Information processing device and information processing method

Abstract

To provide an information processing apparatus capable of stably distributing containers.SOLUTION: An information processing apparatus 2 includes: an information acquisition unit 201A which acquires, on the basis of web access information D8 included in an information storage carrier 100 attached to a container 10 to be filled with contents 11 and constituting a commodity 12, purchase information D12 indicating the type of contents 11 to be purchased, out of contents 11 with which a consumer U7 of the commodity 12 can fill a container with the same specifications as the container 10; and a storage processing unit 201B which stores the purchase information D12 acquired by the information acquisition unit 201A in a container purchase management database 213 serving as a storage unit.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] Conventionally, as a management method for products filled in containers when they are distributed in the distribution market, it is known to use container identification codes attached to the containers to perform inventory management, sales forecasting, marketing, tracing, etc. For example, Patent Document 1 discloses a technology for managing products by reading barcodes printed on the containers with a barcode reader. Also, Patent Document 2 discloses a technology for managing products by reading IC tags attached to the containers with a reader / writer. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-256765 [Patent Document 2] Japanese Patent Application Laid-Open No. 2018-188199 Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Documents 1 and 2, product distribution and inventory are managed using container identification codes attached to containers. For example, when a production system such as high-mix low-volume production is adopted in which containers of the same specifications are used for multiple types of contents, the distribution status of each product filled with each content differs depending on the type of content. Therefore, if consumer information such as what type of content a consumer wants to purchase next can be known in advance, it is considered that this information can be very useful in realizing such a production system.

[0005] The present invention has been made in view of the above-mentioned problems, and has an object to provide an information processing device and an information processing method that enable stable distribution of containers. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: an information acquisition unit that acquires purchase information indicating the type of content that a consumer of the product wishes to purchase from among the contents that can be filled into a container of the same specifications as the container, based on web access information contained in an information storage carrier attached to the container that is filled with the content to constitute the product; The purchasing information acquisition unit includes a storage processing unit that stores the purchasing information acquired by the information acquisition unit in a storage device. [Effects of the Invention]

[0007] According to an information processing device of one aspect of the present invention, based on information included in an information storage carrier attached to a container constituting a product, purchase information indicating the type of content that a consumer of the product wishes to purchase from among the contents that can be filled into a container with the same specifications as the container is acquired and stored in a storage device. Therefore, by utilizing the purchase information, stable distribution of containers can be ensured. For example, in the case of high-mix, low-volume production in which the same containers are used for multiple types of content, the purchase information can be used to accurately predict consumer demand and realize supply plans for distributors, thereby ensuring stable distribution of containers.

[0008] Problems, configurations, and effects other than those described above will become apparent from the detailed description of the invention that follows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an overall view showing an example of a container distribution system 1 according to the present embodiment. [Figure 2] 2 is an explanatory diagram showing an example of each distribution stage of the container 10. FIG. [Figure 3]1 is a block diagram showing an example of an information processing device 2 according to the present embodiment. [Figure 4] FIG. 2 is a functional explanatory diagram showing a container investment management function 200 and an example of information flow. [Figure 5] FIG. 2 is a data configuration diagram showing an example of a distribution management database 210. [Figure 6] FIG. 2 is a data configuration diagram showing an example of a container investment management database 211. [Figure 7] FIG. 2 is a functional explanatory diagram showing a container purchase management function 201 and an example of information flow. [Figure 8] FIG. 2 is a data configuration diagram showing an example of a web management database 212. [Figure 9] FIG. 2 is a data configuration diagram showing an example of a container purchase management database 213. [Figure 10] 1 is a functional explanatory diagram showing a learning function 202 and an example of information flow. [Figure 11] FIG. 2 is a functional explanatory diagram showing a demand and supply management function 203 and an example of information flow. [Figure 12] FIG. 9 is a hardware configuration diagram showing an example of a computer 900 that constitutes each device. [Figure 13] 10 is a flowchart showing an example of a container purchase management method by the container purchase management function 201. [Figure 14] 10 is a flowchart showing an example of a machine learning method performed by the learning function 202. [Figure 15] 10 is a flowchart showing an example of a demand and supply management method performed by the demand and supply management function 203. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. The scope necessary for the explanation to achieve the object of the present invention will be schematically shown, and the scope necessary for explaining the relevant part of the present invention will be mainly explained, and the parts that are omitted from the explanation will be based on publicly known techniques.

[0011] (Configuration of Container Distribution System 1) Fig. 1 is an overall view showing an example of a container distribution system 1 according to this embodiment. Fig. 2 is an explanatory view showing an example of each distribution stage of a container 10.

[0012] The container distribution system 1 includes, as its main component, an information processing device 2. The information processing device 2 is a device that manages various information acquired in the process of containers 10, which are filled with contents 11 and constitute products 12, being distributed in the distribution market, and that realizes multiple functions (described in detail below).

[0013] The information processing device 2 is composed of a general-purpose or dedicated computer (see FIG. 12 described below). Connected to the information processing device 2 via a wired or wireless network 8 are an investor terminal device 3 used by an investor U1, an information user terminal device 4 used by an information user U2, a management company terminal device 5 used by a management company U3 of the information processing device 2, distributor terminal devices 6A-6C used by a container manufacturer U4, a content manufacturer U5, and a container washer U6, respectively, and a consumer terminal device 7 used by a consumer U7. Note that the number of devices 2-7 and the configuration of the network 8 are not limited to the example of FIG. 1.

[0014] The investor terminal device 3, information user terminal device 4, management company terminal device 5, distribution company terminal devices 6A-6C, and consumer terminal device 7 are general-purpose or dedicated computers (see FIG. 12 below), such as stationary computers or portable computers. Programs such as browsers and applications are installed on them, and they accept various input operations and output various information via display screens and voice.

[0015] Container 10 is manufactured using raw materials such as plastic, metal, glass, paper, etc., and may have any shape or size. The specifications of container 10 are determined by the type, raw materials, capacity, shape, size, etc. of container 10. Examples of types of container 10 include plastic bottles made of polyethylene terephthalate (PET) or the like, metal cans made of steel, aluminum, etc., glass bottles, paper cartons, laminated pouches, etc. Container 10 may be a returnable container or a one-way container.

[0016] Here, the condition for multiple containers 10 to have the same specifications is satisfied when, for at least one of the multiple specification items, the numerical value is the same or falls within a specific approximate range, or the contents are the same or fall within a specific similar range. For one specification item, the capacity of the containers 10, two containers 10 may be considered to have the same specifications if both have a capacity of 200 ml, for example, or two containers 10 may be considered to have the same specifications if their capacities are within an approximate range, such as 200 ml and 180 ml. For one specification item, the type of container 10, two containers 10 may be considered to have the same specifications if their types are both glass bottles, for example, or two containers 10 may be considered to have the same specifications if their types are within an approximate range, such as a steel can and an aluminum can.

