WO2021054092A1 - Distribution management system, distribution management method, program, and management server - Google Patents

Abstract

[Problem] Provided are a distribution management system, a distribution management method, a program, and a management server that enable providing a user with flavored nonessential grocery items that satisfy the preferences of the user. [Solution] This distribution management system is provided with a management server, and connects a supplier who supplies nonessential grocery items, a specialist who adds flavor to the nonessential grocery items, and a user who places an order for flavored nonessential grocery items. The management server is configured to execute: storing preference information relating to the order of the flavored nonessential grocery items by the user, supply information relating to the attribute of nonessential grocery items supplied from the supplier, and specialist information relating to flavoring by the specialist; and providing the user with information of the nonessential grocery items flavored so as to satisfy an order by the user, in accordance with the preference information. The preference information includes information on a plurality of categories with the degree of preference added thereto or information relating to a blending ratio of a specific type of blending of a nonessential grocery item supplied from the supplier.

Description

Distribution management system, distribution management method, program and management server

The present invention relates to a distribution management system, a distribution management method, a program, and a management server that provide a user with a flavored luxury item that matches the user's preference.

As for coffee beans as a luxury item, for example, as a coffee ingredient or raw material, coffee beans supplied by a farmer who is a supplier are involved in roasting (considered as flavoring) in the process of passing on to general consumers as users. It is known that expert processing is involved (Patent Document 1).

Japanese Unexamined Patent Publication No. 6-303906

Conventionally, as shown in FIG. 14, the supply chain from farmers who supply coffee beans as a luxury item to users (individual customers, cafe stores, tea stores, Japanese tea stores) has been long. That is, in FIG. 14, 302 is a farmer who is a supplier, 1401 is a coffee bean refiner, 1402 is a local buyer, 1403 is a major domestic buyer, 1404 is an international trading company, 1405 is a domestic trading company, and 1406 is a coffee roaster. , 1407 indicates a cafe, and 304a indicates a user (individual).

For this reason, it was difficult to provide flavored coffee beans to users in a fresh state.

Then, regarding such a luxury item flavored by an expert, it is preferable to provide the user with a product that matches the taste desired by the user.

An object of the present invention is to provide a distribution management system, a distribution management method, a program, and a management server that provide a user with a flavored luxury item that matches the user's preference.

In order to achieve the above object, the distribution management system according to the present invention is provided with a management server, and orders a supplier who supplies the luxury goods, an expert who flavors the luxury goods, and the flavored luxury goods. It is a distribution management system that connects a user to a user via a network, and the management server is a preference information ordered by the user for the flavored luxury item and the luxury item supplied from the supplier. The supply information regarding the attributes of the above and the expert information regarding the flavoring by the expert are stored, and the user's order is adapted based on at least the supply information and the expert information according to the preference information. It is configured to at least provide the user with the flavored information on the luxury item, and the preference information is provided with information with preference for a plurality of categories or supplied by the supplier. It is characterized by providing information on the blending ratio of the luxury product to be blended in a specific species blend.

Further, the distribution management method according to the present invention connects a supplier who supplies the luxury item, an expert who flavors the luxury item, and a user who orders the flavored luxury item via a network. It is a distribution management method, that is, the preference information ordered by the user for the flavored luxury item, the supply information regarding the attributes of the luxury item supplied from the supplier, and the expert regarding the flavoring by the expert. The information is stored in the management server in the first step, and the flavored information of the luxury item that matches the user's order based on at least the supply information and the expert information according to the preference information. The management server has a second step of providing the user with a second step, and the preference information is information to which a degree of preference for a plurality of categories is added, or a specific species of the luxury item supplied from the supplier. It is characterized by providing information on the blending ratio in the blend.

Further, the program according to the present invention comprises a management server, a supplier who supplies the luxury goods, an expert who flavors the luxury goods, and a user who orders the flavored luxury goods via a network. A program used in a distribution management system to be connected to the management server, which supplies the management server with preference information ordered by a user for the flavored luxury item and attributes of the luxury item supplied from the supplier. Depending on the first step of storing the information and the expert information on the flavoring by the expert and the preference information, at least the supply information and the expert information are used to fit the user's order. At least the second step of providing the flavored information on the luxury item to the user is executed, and the preference information is supplied from the information to which the preference degree for a plurality of categories is added or from the supplier. It is characterized by providing information on the blending ratio of the luxury product in a specific kind blend.

Further, the management server according to the present invention is a distribution that connects a supplier that supplies the luxury item, an expert that flavors the luxury item, and a user who orders the flavored luxury item via a network. A management server used in the management system, which is the preference information ordered by the user for the flavored luxury item, the supply information regarding the attributes of the luxury item supplied from the supplier, and the flavor by the expert. The user can store the expert information on the attachment and, in accordance with the preference information, at least the supply information and the flavored information on the luxury item that fits the user's order based on the expert information. The preference information is information in which preference degrees for a plurality of categories are added, or a blending ratio of the preference product supplied from the supplier in a specific kind blend. It is characterized by having information about.

According to the present invention, it is possible to provide a distribution management system, a distribution management method, a program, and a management server that provide a user with a flavored luxury item that matches the user's preference.

Overall configuration diagram of the distribution management system according to the embodiment of the present invention Overall explanatory view of the distribution management method according to the first embodiment Explanatory drawing in which a bottle as a storage container for storing a cup of flavored (including roasted) coffee beans or tea leaves is proposed to a user (customer) as a distribution control method according to an embodiment of the present invention. As a modification of FIG. 3, an explanatory diagram in which a bottle is proposed to user 1 (customer) via a cafe store (user 2). Explanatory drawing of a bottle as a storage container Internal block diagram constituting the management server according to the first embodiment Operation flowchart of the program used in the distribution management method according to the embodiment of the present invention. Explanatory drawing of supply chain which concerns on embodiment of this invention to supply coffee beans or tea leaves Overall explanatory view of the distribution management method according to the second embodiment Explanatory diagram of machine learning according to the second embodiment Internal block diagram constituting the management server according to the second embodiment Overall explanatory view of the distribution management method according to the third embodiment Explanatory diagram of machine learning according to the third embodiment Explanatory diagram of a traditional supply chain that supplies coffee beans or tea leaves Explanatory drawing according to the fourth embodiment Explanatory drawing according to the fifth embodiment Explanatory drawing according to the sixth embodiment Explanatory drawing according to the seventh embodiment Explanatory drawing according to the eighth embodiment Explanatory drawing according to the ninth embodiment Explanatory drawing according to tenth embodiment Explanatory drawing according to eleventh embodiment Explanatory drawing according to the twelfth embodiment

Hereinafter, embodiments of the present invention will be described.

