Information processing method, program, information processing device, and method for generating a learning model

Abstract

To provide an information processing method for proposing suitable beer beverages to customers.SOLUTION: An information processing method according to one aspect comprises: obtaining an attribute of a customer; obtaining answers to multiple questions from the customer; inputting the attribute and multiple answers of the customer to a learning model configured to output beer information pertaining to beer beverages to be proposed, including beer, low-malt beer, new genre beer or beer-taste beverages, when the obtained attribute and the multiple answers of the customer are entered to output beer information pertaining to a beer beverage to be proposed; storing a history of proposed beer beverages in association with the customer according to the generated beer information; and changing the beer beverage to be proposed to the customer according to the history.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing method, a program, an information processing apparatus, and a method for generating a learning model.

Background Art

[0002] Patent Document 1 discloses a recommendation apparatus that analyzes language data for a customer to specify a product (for example, Japanese sake), and outputs a product to be recommended (proposed) to the customer from the products stored in a storage unit that stores information related to the products.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the invention according to Patent Document 1 may propose the product that was previously proposed to the customer again without referring to the past proposal history.

[0005] In one aspect, it is to provide an information processing method or the like for proposing an appropriate beer beverage to a customer.

Means for Solving the Problems

[0006] An information processing method relating to one aspect involves acquiring customer attributes, acquiring answers to multiple questions from the customer, and outputting beer information related to beer beverages, including beer, low-malt beer, new genre beer, or beer-flavored beverages, which should be suggested when the acquired customer attributes and multiple answers are input to a learning model. This learning model outputs beer information, which consists of characteristic values ​​including at least two of the following: alcohol content, bitterness value, chromaticity, and ethyl acetate, for the beer beverages to be suggested when the customer attributes and multiple answers are input to the learning model. The history of beer beverages suggested based on the outputted beer information, which consists of characteristic values ​​including at least two of the following: alcohol content, bitterness value, chromaticity, and ethyl acetate, is stored in association with the customer. last time The first display field for displaying beer information about the proposed beer beverage, last time When displaying a screen that includes a second display field for displaying beer information about a modified beer beverage different from the proposed beer beverage, and a third display field for displaying beer information about a candidate beer beverage that is different from the modified beer beverage, The aforementioned screen is Based on the aforementioned history, when the beer beverage suggested to the customer matches the beer beverage suggested previously: It is something to be displayed. In the first display field This is the beer beverage that was proposed last time. Enter the following into the second display field: teeth Beer beverage after the above modification Display This is characterized by the fact that a computer performs the process. [Effects of the Invention]

[0007] In one respect, it becomes possible to suggest the appropriate beer beverage to the customer. [Brief explanation of the drawing]

[0008] [Figure 1] This is an explanatory diagram showing an overview of the beer beverage proposal system. [Figure 2] This is a block diagram showing an example server configuration. [Figure 3] This is an explanatory diagram showing an example of a record layout in the customer database. [Figure 4]It is an explanatory diagram showing an example of the record layout of a store DB. [Figure 5] It is an explanatory diagram showing an example of the record layout of a product DB. [Figure 6] It is an explanatory diagram showing an example of the record layout of a history DB. [Figure 7] It is an explanatory diagram showing an example of the record layout of an information collection DB. [Figure 8] It is a block diagram showing an example of the configuration of a terminal. [Figure 9] It is a functional block diagram showing the processing operation of a beer beverage proposal system. [[ID=il17]] [Figure 10] It is an explanatory diagram explaining a beer information output model. [Figure 11] It is a flowchart showing the processing procedure when changing to a beer beverage different from the beer beverage to be proposed. [Figure 12] It is a flowchart showing the processing procedure when changing to a beer beverage different from the beer beverage to be proposed. [Figure 13] It is a flowchart showing the processing procedure of a subroutine for proposing a beer beverage. [Figure 14] It is an explanatory diagram showing an example of a screen for accepting customer attributes at a terminal. [Figure 15] It is an explanatory diagram showing an example of a screen for accepting answers to multiple questions at a terminal. [Figure 16] It is an explanatory diagram showing an example of a screen for displaying the beer beverage before change. [Figure 17] It is an explanatory diagram showing an example of a screen for displaying the beer beverage after change. [Figure 18] It is a flowchart showing the processing procedure when accepting the feedback result of a beer beverage. [Figure 19] [[ID=4S]]It is a flowchart showing the processing procedure when changing a beer beverage based on the feedback result. [Figure 20] It is an explanatory diagram showing an example of a screen for accepting feedback at a terminal. [Figure 21]It is a block diagram showing a configuration example of the server according to Embodiment 3. [Figure 22] It is an explanatory diagram showing an example of the record layout of the category classification DB. [Figure 23] It is a flowchart showing the processing procedure when changing to a beer beverage belonging to the same category. [Figure 24] It is a flowchart showing the procedure of the update process of the beer information output model.

Mode for Carrying Out the Invention

[0009] Hereinafter, the present invention will be described in detail based on the drawings showing its embodiments.

[0010] (Embodiment 1) Embodiment 1 relates to a form of changing to a beer beverage different from the beer beverage based on the proposal history of the beer beverage. Beer beverages include beer, sparkling wine, new genres, and beer-taste beverages. The new genre (third beer) is a sparkling wine with less than 50% malt ratio added with spirits derived from wheat, or a product fermented using ingredients other than sugars, hops, water, and malt (such as grains defined by government regulations). In this embodiment, examples of beer beverages are described, but the same applies to other types of beverages such as wine, sake, shochu, and whiskey.