[0017] As shown in FIG. 2, the container 10 is provided with an information storage medium 100 capable of storing container identification information D7 and web access information D8. The container identification information D7 is information for identifying the container 10 and specifying the specifications of the container 10. The container identification information D7 is, for example, a container ID (container identification code) for identifying the container 10 individually or by lot. Note that the container identification information D7 may be omitted and not stored in the information storage medium 100. The web access information D8 is information required for the consumer U7 to access, for example, a web service operated by the management company U3. The web access information D8 includes, for example, a web address (URL) for accessing a web page provided by the web service and link information for launching an app provided by the web service.

[0018] The information storage carrier 100 is configured with a code image such as a one-dimensional code or a two-dimensional code. In this case, a label on which the code image is printed may be attached to or wrapped around the container 10, or may be embedded inside the molded body of the container 10. The information storage carrier 100 is also configured with an electronic tag (IC tag) called RFID or the like. In this case, the electronic tag is embedded inside the molded body of the container 10. Note that multiple information storage carriers 100 may be attached to the container 10; for example, an information storage carrier 100 including container identification information D7 and an information storage carrier 100 including web access information D8 may be attached.

[0019] Contents 11 may be liquid, solid, powder, granules, etc. Examples of types of contents 11 include beverages such as water, juice, and milk, foods, medicines, cosmetics, detergents, etc. Contents 11 may be at room temperature, chilled (refrigerated), or frozen. Furthermore, contents 11 may be heated or unheated during the manufacturing process, and in the case of beverages and foods, they may be fresh or processed products.

[0020] The container 10 is manufactured by, for example, a container manufacturer U4 (container manufacturing stage), and the contents 11 are manufactured by a contents manufacturer U5 (content manufacturing stage). Then, the contents 11 are filled into the container 10 by the contents manufacturer U5 (filling stage), and the product 12 is manufactured. The contents manufacturer U5 is, for example, a beverage manufacturer or a food manufacturer, and when the specifications and quantity of the container 10 are specified and an order is placed with the container 10 to the container manufacturer U4 or the container washer U6, the container 10 corresponding to the order is delivered by the container manufacturer U4 or the container washer U6, and the cost is paid to the container manufacturer U4 or the container washer U6. Then, the information storage medium 100 is configured with a code image, and In this case, for example, in the filling stage, a label on which a code image is printed is attached to the container 10 by the content manufacturer U5. In addition, in the case where the information storage carrier 100 is configured as an electronic tag, the electronic tag is embedded in the container 10 by the container manufacturer U4 in the container manufacturing stage, and the container identification information D7 and web access information D8 are written to the electronic tag by the content manufacturer U5 in the filling stage.

[0021] The product 12 is shipped from the content manufacturer U5 (shipping stage) and transported to, for example, a retail store or warehouse (transportation stage). The product 12 is then sold to a consumer U7 via, for example, a wholesaler, retailer, trading company, etc. (sales stage), and the content 11 is consumed by the consumer U7 (consumption stage). Consumer U7 includes not only general consumers but also businesses, etc. If the container 10 is a returnable container, once the content 11 is consumed by the consumer U7 (consumption stage), the container 10 is collected from the consumer U7 (collection stage) and cleaned by a container washer U6 (cleaning stage). The content 11 is then filled into the cleaned container 10 by the content manufacturer U5 (filling stage), thereby producing the product 12.

[0022] As described above, container 10 is distributed through multiple distribution stages in the distribution market. The multiple distribution stages include, but are not limited to, a container manufacturing stage, a content manufacturing stage, a filling stage, a shipping stage, a transportation stage, a sales stage, a consumption stage, a collection stage, and a cleaning stage. If container 10 is a returnable container, the multiple distribution stages include at least a cleaning stage, a filling stage, and a consumption stage, and container 10 is reused by repeating these distribution stages. In this embodiment, the container 10 is a glass bottle manufactured as a returnable container, and the type of content 11 is a beverage such as water, juice, or milk.

[0023] (Configuration of information processing device 2) 3 is a block diagram showing an example of an information processing device 2 according to this embodiment. The information processing device 2 includes a control unit 20 configured with a processor or the like, a storage unit 21 configured with an HDD, an SSD, a memory or the like, a communication unit 22 that is a communication interface with the network 8, an input unit 23 configured with a keyboard, a mouse or the like, and a display unit 24 configured with a display or the like. Note that the input unit 23 and the display unit 24 may be omitted.

[0024] The storage unit 21 stores various databases (DB) 210 to 215 and an information processing program 216, as well as an operating system, other programs, data, and the like.

[0025] The control unit 20 executes an information processing program 216 stored in the storage unit 21 to implement a container investment management function 200, a container purchasing management function 201, a learning function 202, and a demand / supply management function 203. The control unit 20 includes an information acquisition unit 201A and a storage processing unit 201B as units that implement the container purchasing management function 201. The control unit 20 includes a learning data acquisition unit 202A and a machine learning unit 202B as units that implement the learning function 202. The control unit 20 includes an information acquisition unit 203A, a generation processing unit 203B, and an output processing unit 203C as units that implement the demand / supply management function 203.

[0026] The functions 200 to 203 and the contents of the databases 210 to 215 will be explained below.

[0027] (Container investment management function 200) 4 is a functional explanatory diagram showing the container investment management function 200 and an example of information flow. The control unit 20 of the information processing device 2 mainly uses a distribution management database 210 (see FIG. 5 described later) and a container investment management database 211 (see FIG. 6 described later) to manage the container investment management function 200. Achieve this.

[0028] The information processing device 2 receives investment information D1 from the investor U1 via the investor terminal device 3 (arrow (1a) in FIG. 4), registers the investment information D1 in the container investment management database 211, and generates distribution container setting information D2 based on the investment information D1, in which the container quantity, specifications, and type of contents 11 of the container 10 to be invested in are set, and invests in the container 10 (arrow (1b)). Then, when the container 10 to be invested in is distributed in the distribution market, the information processing device 2, when the information storage carrier 100 attached to the container 10 is read by a reading device (not shown), registers container status data indicating the status of the container 10 at each distribution stage in the distribution management database 210 in association with the container identification information D7 included in the information storage carrier 100, and stores the collected data D3 in the container investment management database 211 as a data set of container status data for multiple containers 10 (arrow (1c)). The reading device may be, for example, a code reader, a camera, an electronic tag reader, or the like, and may be a fixed or handheld type, or may be an embedded type incorporated into a portable computer (such as a smartphone). The reading device is used at each distribution stage in the distribution market, and is installed in various places such as manufacturing plants, logistics centers, transportation means, sales stores, and refrigerators in the homes of consumers U7.

