuously produced and sold, the life of the counterfeit in the market may be long.

Furthermore, the spatial range of motion of a single product is typically small in terms of genuine products. In contrast, in the case of a counterfeit, since a counterfeit having the same QR code may be sold nationwide, the spatial distribution range of a product may be wide in consideration of a counterfeit having the same QR code.

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

In other words, the feature analysis component 120 can 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 number of scans, the scan time span, and/or the distribution of the scan space span. Further, the feature analysis component 120 can determine whether the product is a counterfeit product based on the multivariate-based statistical features of the entries in the database having the same unique code data. Here, the multivariate based statistical characteristics may include characteristics regarding distribution of at least two of the number of scans, the scan time span, and the scan space span. It will be appreciated that more reliable determination results can be obtained when statistical characteristics of a plurality of variables are considered collectively than when statistical characteristics of a single variable are considered. Next, the method 200 proceeds to step S230, and at step S230, the blacklist generating component 130 adds the unique code data determined at step S220 to indicate a counterfeit product to a counterfeit product blacklist. That is, the counterfeit product blacklist includes unique code data determined at step S220 to be indicative of counterfeit products. In other words, the counterfeit product blacklist includes unique code data that is copied and used by counterfeiters.

Further, the blacklist generating section 130 also adds unique code data artificially determined to indicate a counterfeit product to the counterfeit product blacklist. As mentioned earlier, on the package of genuine products, the QR code is printed in a manner different from that of the pattern, text, etc. on the product package. On the other hand, the printing mode of the fake package is the same. This enables distributors, retailers, etc. of the products to visually identify counterfeit products. Alternatively, a distributor, retailer, etc. of a product may identify a counterfeit by detecting the chemical composition of the product. In the event that a counterfeit is identified, a distributor, retailer, etc. of the product may take a QR code of the product, upload a digital image of the QR code, and interpret the QR code as indicating a counterfeit product. Thereby, the blacklist generating section 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 take the product code, upload a digital image of the product code, and state that the product code indicates a counterfeit product. Alternatively, a distributor, retailer, etc. of the product may manually enter a product code indicative of a counterfeit product and state that the product code is indicative of a counterfeit product. Thus, the blacklist generating component 130 can add the corresponding product code to the blacklist. Still alternatively, in the case where the unique code is a Datamatrix code, the Datamatrix code may be processed in a manner similar to that of the QR code.

Further, the blacklist generating section 130 periodically updates the generated blacklist. In particular, as the number of entries in the database increases, component 130 can add new unique code data to the blacklist. Alternatively, in the event that 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 generating section 130 sends the generated blacklist to the counterfeit product determination section 140. It is to be understood that the steps S210-S230 as described before correspond to the generation step of the black list. The use step S240 of the blacklist is described next. It is also understood that the step of generating the black list and the step of using the black list may be parallel steps. Further, an exemplary use example of the blacklist will be described below with reference to fig. 7A and 7B. At step S240, the counterfeit product determination section 140 identifies a counterfeit product 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 section 140 determines whether this QR code (i.e., the corresponding unique code data) is contained in the blacklist. If so, the counterfeit product determination section 140 determines that the product having this QR code is a counterfeit product. If not, the counterfeit product determination section 140 determines that the product having this QR code is genuine.

In the case where it is determined at step S220 that the shampoo product is a counterfeit product, or in the case where it is determined at step S240 that the shampoo product is a counterfeit product, the method 200 proceeds to step S250, and at step S250, the notification part 150 notifies the corresponding electronic device performing scanning that the scanned shampoo product is a counterfeit product. Also, in other cases, the notification part 150 may notify the electronic device that the scanned shampoo product is genuine. That is, the notification component 150 notifies a distributor, a retailer, a consumer, or the like, who performs the scanning, whether the scanned shampoo product is a genuine product or a counterfeit product. Such notification may be performed in various known ways, such as sending a text short message, sending a voice message, and so on.

Hereinafter, the operation of the feature analysis section 120 is described with reference to fig. 3, 4, 5A to 5C.