(First Embodiment)
(Distribution management system)
FIG. 1 shows an overall configuration diagram of a distribution management system according to an embodiment of the present invention including a management server 301. Specialties such as a supplier (farmer) 302 that supplies coffee beans or tea leaves as a luxury item, and a roaster who flavors (including roasting) using only one type or a blend of multiple types of coffee beans or tea leaves. The house 303 and a user (individual) 304a or a user (store) 304b who orders preference information regarding coffee beans or tea leaves as a flavored luxury item are connected as a network via the Internet.

Here, the luxury item means one of foods, beverages, seasonings, spices, smoking substances, and their materials (things that retain the prototype) or raw materials (things that do not retain the prototype) that the user prefers to ingest. Is what you do.

In addition, the preference information includes any one of taste information regarding flavor, preference information regarding health, preference information regarding sensations or desires other than flavor, and preference information regarding atmosphere. More preferably, it contains at least two of these preference information (eg, including flavor preference information and health preference information).

Also, it means mixing different types (for example, coffee beans or tea leaves) from the blend, and various combinations are possible depending on the number of different types and their mixing ratio.

Flavor means a taste including the sense of smell (scent) by the nose and the sense of taste (taste) by the tongue. And flavoring means a process of artificially adding flavor (including roasting and blending). It is also called spice when it comes to artificial flavor. More specifically, it includes processing such as roasting using one kind or a plurality of kinds of blended materials (those that retain the prototype) or raw materials (those that do not retain the prototype). Roasting means the process of roasting (baking) raw beans with fire (this produces sourness and bitterness).

The management server 301 stores the input preference information regarding the flavor of coffee or tea produced by the coffee beans or tea leaves of the user 304a or the user 304b who orders the coffee beans or tea leaves as a flavored luxury item. Further, the management server 301 stores supply information regarding the attributes of coffee beans or tea leaves supplied from the supplier (farmer) 302, and expert information regarding flavoring (including roasting and blending) by the expert 302. ..

Here, the attribute means a type, a characteristic, etc., and the supply information from the supplier (farmer) includes information such as Brazil A farm as the supplier or the supply place.

Expert information includes, for example, eight stages from light roasting to deep roasting when it comes to roasting coffee beans. The eight stages are light roast, cinnamon roast, medium roast, high roast, city roast, full city roast, French roast, and Italian roast in order from the shortest to the longest roasting time. The beans change from bright to black in this order, and from a strong acidity state to a strong bitterness state in this order.

Furthermore, as expert information, in addition to sourness and bitterness, information in categories such as sweetness, sharpness, richness, afterglow, change in flavor when cooled, and intensity of aroma, and in the category of flavors such as sour aroma, anise, and nutmeg. There is information. In addition, there is information in categories such as roast, spices, chocolate, sweetness, flowers, fruits, sourness, vegetables and more.

The management server 301 further provides supply information regarding the attributes of coffee beans or tea leaves supplied from the supplier 302 and expert information by the expert 303 according to the preference information ordered by the user 304a or 304b (FIG. 2). Information on flavored coffee beans or tea leaves that match (match) the user's order is provided to the user based on the unique information in the indicated store 304b or the like, if any).

Here, "fitting to the user's order" means that the user's order is close to his / her own taste. Providing information that matches the user's order includes not only providing coffee (tea) with flavored coffee beans (tea leaves) that is close to one's taste, but also a cafe (teahouse) where it can be drunk. ) Is also included in the recommendation (recommendation).

(Distribution management method)
FIG. 2 shows a distribution management method according to the first embodiment of the present invention. In FIG. 2, reference numeral 211 indicates an input of one's favorite flavor. Specifically, from top to bottom, the strength of the fragrance, sourness, bitterness, sweetness, sharpness, richness, afterglow, and the degree of change in taste when cooled are the length of the horizon (from the reference position at the left end, which is the starting point, to the right end). It is represented by the length of.

212 is a single-origin coffee bean (tea) or a blended coffee (tea).
The data of is shown. Specifically, 212a indicates Blue Mountain, and in the chart shape 212b, clockwise from the upper position, regarding information in categories such as bitterness, sweetness, sharpness, richness, afterglow, change in taste when cooled, strength of aroma, and sourness. , Each degree is expressed by the length in the radial direction.

The code 212c indicates information in categories such as roast, spice, chocolate, sweetness, flowers, fruits, sourness, vegetables, etc. in order from the top. Moreover, 212d means "this coffee". Further, 212e shows information in the flavor category such as sour aroma, anise, and nutmeg from the left side.

Code 213 recommends coffee beans or blended coffee that is close to your flavor as an output, or a cafe where you can drink it.

Reference numeral 214 is the output screen, 214a is the Brazilian A farm, 214b is the city roast, and 214c is the flavor chart in the clockwise direction from the upper position, with bitterness, sourness, sweetness 1, sweetness 2, and changes due to temperature (when cooled). The degree of change in taste), aroma, body, and finish is indicated by the radial length.

In 214d, the left side indicates "your own taste" and the right side indicates "the taste of this coffee". 214e indicates, for example, Shinjuku Cafe α and Ginza Cafe A as the names of the stores. 214f indicates information that can be selected by the user in the flavor category such as anise and lemon. Note that 215 indicates that "if there is home roasting or an original blend, coffee information is also managed by the server".

As described above, in FIG. 2, first, the user (individual) 304a inputs (inputs) his / her preference information to the management server 301. The information to be input includes, for example, the degree of preference (whether the preference is large or small) in each category (parameter) related to the intensity of aroma, sourness, bitterness, sweetness, sharpness, richness, afterglow, and change in flavor when cooled. It is the information of the level). This is information that can be displayed as an area as a chart shape (positioning each category radially and connecting positions (points) indicating the level of each category).

Then, the management server 301 places an order for the user based on the supply information regarding the attributes of coffee beans or tea leaves supplied from the farmhouse 302 and the expert information by the expert 303 according to the preference information ordered by the user. Provide users with information on suitable flavored (including roasted) coffee beans or tea leaves.