[0029] Furthermore, the information processing device 2 generates container distribution information D4 based on the collected data D3 (arrow (1d)), and converts the container distribution information D4 into value media 13 by providing it to the information user U2 via the information user terminal device 4 (arrow (1e)).The information processing device 2 then generates dividend information D6 by distributing the value media 13 based on the investment information D1 (arrow (1f)), registers the dividend information D6 in the container investment management database 211, and pays dividends to the investor U1 based on the dividend information D6 (arrow (1g)).

[0030] Examples of forms of investment in the container 10 include, for example, when a content manufacturer U5 orders a container 10, investing in part or all of the purchase cost of the container 10, or when a container manufacturer U4 or a container washer U6 manufactures or cleans the container 10, investing in part or all of the manufacturing and cleaning costs of the container 10. The container 10 to be invested in is provided to the content manufacturer U5 in response to an order from the content manufacturer U5, and the content manufacturer U5 can receive the container 10 to be invested in without having to pay the invested cost. On the other hand, in exchange for receiving the investment, the content manufacturer U5 consents to the collection of collected data D3 using the container 10 to be invested in, without holding ownership of the container 10 to be invested in, and is required to cooperate in the collection of the collected data D3 as necessary.

[0031] FIG. 5 is a data structure diagram showing an example of the distribution management database 210. The distribution management database 210 has a record for each container identification information D7 (container ID). Each record has fields for registering container status data. In the example of FIG. 5, the database has fields for registering container specifications, manufacturing date and time (cleaning date and time), manufacturing location (cleaning location), filling date and time, filling location, type of filled content, shipping date and time, transportation time, transportation temperature, sales date and time, sales location, consumption date and time, consumption location, storage temperature, etc. Note that the container status data is registered when the container identification information D7 is read by a reading device, but some of the container status data may be acquired by a status detection device (such as a temperature sensor) installed in various locations similar to the reading device. Furthermore, the container status data may be acquired by the information processing device 2 linking with an external system (such as a manufacturing management system, a transportation management system, or a sales management system) or an external app (such as an online shopping app or a refrigerator management app).

[0032] The manufacturing location, cleaning location, filling location, sales location, and consumption location correspond to distribution location information regarding the location of the container 10 as it is distributed through each distribution stage. In addition, when the container 10 is temporarily stored at each distribution stage (for example, when it is stored during the period from the date and time of filling to the date and time of shipping), When the container 10 is stored in a warehouse or other storage location, the manufacturing location, cleaning location, filling location, sales location, and storage location correspond to inventory location information regarding the location of the container 10 when it is stocked. The distribution location information and inventory location information may be recorded, for example, using coordinates such as latitude and longitude indicating each location, or may be recorded by region or area such as an administrative district or mesh division.

[0033] 6 is a data structure diagram showing an example of the container investment management database 211. The container investment management database 211 has a record for each investment ID for associating various information handled by the container investment management function 200. Each record has fields in which, for example, investment information D1, circulation container setting information D2, collected data D3, container circulation information D4, value medium information D5, dividend information D6, etc. can be registered. The container investment management database 211 can be referenced from the management business operator terminal device 5, and editing operations such as adding, deleting, and correcting each piece of data may be performed on the display screen of the management business operator terminal device 5.

[0034] The investment information D1 includes at least the investor U1 and the investment amount by that investor U1. If there are multiple investors U1, the investment amount for each investor U1 is included. The investment information D1 may also include at least one of the specifications of the container 10 to be invested in and the type of content 11 to be filled into the container 10 to be invested in.

[0035] The distribution container setting information D2 includes at least the number of containers 10 to be contributed and a contribution container ID that identifies each of the contribution containers 10 by the container identification information D7. The distribution container setting information D2 may also include at least one of the specifications of the contribution containers 10 and the type of contents 11 to be filled into the contribution containers 10.

[0036] The collected data D3 is composed of container status data for each container 10 acquired by the distribution of the contributed containers 10 in the distribution market. For example, in a record identified by the contribution ID "I001", if the distribution container setting information D2 is set with the container quantity set to "1000" and the contributed container IDs set to "A0001 to A1000", the collected data D3 is composed of container status data for 1000 containers 10, as shown in FIG. 5.

[0037] The container distribution information D4 includes, for example, data analysis results generated by performing data analysis processes such as aggregation, statistical processing, and analytical processing on the collected data D3. Note that the container distribution information D4 may include the collected data D3 itself instead of or in addition to the data analysis results.

[0038] The value medium information D5 includes at least the unit of the value medium 13 and the quantity of the value medium 13. The value medium 13 is the consideration that the information user U2 hands over in exchange for the provision of the container distribution information D4, and for example, when paying in any currency (including digital currency), the quantity of the value medium 13 corresponds to the payment amount.

[0039] The dividend information D6 includes at least the investor U1 and the dividend amount for that investor U1. If there are multiple investors U1, the dividend amount for each investor U1 is included.

[0040] (Container purchasing management function 201) 7 is a functional explanatory diagram showing an example of the container purchase management function 201 and the flow of information. The information acquisition unit 201A and the storage processing unit 201B of the information processing device 2 mainly realize the container purchase management function 201 using a web management database 212 (see FIG. 8 described later) and a container purchase management database 213 (see FIG. 9 described later).

[0041] The information acquisition unit 201A acquires information from the information storage medium 100 attached to the container 10 constituting the product 12. Based on the included container identification information D7 and web access information D8, purchase information D12 is acquired indicating the type of contents 11 (hereinafter referred to as the "type of contents desired to be purchased") that the consumer U7 of the product 12 wishes to purchase from among the contents 11 that can be filled into a container with the same specifications as the container 10 (hereinafter referred to as the "same-specification container"). The conditions for the same-specification container may be set by the management company U3, the container manufacturer U4, etc., and stored in the memory unit 21. In that case, for example, editing operations for the conditions for the same-specification container may be performed on the display screen of the management company terminal device 5 or the distribution company terminal device 6A.

[0042] Specifically, the information acquisition unit 201A acquires purchase information D12 via a web service by accessing the web service based on web access information D8 read from the information storage carrier 100 using the consumer terminal device 7. At this time, the information acquisition unit 201A may further acquire consumer information D13 about the consumer U7 from the consumer terminal device 7 when the web service is accessed.

[0043] For example, a consumer U7 who has purchased a product 12 uses a consumer terminal device 7 to read the information storage medium 100 attached to the container 10, as with the above-described reading device (arrow (2a) in FIG. 7), and accesses a web service based on web access information D8 included in the information storage medium 100 (arrow (2b)). The consumer terminal device 7 then displays a display screen provided by the web service in a browser or app (arrow (2c)). The consumer U7 inputs purchase request data D9, including the type of contents they wish to purchase and the quantity they wish to purchase, on the display screen (arrow (2d)). The information acquisition unit 201A then associates the purchase request data D9 with the container identification information D7 included in the information storage medium 100 and registers it in the web management database 212 (arrow (2e)). Purchase information D12 is then acquired as a data set of purchase request data D9 for multiple containers 10 (consumer U7). The web service may accept the purchase request data D9 as an actual order for the product 12, or as a reservation for the product 12 or as a result of a questionnaire.