[0011] FIG. 1 is an explanatory diagram showing an overview of the beer beverage proposal system. The system of this embodiment includes an information processing device 1 and an information processing terminal 2, and each device transmits and receives information via a network N such as the Internet.

[0012] The information processing device 1 is an information processing device that performs processing, storage, and transmission / reception of various information. The information processing device 1 is, for example, a server device, a personal computer, or a general-purpose tablet PC (personal computer). In this embodiment, the information processing device 1 is assumed to be a server device, and hereinafter it will be read as server 1 for simplicity.

[0013] Information processing terminal 2 is a terminal device that receives customer attributes and answers to multiple questions, as well as receiving and displaying suggested beer beverages. Information processing terminal 2 is an information processing device such as a smartphone, mobile phone, wearable device such as Apple Watch (registered trademark), tablet, or personal computer terminal. For simplicity, information processing terminal 2 will be read as terminal 2 below.

[0014] In this embodiment, Server 1 obtains customer attributes and answers to multiple questions from Terminal 2. Server 1 outputs beer information to a learning model that outputs beer information, including beer, low-malt beer, or beer-flavored beverages, which should be suggested when the acquired customer attributes and multiple answers are input.

[0015] Server 1 suggests beer beverages based on the beer information output by the learning model and stores the history of suggested beer beverages in association with the customer. Server 1 determines whether the beer beverage suggested to the customer matches the beer beverage suggested previously. If Server 1 determines that the beer beverage suggested to the customer matches the beer beverage suggested previously, it changes to a different beer beverage.

[0016] Figure 2 is a block diagram showing an example configuration of Server 1. Server 1 includes a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, a display unit 15, a reading unit 16, and a large-capacity storage unit 17. Each component is connected by bus B.

[0017] The control unit 11 includes arithmetic processing units such as a CPU (Central Processing Unit), MPU (Micro-Processing Unit), and GPU (Graphics Processing Unit), and performs various information processing and control processing related to the server 1 by reading and executing the control program 1P stored in the storage unit 12. In Figure 2, the control unit 11 is described as a single processor, but it may be a multi-processor system.

[0018] The storage unit 12 includes memory elements such as RAM (Random Access Memory) and ROM (Read Only Memory), and stores control programs 1P or data necessary for the control unit 11 to execute processing. The storage unit 12 also temporarily stores data necessary for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing communication-related processing, and transmits and receives information with terminals 2, etc., via the network N.

[0019] The input unit 14 is an input device such as a mouse, keyboard, touch panel, or buttons, and outputs the received operation information to the control unit 11. The display unit 15 is a liquid crystal display or an organic EL (electroluminescence) display, and displays various information according to the instructions of the control unit 11.

[0020] The reading unit 16 reads a portable storage medium 1a, including a CD (Compact Disc)-ROM or DVD (Digital Versatile Disc)-ROM. The control unit 11 may read the control program 1P from the portable storage medium 1a via the reading unit 16 and store it in the large-capacity storage unit 17. Alternatively, the control unit 11 may download the control program 1P from another computer via a network N or the like and store it in the large-capacity storage unit 17. Furthermore, the control unit 11 may also read the control program 1P from the semiconductor memory 1b.

[0021] The large-capacity storage unit 17 includes recording media such as an HDD (Hard disk drive) or an SSD (Solid State Drive). The large-capacity storage unit 17 includes a customer database 171, a store database 172, a product database 173, a history database 174, an information collection database 175, and a beer information output model 176. The customer database 171 stores information about customers. The store database 172 stores information about stores. The product database 173 stores information about products such as beer beverages. The history database 174 stores the history of beer beverage recommendations made to customers. The information collection database 175 stores information about questions and feedback.

[0022] In this embodiment, the storage unit 12 and the large-capacity storage unit 17 may be configured as a single storage device. Furthermore, the large-capacity storage unit 17 may be composed of multiple storage devices. Moreover, the large-capacity storage unit 17 may be an external storage device connected to the server 1.

[0023] In this embodiment, Server 1 is described as a single information processing device, but it may be configured as a distributed system using multiple devices, or it may be composed of virtual machines.

[0024] Figure 3 is an explanatory diagram showing an example of the record layout of Customer DB171. Customer DB171 includes columns for Customer ID, Nickname, Password, Email Address, Gender, Age, Favorite Alcoholic Beverage, Drinking Frequency, Drinking Occasion, and Answers to Questions. The Customer ID column stores the unique ID of each customer to identify them. The Nickname column stores the customer's nickname. The Password column stores the password used for login. The Email Address column stores the customer's email address. The Gender column stores the customer's gender. The Age column stores the customer's age.

[0025] The "Frequently Drinked Alcohol" column stores the types of alcohol the customer drinks most often. The "Drinking Frequency" column stores the frequency of drinking beer beverages. Drinking frequency can be, for example, "Once a week" or "Almost every day." The "Drinking Scene" column stores information about the situations in which the customer wants to drink alcohol. This column can include, for example, "Prefers drinking at home" or "Prefers drinking out." The "Answers to Questions" column stores the customer's answers to multiple questions.

[0026] Figure 4 is an explanatory diagram showing an example of the record layout of Store DB172. Store DB172 includes columns for Store ID, Store Name, Product ID, and Start Date. The Store ID column stores the unique ID of each store to identify it. The Store Name column stores the name of the store. The Product ID column stores the ID of the product (the product being offered) handled by the store. The Start Date column stores the date on which the store began handling the product.