FIG. 2 shows the results of a certain roasting (roasting) and flavoring (spices) as expert information by expert 303 when Blue Mountain: 1 was used as the type of coffee beans. That is, in addition to sourness and bitterness, each degree in categories such as sweetness, sharpness, richness, afterglow, change in flavor when cooled, and intensity of aroma is shown as a region in the chart shape.

Regarding the expert information provided by the expert 303, in FIG. 2, it is possible to select whether or not to add each of the three types of flavoring (spices): sour aroma, anise, and nutmeg. In addition, as evaluation items by expert 303, the degree of each in categories such as roast, spice, chocolate, sweetness, flowers, fruits, sourness, vegetables, etc. is taken into consideration.

In FIG. 2, the management server 301 contains supply information regarding the attributes of coffee beans or tea leaves supplied from the farmer 302, expert information by the expert 303, and unique information in the cafe (information on self-roasting or original blend). (In some cases, coffee farm information) is also stored in the management server 301 as a server management target.

Then, according to the preference information ordered by the user, at least the supply information regarding the attributes of coffee beans or tea leaves supplied from the farmer 302 and the expert information by the expert 303 (unique information in the cafe shop shown in FIG. 2) are obtained. Based on this (including this unique information in some cases), it is possible to provide the user with information on flavored (including roasted) coffee beans or tea leaves that match the user's order.

The information provided by the management server 301 to the user includes information on coffee beans (tea leaves) flavored (including roasted) that are close to the flavor of one's taste, as well as a cafe (teahouse) where the coffee (tea) can be drunk. ) Recommendation (recommended).

In FIG. 2, information such as farmhouse 302 is Brazil A farm, roasting type is city roast, flavoring (spices) is anise, and lemon is provided to the user together with the chart shape shown below. Furthermore, Shinjuku Cafe α and Ginza Cafe A are recommended (recommended) as cafes where you can drink this coffee.

For the chart shape, a flavor chart recorded as a system is used, and the degree (level) of each is shown in the categories of bitterness, sourness, sweetness 1, sweetness 2, temperature change, aroma, body, and afterglow.

Then, in FIG. 2, one's own evaluation (evaluation after drinking) is shown as the area of "own taste (flavor)", and the proposed coffee flavor is used as the area of "this coffee taste (flavor)". It is shown.

Users can use the area of "taste (flavor) of this coffee", which is a type of coffee or tea flavor proposed to be close to their own flavor, and "my taste (flavor)", which is their own evaluation. By comparing with the area of, you can find your favorite position more efficiently and accurately even though it is one selected type.

FIG. 3 shows a distribution in which a bottle 100 as a storage container for storing flavored (including roasted) coffee beans (tea leaves) provided as information conforming to the user's order is provided to the user. Shows the management method. When the user inputs (inputs) his / her favorite flavor as α1, the management server 301 as α2 relates to the bottle 100 as a storage container for storing only one cup of flavored (including roasted) coffee beans or tea leaves. Output information.

311 in FIG. 3 shows a proposal for fresh beans / tea leaves managed in bottles (sometimes there are multiple bottles for one cup). In addition, reference numeral 312 indicates the intensity of fragrance, the aftertaste, the change in taste when cooled, and the like in the menu of "your own blend service" with "input your favorite flavor" with respect to α1. In addition, reference numeral 313 indicates the intensity of fragrance, the aftertaste, the change in taste when cooled, and the like in the menu of "your own blend service" as "feedback of your favorite flavor" with respect to α4.

Here, the bottle 100 will be described in detail later, but it is necessary to maintain the flavor of a cup of flavored (including roasted) coffee beans or tea leaves and to enable tasting of more coffee or tea. It plays an important role. Regarding the latter, it is possible to efficiently and accurately understand (find) the flavor of freshly preserved roasted or wind-bottled coffee or tea (flavor of one's taste) without spending a long time.

While many such bottles 100 are prepared for various types of coffee beans or tea leaves after roasting or flavoring, α3 in FIG. 3 provides the user with information on the desired bottle 100. Therefore, the user can obtain a desired bottle 100 having a flavor close to his / her taste. This allows the user to drink coffee or tea from roasted or flavored coffee beans or tea leaves that is close to his or her favorite flavor at home.

Information about one's favorite flavor is fed back to the management server 301 as α4, and this information is stored in the management server 301.

FIG. 4 shows a modified example of FIG. 411 in FIG. 4 indicates "search for fresh beans / tea leaves managed in bottles per cup", 412 indicates "the clerk drinks what is brewed", and reference numeral 413 indicates reference numeral 312 in FIG. 3 with respect to β1. 414 is similar to reference numeral 313 in FIG. 3 with respect to β6.

In FIG. 4, user 1 (customer) drinks coffee or tea from coffee beans or tea leaves flavored (including roasted) close to his / her favorite flavor at the cafe shop through user 2 (cafe shop). Can be done. That is, when the user conveys his / her favorite flavor to the clerk of the cafe store as β1, the clerk inputs (inputs) the transmitted information to the management server 301 as β2. Then, as β3, the management server 301 provides the cafe shop with information on the bottle 100 as a storage container for storing only one cup of flavored (including roasted) coffee beans or tea leaves.

Then, as β4, the cafe clerk searches for the desired bottle 100 from a large number of bottles, and as β5, provides the user with coffee or tea using fresh coffee beans or tea leaves stored in the desired bottle 100.

Note that information regarding one's favorite flavor is fed back to the management server 301 as β6, and this information is stored in the management server 301.

(Storage container)
FIG. 5 shows a bottle 100 as a storage container. The bottle 100 provided with the stopper 101 is composed of a PET bottle (or a metal bottle) that protects against ultraviolet rays and has a pressure resistance, and contains 10 flavored (including roasted) coffee beans (tea leaves) 102 in a fresh state as a cup. Store only gram to 15 grams. Then, preferably, a gas 103 for suppressing the oxidation of the flavored (including roasted) coffee beans (tea leaves) 102 and improving the storage stability is sealed (filled). As the gas 103, CO2 (carbon dioxide), nitrogen as an inert gas, or the like is used.

When CO2 (carbon dioxide) is generated in the process of oxidizing the coffee beans (tea leaves) 102, CO2 (carbon dioxide), which is the same gas, and an inert gas such as nitrogen are used inside the bottle 100 as a different gas. By encapsulating, the generation of CO2 (carbon dioxide) from the coffee beans (tea leaves) 102 can be suppressed by the internal pressure. As a result, the coffee beans (tea leaves) 102 can be stored in a fresh state without being oxidized.