[0044] Furthermore, if persona data D10 such as the gender, age, preferences, and location of consumer U7 is stored in the memory of the consumer terminal device 7, the information acquisition unit 201A refers to the memory, accepts input of persona data D10 from consumer U7 on the display screen of the consumer terminal device 7, and receives location information detected by the location detection function of the consumer terminal device 7 (arrows (2f) and (2g)). Consequently, the information acquisition unit 201A associates the persona data D10 with container identification information D7 included in the information storage carrier 100 and registers it in the web management database 212 (arrow (2e)). Then, consumer information D13 is acquired as a data set of persona data D10 for multiple containers 10 (consumer U7).

[0045] The information acquisition unit 201A may further acquire container management information D14 including at least one of distribution information when containers with the same specifications are distributed through multiple distribution stages and inventory information when they are stocked at multiple distribution stages (arrow (2h)). The container management information D14 may be acquired, for example, from the distributor terminal devices 6A to 6C, or may be acquired by referring to information registered in each field of the distribution management database 210 (for example, distribution location information, inventory location information, etc.), or may be acquired by the information processing device 2 linking with an external system (such as a manufacturing management system, a transportation management system, or a sales management system).

[0046] The storage processing unit 201B stores the purchasing information D12 acquired by the information acquisition unit 201A in the container purchasing management database 213 (arrow (2i)). If the information acquisition unit 201A acquires consumer information D13 and container management information D14 in addition to the purchasing information D12, the storage processing unit 201B stores the consumer information D13 and container management information D14 in the container purchasing management database 213 (arrow (2i)). Furthermore, the storage processing unit 201B stores the purchasing information D12 and the like acquired by the information acquisition unit 201A in the container investment management database 211. In this case, the purchase information D12 and the like may be provided to the information user U2 in exchange for the value medium 13 as part of the container distribution information D4.

[0047] Figure 8 is a data structure diagram showing an example of the web management database 212. The web management database 212 has a record for each container identification information D7 (container ID). Each record has fields for registering purchase request data D9 and persona data D10 acquired via the web service. In the example of Figure 8, there are fields for registering the acquisition date and time, type of content desired to be purchased, desired purchase quantity, gender, age, preferences, location, etc. of consumer U7.

[0048] The location included in the persona data D10 corresponds to consumer location information regarding the location of consumer U7. The consumer location information is recorded, for example, as coordinates such as latitude and longitude indicating the location of consumer U7 at a specific time (for example, the time of accessing a web service). Note that the consumer location information may also indicate a location such as an address or a range of activities, and in that case, may be recorded as a region or area such as an administrative district or mesh district.

[0049] The web management database 212 can also be associated with the distribution management database 210 via the container identification information D7. In this case, by referencing the distribution management database 210 via the container identification information D7, information registered in each field of the distribution management database 210 (e.g., distribution location information, inventory location information, etc.) can be obtained for each container 10.

[0050] 9 is a data structure diagram showing an example of the container purchasing management database 213. The container purchasing management database 213 has a record for each management ID for associating various pieces of information handled by the container purchasing management function 201. Each record has fields in which, for example, management information D11, purchasing information D12, consumer information D13, container management information D14, etc. can be registered. The container purchasing management database 213 can be referenced from the management company terminal device 5 and the distribution company terminal devices 6A to 6C, and editing operations such as adding, deleting, and correcting each piece of data may be performed on the display screen of the management company terminal device 5 and the distribution company terminal devices 6A to 6C.

[0051] The management information D11 includes at least the specifications of the containers 10 to be managed and management target container IDs that identify the respective management target containers 10 using the container identification information D7. The multiple containers 10 each identified by the management target container IDs are containers with the same specifications.

[0052] The purchasing information D12 is composed of purchase request data D9 for each container 10 acquired via a web service from a consumer U7 of a product 12, which is a container 10 to be managed and filled with contents 11. For example, in a record identified by a container management ID "J001," if the management information D11 specifies that the specifications of the container 10 are "200 ml glass bottles" and the managed container IDs are "B0001 to B1000," the purchasing information D12 is composed of purchase request data D9 for 1,000 containers 10 (200 ml glass bottles) with the same specifications, as shown in FIG. 9. The purchasing information D12 may include the purchase request data D9 for each container 10 itself, or may instead or in addition include data analysis results generated by performing data analysis processes such as aggregation, statistical processing, and analysis on the purchase request data D9 for each container 10.

[0053] The consumer information D13 is composed of persona data D10 (gender, age, preferences, location, etc.) for each container 10 acquired via a web service from a consumer U7 of a product 12 in which the contents 11 are filled in the container 10 to be managed. In the example of FIG. 9, the consumer information D13 is composed of persona data D10 for 1,000 containers 10 (200 ml glass bottles) of the same specifications. Note that the consumer information D13 includes the persona data D10 for each container 10 itself. Alternatively, or in addition, the persona data D10 for each container 10 may include a data analysis result generated by performing data analysis processing such as aggregation processing, statistical processing, and analysis processing on the persona data D10.

[0054] The container management information D14 includes at least one of distribution information when containers with the same specifications as the container 10 to be managed are distributed through multiple distribution stages, and inventory information when the container is stocked at multiple distribution stages. The distribution information is, for example, the number of items in distribution, the distribution rate, the distribution time, the distribution cost, etc. at each distribution stage. The inventory information is, for example, the number of items in stock, the inventory rate, the inventory time, and the inventory cost at each distribution stage.

[0055] The distribution information may include distribution location information. The distribution location information may include, for example, information on each location (manufacturing location, cleaning location, filling location, sales location, consumption location, etc.) when the container 10 is distributed through each distribution stage. In this case, the distribution information may include the number of distributions, distribution rate, distribution time, distribution cost, etc. at each location.

[0056] The inventory information may also include inventory location information. The inventory location information may include, for example, information on each location (manufacturing location, cleaning location, filling location, sales location, storage location, etc.) where the container 10 is stored at each distribution stage. In this case, the inventory information may include the number of items in stock, the inventory rate, the inventory time, the inventory cost, etc. at each location.

[0057] (Learning function 202) 10 is a functional explanatory diagram showing an example of the learning function 202 and the flow of information. The learning data acquisition unit 202A and the machine learning unit 202B of the information processing device 2 mainly use a container purchase management database 213, a learning data management database 214 (learning data storage unit), and a trained model management database 215 (trained model storage unit) to realize the learning function 202.