Fig. 3 is a block diagram illustrating the configuration of an exemplary feature analysis component 120 according to some embodiments of the present disclosure. The feature analysis part 120 may include: a scan number analysis section 122 for determining whether the number of entries having the same unique code data in the database is greater than a preset number; a time span analysis section 124 for determining whether at least one scanning time interval, which indicates a time interval between scanning times of any two scans, is larger than a preset time interval based on time data in an entry having the same unique code data; and a spatial span analysis component 126 for determining whether at least one scan spatial distance is greater than a preset spatial distance based on the geographic location data in the entries having the same unique code data, the scan spatial distance indicating a spatial distance between geographic locations of any two scans. Although not shown, the component 120 may also include an integrated analysis component for determining whether the QR code under consideration indicates a counterfeit product based on the determination of at least one of the components 122, 124, and 126.

The operation of the various components shown in fig. 3 will be described in further detail below. FIG. 4 is a flow chart illustrating an example feature analysis process 220, according to some embodiments of the present disclosure.

First, at step S222, the scan number analyzing section 122 determines whether the number of scans for the same QR code is greater than a preset number. And, the determination result at step S222 is sent to an integrated analysis step S228 described later. Fig. 5A shows a scan number histogram 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 the respective numbers of scans. By comparing with the scanning number histogram of the QR code for the genuine product, it is found that: it is not uncommon to scan the same genuine article 20 or more times. Therefore, at step S228, in the case where the number of scans for the same QR code is greater than, for example, 20, it may be determined that this QR code indicates a counterfeit product.

It can be understood that, in general, in the case of a genuine article, it is not common to scan the same genuine article 20 times. It is extremely rare to scan the same genuine article 100 times. In contrast, in the case of counterfeits, since thousands of counterfeits have the same QR code, it may be common to scan the same QR code several tens of times.

At step S224, the time span analysis section 124 determines whether at least one scanning time interval is greater than a preset time interval among a plurality of scans for the same QR code. For example, component 124 can determine whether a scan interval is greater than a preset interval. For another example, component 124 can determine whether two or more scan intervals are greater than a preset interval. And, the determination result at step S224 is sent to the integrated analysis step S228. Fig. 5B shows the distribution of QR code reproduction time intervals (i.e., scanning time intervals). The horizontal axis in fig. 5B represents the scanning time interval as an argument, which is in units of days, and the vertical axis represents the probability density function for the argument. In fig. 5B, a solid line represents the distribution of reproduction time intervals of the QR codes in the blacklist, and a dotted line represents the distribution of reproduction time intervals of 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 time interval for genuine products is mostly concentrated on 0-25 days, and the scanning time interval for counterfeit products is mostly concentrated on 50-150 days. Thus, at step S228, if there is at least one scanning time interval greater than, for example, 25 days or 50 days with respect to the scanning time interval of the same QR code, it may be determined that this QR code is indicative of a counterfeit product. It will be appreciated that the more scanning time intervals that are larger than the preset time interval, the more reliable the determination result.

It can be appreciated that, in general, in the case of genuine articles, the scanning interval for a single genuine article will not be large since the market lifetime of the single genuine article is not long. In contrast, in the case of a counterfeit, since thousands of counterfeits having the same QR code are continuously sold in the market, the scanning time interval for the counterfeit tends to be large compared to the genuine one.

At step S226, the spatial span analysis section 126 determines whether at least one scanning spatial distance is greater than a preset spatial distance among a plurality of scans for the same QR code. For example, component 126 can determine whether a scan spatial distance is greater than a predetermined spatial distance. As another example, component 124 can determine whether two or more of the scan spatial distances are greater than a predetermined spatial distance. And, the determination result at step S226 is sent to the integrated analysis step S228. Fig. 5C shows the distribution of the QR code reproduction spatial distances (i.e., the scanning spatial distances). The horizontal axis in fig. 5C represents the scan space distance as an argument, which is in kilometers, and the vertical axis represents the probability density function for the argument. In fig. 5C, the solid line represents the distribution of the reproduction spatial distances of the QR codes in the blacklist, and the broken line represents the distribution of the reproduction spatial 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 space distance for the genuine article is mostly concentrated on 0-200 kilometers, and the scanning space distance for the fake article is mostly concentrated on 200-2000 kilometers. Thus, at step S228, if there is at least one scanning spatial distance greater than, for example, 200 kilometers in terms of scanning spatial distances of the same QR code, it may be determined that this QR code is indicative of a counterfeit product. It will be appreciated that the more the scan space distance is greater than the preset space distance, the more reliable the determination results.

It can be appreciated that, in general, in the case of genuine articles, the geographical location at which a scan is made for a single genuine article does not typically vary greatly. In contrast, in the case of a counterfeit, since thousands of counterfeits having the same QR code are widely sold nationwide, the scanning spatial distance to the counterfeit tends to be large compared to the genuine one.