It is also possible to store the flavored (including roasted) coffee beans (tea leaves) 102 in the bottle 100 without filling (filling) the gas 103.

(Inside the management server)
FIG. 6 shows an internal block diagram constituting the management server 301 according to the present embodiment. The management server 301 includes an information input unit 301a, a storage unit 301b, a determination unit (matching unit) 301c, and an information output unit 301d.

The user's preference information is input to the information input unit 301a, and this preference information is stored in the storage unit 301b. In addition, the storage unit 301b stores supply information regarding the attributes of coffee beans or tea leaves supplied from the supplier (farmer) and expert information regarding flavoring (including roasting and blending) by an expert. ing. If there is information in the store 304b (information on self-roasting or coffee farm information if there is an original blend), this is also stored in the storage unit 301b.

The determination unit (matching unit) 301c matches the supply information, the expert information, and the information in the store 304b described above according to the preference information ordered by the user, and flavors the information to match the user's order. Information on coffee beans or tea leaves as a luxury item is provided to the user from the information output unit 301d. Information on flavored coffee beans or tea leaves that match the user's order is output as one type that is close to the user's favorite flavor, and multiple types are output as candidates that are close to the user's taste. Is also good.

If proposed (output) as one type (single origin or blended), the user should obtain a corresponding bottle 100 (FIGS. 3-5) as the desired storage container. To. Also, if multiple types (single origin or blended) are output as candidates that are close to the user's favorite flavor, the user finally selects and selects the desired flavor. A bottle 100 (FIGS. 3 to 5) as a storage container for the above is obtained.

Here, single origin means a single type of coffee beans or tea leaves that are not blended.

As shown in FIG. 6, the information output unit 301d may output (provide) the required supply amount at a predetermined deadline to the farmer 302 as forecast information according to the user's order amount.

(flowchart)
FIG. 7 shows an operation flowchart of the distribution management system and the program used in the distribution management method according to the present embodiment. In S1, the management server 301 stores supply information regarding the attributes of coffee beans or tea leaves supplied from the farmer and expert information regarding flavoring (including roasting and blending) by an expert. In addition, if there is unique information in the cafe (information on home roasting or coffee farm information if there is an original blend), this is also stored. Then, in S2, the management server 301 stores the input user's favorite information.

In S3, according to the favorite information ordered by the user, the matching judgment including the supply information, the expert information, and the above-mentioned information in the cafe, if any, is performed, and whether or not there is a matching candidate is determined. It is judged. If there is a matching candidate, it is determined in S4 whether or not there is one candidate. Then, when there is only one candidate, the desired bottle 100 can be obtained in S6 as having a flavor close to one's favorite.

If there is no matching candidate in S3, return to S2, enter different information as your favorite flavor, and store that information in the management server 301. If there are a plurality of candidates instead of one in S4, those candidates are output to the user in S5. Then, in S6 through the selection by the user, the user can obtain the desired bottle 100 as having a flavor close to his / her taste.

Here, a step of performing machine learning between S2 and S3 (based on a similar chart shape evaluated in the past with respect to the subjective taste information of one's own taste first stored in S2). A step that can be changed to objective information), and a step that evaluates by the user is provided after S6, a loop that returns the evaluation result between S1 and S2 is formed, and it is recommended by machine learning in S3. It is also possible to form a flow of determining whether there is a matching candidate.

(Supply chain)
FIG. 8 shows a supply chain according to the present embodiment for supplying coffee beans or tea leaves. It can be made shorter than the conventional supply chain shown in FIG. According to the supply chain according to the present embodiment, the coffee or tea produced by the coffee beans or tea leaves supplied from the supplier (farmer) 302 is originally based on the treatment by the expert 303 such as roasting with the fresh coffee beans or tea leaves. The flavor of coffee is delivered to the user (individual) 304a. Then, since the original flavor can be delivered to the user, sufficient data can be collected for the user to enjoy the original flavor of coffee or tea.

(Second Embodiment)
FIG. 9 shows a distribution management method in which the management server 301 is provided with a machine learning unit 301e (FIG. 11) that performs chart-shaped pattern matching related to blending as a second embodiment of the present invention. In the present embodiment, it is premised on a blend in which various combinations can be considered depending on the number of different types of coffee beans (tea leaves) and their mixing ratios (more preferably, combinations of roasting treatments are also included).

In FIG. 9, 911 shows the input of one's favorite flavor. Specifically, as a "blend service only for you", the strength of the fragrance, sourness, bitterness, sweetness, sharpness, richness, afterglow, and the degree of change in taste when cooled are the length of the horizon (starting point). It is represented by the length from the reference position at the left end to the right end.

212, 212a to 212e, 215 are the same as those shown in FIG.

913 automatically calculates and outputs a blend of coffee beans that is close to its own flavor as an output (by machine learning).

914 indicates "Oki Matsumoto Blend A" on the output screen. 914a is a flavor chart in which the degree of bitterness, sweetness, sharpness, richness, afterglow, change in taste when cooled, intensity of aroma, and sourness are indicated by radial lengths in order from the upper position. ..

914b shows information in categories such as roast, spice, chocolate, sweetness, flowers, fruits, sourness, vegetables, etc. in order from the top. 914c and 914d mean "this coffee blend" and "order". From the left side, 914e shows information that can be selected by the user in the flavor category such as sour aroma, anise, and nutmeg.

In addition, as a recipe, 914f supplies "50% of coffee beans supplied by farm A and using city roast as roasting, 30% of coffee beans supplied by farm B and using roasted cinnamon, and farm C. 20% of coffee beans that have been roasted and used high roast.

As shown in FIGS. 9 and 10, the user inputs (inputs) his / her favorite flavor to the management server 301, leaving the blending to him / her. Then, as shown in FIG. 10, the management server 301 uses the AI (artificial intelligence) of the machine-learned trained model to determine a chart shape close to the estimated input chart shape as an output chart shape by pattern matching. Then, we propose to the user a blend α that is automatically calculated so that the coffee (tea) has a flavor close to that of ours.

Here, AI preliminarily machine-learns the chart shape n for various blends n as teacher data to form a trained model.

In FIG. 10, 1010 is teacher data, 1011 is blend 1, 1021 is chart shape 1, 1012 is blend 2, 1022 is chart shape 2, 101n is blend n, and 102n is chart shape n. In addition, 1031 is an AI-learned model, and 1032 is "input your favorite flavor (blend is left to you)". 1033 indicates input chart shape estimation, 1034 indicates pattern matching, 1035 indicates output chart shape, and 1036 indicates blend α.