[0058] The learning data acquisition unit 202A acquires learning data D17 consisting of market information D15 of the container 10 to be learned and supply and demand information D16 of the container 10. The learning data D17 is data used as training data, verification data, and test data in supervised learning. The supply and demand information D16 is data used as a correct answer label in supervised learning.

[0059] The market information D15 that constitutes the input data for the learning data D17 includes purchasing information D12 that indicates the type of contents 11 that a consumer U7 of the product 12 made up of the container 10 wishes to purchase from among the contents 11 that can be filled into a container with the same specifications as the container 10, as information obtained based on the container identification information D7 and web access information D8 contained in the information storage carrier 100 attached to the container 10 to be learned.

[0060] The purchasing information D12 is preferably a data analysis result based on the purchase desire data D9 of multiple containers 10 (consumer U7) taking into account the input to the learning model D18. For example, as shown in Fig. 10, the purchasing information D12 indicates the proportion of each type of content desired to be purchased, and is expressed as the proportion of those wishing to purchase "water," "juice," and "milk." The purchasing information D12 may also be for a single type of content 11, and may be expressed as the proportion of those wishing to purchase "water," for example.

[0061] The market information D15 may further include consumer information D13 about a consumer U7 of the product 12 made up of the container 10 of interest. The market information D15 may also include container management information D14 including at least one of distribution information when a container having the same specifications as the container 10 of interest is distributed through multiple distribution stages and inventory information when the container is stocked at multiple distribution stages. It may further include:

[0062] The demand and supply information D16 constituting the output data of the learning data D17 is information relating to the demand or supply of a product 12 in which contents 11 are filled in a container having the same specifications as the learning target container 10. The demand and supply information D16 includes, for example, at least one of demand forecast information relating to the demand forecast for the product 12 as information relating to the demand of the product 12, and supply plan information relating to the supply plan for the product 12 as information relating to the supply of the product 12.

[0063] Taking into consideration the output from the learning model D18, the demand forecast information is expressed, for example, as a numerical value indicating the degree of demand for each type of content 11 (a numerical value normalized in the range of 0 to 1 as shown in FIG. 10) or a score for each demand classification (such as "low," "normal," or "high") for each type of content 11. Note that the demand forecast information may be for a single type of content 11, and may be expressed as a numerical value or score indicating the degree of demand for "water," for example.

[0064] The supply plan information is expressed, taking into consideration the output from the learning model D18, as, for example, a numerical value indicating the supply rate for each type of content 11 (a numerical value normalized in the range of 0 to 1 as shown in FIG. 10) or a score for each demand classification (such as "low," "normal," or "high") for each type of content 11. Note that the supply plan information may be for a single type of content 11, and may be expressed as, for example, a numerical value or classification value indicating the supply rate for "water."

[0065] For example, the learning data acquisition unit 202A acquires market information D15 constituting the learning data D17 by referring to the purchase information D12, consumer information D13, and container management information D14 registered in the container purchasing management database 213, or by accepting input operations from the management company terminal device 5 and the distribution company terminal devices 6A to 6C. The learning data acquisition unit 202A also acquires demand and supply information D16 constituting the learning data D17 by referring to demand and supply information D16 registered as actual values, planned values, simulation values, etc. in external systems (such as a manufacturing management system, a transportation management system, or a sales management system), or by accepting input operations from the management company terminal device 5 and the distribution company terminal devices 6A to 6C.

[0066] The learning data management database 214 stores a plurality of sets of learning data D17 acquired by the learning data acquisition unit 202A.

[0067] The machine learning unit 202B performs machine learning using multiple sets of training data D17 stored in the training data management database 214. That is, the machine learning unit 202B inputs multiple sets of training data D17 to the training model D18 and causes the training model D18 to learn the correlation between the market information D15 and the supply and demand information D16 included in the training data D17, thereby generating a trained training model D18. When performing machine learning, the machine learning unit 202B can employ any method, such as online training, batch training, or mini-batch training. Note that the machine learning unit 202B may perform predetermined pre-processing on input data (market information D15) to be input to the training model D18, or may perform predetermined post-processing on output data (supply and demand information D16) output from the training model D18.

[0068] The learning model D18 employs, for example, a neural network structure and includes an input layer 110, an intermediate layer 111, and an output layer 112. Synapses (not shown) that connect each neuron are laid between each layer, and each synapse is associated with a weight. A group of weight parameters consisting of the weights of each synapse is adjusted by machine learning.

[0069] The input layer 110 has a number of neurons corresponding to the market information D15 as input data. , each value of the market information D15 is input to each neuron. The output layer 112 has neurons corresponding in number to the demand and supply information D16 as output data, and prediction results (inference results) of the demand and supply information D16 for the market information D15 are output as output data. When the learning model D18 is configured as a regression model, the demand and supply information D16 is output as a numerical value (normalized in the range of 0 to 1) indicating the demand level for each type of content 11 and a numerical value (normalized in the range of 0 to 1) indicating the supply level for each type of content 11, as shown in FIG. 11. When the learning model D18 is configured as a classification model, the demand and supply information D16 is output as a score for each demand classification for each type of content 11, for example, and a score for each supply classification for each type of content 11.

[0070] The trained model management database 215 stores the trained learning model D18 (specifically, a group of adjusted weight parameters) generated by the machine learning unit 202B. The trained learning model D18 stored in the trained model management database 215 may be provided to another system via the network 8, a recording medium, or the like. The trained learning model D18 may also be stored in the container investment management database 211. In that case, the trained learning model D18 may be provided to the information user U2 as part of the container distribution information D4 in exchange for the value medium 13.

[0071] 10 has been described, multiple data configurations with different conditions may be employed, such as the machine learning method, the specifications of the container 10, the type of data included in the market information D15, and the type of data included in the supply and demand information D16. In this case, the learning data acquisition unit 202A acquires multiple types of learning data D17 corresponding to the multiple data configurations with different conditions, and the machine learning unit 202B performs machine learning using each of the learning data D17, and stores the trained learning model D18 in the trained model management database 215.

[0072] (Demand supply management function 203) 11 is a functional explanatory diagram showing an example of the flow of information and the demand and supply management function 203. The information acquisition unit 203A, the generation processing unit 203B, and the output processing unit 203C of the information processing device 2 mainly use the container purchase management database 213 and the trained model management database 215 to realize the demand and supply management function 203.

[0073] The information acquisition unit 203A acquires market information D15 for the container 10 that is the prediction target. For example, the information acquisition unit 203A refers to the container purchase management database 213 and acquires the purchase information D12 registered in the container purchase management database 213 as the market information D15 for the container 10 that is the prediction target. Note that the market information D15 for the container 10 that is the prediction target may further include at least one of consumer information D13 and container management information D14 in addition to the purchase information D12, as shown in FIG. 11 .

