Possibility information generation system, program and possibility information generation method
The system addresses unauthorized product distribution by generating and visualizing distribution risk using location and authenticity data, enhancing prevention efforts.
Patent Information
- Application Number
- JP2024055457
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-03-29
- Publication Date
- 2025-10-10
AI Technical Summary
Existing systems fail to effectively identify and prevent unauthorized distribution of products, such as counterfeits and parallel imports, along the distribution route, despite authenticity determinations at various centers.
A possibility information generation system that utilizes location and distribution route data, authenticity determination data, and machine learning to generate and visualize the likelihood of unauthorized distribution, providing actionable insights to manufacturers and sellers.
Enables manufacturers and sellers to identify high-risk distribution centers and channels, reducing the need for computational and human resources to combat unauthorized distribution.
Smart Images
Figure 2025153141000001_ABST
Abstract
Description
[Technical Field]
[0001] The present invention relates to a possibility information generation system, a program, and a possibility information generation method. [Background technology]
[0002] Patent Document 1 describes the information processing method as follows: "An information processing device executes a product information acquisition step in which an information processing device acquires product identification information capable of identifying the product to be matched from a serial code attached to the product to be matched or image information of the product to be matched; an optical image acquisition step in which an optical image information relating to an optical image obtained from a fine line pattern attached to the product to be matched; and a matching step in which an information processing device generates a matching result by matching the acquired optical image information with correct answer information relating to a correct answer label of the optical image corresponding to the product identification information." [Prior art document] [Patent documents] [Patent Document 1] International Publication No. 2022 / 054869 Summary of the Invention
[0003] In a first aspect of the present invention, there is provided a possibility information generation system comprising: a location information acquisition unit that acquires location information of an authenticity determination center that performs authenticity determination of a product; a distribution route information acquisition unit that acquires information regarding the product's distribution route including the authenticity determination center; an authenticity determination data acquisition unit that acquires product authenticity determination data from a terminal at the authenticity determination center; a possibility information generation unit that uses the location information and / or information regarding the distribution route and the authenticity determination data to generate illegal distribution possibility information that indicates the possibility of the product being distributed illegally in a manner not intended by the authenticity determination center and / or the product's manufacturer or seller at the distribution route; and a possibility information output unit that outputs the illegal distribution possibility information to a terminal used by a user of the authenticity determination data.
[0004] The authenticity determination data may include at least one of the result of the authenticity determination, the date and time when the authenticity determination was performed, and the authenticity determination location where the authenticity determination was performed.
[0005] Any of the above-described possibility information generation systems may further include a statistical data generation unit that generates, based on the authenticity determination data, unauthorized distribution statistical data including at least one of detection status including the number of times unauthorized distribution products were detected at the authenticity determination site and / or the ratio of the number of detections to the number of times authenticity determinations were performed, detection status during a predetermined period, detection status by product type, and date and time of the most recent detection of an unauthorized distribution product. The possibility information generation unit may generate unauthorized distribution possibility information using the unauthorized distribution statistical data.
[0006] The possibility information generation unit may use the illegal distribution statistical data to generate illegal distribution possibility information that indicates the possibility of illegal distribution at the authenticity determination center and / or distribution channel in stages based on predetermined criteria.
[0007] The possibility information output unit may output the unauthorized distribution possibility information that has been rearranged and / or narrowed down in accordance with the specified conditions.
[0008] Any of the above-described possibility information generation systems may further include a heat map generation unit that generates a heat map that represents at least a portion of the illegal distribution possibility information on map information. The possibility information output unit may output the heat map.
[0009] The authenticity determination site may include multiple sites along the product distribution route.
[0010] Users of the authentication data may include product manufacturers and / or distributors.
[0011] Any of the above-described possibility information generation systems may further include a machine learning unit that generates a prediction model that predicts the possibility of unauthorized distribution from at least a portion of the authenticity assessment data. The possibility information generation unit may generate the unauthorized distribution possibility information using the prediction model.
[0012] In any of the above-described possibility information generation systems, at least one of location information, information regarding distribution routes, authenticity determination data, and information regarding the possibility of unauthorized distribution may be recorded on a ledger on a blockchain network.
[0013] In a second aspect, the present invention provides a program that, when executed by a computer, causes the computer to function as any one of the above-described possibility information generation systems.
[0014] In a third aspect of the present invention, there is provided a possibility information generation method in which any of the above-mentioned possibility information generation systems executes the following steps: a location information acquisition step of acquiring location information of multiple authenticity determination centers that perform product authenticity determination; a distribution route information acquisition step of acquiring information regarding the product's distribution route between the multiple authenticity determination centers; an authenticity determination data acquisition step of acquiring product authenticity determination data from terminals of the multiple authenticity determination centers; a possibility information generation step of using the location information and / or information regarding the distribution route and the authenticity determination data to generate illegal distribution possibility information indicating the possibility of the product being illegally distributed, that is, distributed in a manner not intended by the product's manufacturer or seller at the multiple authenticity determination centers and / or distribution routes; and a possibility information output unit step of outputting the illegal distribution possibility information.
[0015] The above summary of the invention does not list all of the features of the present invention, and subcombinations of these features may also be inventions. [Brief explanation of the drawings]
[0016] [Figure 1] 1 shows an example of an authenticity determination system 10 according to this embodiment. [Figure 2] 10 shows an example of a processing flow of the possibility information generation method of this embodiment. [Figure 3] 8 shows an example of location information and distribution route information 800. [Figure 4] An example of the authenticity determination data 900 is shown. [Figure 5] An example of a subflow of S1090 is shown below. [Figure 6] An example of the irregular distribution statistical data 1000 is shown. [Figure 7] An example of the irregular distribution statistical data 1100 is shown. [Figure 8] An example of unauthorized distribution possibility information 1200 is shown. [Figure 9] An example of unauthorized distribution possibility information 1300 is shown. [Figure 10] An example of a subflow of S1090 is shown below. [Figure 11] An example of unauthorized distribution possibility information 1400 is shown. [Figure 12] An example of a subflow of S1090 is shown below. [Figure 13] An example of a heat map 1500 is shown. [Figure 14] An example of a heat map 1600 is shown. [Figure 15] 22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION
[0017] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.