[0027] Figure 5 is an explanatory diagram showing an example of the record layout of the product database 173. The product database 173 includes columns for product ID, product type, brand, and characteristics. In this embodiment, the product is described as a beer beverage. The product ID column stores the ID of each beer beverage, which is used to uniquely identify each beer beverage. The product type column stores the type of beer beverage. Types of beer beverages include beer, low-malt beer, new genre beer, and beer-flavored beverages.

[0028] Beer is a beverage made by fermenting malt, hops, and water. Alternatively, beer is a beverage made by fermenting malt, hops, water, and barley or other items specified by government ordinance, provided that the total weight of the items specified by government ordinance does not exceed 50% of the weight of the malt. Happoshu is a beverage that uses malt or barley as part of its ingredients and is effervescent (with an alcohol content of less than 20%), and uses less malt than beer. The new genre is classified into two categories: other brewed alcoholic beverages and liqueurs. Other brewed alcoholic beverages (sparkling) are beverages made by fermenting grains, sugars, and other items as ingredients, with an alcohol content of less than 20% and an extract content of 2% or more. Liqueurs (sparkling) are happoshu with a malt ratio of less than 50% to which spirits are added, and have an extract content of 2% or more. Beer-flavored beverages are beverages that contain no alcohol or contain less than 1% alcohol.

[0029] The Brand column stores the brand of the beer beverage. The Characteristics column includes Original Extract (OE), True Extract (ER), True Fermentation Degree (RDF), Appearance Extract (AE), Appearance Fermentation Degree (ADF), and Alcohol Content columns.

[0030] The Original Extract column stores values ​​indicating the concentration of the original extract in the beer beverage. The True Extract column stores values ​​indicating the concentration of the true extract (soluble evaporation residue) in the beer beverage. The True Fermentation Degree column stores values ​​indicating the true fermentation degree. The Appearance Extract column stores values ​​indicating the concentration of the appearance extract in the beer beverage. The Appearance Fermentation Degree column stores values ​​indicating the appearance fermentation degree. The Alcohol Content column stores the alcohol content of the beer beverage.

[0031] Furthermore, regarding the characteristics of beer beverages, in addition to the original extract, true extract, true fermentation degree, appearance extract, appearance fermentation degree, and alcohol content exemplified above, other characteristic parameters may also be included. For example, the characteristics may further include characteristics such as appearance final fermentation degree, bitterness value, extract content, residual fermentation degree, pH, color, ethyl acetate, n-propanol, isobutanol, isoamyl alcohol, isoamyl acetate, amyl alcohol, active amyl alcohol, acetaldehyde, diacetyl, amino acid sulfate ions, phosphate ions, citric acid, succinic acid, malic acid, acetic acid, lactic acid, isobutyric acid, caprylic acid, capric acid, total polyphenols, β-glucan, turbidity, foam retention time, carbon dioxide, linalool, α-acids, iso-α-acids, S-fraction analysis values, the yeast used (top-fermenting yeast or bottom-fermenting yeast), information on whether or not a Japanese regional name is included in the product name, or information on whether or not malt, hops, wheat, sugars, spices, fruits, vegetables, rice, or miso are used as raw materials.

[0032] Figure 6 is an explanatory diagram showing an example of the record layout of History DB174. History DB174 includes columns for Customer ID, Proposed Beer Beverage ID, Feedback Result, and Proposal Date & Time. The Customer ID column stores the customer ID that identifies the customer. The Proposed Beer Beverage ID column stores the beer beverage ID of the beer beverage that was proposed to the customer. The Feedback Result column stores the feedback results for the proposed beer beverage. The feedback results are, for example, answers to multiple items of the feedback. The Proposal Date & Time column stores the date and time information of when the beer beverage was proposed to the customer.

[0033] Figure 7 is an explanatory diagram showing an example of the record layout of the information collection DB 175. The information collection DB 175 includes a Management ID column, a Type column, a Problem ID column, a Problem column, and a Candidate Answer column. The Management ID column stores the ID of the data to be collected, which is uniquely identified to identify the data for each information collection. The Type column stores the type of data to be collected. For example, the Type column stores "Question" or "Feedback". The Problem ID column stores the Question ID or Feedback Item ID to be submitted to the customer. The Problem column stores the Question or Feedback Item to be submitted to the customer. The Candidate Answer column stores the answer choices for the problem.

[0034] Figure 8 is a block diagram showing an example configuration of terminal 2. Terminal 2 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, and a display unit 25. Each component is connected by bus B.

[0035] The control unit 21 includes an arithmetic processing unit such as a CPU or MPU, and performs various information processing and control processing related to the terminal 2 by reading and executing the control program 2P stored in the storage unit 22. In Figure 8, the control unit 21 is described as a single processor, but it may be a multiprocessor. The storage unit 22 includes memory elements such as RAM and ROM, and stores the control program 2P or data necessary for the control unit 21 to perform processing. The storage unit 22 also temporarily stores data necessary for the control unit 21 to perform arithmetic processing.

[0036] The communication unit 23 is a communication module for performing communication-related processing, and it sends and receives information with the server 1, etc., via the network N. The input unit 24 may be a keyboard, mouse, or a touch panel integrated with the display unit 25. The display unit 25 is a liquid crystal display or an organic EL display, etc., and displays various information according to the instructions of the control unit 21.

[0037] Figure 9 is a functional block diagram showing the processing operation of the beer beverage suggestion system. Terminal 2 accepts input of customer attributes. Customer attributes include gender, age, most frequently consumed alcoholic beverage, frequency of beer beverage consumption, and drinking occasion (e.g., prefers drinking at home, prefers drinking out, etc.). Terminal 2 retrieves multiple questions that have been pre-stored in the information collection DB 175 of Server 1 from Server 1 and displays the retrieved questions on the screen.