[0074] The generation processing unit 203B generates supply and demand information D16 for the market information D15 of the container 10 to be predicted, based on the supply and demand information D16 output by inputting the market information D15 of the container 10 to be predicted acquired by the information acquisition unit 203A as input data into the learning model D18. The generation processing unit 203B may perform predetermined pre-processing on the input data (market information D15) to be input to the learning model D18, or may perform predetermined post-processing on the output data (supply and demand information D16) output from the learning model D18.

[0075] The learning model D18 used in the generation processing unit 203B is a learned learning model D18 stored in the learned model management database 215. When the learning model D18 is stored in the learned model management database 215, the generation processing unit 203B may selectively or in parallel use multiple learning models D18, for example, in accordance with the type of data contained in the market information D15 and the demand and supply information D16.

[0076] The output processing unit 203C performs output processing for outputting the demand and supply information D16 generated by the generation processing unit 203B. For example, the output processing unit 203C may transmit the demand and supply information D16 to the management company terminal device 5 or the distribution company terminal devices 6A to 6C. The output processing unit 203C may also store the demand and supply information D16 in the container purchase management database 213 or the container investment management database 211. In this case, the demand and supply information D16 stored in the container investment management database 211 may be provided to the information user U2 as part of the container distribution information D4 in exchange for the value medium 13.

[0077] FIG. 12 is a hardware configuration diagram showing an example of a computer 900 that constitutes each device.

[0078] Each of the devices 2 to 7 in the container distribution system 1 is configured by a general-purpose or dedicated computer 900. As shown in Fig. 12, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0079] The processor 912 is composed of one or more arithmetic processing devices (such as a central processing unit (CPU), a micro-processing unit (MPU), a digital signal processor (DSP), or a graphics processing unit (GPU)), and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (such as a DRAM or SRAM) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0080] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrated into one device, such as a touch panel display. The storage device 920 is composed of, for example, an HDD, an SSD, etc., and functions as a storage unit. The storage device 920 stores various data necessary for executing the operating system and the program 930.

[0081] The communication I / F unit 922 is connected to a network 940 such as the Internet or an intranet (which may be the same as the network 8 in FIG. 1) by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication standard. The external device I / F unit 924 is connected to an external device 950 such as a camera, printer, scanner, or reader / writer by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication standard. The I / O device I / F unit 926 is connected to an I / O device 960 such as various sensors and actuators, and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, to and from the I / O device 960. The media input / output unit 928 is connected to, for example, a DVD (Digital Versatile Disc) drive, a CD (Compact Disc) drive, or the like. It is composed of a drive device such as an ISC drive, a memory card slot, and a USB connector, and reads and writes data from and to media (non-transitory storage media) 970 such as DVDs, CDs, memory cards, and USB memory.

[0082] In the computer 900 having the above configuration, the processor 912 loads a program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be recorded on the medium 970 in an installable file format or an executable file format and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by being downloaded via the communication I / F unit 922 over the network 940. Furthermore, the computer 900 may implement various functions realized by the processor 912 executing the program 930 using hardware such as an FPGA (field-programmable gate array) or an ASIC (application specific integrated circuit).

[0083] The computer 900 is an electronic device of any type, such as a desktop computer or a portable computer, and may be a client computer, a server computer, or a cloud computer.

[0084] (Operation of container distribution system 1) Below, we will explain the functions 201-203 realized by the information processing device 2 as the operation of the container distribution system 1. Note that the information processing device 2 accesses each of the databases 210-215 to register and refer to each of the information D1-D16, but in the following explanation, the access operation will be omitted as appropriate.

[0085] (Container purchasing management method) FIG. 13 is a flowchart showing an example of a container purchase management method performed by the container purchase management function 201.

[0086] First, in step S100, a consumer U7 who has purchased a product 12 uses a consumer terminal device 7 to read the information storage medium 100 attached to the container 10 that constitutes the product 12. As a result, the consumer terminal device 7 accesses a web service based on the web access information D8 included in the information storage medium 100, and displays a display screen based on the web service.

[0087] Next, in step S110, when the consumer terminal device 7 receives input of purchase request data D9 including the type of content desired to be purchased and the desired quantity to be purchased from the consumer U7 on its display screen, it transmits the purchase request data D9 together with the container identification information D7 contained in the information storage carrier 100 to the information processing device 2.

[0088] Then, in step S120, the information acquisition unit 201A of the information processing device 2 receives the purchase request data D9 and the container identification information D7 from the consumer terminal device 7, and associates the purchase request data D9 with the container identification information D7 and registers it in the web management database 212. Note that if persona data D10 is acquired along with the purchase request data D9, the persona data D10 is also registered in the web management database 212.

[0089] The above steps S100 to S120 are performed by each of the multiple consumers U7 who purchase the product 12, and the web management database 212 accumulates purchase preference data D9 for the multiple containers 10 (consumers U7).

[0090] Next, in step S130, the information acquisition unit 201A refers to the web management database 212 and acquires purchase information D12 as a data set of purchase preference data D9 for multiple containers 10 (consumer U7). If persona data D10 is also registered in the web management database 212, the information acquisition unit 201A acquires consumer information D13 as a data set of persona data D10 for multiple containers 10 (consumer U7). Furthermore, the information acquisition unit 201A may acquire container management information D14.

[0091] Next, in step S140, the storage processing unit 201B stores the purchase information D12 acquired by the information acquisition unit 201A in the container purchase management database 213. If the information acquisition unit 201A has acquired consumer information D13 and container management information D14, the information acquisition unit 201A stores the consumer information D13 and container management information D14 in the container purchase management database 213.

[0092] In this manner, the series of steps in the container purchasing management method shown in Fig. 13 is completed. In the container purchasing management method, steps S100 to S130 correspond to an information acquisition step, and step S140 corresponds to a storage processing step.

[0093] As described above, according to the container purchase management function 201 and container purchase management method (information processing method) of the information processing device 2 of this embodiment, based on the web access information D8 included in the information storage carrier 100 attached to the container 10 constituting the product 12, the consumer U7 of the product 12 acquires purchase information D12 indicating the type of content 11 that the consumer U7 wishes to purchase from among the contents 11 that can be filled into a container with the same specifications as the container 10, and the purchase information D12 is stored in the container purchase management database 213 (storage device). Therefore, by utilizing the purchase information D12, the containers 10 can be distributed stably.

[0094] For example, in the case of high-mix, low-volume production in which containers of the same specifications are used for multiple types of contents 11, it is possible to accurately predict consumer U7 demand and to accurately plan the supply of distribution businesses (container manufacturers U4, content manufacturers U5, container cleaners U6, etc.) based on the purchasing information D12, thereby ensuring stable distribution of containers 10. In particular, by acquiring the above-described purchasing information D12 for the invested containers 10, it is possible to accurately predict demand and plan the supply of products 12 using the invested containers 10, promoting the distribution of the invested containers 10 in the distribution market, and thereby quickly and reliably collecting collected data D3 from the invested containers 10. This ensures that dividends on investments can be reliably paid, thereby improving the added value of the container investment management function 200.

