GB2573766A

GB2573766A - Estimating a counterfeit probability of a product

Info

Publication number: GB2573766A
Application number: GB1807844.4A
Authority: GB
Inventors: Dichler Werner; Arnold Birgit
Original assignee: Eaton Intelligent Power Ltd
Current assignee: Eaton Intelligent Power Ltd
Priority date: 2018-05-15
Filing date: 2018-05-15
Publication date: 2019-11-20
Also published as: WO2019219343A1; GB201807844D0

Abstract

A method for estimating a counterfeit probability of a product (1; Fig 1) wherein during an manufacturing event of the product static data 2 associated with the product is generated which is applied to a machine readable label (3; Fig 1) attached to the product and uploaded to a server (4; Fig 1), wherein during a read out event the static data of the machine readable label is read out via a mobile device (5; Fig 1) of a user, wherein a probability value 7 of the product being counterfeit is chosen from a set of at least three different values based on dynamic data (6; Fig 1) associated with the static data from the server, wherein the probability value of the product is sent to the mobile device and displayed. The label may be a passive printed label with the static data provided as machine-readable code.

Description

ESTIMATING A COUNTERFEIT PROBABILITY OF A PRODUCT

The present invention relates to a method for estimating a counterfeit probability of a product.

Usually, the disadvantages of counterfeit products are primarily seen in the economic damage for the manufacturer of the genuine product, usually luxury or branded goods, since the sale of counterfeit products damages the sales, or diminishes the exclusiveness of the genuine product. However, it is an increasing issue that also safety relevant products like circuit breakers are counterfeited. In this case, the counterfeited products often resemble the appearance of the genuine products, but the used technology is inferior or dysfunctional. The user of the counterfeited products, who trust the brand of the genuine product, is therefore exposed to a risk of high damages or even personal injuries.

A method for detecting counterfeit products is disclosed in the US 2012/0187185 A1. Hereby a label with a QR code containing a unique identification number of the product is scanned via a mobile device and checked on a server. Several methods are disclosed to determine, whether the unique identification number is from a counterfeit product or not. One method works according to the “one to many” principle, meaning that inconsistencies, like the same unique identification number being scanned at different locations, are added up until a threshold is reached. When the threshold is reached the unique identification number is flagged as being counterfeited, and a counterfeit alert is issued. This method is easy to implement, however, the user is warned about his product being counterfeited only after the threshold is reached. A different method is that each consumer has to register his product after a purchase, and the unique identification number is flagged as counterfeited if another user tries to register the same unique identification number. This method is safer than the first one, however, it is harder to implement and needs a high level of control of the distributing network of the product.

A disadvantage of the known methods is, therefore, that they are unreliable or hard to implement.

The object of the present invention is therefore to specify a method mentioned above, which avoids the mentioned disadvantages, which reduces the risk for a user to be harmed by counterfeited products, and which is easy to use and implement.

This is achieved according to the invention by the features of claim 1.

The advantage thus results that the user of the product himself can estimate the risk that the product is counterfeited or not. Due to the use of at least three probability values, small inconsistencies, which are usually too small to flag the product as being counterfeited, can be communicated to the user. The alerted user can decide himself if he is willed to use the product or not, perform further tests with the product, or consult the supplier of the product. To detect those inconsistencies a static data is applied in a machine-readable way on the product itself, which can easily be read out by the user with a mobile device. Further, a dynamic data associated with the static data is provided by a server. By analyzing the static data together with the dynamic data a probability value of the probability of the product being counterfeited can be estimated and returned to the user. Therefore the user can be notified of inconsistencies before a threshold has to be reached. Further conditions can be incorporated in the estimation, which does not clearly indicate a counterfeited product. The method is further easy to use, since no additional point of sales devices, obligatory registrations, or control over the distribution network is necessary.

The invention furthermore relates to a system for estimating the counterfeit probability according to claim 15.

The object of the invention is therefore furthermore to specify a system of the type mentioned above, which avoids the mentioned disadvantages, which reduces the risk for a user to be harmed by counterfeit products, and which is easy to use and implement.

This is achieved according to the invention by the features of claim 15.

The advantages of the system correspond to the advantages of the method for estimating the counterfeit probability of a product.

The dependent claims relate to further advantageous embodiments of the invention.