[0018] 1 shows an example of an authenticity determination system 10 according to this embodiment. The authenticity determination system 10 includes a possibility information generation system 100, a terminal 500 at an authenticity determination location 400, and a terminal 700 used by a user 600 of the authenticity determination data.
[0019] The possibility information generation system 100 of this embodiment visualizes distribution centers, etc. that have a high rate of contamination with unauthorized products based on location information of authenticity determination centers that perform product authenticity determination and / or information regarding the product distribution route, and product authenticity determination data, and presents this to product manufacturers, etc.
[0020] In the possibility information generation system 100 of this embodiment, a product refers to an item manufactured for sale. The product may be, for example, a bag, a wallet, clothing, cosmetics, jewelry, ceramics, watches, food, etc. The product may be a high-priced product such as a brand-name product. In particular, in this embodiment, the target may be a product for which counterfeits are likely to be distributed (for example, a high-priced product and / or a product that is well-known by consumers). In this specification, the term "product" refers to a single and / or multiple items.
[0021] An authentic product is a product that is at least manufactured by a legitimate manufacturer. In one embodiment (e.g., where a legitimate manufacturer and / or legitimate seller only allows certain distribution channels), an authentic product may be treated as not including unauthorized distribution. In another embodiment, any product manufactured by a legitimate manufacturer may be treated as authentic, regardless of distribution channel.
[0022] As the sale of products via e-commerce expands, authenticity assessment may be performed to distinguish and remove unauthorized products from genuine products during the product distribution process. Here, unauthorized products include counterfeit products and illegally distributed products. Counterfeit products refer to imitations of products manufactured by parties other than legitimate manufacturers. Illegally distributed products include products (e.g., parallel imports) that were manufactured by legitimate manufacturers but distributed through unauthorized distribution channels not intended by the legitimate manufacturers and / or legitimate sellers.
[0023] Even if authenticity determination is performed at an authenticity determination center set up along a product's distribution route and unauthorized products are eliminated, there is a possibility that unauthorized products may sneak into the distribution route at a later distribution stage. In order to prevent unauthorized products from entering the distribution route, there is a need for technology to identify distribution centers where unauthorized products are likely to be mixed in.
[0024] Possibility information generation system 100 of this embodiment includes input unit 110, storage unit 120, display unit 130, communication unit 140, and calculation unit 200.
[0025] The input unit 110 inputs necessary information and / or instructions to the calculation unit 200. The input unit 110 may be an input device such as a keyboard or a mouse connected to the calculation unit 200. The input unit 110 may input information and / or instructions via communication with an external terminal or the like, but is not limited to this.
[0026] The storage unit 120 stores information input by the input unit 110, information processed by the calculation unit 200, etc. The storage unit 120 may store location information, distribution route information, authenticity determination data, information on the possibility of unauthorized distribution, statistical data on unauthorized distribution, etc., which will be described later. The storage unit 120 may be a storage device such as a memory or a hard disk. The storage unit 120 may be called by the calculation unit 200 and provide the stored data to the calculation unit 200 as needed.
[0027] The display unit 130 displays the processing results of the calculation unit 200. As an example, the display unit 130 may be an output device such as a monitor connected to the calculation unit 200, but is not limited to this.
[0028] The communication unit 140 is connected via the network 300 to a terminal 500 at an authenticity determination site 400 that performs product authenticity determination, and to a terminal 700 used by a user 600 of the authenticity determination data.
[0029] The authenticity determination site 400 is a site where authenticity determination of a product is performed. The authenticity determination site 400 may include multiple sites in the distribution route of the product. In the authenticity determination system 10 of this embodiment, authenticity determination means determining whether a product is genuine or a counterfeit. The authenticity determination site 400 may be a site (e.g., a factory, warehouse, distribution center, etc.) of a distributor, manufacturer, and / or seller of the product where authenticity determination is performed on the product.
[0030] At the authenticity determination base 400, for example, authenticity determination may be performed using an authenticity determination device that reads the anti-counterfeit label attached to the product.
[0031] The anti-counterfeit label may be, for example, a high-definition pattern formed on a transparent film that has low visibility and is difficult to counterfeit. Information indicating that the product is genuine may be recorded in a predetermined manner on the high-definition pattern, and by reading the pattern and decoding the recorded information, it is possible to determine whether the product is genuine or a counterfeit. As an example, the anti-counterfeit label may be Akliteia (registered trademark) manufactured by Asahi Kasei.
[0032] The terminal 500 may include an authenticity determination device. The authenticity determination device may have a communication function, be directly connected to the network 300, and output authenticity determination data to the possibility information generation system 100.
[0033] The user 600 is a person who uses the authenticity determination data. The user 600 may include a product manufacturer and / or seller. The terminal 700 of the user 600 may acquire the unauthorized distribution possibility information described below and display it so that the user 600 can view it. The terminal 700 may accept, from the user 600, specifications of conditions for sorting and / or narrowing down the unauthorized distribution possibility information described below, and output the conditions to the possibility information generation system 100.
[0034] The calculation unit 200 is connected to the input unit 110, the storage unit 120, the display unit 130, and the communication unit 140, and generates unauthorized distribution possibility information, generates a heat map, and the like based on information acquired via the input unit 110 and the communication unit 140. The calculation unit 200 is a computer. The calculation unit 200 may be provided in a single computer, or each function may be distributed and provided in multiple computers.