[0038] The questions are designed to gather information for suggesting beer beverages that suit the customer's attributes. For example, the questions could be "Do you like ethnic food?", "Do you prefer sweet or dry sake?", or "Are you someone who is sensitive to trends?". In this embodiment, questions pre-stored in the information gathering DB 175 of server 1 were sent to terminal 2, but this is not the only option. For example, the questions could be pre-stored in the storage unit 22 of terminal 2. Terminal 2 receives input from the customer with answers to multiple questions.

[0039] Terminal 2 sends the acquired customer attributes and multiple responses to Server 1. Server 1 receives the customer attributes and multiple responses sent from Terminal 2 and stores them in Customer DB 171. Specifically, Server 1 assigns a customer ID and stores the nickname, gender, age, frequently consumed alcoholic beverages, frequency of beer consumption, drinking occasions, and multiple responses as a single record in Customer DB 171.

[0040] Server 1 outputs beer information to a beer information output model 176, which outputs beer information about beer, low-malt beer, or beer-flavored beverages that should be suggested when customer attributes and multiple responses are input. The beer information includes multiple characteristic values ​​that indicate the characteristics of the beer beverage. The process of outputting beer information using the beer information output model 176 will be described later.

[0041] Server 1 proposes beer beverages based on the beer information output from the beer information output model 176. Specifically, Server 1 calculates the similarity between multiple characteristic values ​​of beer beverages output from the beer information output model 176 and multiple characteristic values ​​associated with various beer beverages stored in the product DB 173. For the similarity calculation process, conventionally known methods can be used, such as a method that vectorizes the characteristic values ​​of beer beverages and calculates the similarity using the distance between vectors, clustering such as k-means, or cosine similarity.

[0042] Server 1 extracts the beer beverage with the highest similarity from product DB 173 as the beer beverage to be proposed. However, this process is not limited to simply extracting beer beverages with high similarity. For example, it can prioritize proposing beer beverages with high similarity within a specified period from the registration date of the beer beverage. Specifically, if a predetermined similarity threshold (e.g., 70%) is set in advance, Server 1 retrieves all beer beverage IDs from product DB 173 whose calculated similarity is equal to or greater than the predetermined threshold. Based on the retrieved beer beverage IDs, Server 1 extracts beer beverages from store DB 172 within the specified period. The specified period is, for example, within 3 months from the date the beer beverage was first handled (registration date). From the extracted beer beverages within the specified period, Server 1 extracts the beer beverage with the highest similarity.

[0043] Server 1 stores the extracted beer beverage in the history database 174, associating it with the customer ID. Specifically, Server 1 stores the extracted beer beverage ID and extraction date and time as a single record in the history database 174, associating it with the customer ID. Based on the customer ID, Server 1 retrieves the beer beverage previously suggested to that customer from the history database 174. Server 1 determines whether the extracted beer beverage matches the previously suggested beer beverage.

[0044] If Server 1 determines that the extracted beer beverage matches the previously proposed beer beverage, it changes it to a different beer beverage. For example, as an example of beer beverage proposals based on similarity as described above, it changes the beer beverage to the next most similar beer beverage. Note that if the previous proposal date was more than a predetermined period (e.g., six months), it is not necessary to change the beer beverage proposed to the customer through the beer information output model 176.

[0045] Furthermore, the process of changing beer beverages is not limited to the similarity of the characteristic values ​​of the beer beverages. For example, from among several beer beverages classified in the same category as the proposed beer beverage, the beer beverage with the highest popularity ranking may be extracted, and the proposed beer beverage may be changed to the extracted beer beverage. Also, the various processes described above may be executed on terminal 2.

[0046] Server 1 sends the modified beer beverage to Terminal 2 as the beer beverage being proposed this time. Terminal 2 receives the beer beverage sent from Server 1 and displays it on its screen. If Server 1 determines that the extracted beer beverage does not match the beer beverage proposed last time, it sends the beer beverage to Terminal 2 as is without making any changes.

[0047] Next, we will explain the process of outputting beer information using the beer information output model 176. Figure 10 is an explanatory diagram illustrating the beer information output model 176. The beer information output model 176 is used as a program module that is part of the artificial intelligence software. The beer information output model 176 is a machine learning model with pre-trained parameters. For example, Server 1 uses customer attributes and answers to multiple questions as training data to generate (construct) the beer information output model 176. The training data is combination data created by associating beer information with customer attributes and multiple answers.

[0048] As shown in Figure 10, Server 1 generates a decision tree model as the beer information output model 176. For example, Server 1 generates the beer information output model 176 using gradient boosting, a type of ensemble learning. That is, Server 1 generates one weak classifier (decision tree) using the training data, and then sequentially generates the next weak classifier based on the error (more precisely, residual) between the predicted value and the actual value by the generated weak classifier. Server 1 sequentially generates weak classifiers by referring to the gradient of the loss function defined by the error between the predicted value and the actual value, taking into account the learning results of the previous weak classifier, and generates the final classifier, i.e., the beer information output model 176.

[0049] In addition to the learning methods described above, other ensemble learning methods may be used, such as bagging, which generates multiple weak classifiers in parallel. Furthermore, the beer information output model 176 may be generated using learning methods other than ensemble learning. For example, Server 1 generates the beer information output model 176 using the random forest method. Specifically, based on the learning data sampled from the training data, Server 1 randomly selects feature items to be used for identification (classification) at non-terminal nodes to create multiple decision trees with low correlation, and generates the beer information output model 176 using these multiple decision trees.