Reference is hereby expressly made to the wording of the patent claims, whereby the claims are incorporated at this point into the description by reference and are considered to be reproduced verbatim.

The invention will be described in greater detail hereafter with reference to the appended figures, which merely illustrate a preferred embodiment by way of example. In the figures:

Figure 1 shows a preferred embodiment of the system as a schematic;

Figure 2 shows a preferred embodiment of a verification of the static data; and

Figure 3 shows a preferred embodiment of the website user interface.

Figures 1 to 3 show at least parts of a preferred embodiment of a method for estimating the counterfeit probability of a product. The counterfeit probability is hereby the probability of a product being a counterfeit instead of a genuine product.

It is provided that during a manufacturing event of the product 1 a static data 2 associated with the product 1 is generated, which static data 2 is applied to a machine-readable label 3 attached to the product 1 on one hand and uploaded to a server 4 on the other hand. The manufacturing event describes the situation where the product 2 is being produced and labeled by the manufacturer. For the product or a patch of several products, a static data 2 is generated. The word static means in this case that this static data 2 is fixed and does not change afterwards anymore. This static data 2 can contain manufactures detail, date of manufacturing, or a serial number. This static data 2 is applied to the product 1 itself via a machine-readable label 3. The machine-readable label 3 is a label which can be easily read out via a proper read-out unit 12. The machine-readable label 3 can be stick to the product 2 or can be directly applied to a casing of the product by engraving or printing. Further, the static data 2 is uploaded to a server 4 for further use. After the manufacturing event, the product can be distributed on the market.

In particular, it can be provided that the product is an electrical device, in particular, a safety-relevant electrical device, especially a circuit breaker. For electrical devices, it is quite easy to resemble the appearance and it is usually forbidden to open the casing of such a device, making it very hard for a user to check if the product is working properly. The invention is especially useful for safety-relevant electrical devices like circuit breakers, since in this case already one malfunctioning counterfeited product 1 can lead to enormous damages.

It is further provided that during a read-out event the static data 2 of the machinereadable label 3 is read out via a mobile device 5 of a user, wherein a probability value 7 of the counterfeit probability of the product 1 is chosen from a set of at least three different values based on a dynamic data 6 associated with the static data 2 from the server 4. The read-out event is initiated by the user, in particular after the purchase of the product 1. For the read-out event, the user scans the machine-readable label 3 with a read-out unit 12 of a mobile device 5. The mobile device 5 can, in particular, be a smartphone or a tablet PC.

The probability value 7 is a value being determined, in particular by the calculation unit 18 of the server 4, which estimates the probability of the product being counterfeited, therefore not being genuine. The probability value 7 is chosen from a set of at least three different values, meaning that the probability value 7 itself is a single value, however, this single value is from a value set with at least three different values. The probability value 7 is therefore not just a binary, but can also provide at least one value between not being counterfeited and being counterfeited. The calculation or other means for determining the probability value 7 is done based on the dynamic data 6 associated with the static data 2 from the server 4.

The word dynamic data 6 means in this case that this dynamic data 6 can be changed or increased with more information over time. This dynamic data 6 can be statistical or historical information about the product 1 being associated with the static data 2, like previous read-out events, status data of the product 1, like the product 1 being sold or the designated market for the product 1, or data related to the operational environment of the product 1 associated to the static data 2, like place or day of installation.

It is further provided that the probability value 7 of the product 1 is sent to the mobile device 5 and outputted to the user of the mobile device 5. The probability value 7 is outputted to the user by the mobile device 5.

A communication between the mobile device 5 and the server 4 can be done with usual telecommunication technologies, like WLAN or a public network.

It is understood that the term server 4 can be interpreted in a very broad way. The server 4 can be a single computer connected to the internet, a part of a server farm or a program running in a cloud. The server 4 comprises communication means 28 to send and receive data from several mobile devices 5 and the manufacturer. Further, the server 4 comprises some kind of database 17, in which the static data 2 of the manufacturer and the dynamic data 6 is saved. Further, the server 4 can, in particular, comprise some kind of calculation unit 18, to perform calculations with the static data 2 being received by the mobile device 5 and the dynamic data 6, to choose the probability value 7.

It can be provided that the mobile device 5 comprises a display as outputting means 14 to display the probability value 7 graphically to the user. The probability value 7 can be outputted as a number or a graphic, like a filled bar. Alternatively the probability value 7 could also be outputted as an audio.