[0035] The calculation unit 200 may include a location information acquisition unit 205, a distribution route information acquisition unit 210, an authenticity determination data acquisition unit 215, a possibility information generation unit 220, a statistical data generation unit 225, a heat map generation unit 230, a possibility information output unit 235, a condition specification reception unit 240, and a machine learning unit 245.
[0036] The location information acquisition unit 205 acquires location information (sometimes simply referred to as "location information") of the authenticity determination location 400. The location information may include information indicating the address of the authenticity determination location 400. The location information acquisition unit 205 may acquire location information of multiple authenticity determination locations 400 along the distribution route.
[0037] The distribution route information acquisition unit 210 acquires information about the distribution route of a product (sometimes simply referred to as "distribution route information"). The distribution route may include the route through which the product passes during distribution. The distribution route may include, for example, the route along which the product moves between the product's manufacturer, distributor, and / or seller. In the possibility information generation system 100, the distribution route information acquisition unit 210 may acquire distribution route information between an authenticity determination location 400 and another authenticity determination location 400.
[0038] The authenticity determination data acquisition unit 215 acquires product authenticity determination data. The authenticity determination data is data acquired by performing an authenticity determination on a product, and includes at least the result of the authenticity determination on the product. The result of the authenticity determination indicates whether the product is genuine or an unauthorized product. Details of the authenticity determination data will be described later.
[0039] The possibility information generating unit 220 generates unauthorized distribution possibility information using the location information and / or distribution route information and the authenticity assessment data. The unauthorized distribution possibility information is information indicating the possibility that products include unauthorized distribution products at the authenticity assessment base and / or the distribution route.
[0040] Unauthorized distribution refers to a situation in which a product is distributed in a manner not intended by the product manufacturer or seller at the authentication assessment center and / or distribution channel. Specifically, unauthorized distribution products include counterfeit products and illegally distributed products. Counterfeit products refer to imitations manufactured by parties other than legitimate manufacturers. Unauthorized distribution products include products (e.g., parallel imports) manufactured by legitimate manufacturers but distributed through unauthorized distribution channels not intended by the legitimate manufacturers and / or sellers. Details of the unauthorized distribution possibility information will be provided later.
[0041] The possibility information generating unit 220 may generate the illegal distribution possibility information based on illegal distribution statistical data, which will be described later.
[0042] The statistical data generation unit 225 generates unauthorized distribution statistical data based on the authenticity determination data. The unauthorized distribution statistical data is statistical data related to unauthorized distribution of products, generated by processing data included in the authenticity determination data. Details of the unauthorized distribution statistical data will be described later.
[0043] The heat map generation unit 230 generates a heat map that displays at least a portion of the unauthorized distribution possibility information on map information. The heat map in this embodiment may be a heat map that visually displays information indicating the possibility of unauthorized distribution on map information displayed as an image. Details of the heat map will be described later.
[0044] The possibility information output unit 235 outputs the information on the possibility of unauthorized distribution to the terminal 700 used by the user 600 of the authenticity determination data. The possibility information output unit 235 may also output the above-mentioned heat map to the terminal 700.
[0045] The condition specification receiving unit 240 receives specification of conditions for sorting and / or narrowing down the unauthorized distribution possibility information from the terminal 700 of the authenticity determination data user 600. The condition specification may specify sorting in order of priority and / or hiding of items included in the unauthorized distribution possibility information and / or unauthorized distribution statistical data (for example, the above-mentioned location information and / or distribution route information, detection status, product type, and detection date and time, etc.).
[0046] The possibility information generation system 100 of this embodiment may include a machine learning unit 245. The machine learning unit 245 generates a prediction model that predicts the possibility of unauthorized distribution from at least a portion of the authenticity assessment data. The machine learning unit 245 may generate the prediction model by tree analysis such as a regression tree or a random forest, a neural network, a Bayesian analysis, or a combination thereof, using as training data the results of the authentication of the authenticity assessment data, the date and time when the authenticity assessment was performed, the authenticity assessment location, and unauthorized distribution possibility information generated based on the authenticity assessment data. In this case, the possibility information generation unit 220 may generate the unauthorized distribution possibility information using the prediction model.
[0047] The possibility information generation system 100 of this embodiment makes it possible to grasp the status of unauthorized distribution of products for each authenticity determination location and / or distribution channel, visualize distribution locations etc. where unauthorized product contamination rates are high, and present this to product manufacturers etc. In addition, the possibility information generation system 100 of this embodiment makes it possible to reduce the computational resources and human resources required to visualize the status of unauthorized product distribution.
[0048] In this embodiment, a program is provided that is executed by a computer to cause the computer to function as the above-described possibility information generation system 100. The program may be stored in the storage unit 120 of the possibility information generation system 100.
[0049] Fig. 2 shows an example of a processing flow of the possibility information generation method of this embodiment. By performing the processing flow from S1010 to S1120 in Fig. 2, the authenticity determination system 10 of this embodiment visualizes distribution centers, etc. that have a high rate of contamination by unauthorized products, based on location information of authenticity determination centers that perform product authenticity determination and / or information about the product distribution route, and product authenticity determination data, and presents this to product manufacturers, etc.
[0050] For ease of explanation, the processes from S1010 to S1120 will be explained in order, but at least some of these processes may be executed in parallel, or the steps may be interchanged as long as it does not deviate from the spirit of the present invention, or some steps may be omitted as long as it does not deviate from the spirit of the present invention.
[0051] In the processing flow shown in FIG. 2, the possibility information generation system 100 executes a location information acquisition step S1020, a distribution route information acquisition step S1040, an authenticity determination data acquisition step S1080, a possibility information generation step S1090, and a possibility information output step S1110.
[0052] 2, first, in S1010, the terminal 500 at the authenticity determination location 400 outputs location information of the authenticity determination location 400 to the possibility information generation system 100. The location information may represent the location of the authenticity determination location 400 and / or the location of the authenticity determination device when performing the authenticity determination. The location may be represented using a country, state, prefecture, city, ward, town, or village, street address, etc., or may be represented by the longitude and latitude of the location.