[0050] Furthermore, dimensionality reduction may be applied to the input data entered into the beer information output model 176.

[0051] As described above, Server 1 generates a beer information output model 176 using customer attributes and answers to multiple questions as training data. In this embodiment, customer attributes and answers to multiple questions are used as training data, but this is not the only option. For example, information such as temperature, humidity, season, date and time, and the type of food the store serves may also be included in the training data.

[0052] Server 1 retrieves customer attributes and answers to multiple questions from customer DB 171 and uses them as training input parameters to generate a beer information output model 176 so that the output parameters approximate the ground truth values. Beer information consists of multiple characteristic values ​​that indicate the characteristics of beer beverages. Specifically, Server 1 generates the beer information output model 176 by training a machine learning model (such as a decision tree model) with parameters using the multiple characteristic values ​​of beer beverages associated with the input customer attributes and multiple answers as ground truth values.

[0053] Server 1 uses the generated beer information output model 176 to output estimation results for several characteristic values ​​of the beer beverage. As shown in the figure, the estimated values ​​for "Original Extract," "True Extract," "True Fermentation Degree," "Appearance Extract," "Appearance Fermentation Degree," "Alcohol Content," "Extract Content," "Residual Fermentation Degree," "pH," and "Color" are output to the customer as "13.8," "5.5," "60.5," "3.8," "70.3," "5%," "22.6," "6.7," "5.2," and "260," respectively. Note that the estimation results output from the output layer are not limited to the format described above. For example, the estimation results may be discrete values ​​indicating the presence or absence of breweries or adjuncts (e.g., values ​​of "0" or "1"), or continuous probability values ​​(e.g., values ​​in the range from "0" to "1"). Although the above explanation uses the characteristic values ​​of the beer beverage, the illustration and explanation of other characteristic values ​​(e.g., values ​​of ethyl acetate, isobutanol, isoamyl alcohol, etc.) as output results are omitted.

[0054] Furthermore, training data can be created based on customer feedback, and the beer information output model 176 can be retrained using the created training data. Specifically, Server 1 retrieves customer feedback results from History DB 174 based on the customer ID. Server 1 analyzes the retrieved feedback results (answers to feedback items) and extracts beer beverage IDs that match the customer's preferences. For example, if the customer's response to the feedback item "How did you like the taste of the beer you drank?" is "It was delicious" or "It was very delicious," Server 1 extracts the beer beverage ID for that beer beverage. Alternatively, if the customer's response to the feedback item "How many beers have you drunk now?" is "3 or more," Server 1 extracts the beer beverage ID for that beer beverage.

[0055] Server 1 reads multiple characteristic values ​​of beer beverages from product DB 173 based on the extracted beer beverage ID. Server 1 associates the read multiple characteristic values ​​of beer beverages with customer attributes and multiple responses to create training data for combinations. Server 1 retrains the beer information output model 176 using the created training data, in the same manner as the learning process described above. Note that the training data is not limited to collecting beer beverages corresponding to high ratings such as "delicious" or "very delicious," but may also be created based on beer beverages corresponding to low ratings such as "not delicious" or "not very good."

[0056] In this embodiment, the beer information output model 176 is described as a decision tree, but the beer information output model 176 is not limited to a decision tree and may be a pre-trained model constructed using any learning algorithm other than a decision tree, such as a CNN (Convolutional Neural Network), R-CNN (Regions with Convolutional Neural Networks), SVM (Support Vector Machine), Bayesian network, or regression tree.

[0057] In this embodiment, an example was described in which the beer information output from the beer information output model 176 consists of multiple characteristic values ​​of a beer beverage, but this is not limited to this example. For example, the beer information relating to a beer beverage may be a beer beverage ID. That is, customer attributes and multiple responses are input to the beer information output model 176, and the beer beverage ID of the beer beverage to be suggested is output.

[0058] Figures 11 and 12 are flowcharts illustrating the processing procedure when changing to a different beer beverage than the one initially proposed. Note that the customer's basic information (e.g., nickname, email address, etc.) is pre-registered in the customer database 171. Specifically, terminal 2 sends the received basic information to server 1. Server 1 receives the basic information sent from terminal 2. Server 1 assigns a customer ID and stores the received basic information in customer database 171, associating it with the customer ID. Then, it performs a login process for registered customers.

[0059] The control unit 21 of terminal 2 determines whether the customer is already logged in (step S291). If the control unit 21 determines that the customer is already logged in (YES in step S291), it proceeds to step S201, which will be described later. If the control unit 21 determines that the customer is not logged in (NO in step S291), the control unit 21 sends authentication information, including the customer ID and password, to the server 1 via the communication unit 23 (step S292). Note that the authentication information is not limited to the customer ID and password, but may also include information for fingerprint authentication or facial recognition, for example.

[0060] The control unit 11 of server 1 receives authentication information transmitted from terminal 2 via the communication unit 13 (step S191). The control unit 11 performs authentication processing based on the received authentication information (step S192) and transmits the authentication result (login successful, login failed, etc.) to terminal 2 via the communication unit 13 (step S193). The control unit 21 of terminal 2 receives the authentication result transmitted from server 1 via the communication unit 23 (step S293). The control unit 21 determines whether the login was successful or not based on the received authentication result (step S294).

[0061] If the control unit 21 determines that the login was unsuccessful (NO in step S294), it proceeds to step S291. If the control unit 21 determines that the login was successful (YES in step S294), it accepts the customer's attributes via the input unit 24 (step S201). Note that this is not limited to accepting customer attributes via login. For example, if customer attributes can be obtained using local storage or cookies, the input of customer attributes may be omitted. The control unit 11 of the server 1 obtains multiple questions from the information collection DB 175 of the large-capacity storage unit 17 (step S101). The control unit 11 transmits the obtained multiple questions to the terminal 2 via the communication unit 13 (step S102).