Next, at step S228, the comprehensive analysis component determines whether the QR code in question 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 in the case where any one 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 in question indicates a counterfeit product in the case where any two of the determination results obtained at steps S222, S224, and S226 are affirmative decisions. For another example, it may be determined that the QR code under consideration indicates a counterfeit product in the case where all of the determination results obtained at steps S222, S224, and S226 are affirmative decisions.

It should be understood that the preset number, the preset time threshold and the preset distance threshold as previously described are values preset empirically. These preset values vary between different categories of individual consumer products. For example, in the case of shampoo products, the market lifetime is generally short, such as several months, whereas in the case of spatula products, the market lifetime is generally long, such as several years, so that the time thresholds preset for these two types of products can be different. Further, when two or more determination results are considered, a lower preset numerical value may be employed than the case where a single determination result is considered.

In some embodiments of the present disclosure, the feature analysis component 120 can determine whether the personal consumer product having the same unique code data is a counterfeit product based on the time data and the geographic location data in the entries having the same unique code data. More specifically, component 120 can determine whether the product is a counterfeit product based on a ratio of a scan time interval to a scan space distance for any two scans. For example, if it is determined that two scans for a certain QR code occur at approximately the same time, however, the spatial separation between the geographic locations of the two scans is large, such as across provinces/cities, then it may be determined that this QR code is indicative of a counterfeit product.

In some embodiments of the present disclosure, a personal consumer product may have an outer identification code printed on the outer package of the product and an inner identification code printed on the inner package of the product. The external identification code and the internal identification code are different identification codes (different in form or different in content). And, in response to multiple scans of the internal identification codes of multiple personal consumer product, building a database as previously described. Thus, the difficulty of copying the identification code by a counterfeiter can be improved. Next, an exemplary use example of the blacklist is described with reference to fig. 7A and 7B. More specifically, an exemplary Web-based application is described with reference to fig. 7A and 7B, in accordance with some embodiments of the present disclosure. This Web-based application may be implemented by a server provided by the product manufacturer. The functionality of this application is described below from the perspective of the user of this application. Here, the user of the Web-based application may be a consumer, retailer, distributor, investigator, law enforcement, and the like. In this example, a user uses a Web-based application to query whether a product is a counterfeit product, which may include the following 8 steps. In this example, sanitary napkin products are described as examples of personal consumer product and product codes are described as examples of identification codes.

Step 1, a user connects to a 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 a corresponding Web page by accessing a preset Web address.

And 2, optional login step. The user may choose to access the Web page real-name or anonymously.

And 3, confirming the permission option by the user. For example, the user may permit the application to access its GPS data or IP address, permit the application to use a camera, and so forth.

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

Step 5, the Web-based application displays a fake product blacklist to the user via a Web page. The counterfeit product blacklist includes product codes that have been copied and used by counterfeiters. The application may display the blacklist via a GUI as shown in fig. 7A. The table in fig. 7A corresponds to an exemplary blacklist, where the blacklist includes a barcode, a product code copied by a counterfeiter, and an image of the copied product code. Here, the barcode is used to manage the copied product codes by classification. The rectangular box at the top of fig. 7A is used to provide intelligent lookup functionality, as described later.

Step 6, the user types the product code "07/12/2024825027021703: 029739" on the sanitary napkin product digit by digit into a rectangular box. Symbols and spaces may be omitted during typing. Incorrectly entered digits may be deleted during the typing process. Accordingly, the product codes in the blacklist that match the digits that have been entered are displayed in the table in FIG. 7A. As shown in FIG. 7A, in the event that the user has typed "07", the user finds the product code for the product, as shown in the first row of the table in FIG. 7A.

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

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, step 8. If the user selects "no" in the GUI as shown in fig. 7B, the Web-based application returns to step 3 as previously described. If the user selects "re-enter product code" in the GUI as shown in FIG. 7B, the Webbased application returns to step 5, previously described. 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 fig. 7A and 7B are merely exemplary, and the present disclosure is not limited thereto. For example, in the aforementioned step 6, instead of the product code being entered by the user, the QR code or Datamatrix code on the product package may be scanned by the user, thereby obtaining the ID of the product via decoding. Still alternatively, the user may photograph the product code on the product package, thereby obtaining the product code via OCR technology. In addition, Web-based applications can display the distribution of scan activities of interest to the user via Web pages. For example, a Web-based application can mark the location of a scanning activity for the same identification code within a time period of interest on a map. Alternatively, the Web-based application can mark geographic location distributions on the map that are identified as counterfeit products.