In such an embodiment, as an output of information, it is possible to propose a blend using machine learning from the registered coffee bean (tea leaf) information (including single origin and already blended). ..

For the blend α proposed to the user, output (suggest) so that the desired bottle 100 (FIGS. 3 to 5) can be obtained. As machine learning, in addition to supervised learning, unsupervised learning can also be used.

(Third Embodiment)
FIG. 12 shows the user's preference information regarding the flavor of coffee or tea from flavored (including roasted) coffee beans or tea leaves, in which the user specifies a preferred formulation, that is, on the premise of his or her own blend formulation. The distribution management method according to the third embodiment of the present invention is shown. Similar to the second embodiment shown in FIG. 9, the management server 301 includes a machine learning unit 301e (FIG. 11) that performs pattern matching of chart shapes related to blending.

In FIG. 12, 1211 is "input your own blend formulation", and the recipe "your own blend service" is "50% of coffee beans supplied from farm A and using city roast as roasting, farm B". 30% of coffee beans supplied from and roasted with cinnamon roast, and 20% of coffee beans supplied from farm C with high roasted roasted.

212, 212a to 212e, 215 are the same as those shown in FIG.

1213 indicates "automatically calculate and output the flavor and taste of your own coffee blend as output (by machine learning)".

1214 indicates "Oki Matsumoto original blend" on the output screen. 1214a is a flavor chart in which the degree of bitterness, sweetness, sharpness, richness, afterglow, change in taste when cooled, intensity of aroma, and sourness are indicated by radial lengths in order from the upper position. ..

1214b shows information in categories such as roast, spice, chocolate, sweetness, flowers, fruits, sourness, vegetables, etc. in order from the top. 1214c and 1214d mean "ordered blend" and "Matsumoto-sama's taste". From the left side, 1214e indicates information that can be selected by the user in the flavor category such as sour aroma, anise, and nutmeg.

In such an embodiment, the user inputs (inputs) his / her favorite flavor to the management server 301 together with the blending ratio of the specific species blend for the blend. Regarding the blending ratio of the specific species blend, for example, 50% of coffee beans supplied from farm A and using city roast for roasting and cinnamon roast supplied from farm B for roasting were used as their own blending blend. 30% of coffee beans and 20% of coffee beans supplied from farm C and roasted using high roast.

Regarding the flavor of one's taste, the user inputs (inputs) to the management server 301 whether or not to add each of sour incense, anise, and nutmeg, for example.

Then, the management server 301 uses the AI (artificial intelligence) of the machine-learned trained model shown in FIG. 13 to determine a chart shape close to the estimated input chart shape as an output chart shape by pattern matching, and self-determines it. We propose to the user a specific type blend Δβ whose blending ratio is finely adjusted so that the coffee (brown) has a flavor close to that of.

Here, AI forms a trained model by machine learning the chart shape n as teacher data for a specific species blend of various blending ratios Δn in which the input blending ratio is finely adjusted.

Regarding the specific species blend Δβ proposed to the user, the output (proposal) is made so that the desired bottle 100 (FIGS. 3 to 5) can be obtained. As machine learning, in addition to supervised learning, unsupervised learning can also be used.

In FIG. 13, 1310 is the teacher data, 1311 is the “specific species blend of the compounding ratio Δ1”, 1321 is the chart shape 1, 1312 is the “specific species blend of the compounding ratio Δ2”, 1012 is the blend 2, and 1322 is the chart shape 2. 131n indicates a “specific species blend having a blending ratio of Δn”, and 132n indicates a chart shape n. Further, 1331 indicates an AI-learned model, and 1332 indicates "input in advance for a specific species blend (input the flavor of one's taste as well as the blending ratio)". 1333 indicates the estimation of the input chart shape, 1334 indicates the pattern matching, 1335 indicates the output chart shape, and 1336 indicates the “specific species blend of the compounding ratio Δβ”.

(Fourth Embodiment)
In the fourth embodiment shown in FIG. 15, as the preference information ordered by the user, the taste information regarding the flavor in the first embodiment shown in FIG. 2 and the preference information regarding health (to the mind and body by ingestion). Caffeine was added as an effect). If the amount of caffeine is large, it is judged that it is not good for the health of some users, so it is possible to order coffee with a reduced amount of caffeine.

In FIG. 15, 211 to 215 are the same as those shown in FIG. Then, 1501 is taken as a degree of caffeine as an ingested component from the reference left position to the right with the length of the horizon as "the input of the predetermined ingested component and the amount of the ingested component that should be noted for oneself". The amount of caffeine, which indicates the amount of the component, may be continuously indicated as the length of the horizon, or the degree such as normal or small may be indicated in a plurality of steps.

(Fifth Embodiment)
In the fifth embodiment shown in FIG. 16, caffeine is added as preference information regarding health to the taste information regarding flavor in the second embodiment shown in FIG. 9 as preference information ordered by the user. Is. If the amount of caffeine is large, it is judged that it is not good for the health of some users, so it is possible to order coffee with a reduced amount of caffeine.

In FIG. 16, 911, 913, 914, 212, 215 are the same as those shown in FIG. Then, 1501 is taken as a degree of caffeine as an ingested component from the reference left position to the right with the length of the horizon as "the input of the predetermined ingested component and the amount of the ingested component that should be noted for oneself". The amount of caffeine, which indicates the amount of the component, may be continuously indicated as the length of the horizon, or the degree such as normal or small may be indicated in a plurality of steps.

(Sixth Embodiment)
In the fifth embodiment shown in FIG. 17, caffeine is added as preference information regarding health to the taste information regarding flavor in the third embodiment shown in FIG. 12 as preference information ordered by the user. Is. If the amount of caffeine is large, it is judged that it is not good for the health of some users, so it is possible to order coffee with a reduced amount of caffeine.

In FIG. 17, 1211, 1213, 1214, 212, 215 are the same as those shown in FIG. Then, 1501 is taken as a degree of caffeine as an ingested component from the reference left position to the right with the length of the horizon as "the input of the predetermined ingested component and the amount of the ingested component that should be noted for oneself". The amount of caffeine, which indicates the amount of the component, may be continuously indicated as the length of the horizon, or the degree such as normal or small may be indicated in a plurality of steps.