[0062] The control unit 21 of terminal 2 receives multiple questions transmitted from server 1 via the communication unit 23 (step S202), and displays the received multiple questions via the display unit 25 (step S203). The control unit 21 receives input of answers to the multiple questions from the customer via the input unit 24 (step S204). The control unit 21 transmits the customer ID, the received customer attributes, and the multiple answers to server 1 via the communication unit 23 (step S205).

[0063] The control unit 11 of server 1 receives the customer ID, customer attributes, and multiple responses transmitted from terminal 2 via the communication unit 13 (step S103). The control unit 11 stores the received customer attributes and multiple responses in the customer DB 171, associating them with the customer ID (step S104). The control unit 11 outputs beer information using the beer information output model 176, which outputs beer information related to beer beverages, including beer, low-malt beer, or beer-flavored beverages, that should be suggested when customer attributes and multiple responses are input (step S105). The control unit 11 suggests beer beverages based on the beer information output from the beer information output model 176 (step S106). The beer beverage suggestion subroutine will be described later.

[0064] The control unit 11 stores the proposed beer beverage in the history DB 174, associating it with the customer ID (step S107). Based on the customer ID, the control unit 11 retrieves the previously proposed beer beverage from the history DB 174 and determines whether the proposed beer beverage matches the previously proposed beer beverage via the beer information output model 176 (step S108). If the control unit 11 determines that the proposed beer beverage does not match the previously proposed beer beverage via the beer information output model 176 (NO in step S108), it proceeds to the process described in step S111.

[0065] If the control unit 11 determines that the beer beverage suggested to the customer via the beer information output model 176 matches the previously suggested beer beverage (YES in step S108), it changes it to a different beer beverage (step S109). For example, if the control unit 11 suggests beer beverages based on similarity, it changes the beer beverage suggested to the customer via the beer information output model 176 to the next most similar beer beverage. The control unit 11 updates the history DB 174 by associating the changed beer beverage with the customer ID (step S110).

[0066] The control unit 11 transmits the proposed beer beverage to the terminal 2 via the communication unit 13 (step S111). The control unit 21 of the terminal 2 receives the beer beverage transmitted from the server 1 via the communication unit 23 (step S206), and displays the received beer beverage on the screen via the display unit 25 (step S207).

[0067] In this embodiment, an example was described in which a beer beverage suggested to the customer via the beer information output model 176 is changed to a different beer beverage if it matches the previously suggested beer beverage. However, this is not the only example. For instance, based on the history of beer beverages suggested to the customer, if the same beer beverage has been suggested a predetermined number of times consecutively (e.g., three times), it may be changed to a different beer beverage.

[0068] Furthermore, the method of change is not limited to the above-described method; for example, the beer beverage could be changed to a different one for each glass. For example, for the first glass of beer, the beer beverage suggested to the customer through the beer information output model 176 could be changed to the next most similar beer beverage. For the second glass of beer, the beer beverage suggested to the customer through the beer information output model 176 could be changed to the third most similar beer beverage.

[0069] Figure 13 is a flowchart showing the processing procedure of a subroutine that suggests beer beverages. The control unit 11 calculates the similarity between multiple characteristic values ​​of beer beverages output from the beer information output model 176 and multiple characteristic values ​​associated with various beer beverages stored in the product DB 173, for example, using cosine similarity (step S10). The control unit 11 obtains a predetermined threshold for similarity (for example, 70%) from the storage unit 12 (step S11). The control unit 11 obtains all beer beverage IDs from the product DB 173 whose calculated similarity is equal to or greater than the predetermined threshold (step S12).

[0070] The control unit 11 extracts beer beverages from the store database 172 within a specified period (for example, within 3 months from the start of sales) based on the acquired beer beverage ID (step S13). The control unit 11 extracts the beer beverage with the highest similarity from among the extracted beer beverages within the specified period (step S14) and returns to the original process that called the subroutine.

[0071] Figure 14 is an explanatory diagram showing an example of a screen for receiving customer attributes on terminal 2. The screen includes a customer attribute input field 11a and a confirmation button 11b. The customer attribute input field 11a is an input field for receiving customer attribute input. The confirmation button 11b is a button for sending the customer attributes entered by the customer to server 1. As shown in the figure, customer attributes (customer information) include nickname, gender, age, most frequently consumed alcoholic beverage, frequency of beer consumption, drinking occasion (drinks at home or drinks out), etc.

[0072] When terminal 2 receives a touch (click) operation on the confirmation button 11b, it sends the customer attributes entered in the customer attribute input field 11a to server 1. Server 1 receives the customer attributes sent from terminal 2 and stores the received customer attributes in customer DB 171, associating them with the customer ID. If the customer attributes have been received once and stored in customer DB 171, or if the customer attributes have been retrieved from local storage, terminal 2 may omit the process in step S201 (Figure 11) from the second time onward. Authentication information for login, including the customer ID, may be entered on this screen, or it may be entered on a separate login screen.

[0073] Figure 15 is an explanatory diagram showing an example of a screen on terminal 2 that accepts answers to multiple questions. The screen includes a progress display area 12a, a question display area 12b, and answer candidate buttons 12c. The progress display area 12a is a display area for showing the progress of answering questions. For example, the numbers of questions that have been answered and are being answered may be shown with solid lines, and the numbers of unanswered questions may be shown with dotted lines. The question display area 12b is a display area for showing the content of the questions. The answer candidate buttons 12c are buttons for selecting answer candidates (options) for a question.