It will be appreciated that the above describes a situation in which the identification code of the personal consumer product is a unique code. However, the present disclosure is not limited thereto. Alternatively, the identification code of the personal consumer product may be a common code shared by a plurality (or group) of personal consumer product. For example, as previously described, during high-speed manufacturing of products, it is possible that a group of products have identical identification codes. As another example, in the case of using a portion "DDDDPPPPLL HH: MM: SS" of the product code as shown in FIG. 6A as the identification code, a group of products that are offline at the same time can be made to share the same identification code. This set of products may be, for example, more than 2 and less than 20 products. Alternatively, the set of products may be, for example, more than 2 and less than 10 products. Still alternatively, the set of products may be, for example, more than 2 and less than 5 products. Even if one identification code is shared by a group of products as described above, the method and apparatus according to the present disclosure can recognize counterfeit products.

The above describes a method and apparatus for identifying counterfeit personal consumer product according to the present disclosure. By way of data analysis, the method and apparatus of the present disclosure can reliably identify counterfeit products. Further, based on the constructed database, it is possible to determine the areas and periods of time in which counterfeit products exist, which may be meaningful for product manufacturers to fight against counterfeiting.

Hardware implementation

With reference to fig. 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, for example, by computing device 800. Computing device 800 may be any machine configured to perform processing and/or computing, and may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a smart phone, a portable camera, or any combination thereof. The above-described server 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 components capable of connecting with bus 802 or communicating 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, a keyboard, a touch screen, a microphone, and/or a remote control. Output device 808 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, and/or a printer. Computing device 800 may also include or be connected with non-transitory storage device 810, non-transitory storage device 810 may be any storage device that is non-transitory and that may implement a data storage library, and may include, but is not limited to, disk drives, optical storage devices, solid state storage, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, compact disks, or any other optical medium, ROM (read only memory), RAM (random access memory), cache memory, and/or any other memory chip or cartridge, and/or any other medium from which a computer may read data, instructions, and/or code. 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 a communication device 812. The communication device 812 may be any type of device or system capable of communicating with external apparatus and/or with a network and may include, but is not limited to, a modem, a network card, an infrared communication device, wireless communication equipment, and/or a chipset such as a bluetooth (TM) device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication facility, and the like.

The bus 802 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA (eisa) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

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

Software elements may reside in the working memory 814, including but not limited to an operating system 816, one or more application programs 818, drivers, and/or other data and code. Instructions for performing the methods and steps described above may be included in one or more applications 818, and the components of apparatus 100 or 300 described above may be implemented by processor 804 reading and executing the instructions of one or more applications 818. More specifically, database construction component 110 can be implemented, for example, by processor 804 when executing application 818 having instructions to perform step S210. The feature analysis component 120 can be implemented, for example, by the processor 804 when executing the application 818 having instructions to perform step S220. Similarly, blacklist generating component 130, counterfeit product determination component 140, notification component 150 can be implemented, for example, by processor 804 when executing application 818 having instructions to perform steps S230, S240, S250, respectively. Further, the scan number analyzing section 122, the time span analyzing section 124, and the space span analyzing section 126 may be implemented, for example, by the processor 804 when executing the application program 818 having instructions to execute steps S222, S224, S226, respectively. Executable code or source code for the instructions of 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 read into the working memory 814, possibly compiled and/or installed. Executable code or source code for the instructions of the software elements may also be downloaded from a remote location.

From the above embodiments, it is apparent to those skilled in the art that the present disclosure can be implemented by software and necessary hardware, or can be implemented by hardware, firmware, and the like. Based on this understanding, embodiments of the present disclosure may be implemented partially in software. The computer software may be stored in a computer readable storage medium, such as a floppy disk, hard disk, optical disk, or flash memory. The computer software includes a series of instructions that cause a computer (e.g., a personal computer, a service station, or a network terminal) to perform a method or a portion thereof according to various embodiments of the present disclosure.

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

The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as “40 mm” is intended to mean “about 40 mm.”

Every document cited herein, including any cross referenced or related patent or application and any patent application or patent to which this application claims priority or benefit thereof, is hereby incorporated herein by reference in its entirety unless expressly excluded or otherwise limited. The citation of any document is not an admission that it is prior art with respect to any invention disclosed or claimed herein or that it alone, or in any combination with any other reference or references, teaches, suggests or discloses any such invention. Further, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition assigned to that term in this document shall govern.