The advantage thus results that the user of the product 1 himself can estimate the risk that the product 1 is counterfeited or not. Due to the use of at least three probability values 7, small inconsistencies, which are usually too small to flag the product 1 as being counterfeited, can be communicated to the user. The alerted user can decide himself if he is willed to use the product 1 or not, perform further tests with the product 1, or consult the supplier of the product 1. To detect those inconsistencies a static data 2 is applied in a machine-readable way on the product 1 itself, which can easily be read out by the user with a mobile device 5. Further, a dynamic data 6 associated with the static data is provided by a server 4. By analyzing the static data 2 together with the dynamic data 6 a probability value 7 of the probability of the product 1 being counterfeited can be chosen and returned to the user. Therefore the user can be notified of inconsistencies before a threshold has to be reached. Further conditions can be incorporated in the estimation, which does not clearly indicate a counterfeited product. The method is further easy to use, since no additional point of sales devices, obligatory registrations, or control over the distribution network is necessary.

Further a system 11 for estimating the counterfeit probability of a product 1, comprising a machine-readable label 3 with a static data 2 associated with the product 1 being attached to the product 1, and a mobile device 5 containing a read-out unit 12 to read out the static data 2 of the machine-readable label 3, a communication unit 13 for communication with a server 4, and outputting means 14 for outputting a probability value 7 to a user, further comprising a server 4 with a database containing dynamic data 6 associated with the static data 2, wherein the probability value 7 is chosen from a set of at least three different values based on a dynamic data 6 associated with the static data 2 from the server 4. A schematic of a preferred embodiment of the system 11 and its components are shown in fig. 1.

In particular, it can be provided that that the probability value 7 is chosen from a set of at least five, in particular at least ten, different values. This way, different degrees of likelihood that the product 1 is counterfeited can be outputted to the user, like a very small, small, medium and high, and absolute chance of the product 1 being counterfeited. The user can then decide for each value of the probability value 7, which action to take.

In particular, it can be provided that the probability value 7 is a percentage. The probability value 7 can, in particular, be chosen from a set of values containing 0%, 100%, and at least one value between 0% and 100%. This way the probability value 7 can be provided to the user in an easy to understand manner.

Particularly it can be provided that during a read-out event the static data 2 of the machine-readable label 3 is sent to the server 4. It can further be provided that the probability value 7 of the counterfeit probability of the product 1 is determined by the server 4 based on the dynamic data 6, wherein the probability value 7 of the product 1 is sent to the mobile device 5 and outputted to the user of the mobile device 5. This way the calculations can be performed in the secure environment of the server 4.

Alternatively the mobile device 5 could request the dynamic data 6 from the server 4 in the read-out event, wherein the calculations are performed on the mobile device.

It can particularly be provided that the machine-readable label 3 is a passive printed label, with the static data 2 being provided as a machine-readable printed code 10. A passive printed label means a printed label which does not use any special ink or other additional safety features. The printed code 10 can be a barcode, a 2D barcode, in particular, a QR code. In this case, the read-out unit 12 of the mobile device 5 can be configured as a camera, which is very common for mobile devices 5. Therefore no special equipment is required by the user to read out the static data 2.

Further machine-readable label 3 can contain readable information 27 with printed letters and numbers.

In an alternative embodiment, it can be provided that the machine-readable label 3 is configured as an RFID chip.

Further it can be provided that the static data 2 contains a link 8 to a verification website 9 associated with the server 4, and that the mobile device 5 opens the verification website 9 when the static data 2 is read out. Hereby the user can use a common code reader application, like a QR reader application, on his mobile device 5. Upon reading out the machine-readable label 3 via the mobile device 5, the verification website 9 of the link 8 in the static data 2 is opened in a browser of the mobile device 5. The static data 2 is then, in particular, uploaded to the website verification website 9 so that the server 4 can determine the probability value 7. The outputting of the probability value 7 can also be done via the verification website 9. It is therefore not necessary for the user to have a specific program or mobile application on his mobile device 5, thus making it easier for the user to use the service.

A preferred embodiment of the graphical user interface of the verification website 9 is shown in Fig. 3. The left image shows the verification website 9 after reading out the static data 2, with the static data 2 and a user data 16 being presented to the user. After pressing the button 19 on the bottom, the probability value 7 is determined by the server 4 and outputted to the user on the verification website 9, as shown in the right image in Fig. 3.