(7th Embodiment)
In the seventh embodiment shown in FIG. 18, regarding the luxury product flavored by an expert, as the preference information ordered by the user, the salt content and sugar content when ingesting the luxury product, which is the preference information regarding health, It allows you to quantitatively enter the amount of fat, vitamins, calories, calcium, and caffeine.

Here, in the present embodiment and the embodiment described later, the supplier includes a person who supplies the luxury goods as a whole, and a person who supplies any (at least one) of those materials and raw materials. Then, the expert for such a supplier becomes a person who flavors the luxury goods supplied from the supplier.

That is, when considering curry as a luxury item, the supplier includes a person who supplies the entire curry and a person who supplies a plurality of materials and raw materials constituting the curry.

Then, the expert supplies the curry when the supplier supplies the entire curry, the person who flavors the curry, and when the supplier supplies at least one of the plurality of ingredients and raw materials constituting the curry. Be a person who flavors at least one of the ingredients and raw materials.

FIG. 18 shows a case where the user selects a favorite product on the assumption that the supplier supplies the food (foodstuff, meal, or confectionery) or the beverage as a luxury product as a whole. The same applies to the case of supplying materials and raw materials as products.

In FIG. 18, the input screen 1800 shows "input of quantitative data". 1801 shows palatability information (quantitative data), and the degree of salt, sugar, fat, vitamin A, calories, calcium, caffeine, and sweetness can be input by the user in order from the top. It shows that 10 g, 10 mg of vitamin A, 450 kcal of calories, and 3 of sweet level were input.

In addition, the input screen 1802 shows ingredients / meals / confectionery, and one or more of chocolate, cake, cookies, ingredients, ramen, curry, boiled food, pickles, and breakfast set can be selected (checked) as confectionery from the top. The figure shows that the curry has been selected (checked). Furthermore, coffee and tea (black tea, green tea) as beverages, foodstuffs, hamburgers as meals, rice balls, sushi, steak, etc. can be selected.

In addition, 1803 is an existing restaurant (other company), 1804 is its own company, 1805 is a meal information server, 1806 is an AI server, and 1807 is a UTPUT screen, which indicates "proposal of personalized ingredients / meals / confectionery".

1807a is a "Matsumoto Dai-sama's curry recipe", and on the left side of the screen, there is a flavor chart (sourness, sweetness, saltiness, spiciness, astringency, umami, numbness in order from the top position) The degree of is shown as the radial length. In addition, iron, folic acid, vitamin A, and calories can be displayed on the right side of the screen as health information.

In 1807b, photos of curry-related products are displayed as multiple candidates that match the preference information ordered by the user.

In such an embodiment, the preference information shown in 1801 is information to which the degree of preference for a plurality of categories is added. Specifically, INPUT1 inputs preference information of information terminal devices (smartphones (smartphones), notebook PCs, tablet terminals, etc., which are not limited to those that can be moved with the movement of the user, including desktop PCs). Indicates the input screen to be input. This input screen may include a plurality of different pages, in which case the inputs shown in FIGS. 2, 9 and 12 can be made on one page, and the inputs of the present embodiment can be made on different pages. ) Can be made possible.

INPUT2 is a screen different from INPUT1 and allows you to select the type of luxury item flavored by an expert. In the figure, chocolate, cake, cookie, ramen, curry, boiled food, pickles, breakfast set can be selected, and as mentioned above, coffee and tea (tea, green tea), hamburger, rice ball, sushi, steak You can also select.

As for flavoring by experts, in the case of chocolate, the ratio of seasonings (milk, sugar, etc.) to be added is changed according to the origin of the ingredients or raw materials (cacao, etc.). Also, add a special flavor (vanilla essence, etc.).

In the case of cookies, the ingredients or ingredients (flour, sugar, butter, etc.) are mixed to add flavor. Further, the seasoning to be added (l cocoa, etc.) is changed, and another seasoning to be added (almond, nuts, chocolate, etc.) is changed for flavoring.

In the case of simmered dishes, the ingredients or ingredients (vegetables / meat) are mixed to add flavor. In addition, the amount of seasoning (sugar, soy sauce, liquor) to be added is changed to add flavor. Then, in the case of a breakfast set, these are combined.

The meal information server 1805 shown in FIG. 18 is shared by existing restaurants (other companies) and the company, and the combination of the meal information server 1805 and the AI server 1806 constitutes the management server of the present invention, but only the AI server. Can also be configured as the management server of the present invention.

Then, in the present embodiment, the information suitable for the user's order is a chart including any one of sweetness, bitterness, saltiness, pungent taste, astringency, umami, numbness, and sourness on the screen (OUTPUT screen) of the information terminal device. Displayed as a shape.

Then, information on a plurality of candidate curry products that match the taste information ordered by the user is displayed on the same OUTPUT screen (when curry is selected as the curry product, a plurality of types of curry products are displayed). .. From these plurality of candidates, the user can select the most preferable one in consideration of the manufacturer, the place of manufacture, the price, and the like.

The chart shape described above may be deleted from the OUTPUT screen.

(8th Embodiment)
The eighth embodiment shown in FIG. 19 differs from the seventh embodiment shown in FIG. 18 in that qualitative data is input as preference information (INPUT 1). In FIG. 19, 1802-1807 are the same as those described in FIG.

In FIG. 19, 1901 shows palatability information (qualitative data), and from top to bottom, tension up (mood), want to lose weight (desire), at the beach (place), diabetes (illness), good for hair (efficacy), drowsiness. Prevention (effect), romantic (atmosphere), mint scent, and sugar restriction can be displayed.

That is, by category, preference information on flavor (mint scent), preference information on health (effective for diabetes, good for hair, lipid restriction), preference information on sensations other than flavor (prevention of drowsiness) or desire. You can enter your favorite information (want to raise your tension, want to lose weight) and your favorite information about the atmosphere (feeling at the beach, related to romanticism). Here, it goes without saying that the user's preference can be added as shown in FIG. 18, for example.

(9th Embodiment)
In the ninth embodiment shown in FIG. 20, in contrast to the seventh embodiment shown in FIG. 18, shops matching the user's preference information are displayed on the OUTPUT screen that displays the information provided to the user. It differs in that.

In FIG. 20, 1801-1806,1807a are the same as those described in FIG. In FIG. 20, 2000 indicates “quantitative data input (shop output)” and OUTPUT screen 2007 indicates “shop proposals based on personalized recipes”. 2007b displays the information of "curry shop HANA" as the shop information with a photograph.