[0074] Server 1 retrieves multiple questions stored in the information collection DB 175 and sends them to Terminal 2. Terminal 2 receives the multiple questions sent from Server 1 and displays them on its screen. The questions are, for example, "Do you prefer Japanese or Western-style breakfast? Please answer with the most common answer." When Terminal 2 receives a touch operation on any of the answer candidate buttons 12c, it retrieves the corresponding answer for that question and displays the next question. For example, if Terminal 2 receives a touch operation on the answer candidate button 12c for "Japanese-style breakfast," it retrieves the answer "Japanese-style breakfast." Terminal 2 then displays the next question in the question display field 12b.

[0075] Terminal 2 sends the customer ID and multiple answers selected by the answer candidate button 12c to Server 1. Server 1 receives the customer ID and multiple answers sent from Terminal 2 and stores the received multiple answers in Customer DB 171, associating them with the customer ID.

[0076] Figure 16 is an explanatory diagram showing an example of a screen displaying the beer beverage before the change. Figure 16 illustrates an example of a screen when the beer beverage suggested to the customer via the beer information output model 176 does not match the previously suggested beer beverage, and the original beer beverage (the original beer beverage) is suggested to the customer without being changed. The screen includes a character 61, a beer display field 71, an order button 72, and a cancel button 73. Character 61 is a character that provides guidance to the customer. As shown in the figure, terminal 2 displays a character with a troubled expression on character 61 in relation to the original beer beverage.

[0077] Terminal 2 displays character 61 in the display area at the top of the screen, and also displays a comment (text) as character 61's dialogue, recommending a beer beverage to be displayed in the beer display area 71. The dialogue could be, for example, "Here is our recommended beer for you, Mr. / Ms. XX," or "I'll do my best with the next suggestion!" The dialogue is not limited to text; it can also be voice.

[0078] The beer display area 71 is a display area for displaying beer information about the beer beverage suggested to the customer through the beer information output model 176. For example, terminal 2 displays the product name, description, and product image of the suggested beer beverage in the beer display area 71. Terminal 2 also displays a detailed profile of the suggested beer beverage, such as the beer style, alcohol content (concentration), calories, manufacturer (brewery), and production region. Beer style refers to the classification of beer according to ingredients, brewing method, alcohol content, etc., such as IPA (India Pale Ale) and Weizen. In addition, terminal 2 displays a radar chart that represents the flavor (body, acidity, sweetness, aroma, bitterness, etc.) of the beer beverage on multiple scales. The radar chart may be automatically created by, for example, server 1 processing multiple characteristic values ​​of the beer beverage stored in product DB 173, or it may be created manually by the system administrator.

[0079] The order button 72 and the cancel button 73 are operation objects for selecting whether or not to order (drink) the suggested beer beverage. If the order button 72 is pressed, the suggested beer beverage is ordered. In this case, for example, server 1 processes the order for the beer beverage and notifies the store staff of the order. If the order button 72 is pressed, a confirmation screen may be displayed first for entering the order quantity or confirming the order details. If integration with the store's ordering system is possible, the order processing for the suggested beer beverage may be performed by the store's server. If the cancel button 73 is pressed, the suggested beer beverage is not ordered and the process ends. In this case, for example, server 1 may select another beer beverage (such as the next most similar beer beverage) and display it on the suggestion screen, or it may end the series of suggestion processes.

[0080] Figure 17 is an explanatory diagram showing an example of a screen displaying the changed beer beverage. Note that content that overlaps with Figure 16 is denoted by the same reference numerals and its explanation is omitted. Figure 17 illustrates an example screen when, if the beer beverage proposed to the customer via the beer information output model 176 (the beer beverage before the change) matches the previously proposed beer beverage, a different beer beverage (the changed beer beverage) is proposed to the customer.

[0081] If Server 1 determines that the beer beverage suggested to the customer via the beer information output model 176 matches the previously suggested beer beverage, it sends beer information about the changed beer beverage and a character (image or icon, etc.) to guide the customer to Terminal 2. Terminal 2 displays the received beer information about the changed beer beverage in the beer display field 71. Similar to Figure 16, the beer information includes the product name, description, product image, beer style, alcohol content (concentration), calories, manufacturer (brewery), production region, or a radar chart representing the flavor of the changed beer beverage on multiple scales.

[0082] Terminal 2 displays the received character on character 61. If Server 1 changes the beer beverage it suggested to the customer via the beer information output model 176, it changes the display of character 61. Specifically, Server 1 changes the facial expression and comment of character 61 from the previous state. For example, as shown in Figure 17, if Server 1 suggests a changed beer beverage, it changes the facial expression of character 61 from the screen in Figure 16 to a confident expression. Alternatively, Terminal 2 may display a line such as "A recommendation for thoughtfulness!" as a comment for character 61.

[0083] The screen also further includes a pre-change beer display field 74. The pre-change beer display field 74 is a display field for displaying beer information about the beer beverage before the change. Server 1 acquires beer information about the beer beverage before the change and transmits it to terminal 2. Terminal 2 displays the beer information about the beer beverage before the change transmitted from server 1 in the pre-change beer display field 74. The beer information displayed in the pre-change beer display field 74 includes, for example, the product name and description of the beer beverage before the change.