In an alternative embodiment, a mobile application or program is installed on the mobile device 5, which determines the probability value 7.

According to a preferred embodiment it can be provided that a digital signature 15 is generated based on the static data 2, which digital signature 15 is applied together with the static data 2 to the machine-readable label 3, wherein the digital signature 15 is used by the server 4 to verify the static data 2. The digital signature 15 ensures that it is impossible for a counterfeiter to copy and alter the static data 2 provided on the machine-readable label 3.

It can be particularly provided that the machine-readable printed code 10 contains the static data 2, the link 8, and the digital signature 15.

A preferred embodiment of how the digital signature 15 works is provided in fig. 2. The static data 2 is used as a seeding number for a hash generator 20 to generate a hash number 21. The hash number 21 is used in a signature algorithm 22 together with a private key 23 to generate the digital signature 15. The digital signature 15 itself is provided on the machine-readable printed code 10 on the machinereadable label 3. In the read-out event, two hash number 21 are generated. A first hash number 21 is generated with the same hash generator 20 from the static data 2 provided on the machine-readable label 3. The second hash number 21 is generated by the digital signature 15, a public key 24 belonging to the private key 23, and a verification algorithm 25. Both hash numbers 21 are compared in an integrity check 26. If both hash numbers 21 are equal, the integrity check 26 is passed. A difference between both hash numbers 21 leads to a failure of the integrity check 26, meaning that the information on the machine-readable label 3 is not genuine.

In particular, it can be provided that in the read-out event a user data 16 is generated by the mobile device 5, which user data 16 is used for choosing the probability value 7. The user data 16 means data provided or generated by the user himself or by the mobile device 5. The user data 16 is in particular generated after reading out the static data 2 of the product. This user data 16 can contain a time stamp of the read-out event, an identification number of the mobile device 5, the reason for the read-out event, and/or the position of the user in the market, like being an end customer or a dealer. Hereby the user data 16 can be sent to the server 4 together with the static data 2. The determination of the probability value 7 is then based on the dynamic data 6 and the user data 16. This information can be very helpful to estimate the probability value 7, for example when the same static data is read out by different users, indicating that the machine-readable label 3 has been copied and applied to counterfeited products.

Particularly it can be provided that the user data 16 contains location information of mobile device 5. The location information of mobile device 5 can especially be the GPS coordinates of the mobile device 5 at the read-out event. It can be provided that the mobile device 5 comprises a GPS unit 29. Especially the location information of mobile device 5 is very helpful since the read-out of the same static data 2 on different locations can be a strong indication of the machine-readable label 3 being copied.

Especially it can be provided that the read-out event is saved by the server 4 and added to the dynamic data 6. Therefore a history of the read-out events can be used to recognize a pattern which could indicate a counterfeit of the given static data 2.

Further, it can be provided that the probability value 7 is chosen by additionally using the dynamic data 6 of similar static data 2. Similar static data 2 means static 2 being in the vicinity to the static data 2 of the machine-readable label 3, like the neighboring serial numbers, or the serial numbers of the same production patch of the product. For example, if all the neighboring serial numbers were located by the different read-out events in a far geographic location, this might be a hint of the read out static data 2 to be from a counterfeited product. A different example is an increase in the probability of a product 1 being counterfeited if at least a large number of products 1 with a similar static data 2 have been counterfeited in the recent past.

It can, in particular, be provided that the probability value 7 is chosen by taking into account different conditional values. This means that the probability value 7 is not just determined based on a single condition, but that different possible conditions are combined to determine the probability value 7. This has the advantage that a far more realistic estimation of a product 1 being a counterfeit can be done.

The conditional values not being based on the user information 16 can be for example be the frequencies of read-out events, the duration between manufacturing and the first read-out event, or a failed integrity check 26 of the digital signature 15.

It can be provided that at least one conditional value is dependent on the number of occurrences of different user data 16 for the same static data 2. The conditional values can be based on the user information 16, for example the count of different end-customers for the same static data 2, the count of a first geographic distance, like 100 km to 999 km, between two read-out events for the same static data 2, the count of a second geographic distance, like larger than 1000 km, between two read-out events for the same static data 2, and/or the count of dealer entries after end customer entries with the same static data 2.