[0053] If the terminal 500 includes an authenticity determination device, the authenticity determination device may output the location information directly to the possibility information generation system 100 via the network 300 in S1010.
[0054] After S1010, in S1020, the location information acquisition unit 205 acquires the location information of the authenticity determination location. If the possibility information generation system 100 has already acquired the location information, the processes of S1010 and S1020 may be omitted.
[0055] Next, in S1030, the terminal 500 outputs product distribution route information, including the authenticity determination location 400, to the possibility information generation system 100. The distribution route information may include the authenticity determination location 400 through which the product passes during distribution and the product transportation route (air, sea, road, etc.) between the authenticity determination locations 400.
[0056] For example, the distribution route information may include information indicating the distribution route of the product from the base (factory, warehouse, etc.) of the product manufacturer or seller to the base of a distributor (e.g., a fulfillment center that performs product collection, sorting, unpacking, inspection, storage, picking, packing, delivery, etc.) (e.g., the area where the fulfillment center receives products), and information indicating the distribution route of the product from the base of a distributor (e.g., a fulfillment center) to the base of another distributor (e.g., a distribution center that delivers products to consumers, etc.).
[0057] After S1030, in S1040, the distribution route information acquisition unit 210 acquires the distribution route information. If the possibility information generation system 100 has already acquired the distribution route information, the processes of S1030 and S1040 may be omitted.
[0058] The processes of S1010 and S1020, and S1030 and S1040 may be executed simultaneously. For example, the possibility information generation system 100 may acquire distribution route information at the same time as acquiring location information.
[0059] Fig. 3 shows an example of location information and distribution route information 800. In the example shown in Fig. 3, the location information and distribution route information 800 includes information 810 indicating an authenticity determination location, location information 820 for each authenticity determination location, and distribution route information 830.
[0060] In the example shown in Fig. 3, the information 810 indicating the authenticity determination location indicates the name of the authenticity determination location 400, and the location information 820 indicates the address of the location of each authenticity determination location 400. For example, for "A (fulfillment center)" (No. 1) as information 810 indicating the authenticity determination location, the location information 820 indicates the address, such as the location "XX, XX City, T Prefecture." Furthermore, in the example of No. 1, the distribution route information 830 indicates the distribution route by which products are shipped from manufacturers X and Y to A. Furthermore, in No. 2, "B (distribution center)" is indicated as information 810 indicating the authenticity determination location, the location information 820 indicates the location "XX, XX Ward, T City," and the distribution route information 830 indicates the distribution route by which products are shipped from A to B.
[0061] The distribution route information 830 may be information showing the distribution route of a product on map information, as shown in an example of a heat map described later. For example, for "A (fulfillment center)" shown as the authenticity determination site 400 in Fig. 3, the distribution route information 830 may show on map information the region where the authenticity determination site A receives products.
[0062] After S1040, in S1050, the authenticity determination site 400 performs an authenticity determination of the product. The authenticity determination may be performed by applying a known authenticity determination technique. For example, the authenticity determination may be performed using an authenticity determination device that reads an anti-counterfeit label attached to the product. For the anti-counterfeit label, the above description of the anti-counterfeit label may be applied as is. In S1050, the authenticity determination may be performed using an RFID tag.
[0063] Next, in S1060, the terminal 500 generates authentication data. The authentication data may include at least one of the result of the authentication, the date and time when the authentication was performed, and the authentication location 400 that performed the authentication.
[0064] The authenticity determination data may further include product information related to the product for which the authenticity determination was performed. The product information may include, for example, information related to the product manufacturer, product name, and / or product type. By including product information, it is possible to generate unauthorized distribution statistical data categorized by product type, etc., and to grasp the possibility of unauthorized distribution depending on the product type, etc.
[0065] Fig. 4 shows an example of the authenticity determination data 900. In the example of Fig. 4, the authenticity determination data 900 includes, for each product for which an authenticity determination has been performed, information on a manufacturer 910, a product name 920, a product type 930, an authenticity determination result 940, information 950 indicating an authenticity determination location, and a date and time 960 on which the authenticity determination was performed.
[0066] The manufacturer 910 indicates the manufacturer, such as a maker, that produces the product. The manufacturer 910 field may include the name of the manufacturer.
[0067] Product name 920 indicates the name of the product. The product name may be the sales name displayed on the product when it is sold, or may be the name the product is called within the manufacturer's company.
[0068] Product type 930 may be, for example, a product type classified according to the product's use (e.g., food, accessories, clothing, and daily necessities, etc.), or may be a product type classified by further subdivided product names (e.g., bags, watches, necklaces, rings, and Western-style sweets, etc.).
[0069] The authenticity determination result 940 is obtained by authenticity determination and indicates whether the product is genuine or an unauthorized product.
[0070] The information 950 indicating the authenticity determination location indicates the authenticity determination location 400 that performed the authenticity determination on the product, and may include the name of the authenticity determination location 400, for example.
[0071] The date and time when the authenticity check was performed 960 indicates the date and time when the authenticity check was performed on the product. The time may be omitted from the check date and time.
[0072] After S1060, in S1070, the terminal 500 outputs the authenticity determination data generated in S1060 to the possibility information generation system 100. The terminal 500 may output the authenticity determination data for each individual product (individual product), or may output the authenticity determination data for multiple products collectively on a regular basis (for example, on a specified date, monthly, weekly, or daily basis).
[0073] Next, in S1080, the authenticity determination data acquisition unit 215 acquires the authenticity determination data of the product from the terminal 500.
[0074] After S1020, S1040, and S1080, in S1090, the possibility information generation unit 220 generates unauthorized distribution possibility information. The unauthorized distribution possibility information indicates the possibility that an unauthorized product is being distributed. The possibility information generation unit 220 may generate the unauthorized distribution possibility information using the above-mentioned location information and / or distribution route information and authenticity determination data.