[0095] (machine learning methods) FIG. 14 is a flowchart showing an example of a machine learning method performed by the learning function 202.

[0096] First, in step S200, the training data acquisition unit 202A of the information processing device 2 acquires a desired number of training data D17 as a preliminary preparation for starting machine learning, and stores the acquired training data D17 in the training data management database 214. The number of training data D17 to be prepared here may be set in consideration of the inference accuracy required for the ultimately obtained training model D18.

[0097] Next, in step S210, the machine learning unit 202B prepares a pre-learning learning model D18 to start machine learning. The pre-learning learning model D18 prepared here is configured with the neural network model exemplified in Fig. 10, and the weights of each synapse are set to their initial values.

[0098] Next, in step S220, the machine learning unit 202B acquires, for example, one set of training data D17 at random from the multiple sets of training data D17 stored in the training data management database 214.

[0099] Next, in step S230, the machine learning unit 202B inputs market information D15 (input data) included in one set of learning data D17 to the input layer 110 of the prepared learning model D18 before learning (or during learning). As a result, supply and demand information D16 (output data) is output as an inference result from the output layer 112 of the learning model D18, and this output data has been generated by the learning model D18 before learning (or during learning). Therefore, in the state before learning (or during learning), the output data output as an inference result indicates information different from the supply and demand information D16 (correct label) included in the learning data D17.

[0100] Next, in step S240, the machine learning unit 202B performs machine learning by comparing the supply and demand information D16 (correct label) included in the set of learning data D17 acquired in step S220 with the supply and demand information D16 (output data) output as an inference result from the output layer 112 in step S230, and performing a process of adjusting the weight of each synapse (backpropagation).In this way, the machine learning unit 202B causes the learning model D18 to learn the correlation between the market information D15 and the supply and demand information D16.

[0101] Next, in step S250, the machine learning unit 202B determines whether a predetermined learning termination condition has been met, for example, based on the evaluation value of an error function based on the demand and supply information D16 (correct label) included in the learning data D17 and the demand and supply information D16 (output data) output as an inference result, or the remaining number of unlearned learning data D17 stored in the learning data management database 214.

[0102] If the machine learning unit 202B determines in step S250 that the learning termination condition is not satisfied and that machine learning should continue (No in step S250), the process returns to step S220, and performs steps S220 to S240 multiple times on the learning model D18 under training using unlearned training data D17. On the other hand, if the machine learning unit 202B determines in step S250 that the learning termination condition is satisfied and that machine learning should end (Yes in step S250), the process proceeds to step S260.

[0103] Then, in step S260, the machine learning unit 202B stores the trained learning model D18 (adjusted weight parameter group) generated by adjusting the weights associated with each synapse in the trained model management database 215.

[0104] This completes the series of steps in the machine learning method shown in Fig. 14. In the machine learning method, step S200 corresponds to a learning data storage step, steps S210 to S250 correspond to a machine learning step, and step S260 corresponds to a trained model storage step.

[0105] As described above, according to the learning function 202 and machine learning method of the information processing device 2 of this embodiment, the learning model D18 is made to learn the correlation between market information D15 of the container 10 to be learned, which includes at least purchasing information D12 as information obtained based on web access information D8 contained in the information storage carrier 100 attached to the container 10 constituting the product 12, and demand and supply information D16 which includes information regarding the demand or supply of products 12 in which contents 11 are filled in containers of the same specifications as the container 10, for each type of contents 11.Therefore, it is possible to provide a learning model D18 which is capable of generating (inferring) demand and supply information D16 of the container 10 to be predicted from the market information D15 of the container 10.

[0106] (Demand supply management method) FIG. 15 is a flowchart showing an example of a demand and supply management method performed by the demand and supply management function 203.

[0107] First, in step S300, the information acquisition unit 203A of the information processing device 2, for example, receives a prediction target container ID that identifies the prediction target container 10 using container identification information D7 from the management business operator terminal device 5, and then refers to the container purchase management database 213 to identify the management ID that includes the prediction target container ID. The information acquisition unit 203A then acquires the purchase information D12, consumer information D13, and container management information D14 associated with the management ID as market information D15 for the prediction target container 10. Note that the information acquisition unit 203A may acquire the market information D15 for the prediction target container 10 by receiving other information, such as the specifications of the container 10, instead of the container identification information D7.

[0108] Next, in step S310, the generation processing unit 203B inputs the market information D15 of the container 10 to be predicted, acquired in step S300, into the learning model D18, thereby generating demand and supply information D16 for the market information D15 as output data.

[0109] Next, in step S320, the output processing unit 203C performs output processing for outputting the demand and supply information D16 of the container 10 to be predicted, which was generated in step S310, and transmits the demand and supply information D16 to the management company terminal device 5. Then, the management company terminal device 5 displays a display screen based on the demand and supply information D16, thereby presenting the demand and supply information D16 of the container 10 to the management company U3. Note that the destination of the demand and supply information D16 may be the distribution company terminal devices 6A to 6C in addition to or instead of the management company terminal device 5.

[0110] In this way, the series of steps in the demand and supply management method shown in Fig. 15 is completed. In the demand and supply management method described above, step S300 corresponds to an information acquisition step, step S310 corresponds to a generation processing step, and step S320 corresponds to an output processing step.

[0111] As described above, according to the demand and supply management function 203 and demand and supply management method (information processing method) of the information processing device 2 of this embodiment, based on the web access information D8 included in the information storage carrier 100 attached to the container 10 constituting the product 12, purchase information D12 indicating the type of contents 11 that a consumer U7 of the product 12 wishes to purchase from among the contents 11 that can be filled into a container with the same specifications as the container 10 is obtained as market information D15 for the container 10 to be predicted, and by inputting this market information D15 into the learning model D18, demand and supply information D16 is generated that includes information on the demand or supply of the product 12 in which the contents 11 are filled into containers with the same specifications as the container 10, for each type of content 11. Therefore, by utilizing the demand and supply information D16, the containers 10 can be distributed stably.

[0112] For example, by treating the invested container 10 as the predicted container 10, the above-described supply and demand information D16 is generated for the market information D15 of the invested container 10. Then, by using the demand and supply information D16 to stimulate the distribution of the invested container 10 in the secondary market, the collected data D3 from the invested container 10 can be collected quickly and reliably. This allows dividends on the investment to be reliably paid, thereby improving the added value of the container investment management function 200.

[0113] Furthermore, if the container management information D14 included in the market information D15 includes distribution location information and inventory location information, the demand and supply information D16 described above is generated taking into consideration the positions of the containers 10 at each distribution stage, i.e., the distribution status of the containers 10 along the distribution flow line, the status of the containers 10 being held up, etc. This allows for more accurate demand forecasts and supply plans to be realized according to the distribution status of the containers 10.