Here, the display of the store may be displayed as a plurality of candidates. Also,
In addition to displaying the store (introducing the store), the product may be delivered by delivery from that store.

(10th Embodiment)
In the tenth embodiment shown in FIG. 21, the user preference information is displayed on the OUTPUT screen for displaying the information provided to the user with respect to the eighth embodiment shown in FIG. 19, as in the ninth embodiment. The difference is that the shops that match the information are displayed. In FIG. 21, 2100 indicates “qualitative data input (shop output)”, and 1802 to 1806 and 1807a are the same as those described in FIG. Further, 1901 is the same as that shown in FIG. 19, and 2007 is the same as that shown in FIG. Here, it goes without saying that the user's preference can be added as shown in FIG. 20, for example.

Also in the present embodiment, as described in the ninth embodiment, the display of the store may be displayed as a plurality of candidates. In addition to displaying the store (introducing the store), the product may be delivered from the store by delivery.

(11th Embodiment)
The eleventh embodiment shown in FIG. 22 is, as opposed to the seventh embodiment shown in FIG. 18, as preference information regarding a plurality of materials or raw materials constituting a luxury product (for example, curry) flavored by an expert. The difference is that the user can input the amount of them (blend information). In addition, it can be said that this embodiment corresponds to the same type as the embodiment (FIGS. 12 and 17) relating to the blending ratio in the specific species blend of the luxury product.

In FIG. 22, 2200 indicates “input of quantitative data”, and 1802 to 1807 are the same as those described in FIG. In 2201, when curry is selected in INPUT2, cumin, chili, oregano, bird soup, pork soup, garlic, cardamom, Japanese pepper, and cinnamon can be input in order from the top in INPUT2.

Here, in INPUT2 of FIG. 22, for example, a spice as a luxury item (for example, a spice mix in which a favorite component related to health and a favorite component related to flavor are mixed) and a luxury item separately from curry as a luxury item. It is also possible to allow the user to select a seasoning as a luxury item (especially a prepared sauce) in addition to the ramen (including soup base).

In this case, for example, when the user selects at least one of the curry as a favorite item and the spice as a favorite item (for example, ice cream mix) in INPUT2, at least one of the selected curry and the spice is displayed on the OUTPUT screen. Delivered to users as a product.

Then, when the user selects both the curry as a luxury item and the spice as a luxury item (for example, sui ice mix) in INPUT2, the curry base of INPUT1 and the spice of INPUT2 (for example, suiice mix) are displayed on the OUTPUT screen of FIG. As a curry recipe 1807a in which both elements of) are intertwined, a flavor chart and health information (iron, folic acid, vitamin A, calories, etc.) can be displayed.

If the user can recognize the spice mix as a luxury item related to curry or the ingredients of the prepared sauce related to ramen in advance, the user selects it with INPUT1 instead of selecting it with INPUT2. You can also do it.

(12th Embodiment)
In the twelfth embodiment shown in FIG. 23, the OUTPUT screen for displaying the information provided to the user is adapted to the user preference information shown in FIG. 20 with respect to the eleventh embodiment shown in FIG. The difference is that the screen is where the store is displayed. In addition, it can be said that this embodiment corresponds to the same type as the embodiment (FIGS. 12 and 17) relating to the blending ratio in the specific species blend of the luxury product.

In FIG. 23, 2300 indicates "input of quantitative data (store output)", and 1802-1806 and 2007 are the same as those described in FIG. 20. In 2201, when curry is selected in INPUT2, cumin, chili, oregano, bird soup, pork soup, garlic, cardamom, Japanese pepper, and cinnamon can be input in order from the top in INPUT2.

Also in this embodiment, as described in the above-described embodiment, the display of the store may be displayed as a plurality of candidates. In addition to displaying the store (introducing the store), the product may be delivered from the store by delivery.

Here, in INPUT2 of FIG. 23, for example, a spice as a luxury item (for example, a spice mix in which a favorite component related to health and a favorite component related to flavor are mixed) or a luxury item separately from curry as a luxury item. It is also possible to allow the user to select a seasoning as a luxury item (especially a prepared sauce) in addition to the ramen (including soup base).

In this case, for example, when the user selects at least one of the curry as a luxury item and the spice as a luxury item (for example, ice cream mix) in INPUT2, at least one of the selected curry and spice is displayed on the OUTPUT screen. Delivered to users by delivery from the store.

Then, when the user selects both curry as a luxury item and spice as a luxury item (for example, sui ice mix) in INPUT2, on the OUTPUT screen of FIG. 23, the curry base of INPUT1 and the spice of INPUT2 (for example, sui ice mix) are selected. As a curry recipe 1807a in which both elements of) are intertwined, a flavor chart and health information (iron, folic acid, vitamin A, calories, etc.) can be displayed.

If the user can recognize the spice mix as a luxury item related to curry or the ingredients of the prepared sauce related to ramen in advance, the user selects it with INPUT1 instead of selecting it with INPUT2. You can also do it.

(Modification example)
Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and modifications can be made within the scope of the gist thereof. For example, the input of the user to the management server 301 is not limited to the one described in the above-described embodiment, and any information according to one's preference can be used.

And, the combination of each of the above-described embodiments is also possible.

In the present invention as described above, the preference information includes at least two of taste information regarding flavor, preference information regarding health, preference information regarding sensations or desires other than flavor, and preference information regarding atmosphere, or health-related information. It may include information on at least two components that are ingested as preference information, or information on at least two ingredients or ingredients that make up the luxury item flavored as preference information on flavor.

Further, the luxury item is not limited to the above-mentioned items, and may be alcohol, tobacco, or the like, and as information on the flavored luxury item provided to the user, the flavored preference that matches the user's preference. The store that handles (sells) the product may be displayed (introducing the store), and the favorite product may be delivered to the user by delivery from the store.

Further, a mode in which a recording medium on which a software program that realizes the functions of the above-described embodiment is recorded is supplied to the management server, and the management server reads and executes the program stored in the recording medium is also within the scope of the present invention. .. In this case, the program itself read from the storage medium realizes the functions of the above-described embodiment, and the storage medium that stores the program constitutes the present invention.

As a storage medium for supplying such a program, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a non-volatile memory card, a ROM, a DVD, or the like is used. be able to.

