Method for promoting actions of a target group and system thereof

Abstract

This invention provides a computer-implemented method to perform operations to promote a target group to read an identifier with a device. In one embodiment, said method, comprises the steps of: a) Reading and verifying validity of said identifier associated with a specific data class, wherein said identifier triggers a data processing hardware to receive an input data unique to said identifier; and b) Providing an output, R, paired with said specific data class, wherein said output is of sufficient value to trigger said target group to repeat step (a) when there is another identifier.

Description

METHOD FOR PROMOTING ACTIONS OF A TARGET GROUP AND SYSTEM THEREOFFIELD OF THE INVENTION

[0001] The present invention relates to methods for promoting actions to be taken by a target group, particularly, reading of identifiers.BACKGROUND OF THE INVENTION

[0002] The emergence of internet technology has been one of the most important driving forces behind the globalization of business worldwide. Counterfeiting is, unfortunately, on the increase and the international criminal rings who profit from this trade respect neither borders nor legality. They are always ready to prey on the vulnerable and take advantage of any lack of coordination. The harm caused by IP crime hits company profits, tax revenues and endangers the public as has been shown so graphically by the rise in fake medicines and personal protective equipment in the wake of the Covid-19 pandemic. According to a 2019 study by the Organization for Economic Cooperation and Development and the EUIPO on trends in illicit trade, within the past 3 years the value of counterfeit goods had reached USD 509 billion, equal to 3.3 % of global trade.

[0003] Anti-counterfeiting technologies help determine whether a product is genuine or fake, or has otherwise been subject to fraudulent activities. They may use different methods by performing functions including authentication, tracking / tracing and anti- tampering / anti- alteration.

[0004] Anti-counterfeiting technologies differ according to the combination of essential functions they perform, the methods they use to perform them and their mode of inspection - whether they can be verified by human senses or whether a special device is required. However, the characteristic shared by all anti-counterfeiting methods is the use of specific marking devices, known as markers. These are joined inseparablyto the products (by various techniques) and contain the specific information that enables the technology to perform its essential function.

[0005] Broadly speaking anti-counterfeiting technologies could be classified as follows: Electronic technologies - where an electronic identification / authentication / tracking device is associated with the product; Marking technologies - where a visible or invisible marker is incorporated into the product; Chemical & physical technologies - where chemical or physical processes or substances are used to mark and verify the product; Mechanical technologies - where material elements or processes are applied to the product for authentication and / or anti-tampering purposes; Technologies for digital media - where information is embedded in or extracted from digital files, computers and electronic devices; Shared ledger technology (blockchain) - a tool that can be used in combination with other technologies to boost overall anticounterfeiting defenses.

[0006] This invention provides a further solution to the problems that have not been solved by the anti-counterfeiting technologies described above.SUMMARY OF THE INVENTION

[0007] This invention provides a computer-implemented method to perform operations to promote a target group to read an identifier with a device. In one embodiment, said computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations to promote a target group to read an identifier with a device, comprising the steps of: a) Reading and verifying validity of said identifier associated with a specific data class, wherein said identifier triggers said data processing hardware to receive an input data unique to said identifier; and b) Providing an output, R. paired with said specific data class, wherein said output is of sufficient value to trigger said target group to repeat step (a) when there is another identifier; wherein value of said output to be paired with said specific dataclass is determined by the steps of: i) Determining probability of said target group to read said identifier at different values of said output, Pf ) ,' ii) Obtaining a first reference value, S, and a second reference value, C, for said specific data class; iii) Estimating propensity for said target group to not read said identifier with a function, P = f C , S , P (Ryy, iv) Determining a value of said output when P equals said second reference value, C; and v) Pairing said value of step (iv) with said specific data class.