[0075] The possibility information generating unit 220 may generate unauthorized distribution possibility information for each authenticity assessment location and / or distribution channel. The possibility information generating unit 220 may generate unauthorized distribution possibility information for each product type (the above-mentioned description of product types may be applied as is) and / or for each manufacturer. The possibility information generating unit 220 may generate unauthorized distribution possibility information for a predetermined period and / or region.
[0076] In S1090, when the possibility information generating unit 220 generates the illegal distribution possibility information, the statistical data generating unit 225 may generate illegal distribution statistical data.
[0077] The illegal distribution statistical data may include at least one of the detection status (sometimes simply referred to as "detection status") of illegally distributed products detected at an authenticity assessment center, the detection status over a predetermined period of time, the detection status by product type, and the date and time of the most recent detection of an illegally distributed product.
[0078] The detection status may include the number of times unauthorized distribution products were detected at the authentication determination site and / or the ratio of the number of times the products were detected to the number of times authentication was performed. The detection status may include the number of times the products were detected and / or the ratio of the number of times the products were detected to the number of times authentication was performed during a predetermined period.
[0079] In S1090, the possibility information generating unit 220 may generate illegal distribution possibility information using illegal distribution statistical data. The possibility information generating unit 220 may generate illegal distribution possibility information that indicates, in stages, the possibility of illegal distribution at the authenticity assessment location 400 and / or the distribution channel according to predetermined criteria.
[0080] The specific procedures for generating the illegal distribution statistical data and generating the illegal distribution possibility information using the illegal distribution statistical data will be described in the subflows below.
[0081] In S1090, the possibility information generating unit 220 may generate a heat map that displays at least a portion of the unauthorized distribution possibility information on map information. The heat map may display, on map information, the authenticity determination base 400 and / or distribution route information, and the possibility that unauthorized distribution of products is occurring at the authenticity determination base 400 and / or distribution route.
[0082] In the heat map, the possibility of unauthorized distribution of products may be visually represented using symbols such as dots and circles to make it easier to understand. The specific procedure for generating a heat map using unauthorized distribution statistical data will be explained in the subflow below.
[0083] In S1090, the possibility information generation unit 220 may generate unauthorised distribution possibility information that has been rearranged and / or narrowed down in accordance with the specified conditions. At this time, the terminal 700 of the user 600 may output, prior to S1090, to the possibility information generation system 100 in S1100, specification of the conditions for rearranging and / or narrowing down. The rearranging and / or narrowing down of the unauthorised distribution possibility information based on the specified conditions will be described in a subflow below.
[0084] Next, the subflow of S1090 will be described. Fig. 5 shows an example of the subflow of S1090. By performing the subflow of Fig. 5, the possibility information generation system 100 may generate illegal distribution possibility information that indicates the possibility of illegal distribution in stages based on illegal distribution statistical data obtained by statistically processing authenticity determination data.
[0085] 5, first, in S2010, the statistical data generation unit 225 classifies the authenticity determination data acquired in S1080 by product type. Regarding the product type, the description of the product type in the authenticity determination data shown in FIG. 4 may be applied.
[0086] Next, in S2020, the statistical data generation unit 225 classifies the authenticity determination data by location information. The statistical data generation unit 225 may classify the authenticity determination data by each authenticity determination location 400 and / or by region to which the authenticity determination location 400 belongs (for example, within the same country or the same prefecture).
[0087] Next, in S2030, the statistical data generation unit 225 classifies the authenticity determination data by distribution route. The statistical data generation unit 225 may classify the authenticity determination data by the same distribution route and / or by region to which the distribution route belongs (for example, within the same country or the same region, etc.).
[0088] 5, any one or more of the processes from S2010 to S2030 may be omitted. For example, if the user 600 desires unauthorized distribution information for each product type, only classification by product type may be performed.
[0089] The order of the processes from S2010 to S2030 may be reversed. For example, the authenticity determination data may be classified by distribution channel in S2030, and then classified by product type in S2010.
[0090] Next, in S2040, the possibility information generation unit 220 uses the illegal distribution statistical data to generate illegal distribution possibility information that indicates in stages the possibility of illegal distribution at the authenticity determination location 400 and / or distribution channel based on predetermined criteria.
[0091] The possibility information generation unit 220 may express the possibility that an unofficially distributed product is in circulation as a scale rating such as "3", "2", "1" or "high", "medium", "low" etc., categorized according to predetermined criteria, or may express it as a percentage such as "80%", "50%", "20%", etc.
[0092] 6 shows an example of unauthorized distribution statistical data 1000. The unauthorized distribution statistical data 1000 may include a product type 1010, a manufacturer 1020, and a detection situation 1030.
[0093] In the example of Figure 6, the authenticity determination data is classified by product type 1010, manufacturer 1020, and authenticity determination location 400 ("Location A" to "Location E" in the detection status 1030 column), and for each authenticity determination location 400, the detection status 1030 shows the ratio (percentage) of the number of detections to the cumulative number of authenticity determinations performed.
[0094] The example in Fig. 6 also shows the total detection status for each type of product ("Bags (total)," "Necklaces (total)," and "Western-style confectionery (total)") at each authenticity determination location 400. Note that in the example in Fig. 6, "Location A" to "Location E" set as authenticity determination locations 400 correspond to "A (fulfillment center)" to "E (distribution center)" of the authenticity determination locations 400 in the example shown in Fig. 3.
[0095] 7 shows an example of unauthorized distribution statistical data 1100. The unauthorized distribution statistical data 1100 may include a product type 1110, a manufacturer 1120, and a detection status 1130.
[0096] In the example of Fig. 7, the descriptions of 1010 and 1020 shown in Fig. 6 may be applied to product type 1110 and manufacturer 1120. In the example of Fig. 7, detection status 1130 shows the number of detections and the most recent detection date and time during a predetermined period (July 1, 2023 to June 30, 2024).