It can, in particular, be provided that an intermediate probability value of the counterfeit probability of the product 1 is calculated based on the dynamic data 6, wherein the pre-calculated intermediate probability value is compared to the set of at least three different values to choose the probability value 7. This means that an intermediate probability value is calculated and that based on this intermediate probability a fitting probability value 7 is chosen from the set at least three different values. Herby the intermediate probability value is the result of a calculation and the probability value 7 is the value being outputted to the user.

According to a first preferred embodiment, it can be provided that each conditional value is associated with a specific increase of the intermediate probability value, and that the intermediate probability value of the counterfeit probability of the product 1 is calculated by summing up the specific increases by each conditional value. In particular, a table can be provided containing different conditions, and an increase of the counterfeit probability for a given number of occurrences of this condition. For example, the condition can be the count of different end customers. In the case of 2 to 10 occurrences the intermediate probability value is increased by 10%, in the case of 11 to 50 occurrences, the intermediate probability value is increased by 50%, and in the case of more than 50 occurrences the intermediate probability value is increased by 100%. The values for these tables can be taken out of experiences. Of course, these values can be adapted to different conditions and products. Each range of the percentage of the intermediate probability value can then be associated with one value of the set of at least three values of the probability value 7. By summing up the specific increases by each conditional value an easy to implement method can be applied to calculate the probability value 7.

According to a second preferred embodiment, it can be provided that the counterfeit probability of the product 1 is chosen by an artificial intelligence taking into account the conditional values. In this case, an artificial intelligence, like a neurological network, can be trained with historical or simulated cases of counterfeited products 1 to recognize the patterns in the dynamic data 6 and/or user data 16 being usual for counterfeited products 1 based on the different conditional values. This way a more accurate estimation of the probability value 7 can be obtained.

In particular, it can be provided that upon choosing of the probability value 7 the reasons for an increase of the probability value 7 are determined and outputted to the user of the mobile device 5 together with the probability value 7 of the product

1. The user is therefore not just provided the probability value 7, but also reasons for this probability value 7. This gives the user the possibility to better assess the probability value 7. For example, if the user knows that the product 1 is second hand, he can ignore an increase of the probability value 7 caused by a different user having read out the same machine-readable label 3 in the past. If the product 1 is sold as being brand new to the user, the same increase of the probability value 7 may be a strong hint of the product 1 being counterfeited.

Claims

1. Method for estimating a counterfeit probability of a product (1), wherein during an manufacturing event of the product (1) a static data (2) associated with the product (1) is generated, which static data (2) is applied to a machine-readable label (3) attached to the product (1) on one hand and uploaded to a server (4) on the other hand, wherein during a read-out event the static data (2) of the machine-readable label (3) is read out via a mobile device (5) of a user, wherein a probability value (7) of the counterfeit probability of the product (1) is chosen from a set of at least three different values based on a dynamic data (6) associated with the static data (2) from the server (4), wherein the probability value (7) of the product (1) is sent to the mobile device (5) and outputted to the user of the mobile device (5).

2. Method according to claim 1, characterized in that the static data (2) contains a link (8) to a verification website (9) associated with the server (4), and that the mobile device (5) opens the verification website (9) when the static data (2) is read out.

3. Method according to claim 1 or 2, characterized in that the machinereadable label (3) is a passive printed label, with the static data (2) being provided as a machine-readable printed code (10).

4. Method according to one of the claims 1 to 3, characterized in that a digital signature (15) is generated based on the static data (2), which digital signature (15) is applied together with the static data (2) to the machine-readable label (3), wherein the digital signature (15) is used by the server (4) to verify the static data (2).

5. Method according to one of the claims 1 to 4, characterized in that the probability value (7) is a percentage.

6. Method according to one of the claims 1 to 5, characterized in that the read-out event is saved by the server (4) and added to the dynamic data (6).

7. Method according to one of the claims 1 to 6, characterized in that in the read-out event a user data (16) is generated by the mobile device (5), which user data (16) is used for choosing the probability value (7).

8. Method according to claim 7, characterized in that the user data (16) contains location information of the mobile device (5).

9. Method according to one of the claims 1 to 8, characterized in that the probability value (7) is chosen by additionally using the dynamic data (6) of similar static data (2).