Further, in the second and third embodiments in which a machine learning unit is provided to perform pattern matching of chart shapes, blending (multiple different types) is premised, but a single type may be premised. In addition, when matching the pattern of the chart shape, the user's favorite information regarding the flavor of coffee or tea by flavored (including roasted) coffee beans or tea leaves is his or her favorite flavor such as sourness or bitterness shown in FIG. In addition, the extraction method at the stage of drinking coffee (tea) with coffee beans (tea leaves), the amount of additives (milk, syrup, etc.) after extraction and the presence or absence of addition may be input. ..

Further, in the second and third embodiments described above, pattern matching for the chart shape according to the user's preference information regarding the flavor of coffee or tea by flavored (including roasted) coffee beans or tea leaves will be described. However, instead of this, you may try to match by specs (numbers).

Although the storage container shown in FIG. 5 has been described as being used in the above-mentioned distribution management method, it can also be used as a storage container independent of the above-mentioned distribution management method. That is, in a situation where the management server 301 is not involved, it can be simply used as a storage container (specifically, flavored (including roasted) coffee beans (tea leaves) are carbon dioxide as a gas for enhancing storage stability. Only 10 to 15 grams can be stored as a cup in a state in which carbon, nitrogen, etc. are enclosed (fresh state)).

Then, regarding the amount to be stored in the storage container, it was shown that the predetermined amount corresponding to the container used by the user is to be stored as a cup corresponding to the cup used by the user, but the container used by the user is a water bottle or a pot. It may be a predetermined amount corresponding to.

Needless to say, the flavored luxury items (for example, coffee beans and tea leaves) can be provided to users as themselves or after being processed (dried, powdered, etc.) as necessary.

301 ... Management server, 302 ... Supplier (farmer), 303 ... Expert, 304a ... User (individual), 304b ... User (store)

Claims (14)

1. It is a distribution management system that connects a supplier who supplies a luxury item with a management server, an expert who flavors the luxury item, and a user who orders the flavored luxury item via a network. hand,
   The management server
   Preference information ordered by the user for the flavored luxury item, and
   Supply information regarding the attributes of the luxury item supplied from the supplier, and
   Expert information on flavoring by the experts and
   To remember and
   To provide the user with information on the luxury item flavored according to the user's order based on at least the supply information and the expert information according to the preference information.
   And is configured to run at least
   The distribution management system is characterized in that the preference information includes information on adding preference degrees to a plurality of categories or information on a blending ratio of the favorite products supplied from the supplier in a specific kind blend.
2. The preference information includes at least two of flavor preference information, health preference information, non-flavor sensation or desire preference information, and atmosphere preference information, or is taken as health preference information. The distribution management according to claim 1, further comprising information on at least two components, or information on at least two materials or raw materials constituting the luxury product flavored as taste information regarding flavor. system.
3. The preference information includes health preference information, and the health preference information includes any of the flavored salt, sugar, fat, vitamins, calories, calcium, and caffeine of the luxury product. The distribution management system according to claim 2, wherein the distribution management system is characterized in that.
4. The distribution management system according to any one of claims 1 to 3, wherein the attribute of the luxury item includes a supplier or a supply place of the luxury item.
5. The distribution management system according to any one of claims 1 to 4, wherein the flavoring by the expert uses one kind or a plurality of kinds of blended materials or raw materials.
6. The information on the luxury item provided to the user is characterized by including either information on a storage container for storing the flavored luxury item or information on a store that provides the flavored luxury item. The distribution management system according to any one of claims 1 to 5.
7. The information on the luxury item provided to the user includes information on a store that provides the flavored luxury item, and is characterized in that the luxury item flavored by delivery is delivered to the user from the store. The distribution management system according to any one of claims 1 to 6.
8. The management server is characterized in that the management server provides the user with information suitable for the user's order as a chart shape including any one of sweetness, bitterness, saltiness, pungentness, astringency, umami, numbness, and sourness. The distribution management system according to any one of items 7 to 7.
9. The distribution management system according to any one of claims 1 to 8, wherein the management server provides information suitable for the user's order to the user as a plurality of candidates.
10. The distribution management system according to any one of claims 1 to 9, wherein the management server includes a machine learning unit and performs matching according to the preference information according to a learned model.
11. The luxury item supplied by the supplier is any one of foods, beverages, seasonings, spices, smoking substances, and materials or raw materials thereof, according to any one of claims 1 to 10. Distribution management system.
12. It is a distribution management method that connects a supplier who supplies a luxury item, an expert who flavors the luxury item, and a user who orders the flavored luxury item via a network.
    Preference information ordered by the user for the flavored luxury item, and
    Supply information regarding the attributes of the luxury item supplied from the supplier, and
    Expert information on flavoring by the experts and
    The first step that the management server remembers,
    A second step in which the management server provides the user with information on the favorite product, which is flavored according to the user's order based on at least the supply information and the expert information according to the preference information.
    Have,
    The distribution management method is characterized in that the preference information includes information on adding preference to a plurality of categories or information on a blending ratio of the favorite product in a specific kind blend supplied from the supplier. ..
13. Used in a distribution management system that has a management server and connects a supplier who supplies luxury goods, an expert who flavors the luxury goods, and a user who orders the flavored luxury goods via a network. It is a program that can be
    To the management server
    Preference information ordered by the user for the flavored luxury item, and
    Supply information regarding the attributes of the luxury item supplied from the supplier, and
    Expert information on flavoring by the experts and
    The first step to remember
    A second step of providing the user with information on the luxury product flavored according to the user's order based on at least the supply information and the expert information according to the preference information.
    At least run
    The preference information is a program including information on adding preference degrees to a plurality of categories or information on a blending ratio of the favorite products supplied from the supplier in a specific kind blend.
14. It is a management server used in a distribution management system that connects a supplier who supplies a luxury item, an expert who flavors the luxury item, and a user who orders the flavored luxury item via a network. hand,
    Preference information ordered by the user for the flavored luxury item, and
    Supply information regarding the attributes of the luxury item supplied from the supplier, and
    Expert information on flavoring by the experts and
    To remember and
    To provide the user with information on the luxury item flavored according to the user's order based on at least the supply information and the expert information according to the preference information.
    And is configured to run at least
    The management server is characterized in that the preference information includes information with preference degrees for a plurality of categories added, or information regarding a blending ratio of the preference products supplied from the supplier in a specific kind blend.
    
    
    
    
    

Images