[0097] The example in Fig. 7 also shows the total detection status for each type of product ("Bags (total)," "Necklaces (total)," and "Western-style confectionery (total)") at each authenticity determination location 400. Note that in the example in Fig. 7, "Location A" to "Location E" set as authenticity determination locations 400 correspond to "A (fulfillment center)" to "E (distribution center)," respectively, of the authenticity determination locations 400 in the example shown in Fig. 3.
[0098] As shown in FIGS. 6 and 7, the detection status of unauthorized distribution at the authentication determination site 400 can be grasped by generating unauthorized distribution statistical data by statistically processing the authentication determination data.
[0099] 8 shows an example of the unauthorized distribution possibility information 1200. The unauthorized distribution possibility information 1200 is an example generated based on the unauthorized distribution statistical data 1000 shown in FIG.
[0100] The unauthorized distribution possibility information 1200 may include information 1210 indicating the authenticity determination location, distribution channel information 1220 , a criterion 1230 , and a score 1240 .
[0101] In the example shown in FIG. 8, information 1210 indicating the authenticity determination location and distribution route information 1220 correspond to information 810 indicating the authenticity determination location and distribution route information 830 shown in FIG. 3, respectively.
[0102] Criteria 1230 indicates the criteria for expressing the possibility of unauthorized distribution with a graded score 1240. In the example shown in Fig. 8, the criteria 1230 assigns a score of "1" indicating a low possibility of unauthorized distribution of products when the detection situation is 0%, a score of "2" indicating a high possibility of unauthorized distribution of products when the detection situation is greater than 0% and less than or equal to 3%, and a score of "3" indicating a high possibility of unauthorized distribution of products when the detection situation is greater than 3%.
[0103] In the example shown in Figure 8, in addition to a score of 1240 for each authenticity assessment location 400, the distribution routes are represented as "Manufacturer X, Y → A," "A → B," "A → C," "Seller Z → D," and "D → E," and a score of 1240 is assigned to each distribution route.
[0104] Here, "Manufacturer X, Y → A" and "Seller Z → D" respectively indicate the distribution route from product manufacturers X and Y to authenticity determination base A (which may include the target receiving area of authenticity determination base A) and the distribution route from seller Z to authenticity determination base D (which may include the target receiving area of authenticity determination base D). Also, "A → B", "A → C", and "D → E" respectively indicate the distribution route from authenticity determination base A to B, the distribution route from authenticity determination base A to C, and the distribution route from authenticity determination base D to E.
[0105] In the example shown in Figure 8, to simplify data processing, it is assumed that each authenticity assessment center performs an authenticity assessment on all products, and any unauthorized products detected at each authenticity assessment center are removed from the distribution channel. Therefore, in the example of Figure 8, the scores 1240 for the distribution channels "Manufacturer X, Y → A," "A → B," "A → C," "Seller Z → D," and "D → E" match the scores 1240 at the authenticity assessment centers A, B, C, D, and E, respectively.
[0106] In the example of Figure 8, the score 1240 of the authenticity determination location 400 described above and the score 1240 for each distribution location were assigned based on the detection status of "Bags (total)", "Necklaces (total)", and "Western-style confectionery (total)" in Figure 6, respectively.
[0107] In the unauthorized distribution possibility information 1200 shown in Fig. 8, for example, since the detection rate of bags (total) at authenticity determination location A in Fig. 6 was 2.5%, a score of "2" was assigned to the bags at authenticity determination location A. Also, since the detection rate of necklaces (total) at authenticity determination location B in Fig. 6 was 3.6%, a score of "3" was assigned to the necklaces in the distribution route "A → B".
[0108] As in the example shown in FIG. 8, by expressing the possibility of unauthorized distribution in stages, it is possible to provide the user 600 of the authenticity determination data with information on the possibility of unauthorized distribution that is easy to understand intuitively.
[0109] 9 shows an example of the unauthorized distribution possibility information 1300. The unauthorized distribution possibility information 1300 is an example generated based on the unauthorized distribution statistical data 1100 shown in FIG.
[0110] The explanations of the information 1310 indicating the authenticity determination location, distribution route information 1320, criteria 1330, and score 1340 included in the unauthorized distribution possibility information 1300 may be the same as the explanations of the information 1210 indicating the authenticity determination location, distribution route information 1220, criteria 1230, and score 1240 in Figure 8.
[0111] In the example shown in Figure 9, the criteria 1330 is such that if there are 0 detections or if no unauthorized distribution products have been detected within the last 6 months from June 30, 2024, a score of "1" is assigned, indicating a low possibility that unauthorized distribution products exist; if there are 1 to 100 detections and unauthorized distribution products have been detected within the last 6 months from June 30, 2024, a score of "2" is assigned, indicating a high possibility that unauthorized distribution products exist; and if there are 101 or more detections and unauthorized distribution products have been detected within the last 6 months, a score of "3" is assigned, indicating a high possibility that a large number of unauthorized distribution products are in circulation.
[0112] In the unauthorized distribution possibility information 1300 shown in Fig. 9, for example, the number of times Western-style pastries (total) were detected at authenticity determination site A in Fig. 7 was 79, but no unauthorized distribution products were detected in the most recent six months, so a score of "1" was assigned to the Western-style pastries at authenticity determination site A. Also, the number of times bags (total) were detected at authenticity determination site D in Fig. 7 was 124, and since detections occurred within the most recent six months, a score of "3" was assigned to the bags in the distribution route "Seller Z → D."
[0113] As shown in the example in Figure 9, by generating information on the possibility of unauthorized distribution that reflects the most recent detection status, the current state of unauthorized distribution can be grasped, and product manufacturers and others can promote countermeasures against counterfeit products that are appropriate to the current situation.