10. Method according to one of the claims 1 to 9, characterized in that the probability value (7) is chosen by taking into account different conditional values.

11. Method according to one of the claims 1 to 10, characterized in that an intermediate probability value of the counterfeit probability of the product (1) is calculated based on the dynamic data (6), wherein the pre-calculated intermediate probability value is compared to the set of at least three different values to choose the probability value (7).

12. Method according to claim 10 and 11, characterized in that each conditional value is associated with a specific increase of the intermediate probability value, and that the intermediate probability value of the counterfeit probability of the product (1) is calculated by summing up the specific increases by each conditional value.

13. Method according to claim 10, characterized in that the counterfeit probability of the product (1) is chosen by an artificial intelligence taking into account the conditional values.

14. Method according to one of the claims 1 to 12, characterized in that upon choosing of the probability value (7) the reasons for an increase of the probability value (7) are determined and outputted to the user of the mobile device (5) together with the probability value (7) of the product (1).

15. System (11) for estimating whether a product (1) is genuine according to the method of any one of claims 1 to 14, the system (11) comprising:

a machine-readable label (3) attached to the product (1) and comprising a static data (2) associated with the product (1);

a server (4) with a database containing a dynamic data (6) associated with the static data (2); and a mobile device (5) containing:

a read-out unit (12) to read out the static data (2) of the machinereadable label (3), a communication unit (13) for communication of the static data (2) to the server (4), and outputting means (14) for outputting a probability value (7) to a user, wherein the probability value (7) is estimated based on the static data (2) and the dynamic data (6) associated with the static data (2) and stored at the server (4), wherein the probability value (7) is chosen from a set of at least three different values.

15. System (11) for estimating a counterfeit probability of a product (1), in particular according the method of one of the claims 1 to 13, comprising a machine-readable label (3) with a static data (2) associated with the product (1) being attached to the product (1), and a mobile device (5) containing a read-out unit (12) to read out the static data (2) of the machine-readable label (3), a communication unit (13) for communication with a server (4), and outputting means (14) for outputting a probability value (7) to a user, further comprising a server (4) with a database containing dynamic data (6) associated with the static data (2), characterized in that the probability value (7) is chosen from a set of at least three different values based on a dynamic data (6) associated with the static data (2) from the server (4).

08 01 19

Amendments to the claims have been filed as follows:

1. A method for estimating whether a product (1) is genuine, the method comprising:

during a manufacturing event of the product (1), generating a static data (2) associated with the product (1), which static data (2) is applied to a machinereadable label (3) attached to the product (1) on one hand and uploaded to a server (4) on the other hand, and during a read-out event:

reading the static data (2) of the machine-readable label (3) via a mobile device (5) of a user, sending the static data (2) to the server (4), and estimating a probability value (7) for the product (1) based on the static data (2) and a dynamic data (6) associated with the static data (2) and stored at the server (4), wherein the probability value (7) of the product (1) is chosen from a set of at least three different values and is provided for output to the user of the mobile device (5).

08 01 19

6. Method according to one of the claims 1 to 5, characterized in that the readout event is saved by the server (4) and added to the dynamic data (6).

7. Method according to one of the claims 1 to 6, characterized in that during the read-out event a user data (16) is generated by the mobile device (5), which user data (16) is used in estimating the probability value (7).

9. Method according to one of the claims 1 to 8, characterized in that the probability value (7) is estimated by additionally using the dynamic data (6) of similar static data (2).

10. Method according to one of the claims 1 to 9, characterized in that the probability value (7) is estimated by taking into account different conditional values.

11. Method according to one of the claims 1 to 10, characterized in that an intermediate probability value for the product (1) is calculated based on the dynamic data (6), wherein the pre-calculated intermediate probability value is compared to the set of at least three different values to choose the probability value (7).

12. Method according to claim 10 and 11, characterized in that each conditional value is associated with a specific increase of the intermediate probability value, and that the intermediate probability value for the product (1) is calculated by summing up the specific increases by each conditional value.

13. Method according to claim 10, characterized in that the counterfeit probability of the product (1) is estimated by an artificial intelligence taking into account the conditional values.

14. Method according to one of the claims 1 to 12, characterized in that upon

08 01 19 estimating the probability value (7), reasons for an increase of the probability value (7) are determined and provided for output to the user of the mobile device (5) together with the probability value (7) of the product (1).