[0008] This invention also provides a system for implementing the method of this invention. In one embodiment, said system comprises: a) One or more identifiers; b) a device capable of reading said identifier; c) a data processing hardware capable of receiving data from said device, comprising: i) a processor; ii) memory; and iii) program instructions, stored in the memory, that upon execution by the processor cause said data processing hardware to perform operations for implementing said method of this invention.BRIEF DESCRIPTION OF THE FIGURES

[0009] FIGURE 1 is an illustration of the method in one embodiment of this invention.DETAILED DESCRIPTION OF THE INVENTION

[0010] This invention provides a computer-implemented method to perform operations to promote a target group to read an identifier with a device. In one embodiment, said computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations to promote a target group to read an identifier with a device, comprising the steps of: a) Reading and verifying validity of said identifier associated with a specific data class, wherein said identifier triggers said data processing hardware to receive an input data unique to said identifier; and b) Providing an output, R. paired with said specific data class, wherein said output is of sufficient value to trigger said target group to repeat step (a) when thereis another identifier; wherein value of said output to be paired with said specific data class is determined by the steps of: i) Determining probability of said target group to read said identifier at different values of said output, Pf ) ,' ii) Obtaining a first reference value, S, and a second reference value, C, for said specific data class; iii) Estimating propensity for said target group to not read said identifier with a function, P = f C , S , P (Ryy, iv) Determining a value of said output when P equals 0; and v) Pairing said value of step (iv) with said specific data class.

[0011] In one embodiment, said identifier has a time-limited validity after being read.

[0012] In one embodiment, said different values of step(i) comprises a value proportional to time required for completing step (a) and value of time for said target group.

[0013] In one embodiment, said value of time is average hourly rate of said target group.

[0014] In one embodiment, said step (i) comprises estimation with logistic regression:where b0and bi are constants.

[0015] In one embodiment, said estimation is performed on survey data derived from said target group.

[0016] In one embodiment, said specific data class is a branded good.

[0017] In one embodiment, said first reference value, S, comprises selling price of a counterfeit good corresponding to said branded good.

[0018] In one embodiment, said second reference value comprises cost for producing said counterfeit good.

[0019] In one embodiment, said second reference value comprises a value that detersproduction of said counterfeit good.

[0020] In one embodiment, said function of step (iii) is: P = [1 — P(R)]S — C.

[0021] In one embodiment, said output is a monetary reward.

[0022] In one embodiment, said step (i) comprises estimation with logistic regression:where b0, bi bnare constants, Xnis an assigned value associated with one or more variables comprising government policy, guild’s intervention, consumer protection group ’ s policy, anti-counterfeiting technologies, number of identifiers used or inflation rate.

[0023] In one embodiment, said identifier is in a form selected from the group consisting of QR code, bar code, RFID tag, NFC tag and character code.

[0024] In one embodiment, said time-limited validity is a period between 0 second to 30 minutes.

[0025] In one embodiment, said device for reading data is a smart device or computer.

[0026] In one embodiment, said function of step (i) or step (iii) is determined by data mining.

[0027] In one embodiment, said data processing hardware comprises an artificial intelligence system for determining said function of step (iii).

[0028] In one embodiment, said artificial intelligence system comprises a machine learning model trained to predict propensity for said target group to not read said identifier with an updated function.

[0029] In one embodiment, said method further comprises the step of determining, by said artificial intelligence system, one or more parameters affecting propensity for said target group to not read said identifier and modifying said function of step (iii).

[0030] In one embodiment, said method further comprises the step of determining, by said artificial intelligence system, an updated value for said first reference value, S, and said second reference value, C, based on number of identifiers that were read.

[0031] This invention also provides a system for implementing the method of this invention. In one embodiment, said system comprises: a) One or more identifiers; b) a device capable of reading said identifier; c) a data processing hardware capable of receiving data from said device, comprising: i) a processor; ii) memory; and iii) program instructions, stored in the memory, that upon execution by the processor cause said data processing hardware to perform operations for implementing said method of this invention.

[0032] EXAMPLE 1

[0033] The present invention is tested with a branded good selling at USD256.58 as said specific data class. The identifier is a QR code on the branded good. The target group would be the consumers for the branded good while the output to be determined is a monetary reward of sufficient value for the consumers to read a QR code on the branded good. The QR code has a one-time validity and could not be validated even if the same QR code is subsequently associated with a counterfeit good. By inducing all consumers to read the QR code to validate their authenticity on every product sold, the market for counterfeit goods would be drastically diminished or even eliminated.

[0034] A survey is conducted on the consumers for the branded good which provides information on the monetary reward sufficient to invoke their scanning of the QR code. The cumulative data from the survey is then used for estimating the probability for consumers to read the QR code using P(v)7= - i+e~r(bho+b R) where the two constants are determined to be b0= —2.894, 6! = 0.406.