[0114] Fig. 10 shows an example of a subflow of S1090. The possibility information generation system 100 may generate unauthorised distribution possibility information that has been sorted and / or narrowed down according to specified conditions by executing the processing flow from S3010 to S3050 in Fig. 10.
[0115] In the subflow shown in FIG. 10, the description of S3010 to S3040 in the subflow shown in FIG. 5 may be applied as is to the description of S3010 to S3040.
[0116] 10, after S3040, in S3050, the possibility information generation unit 220 sorts and / or narrows down the unauthorized distribution possibility information based on specified conditions (sometimes simply referred to as "specified conditions"). The specified conditions may be the specified conditions output by the terminal 700 of the user 600 in S1100 described above.
[0117] The specified conditions may include, for example, conditions for the authenticity determination site 400, product type, manufacturer, detection date and time, and detection situation included in the unauthorized distribution possibility information. For example, the specified conditions may limit the types of products included in the unauthorized distribution possibility information to products of a specific type (e.g., "Western confectionery"), and sort the unauthorized distribution products in descending order of the number of times they have been detected.
[0118] Fig. 11 shows an example of unauthorized distribution possibility information 1400. The unauthorized distribution possibility information 1400 shows unauthorized distribution possibility information generated by the processing flow of the possibility information generation method of this embodiment, which includes the subflow of Fig. 10. The unauthorized distribution possibility information 1400 may include information 1410 indicating the authenticity determination location, distribution channel information 1420, and a score 1430.
[0119] The unauthorized distribution possibility information 1400 includes information 1410 indicating authenticity determination locations and distribution route information 1420 sorted in descending order of score, with the unauthorized distribution possibility information 1300 shown in FIG. 9 limited to Western-style confectionery as the product type.
[0120] As shown in the flow of Figure 10 and the example of Figure 11, by generating information on the possibility of unauthorized distribution by sorting and / or narrowing it down based on specified conditions, a user 600 of the authenticity assessment data can select and view information on the possibility of unauthorized distribution of products, etc. related to their company.
[0121] Fig. 12 shows an example of a subflow of S1090. The possibility information generation system 100 may generate a heat map that displays at least a portion of the illegal distribution possibility information on map information by executing the processing flow from S4010 to S4050 in Fig. 12.
[0122] In the flow shown in FIG. 12, the explanation of S4010 to S4040 in the subflow explained in FIG. 5 may be applied as is to the explanation of S2010 to S2040.
[0123] In the flow shown in FIG. 12, after S4040, in S4050, the heat map generating unit 230 generates a heat map that displays at least a part of the unauthorized distribution possibility information on map information.
[0124] The heat map generation unit 230 may display the possibility of unauthorized distribution (for example, the score in the examples shown in FIGS. 8, 9, and 11) on map information. In addition to the possibility of unauthorized distribution, the heat map generation unit 230 may also display the authenticity determination site 400, distribution route information, the detection status of unauthorized distribution products, the type of product, etc. on the map information.
[0125] In a heat map, the possibility and / or detection status of unauthorized distribution may be represented by symbols such as circles on map information, and the symbols may be visually represented by using different sizes and / or colors depending on the frequency of the possibility and / or detection status of unauthorized distribution.
[0126] 13 shows an example of a heat map 1500. The heat map 1500 is an example of a heat map generated based on the unauthorized distribution possibility information 1200 of FIG.
[0127] In the example of Fig. 13, the location information of authenticity assessment locations A to E, manufacturers X, Y, and seller Z shown in Fig. 8 is represented on a map by symbols 1510. In a heat map 1500, for each authenticity assessment location 400, a score 1520 indicating the possibility of unauthorized distribution for each type of product (bags, necklaces, and Western-style confectionery) is represented by symbols 1530, 1540, and 1550 of different sizes depending on the score.
[0128] 14 shows an example of a heat map 1600. The heat map 1600 is an example of a heat map generated based on the unauthorized distribution possibility information of FIG.
[0129] 14, the distribution routes "Manufacturer X, Y → A," "A → B," "A → C," "Seller Z → D," and "D → E" shown in Fig. 9 are represented on the map as areas 1610, 1612, 1614, 1616, and 1618 surrounded by dotted lines on the map information. In the heat map 1600, for each distribution route, a score 1620 indicating the possibility of unauthorized distribution for each type of product (bags, necklaces, and Western-style confectionery) is represented by symbols 1630, 1640, and 1650 of different sizes depending on the score.
[0130] As shown in the examples of Figures 13 and 14, by visually representing the possibility of unauthorized distribution on map information, it is possible to provide users 600 of the authenticity assessment data with information on the possibility of unauthorized distribution that is easy to understand intuitively.
[0131] After S1090, in S1110, the possibility information output unit 235 outputs the unauthorized distribution possibility information to the terminal 700 used by the user 600. At this time, if a specification of sorting and / or narrowing conditions is accepted from the terminal 700, the possibility information output unit 235 may output the unauthorized distribution possibility information that has been sorted and / or narrowed down. If a heat map has been generated, the possibility information output unit 235 may output the heat map to the terminal 700.
[0132] Next, in S1120, the terminal 700 of the user 600 acquires the unauthorized distribution possibility information. The user 600 can view the unauthorized distribution possibility information via the terminal 700.
[0133] The method flow of this embodiment described above makes it possible to grasp the status of unauthorized distribution of products for each authenticity determination location and / or distribution channel, visualize distribution locations, etc. where unauthorized product contamination rates are high, and present this information to product manufacturers, etc. In addition, the method flow of this embodiment makes it possible to reduce the computational resources and human resources required to visualize the status of unauthorized product distribution.
[0134] 15 illustrates an example of a computer 2200 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.
[0135] A computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.
[0136] The CPU 2212 operates according to programs stored in the ROM 2230 and RAM 2214, thereby controlling each unit. The graphics controller 2216 acquires image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphics controller 2216 itself, and causes the image data to be displayed on the display device 2218.