While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims

CLAIMS What is claimed is:

1. A method for recognizing a counterfeit individual consumable goods product, characterized by, comprising: constructing, in response to a plurality of scans on the identification codes of multiple individual consumable goods products, a database with a plurality of entries, each entry corresponding to one scan and comprising: identification code data representing the scanned identification code, time data representing the time of the scan, and geographical location data representing the geographical location of the scan, wherein the identification code of the individual consumable goods product comprises an unique code which is unique for a single individual consumable goods product or a common code which is shared by two or more individual consumable goods products, and determining, based on a characteristic of the entries with the same identification code data in the database, whether the individual consumable goods product with such same identification code data is a counterfeit product.

2. The method according to claim 1, wherein determining, based on the result of at least one of the following, whether the individual consumable goods product with such same identification code data is a counterfeit product: determining whether the number of the entries with the same identification code data in the database is greater than a pre-set number, determining, based on the time data comprised in the entries with the same identification code data, whether at least one scan time interval, which indicates a time interval between the times of the scan of any two scans, is greater than a pre-set time interval, and determining, based on the geographical location data comprised in the entries with the same identification code data, whether at least one scan spatial distance, which indicates a spatial distance between the geographical locations of any two scans, is greater than a pre-set spatial distance.

3. The method according to claim 2, wherein at least one of the pre-set number, the pre-set time interval, and the pre-set spatial distance varies as the category of the individual consumable goods product changes.

4. The method according to claim 1, wherein determining whether the individual consumable goods product with such same identification code data is a counterfeit product based on the time data and the geographical location data comprised in the entries with the same identification code data.

5. The method according to any one of claims 1-4, further comprising: in the case wherein it is determined that the individual consumable goods product with such same identification code data is a counterfeit product, adding such same identification code data into a list for counterfeit products.

6. The method according to claim 5, further comprising: adding an identification code data which is artificially determined to indicate a counterfeit product into the list for counterfeit products.

7. The method according to claim 6, further comprising: in response to a scan on an identification code of an individual consumable goods product different to said multiple individual consumable goods products, or in response to manually entering, via a webpage, an identification code of an individual consumable goods product different to said multiple individual consumable goods products, determining whether such identification code is comprised in the list for counterfeit products, and determining such individual consumable goods product is a counterfeit product in the case wherein it is determined that such identification code is comprised in the list for counterfeit products.

8. The method according to any one of claims 1-4, wherein the identification code comprises a machine-readable code, wherein the machine-readable code is generated according to a production code of the individual consumable goods product.

9. The method according to claim 8, wherein the machine-readable code is generated according to at least one of a product production date, a product production plant identification, a product manufacturing line identification, a product production time, a counter value assigned to the product, and an alphanumeric code identifying the individual consumable goods product.

10. The method according to any one of claims 1-4, wherein the common code comprises the one shared by 2 or more and 20 or less individual consumable goods products.

11. The method according to any one of claims 1-4, wherein the geographical location data comprise GPS data of an electrical device which scans the identification code or IP address of an electrical device which scans the identification code, wherein the IP address is convertible to the GPS data.

12. The method according to any one of claims 1-4, wherein a digital image of the identification code is obtained by the scan, and the identification code data is acquired from the digital image by: performing, in the case wherein the identification code is a production code, an OCR process on the digital image to recognize the production code in the digital image as the identification code data, or performing, in the case wherein the identification code is a machine-readable code, an decoding process on the machine-readable code in the digital image to acquire the ID of the product as the identification code data.

13. The method according to any one of claims 1-4, wherein each entry of the database further comprises an electrical device identification representing an electrical device which performs the scan, and wherein, in the case wherein there is a plurality of entries which have the same identification code data and the same electrical device identification, merely one of these plurality of entries is considered in the determination of the counterfeit product.

14. The method according to any one of claims 1-4, wherein the individual consumable goods product comprises an external identification code printed on outer packaging of the product and an internal identification code printed on inner packaging of the product, and wherein in response to a plurality of scans on the internal identification codes of multiple individual consumable goods products, constructing the database.

15. The method according to any one of claims 1 -4, wherein the characteristic of the entries with the same identification code data in the database comprises a statistical characteristic of the entries which is based on multi-variant analysis.