[0035] The first reference value, S , is the selling price of a counterfeit goodcorresponding to said branded good. It is assumed that the counterfeit good would be sold at the same price of the branded good i.e. USD256.58.

[0036] The second reference value, C, is the cost for producing the counterfeit good and estimated to be USD 130.5. This is obtained by the cost of producing a similar good and the cost of buying the same packaging as the branded good e.g. a used container of the branded good.

[0037] The propensity for consumers to not read the QR code is assumed to follow the function, P = [1 — P(R)]S — C.

[0038] Based on the above information, when P = C, probability for consumers to read the QR code, P(R), is 0.491 and the monetary reward, R, is determined to be USD 7.048.

[0039] Since P(R) is the probability for consumers to read the QR code, it can be used as a check on the effectiveness of the reward. At least 49.1% of the branded goods sold should have their QR codes scanned if the monetary reward served its purpose. If the monetary reward did not serve its purpose, the monetary reward could be adjusted and the resulting change in number of scanned QR codes used for better estimation in step (i) and / or step (iii). A replacement P(R) could also be obtained from a new survey for better estimation in step (i).

[0040] EXAMPLE 2

[0041] The present invention is tested with a branded watch selling at USD25658 as said specific data class. The identifier is a string of text code inscribed on the branded watch. The target group would be the consumers for the branded watch while the output to be determined is a monetary reward of sufficient value for the consumers to register the string of text code onto a website. The text code has a one-time validity and could not be validated even if the same text code is subsequently associated with a counterfeit good. By inducing all consumers to enter code to validate their authenticity on everyproduct sold, the market for counterfeit goods would be drastically diminished or even eliminated.

[0042] A survey is conducted on the consumers for the branded watch which provides information on the monetary reward sufficient to invoke their registering of the text code. The cumulative data from the survey is then used for estimating theprobability for consumers to register the text code using P(R) =i+e-(bo+biR)where the two constants are determined to be b0= —0.986, b1= 0.025.

[0043] The first reference value, S , is the selling price of a counterfeit watch corresponding to the branded watch. It is assumed that the counterfeit good would be sold at the same price of the branded watch i.e. USD25658.

[0044] The second reference value, C, is the cost for producing the counterfeit watch and estimated to be USD 625.

[0045] The propensity for consumers to not register the text code is determined by an artificial intelligence system. In this example, it follows the function, P = [1 - P( / ?)]5 - C.

[0046] Based on the above information, when P = C, probability for consumers to register the text code, P(R), is 0.976 and the monetary reward, R, is determined to be USD 189.044.

[0047] Since P(R) is the probability for consumers to register the text code, it can be used as a check on the effectiveness of the reward. At least 97.6% of the luxury watch sold should have their text code registered if the monetary reward served its purpose. If the monetary reward did not serve its purpose, the monetary reward could be adjusted and the resulting change in number of registered text codes used for better estimation in step (i) and / or step (iii) by the artificial intelligence system. A replacement / •’( / ?) could also be obtained from a new survey for better estimation in step (i).

[0048] Data showing various results obtained from an embodiment of this invention is shown in Table 1 below.Table 1. Results from various examples of this invention.1. boand bi are derived by logistic regression on a data set from a survey2. The function P = [1 — P( )]S — C is used in step (iii).

[0049] EXAMPLE 3

[0050] When the method of this invention is applied to branded goods, the second reference value, C, can be the cost for producing a corresponding counterfeit good. This can be any cost associated with bringing the counterfeit product to the market e.g. raw materials, transportation, or packaging. For example, for a counterfeit watch, the cost of producing the counterfeit watch may include the cost of producing the watch itself and the cost of buying the same packaging as the branded watch e.g. a used container of the luxury watch. It may or may not include any other costs required to bring the counterfeit watch into the market.

[0051] EXAMPLE 4

[0052] Other variations of this invention, for example, can be as follows.

[0053] The function of step (iii) may be derived from data mining and may comprisefurther parameters other than C, P(R . and S.

[0054] The function describing the propensity for consumers to not register the text code can be determined by an artificial intelligence system and may not necessarily follow the function, P = [1 — P(R)]S — C.

[0055] The function describing the propensity for consumers to not register the text code may also be determined by other tools capable of reflecting the trend in a set of data e.g. curve fitting.