[0137] The communications interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.
[0138] The ROM 2230 stores therein a boot program or the like that is executed by the computer 2200 upon activation, and / or programs that depend on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.
[0139] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. Information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by realizing information manipulation or processing in accordance with the use of the computer 2200.
[0140] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.
[0141] The CPU 2212 may also cause all or a necessary portion of a file or database stored on an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.
[0142] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 2212 may perform various types of processing on data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, the CPU 2212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.
[0143] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 2200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.
[0144] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.
[0145] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]
[0146] 10 Authentication System 100 Possibility Information Generation System 110 input unit, 120 memory unit, 130 display unit, 140 communication unit, 200 calculation unit 205 Location information acquisition unit 210 Distribution Channel Information Acquisition Department 215 Authenticity determination data acquisition unit 220 possibility information generation unit, 225 statistical data generation unit 230 heat map generation unit, 235 possibility information output unit 240 Condition specification reception unit, 245 Machine learning unit 400 Authentication Center 500, 700 terminals 600 users 800 Location information and distribution route information 810 Information indicating authenticity determination location, 820 Location information 830 Distribution Channel Information 900 Authenticity Determination Data 910 Manufacturer, 920 Product name, 930 Product type, 940 Authenticity determination result 950 Information indicating the authentication location, 960 Date and time when the authentication was performed 1000, 1100 Unofficial Distribution Statistics 1010, 1110 Product Type 1020, 1120 Manufacturer 1030, 1130 Detection status 1200, 1300, 1400 Unauthorized circulation possibility information 1210, 1310, 1410 Information indicating the authenticity determination location 1220, 1320, 1420 Distribution channel information 12:30, 13:30 standard 1240, 1340, 1430 scores 1500, 1600 Heatmap 1510, 1530, 1540, 1550, 1630, 1640, 1650 symbols 1520, 1620 score 1610, 1612, 1614, 1616, 1618 area 2200 Computer 2201 DVD-ROM 2210 host controller 2212 CPU 2214 RAM 2216 Graphics Controller 2218 Display Device 2220 Input / Output Controller 2222 communication interface 2224 hard disk drive 2226 DVD-ROM drive 2230 ROM 2240 I / O chip 2242 keyboard
Claims
1. a location information acquisition unit that acquires location information of an authenticity determination site that performs authenticity determination of a product; a distribution channel information acquisition unit that acquires information about the distribution channel of the product, including the authenticity determination base; an authenticity determination data acquisition unit that acquires authenticity determination data of the product from a terminal at the authenticity determination site; a possibility information generation unit that generates, using the location information and / or information related to the distribution channel and the authenticity determination data, unauthorized distribution possibility information indicating the possibility that the product is being unauthorizedly distributed in a manner not intended by the manufacturer or seller of the product at the authenticity determination center and / or the distribution channel; a possibility information output unit that outputs the information on the possibility of unauthorized distribution to a terminal used by a user of the authenticity determination data; A possibility information generation system comprising:
2. The authenticity determination data includes at least one of a result of the authenticity determination, a date and time when the authenticity determination was performed, and an authenticity determination location where the authenticity determination was performed. The possibility information generation system according to claim 1 .
3. a statistical data generation unit that generates, based on the authenticity determination data, unauthorized distribution statistical data including at least one of the following: a detection status including the number of times unauthorized distribution products have been detected at the authenticity determination base and / or a ratio of the number of times the detection has occurred to the number of times the authenticity determination has been performed; the detection status for a predetermined period; the detection status for each type of product; and the date and time of the most recent detection of an unauthorized distribution product; the possibility information generation unit generates the illegal distribution possibility information using the illegal distribution statistical data. The possibility information generation system according to claim 1 .
4. the possibility information generation unit uses the illegal distribution statistical data to generate illegal distribution possibility information that indicates in stages the possibility of illegal distribution at the authenticity determination center and / or the distribution channel according to a predetermined standard; The possibility information generation system according to claim 3 .
5. the possibility information output unit outputs the irregular distribution possibility information that has been rearranged and / or narrowed down according to specified conditions. The possibility information generating system according to claim 4 .
6. a heat map generating unit that generates a heat map that represents at least a part of the unauthorized distribution possibility information on map information; the possibility information output unit outputs the heat map. The possibility information generation system according to claim 1 .
7. The authenticity determination base includes a plurality of bases in the distribution channel of the product, The possibility information generation system according to claim 1 .
8. Users of the authentication data include manufacturers and / or sellers of the products. The possibility information generation system according to claim 1 .
9. a machine learning unit that generates a prediction model that predicts the possibility of unauthorized distribution from at least a portion of the authenticity determination data; the possibility information generation unit generates the unauthorized distribution possibility information using the prediction model. The possibility information generation system according to claim 1 .
10. At least one of the location information, the information on the distribution channel, the authenticity determination data, and the information on the possibility of unauthorized distribution is recorded on a ledger on a blockchain network. The possibility information generation system according to claim 1 .
11. The method is executed by a computer, causing the computer to: The possibility information generation system according to any one of claims 1 to 10 is operated as follows: program.
12. The possibility information generation system according to any one of claims 1 to 10, a location information acquisition stage for acquiring location information of a plurality of authenticity determination locations that perform authenticity determination of the product; a distribution channel information acquisition step of acquiring information about the distribution channel of the product between the plurality of authenticity determination locations; an authenticity determination data acquisition step of acquiring authenticity determination data of the product from terminals of the plurality of authenticity determination locations; a possibility information generation step of generating, using the location information and / or information related to the distribution channel and the authenticity determination data, unauthorized distribution possibility information indicating a possibility that the product is being unauthorizedly distributed, i.e., distributed in a manner not intended by the manufacturer or seller of the product at the plurality of authenticity determination bases and / or the distribution channel; a possibility information output unit for outputting the unauthorized distribution possibility information; Possibility to perform information generation method.