[0114] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0115] In the above embodiment, the multiple functions 200 to 203 provided in the information processing device 2 are described as being realized by one device, but the functions 200 to 203 may be distributed across multiple devices (computers) and realized by multiple devices. In this case, each of the four functions 200 to 203 may be realized by a single device, or any two or three of the four functions 200 to 203 may be realized by a single device.

[0116] In the above embodiment, the case where the information processing device 2 operates according to the flowcharts shown in Figures 13 to 15 has been described, but some of the steps (units) may be omitted or other steps may be added. In this case, the omitted steps (units) may be executed by an external system (such as a manufacturing management system, a transportation management system, a sales management system, or a data analysis system).

[0117] In the above embodiment, a case has been described in which a neural network is used as the learning model D18 for realizing machine learning by the learning function 202, but other machine learning models may also be used. Examples of other machine learning models include tree types such as decision trees and regression trees, ensemble learning such as bagging and boosting, and neural network types (deep learning) such as recurrent neural networks, convolutional neural networks, and LSTM. hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means, etc. Examples include rastering type, principal component analysis, factor analysis, multivariate analysis such as logistic regression, and support vector machines.

[0118] (Inference device, inference method and inference program) The present invention can be provided not only in the form of the information processing device 2 (method or program) according to the above embodiment, but also in the form of an inference device (inference method or inference program). In this case, the inference device (inference method or inference program) can include a memory and a processor, and the processor executes a series of processes. The series of processes includes an information acquisition process (information acquisition step) that acquires at least purchase information as market information for the container to be inferred based on web access information included in an information storage carrier attached to the container, and an inference process (inference step) that infers demand and supply information for the container to be inferred once the market information for the container to be inferred is acquired in the information acquisition process.

[0119] By providing it in the form of an inference device (inference method or inference program), it can be more easily applied to various devices than when it is implemented in an information processing device. It will be naturally understood by those skilled in the art that when the inference device (inference method or inference program) infers demand and supply information, the inference method implemented by the generation processing unit may be applied using a trained learning model generated by the machine learning device and machine learning method according to the above embodiments. [Explanation of symbols]

[0120] 1...container distribution system, 2...information processing device, 3...investor terminal device, 4...information user terminal device, 5...management company terminal device, 6A to 6C...distribution company terminal device, 7...consumer terminal device, 8...network, 10...container, 11...contents, 12...product, 20...control unit, 21...storage unit, 22...communication unit, 23...input unit, 24...display unit, 100...information storage carrier, 200...container investment management function, 201...container purchase management function, 201A...information acquisition unit, 201B...storage processing unit, 202... learning function, 202A... learning data acquisition unit, 202B... machine learning unit, 203...demand and supply management function, 203A...information acquisition unit, 203B...generation processing unit, 203C...output processing unit, 210...distribution management database, 211...Container investment management database, 212...Web management database, 213...Container purchasing management database, 214...Learning data management database, 215...Model management database, 216...Information processing program, D1...investment information, D2...distribution container setting information, D3...collected data, D4...container distribution information, D5...value medium information, D6...dividend information, D7...container identification information, D8...Web access information, D9...Purchase intention data, D10...Persona data, D11...Management information, D12...Purchasing information, D13...Consumer information, D14...Container management information, D15...Market information, D16...Demand and supply information, D17...Learning data, D18...Learning model, U1...Investor, U2...Information user, U3...Manager, U4...Container manufacturer, U5…Contents manufacturer, U6…Container cleaner, U7…Consumer

Claims

1. an information acquisition unit that acquires, for each container, purchase desire data indicating the type of contents that a consumer of the product desires to purchase from among the contents that can be filled into a container of the same specifications as the container, based on web access information contained in an information storage carrier attached to the container that is filled with the contents to constitute the product, and container identification information contained in the information storage carrier; a storage processing unit that associates the purchase request data and the container identification information acquired by the information acquisition unit and stores them in a storage device, The storage device includes: The specifications of the container are associated with a plurality of pieces of container identification information each identifying a container of the same specifications, and are stored as management information; The information acquisition unit performing a data analysis process on the purchase request data for each of the containers associated with the plurality of container identification information stored as the management information, thereby acquiring purchase information including a data analysis result; The storage processing unit storing the purchase information acquired by the information acquisition unit in the storage device; Information processing device.

2. The information acquisition unit Further acquiring container management information including at least one of distribution information when the containers of the same specifications are distributed through a plurality of distribution stages and inventory information when the containers are stocked at the plurality of distribution stages; The storage processing unit storing the container management information acquired by the information acquisition unit in the storage device; The information processing device according to claim 1 .

3. the distribution information includes distribution location information regarding a location of the container when it is distributed, the inventory information includes inventory location information regarding the location where the container is stored; The information processing device according to claim 2 .

4. The plurality of distribution stages include: a cleaning step of cleaning the container; a filling step of filling the container with the contents; and a consumption step in which the content filled in the container is consumed by the consumer. The information processing device according to claim 2 .

5. The information acquisition unit a web service is accessed based on the web access information read from the information storage carrier using a terminal device used by the consumer, and the purchase request data and the container identification information read from the information storage carrier are acquired via the web service. The information processing device according to claim 1 .

6. The information acquisition unit Further acquiring persona data relating to the consumer for each of the containers from the terminal device when the web service is accessed; The storage processing unit The persona data and the container identification information acquired by the information acquisition unit are stored in the storage device in association with each other, The information acquisition unit performing a data analysis process on the persona data for each of the containers associated with the plurality of container identification information stored as the management information, thereby obtaining consumer information including a data analysis result; The storage processing unit storing the consumer information acquired by the information acquisition unit in the storage device; The information processing device according to claim 5 .

7. the persona data includes consumer location information regarding the location of the consumer; The information processing device according to claim 6 .

8. A computer-implemented information processing method, comprising: an information acquisition step of acquiring, for each container, purchase desire data indicating the type of contents that a consumer of the product desires to purchase from among contents that can be filled into a container of the same specifications as the container, based on web access information contained in an information storage carrier attached to the container that is filled with the contents to constitute the product, and container identification information contained in the information storage carrier; a storage processing step of storing the purchase request data and the container identification information acquired in the information acquisition step in a storage device in association with each other, The storage device includes: The specifications of the container are associated with a plurality of pieces of container identification information each identifying a container of the same specifications, and are stored as management information; The information acquisition step includes: performing a data analysis process on the purchase request data for each of the containers associated with the plurality of container identification information stored as the management information, thereby acquiring purchase information including a data analysis result; The storage processing step includes: storing the purchase information acquired in the information acquisition step in the storage device; Information processing methods.

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