[0056] EXAMPLE 5

[0057] The functions modelling the probability of said target group to read said identifier at different values of said output, P(R), in step (i) and / or the propensity for said target group to not read said identifier in step (iii) can be updated with actual data received from the target group when the method of this invention is implemented. The actual data can be the number of identifiers read by the target group (e.g. number of QR code scanned by buyers of luxury watches) and / or the number of items in a specific data class (e.g. number of products sold for luxury watches).

[0058] The functions may also be updated by an artificial intelligence system after each time a product is sold or a QR code is scanned. The updating can also be done after data is collected after a regular period e.g. one month or within a specific period e.g. X’mas season. This is to ensure rewards provided will closely follow consumer behavior.

[0059] The functions may also be adjusted based on the immediate need for a specific data class. For example, when the cost of a product becomes too high, the functions may be adjusted to obtain a minimum monetary reward which will not significantly affect its attractiveness to the consumers.

Claims

What is claimed is:

1. A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations to promote a target group to read an identifier with a device, comprising the steps of: a. Reading and verifying validity of said identifier associated with a specific data class, wherein said identifier triggers said data processing hardware to receive an input data unique to said identifier; and b. Providing an output, R, paired with said specific data class, wherein said output is of sufficient value to trigger said target group to repeat step (a) when there is another identifier; wherein value of said output to be paired with said specific data class is determined by the steps of: i) Determining probability of said target group to read said identifier at different values of said output, P(R) ii) Obtaining a first reference value, S, and a second reference value, C, for said specific data class; iii) Estimating propensity for said target group to not read said identifier with a function, P = f(C, S, P(Ry) iv) Determining a value of said output when P=Q ; and v) Pairing said value of step (iv) with said specific data class.

2. The computer implemented method of claim 1, wherein said identifier has a time-limited validity after being read.

3. The computer implemented method of claim 1, wherein said different values of step(i) comprises a value proportional to time required for completing step (a) and value of time for said target group.

4. The computer implemented method of claim 3, wherein said value of time is average hourly rate of said target group.

5. The computer implemented method of claim 1, wherein said step (i) comprises estimation with logistic regression:where b0and bi are constants.

6. The computer implemented method of claim 5, wherein said estimation is performed on survey data derived from said target group.

7. The computer implemented method of claim 1, wherein said specific data class is a branded good.

8. The computer implemented method of claim 7, wherein said first reference value, S, comprises selling price of a counterfeit good corresponding to said branded good .

9. The computer implemented method of claim 8, wherein said second reference value comprises cost for producing said counterfeit good.

10. The computer implemented method of claim 8, wherein said second reference value comprises a value that deters production of said counterfeit good.

11. The computer implemented method of claim 1, wherein said function of step (iii) is:P = [1 - P(R)]5 - C.

12. The computer implemented method of claim 7, wherein said output is a monetary reward.

13. The computer implemented method of claim 7, wherein said step (i) comprises estimation with logistic regression:where b0, bi bnare constants, Xnis an assigned value associated with one or more variables comprising government policy, guild’s intervention, consumer protection group’s policy, anti-counterfeiting technologies, number of identifiers used or inflation rate.

14. The computer implemented method of claim 1, wherein said identifier is in a form selected from the group consisting of QR code, bar code, RFID tag, NFC tag and character code.

15. The computer implemented method of claim 1, wherein said time-limited validity is a period between 0 second to 30 minutes.

16. The computer implemented method of claim 1, wherein said device for reading data is a smart device or computer.

17. The computer implemented method of claim 1, wherein said function of step (i) or step (iii) is determined by data mining.

18. The computer implemented method of claim 1, wherein said data processing hardware comprises an artificial intelligence system for determining said function of step (iii).

19. The computer implemented method of claim 18, wherein said method further comprises the step of determining, by said artificial intelligence system, one or more parameters affecting propensity for said target group to not read said identifier and modifying said function of step (iii).

20. A system comprising: a. One or more identifiers; b. A device capable of reading said one or more identifiers; c. A data processing hardware capable of receiving data from said device, comprising: i. a processor; ii. memory; and iii. program instructions, stored in the memory, that upon execution by the processor cause said data processing hardware to perform operations for implementing said method of claim 1.

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