[0084] The screen also includes a candidate field 75. The candidate field 75 is a display field for displaying other beer beverages as candidates, other than the modified beer beverage displayed in the beer display field 71. Terminal 2 displays one or more other beer beverages as candidates in the candidate field 75, including product images, product names, etc. Specifically, for example, Server 1 extracts the next most similar beer beverage to the modified beer beverage displayed in the beer display field 71 and sends beer information about the extracted beer beverage to Terminal 2. Terminal 2 receives the beer information about the next most similar beer beverage sent from Server 1 and displays the received beer information in the candidate field 75. Note that the method for selecting other beer beverages other than the modified beer beverage is not limited to similarity. For example, if the release date is used as the selection criterion for the beer beverage to be proposed, the next most recent beer beverage with a release date may also be selected as a candidate, and the selection method for candidate beer beverages depends on the method used.

[0085] Furthermore, the beer information regarding the beer beverage before the change, the beer information regarding the beer beverage after the change, and the beer information regarding the candidate beer beverage after the change may be simultaneously transmitted from server 1 to terminal 2.

[0086] If the order button 72 is pressed, the suggested modified beer beverage is ordered. In this case, for example, server 1 processes the order for the beer beverage and notifies the store staff of the order. When the order button 72 is pressed, a confirmation screen may be displayed first for entering the order quantity or confirming the order details. If integration with the store's ordering system is possible, the order processing for the suggested beer beverage may be performed by the store's server. If the cancel button 73 is pressed, the suggested modified beer beverage is not ordered and the process ends. In this case, for example, server 1 may display the suggested beer beverage shown in the candidate field 75 in the beer display field 71, or it may end the series of suggestion processes.

[0087] The screen also further includes a "Previous Order" button 74a and a "Candidate Order" button 75a. The "Previous Order" button 74a is an operation object for ordering the beer beverage displayed in the "Previous Beer" display field 74. When the "Previous Order" button 74a is pressed, the previous beer beverage is ordered. The "Candidate Order" button 75a is an operation object for ordering the beer beverage displayed in the "Candidate" field 75. When the "Candidate Order" button 75a is pressed, the candidate "Replaced Beer Beverage" is ordered.

[0088] If either the original beer beverage, the modified beer beverage, or a candidate modified beer beverage is ordered, Server 1 stores the order details of the ordered beer beverage in the history DB 174. The order details include, for example, the beer beverage ID, the order quantity, and the order date and time. Furthermore, the beer information output model 176 can be retrained depending on whether or not a beer beverage is ordered. For example, if the customer does not order (drink) the proposed modified beer beverage, the characteristic values ​​of the proposed modified beer beverage are modified (for example, changed to the characteristic values ​​of a different beer beverage ordered by the customer), and the beer information output model 176 is updated with the modified characteristic values ​​as the correct values ​​for retraining. Also, if a candidate modified beer beverage is ordered (wanted to drink), the characteristic values ​​of the proposed modified beer beverage are modified (for example, changed to the characteristic values ​​of the candidate modified beer beverage), and the beer information output model 176 is updated with the modified characteristic values ​​as the correct values ​​for retraining.

[0089] According to this embodiment, based on the customer's attributes and answers to multiple questions, it becomes possible to output beer information regarding beer beverages to be suggested using the beer information output model 176.

[0090] According to this embodiment, based on the history of proposed beer beverages, it is possible to change to a different beer beverage if it matches the previously proposed beer beverage.

[0091] According to this embodiment, by changing the beer beverage to one different from the one proposed previously, it is possible to avoid duplication of proposals, thereby making it possible to propose an appropriate beer beverage to the customer.

[0092] (Embodiment 2) Embodiment 2 relates to a configuration in which, if the beer beverage to be proposed matches the previously proposed beer beverage, the proposed beer beverage is changed to a different one, taking into account the feedback results of the previous proposal. Note that explanations of content that overlaps with Embodiment 1 will be omitted.

[0093] Server 1, via the beer information output model 176, determines that the beer beverage it has suggested to the customer matches the previously suggested beer beverage, and that the feedback result for that beer beverage is negative, then changes it to a different beer beverage. For example, let's consider a scenario where the feedback item for the beer beverage suggested to the customer, "How did you like the taste of the beer you drank?", uses a five-point rating scale with responses such as "Not tasty," "Not great," "Average," "Tasty," and "Very tasty." For example, if Server 1 receives negative feedback from the customer regarding the taste of the previously suggested beer beverage A, such as "Not tasty," "Not great," or "Average," then it changes it to a different beer beverage B.

[0094] Figure 18 is a flowchart illustrating the processing procedure for receiving feedback results for beer beverages. The control unit 11 of server 1 obtains multiple feedback items from the information collection DB 175 of the large-capacity storage unit 17 (step S121). The control unit 11 transmits the beer beverage ID proposed to the customer and the multiple feedback items obtained to terminal 2 via the communication unit 13 (step S122).

[0095] The control unit 21 of terminal 2 receives the beer beverage ID and multiple feedback items transmitted from server 1 via the communication unit 23 (step S221), and displays the received multiple items via the display unit 25 (step S222). The control unit 21 receives the customer's answers to the multiple items via the input unit 24 (step S223). The control unit 21 transmits the customer ID, beer beverage ID, and feedback results including the received multiple answers to server 1 via the communication unit 23 (step S224).

[0096] The control unit 11 of server 1 receives the feedback results transmitted from terminal 2 via the communication unit 13 (step S123). The control unit 11 stores the received feedback results in the history DB 174 of the large-capacity storage unit 17 (step S124). Specifically, the control unit 11 stores the answers to multiple items of the feedback in the feedback result column, associated with the customer ID and the beer beverage ID.

[0097] Figure 19 is a flowchart showing the processing procedure when changing the beer beverage based on feedback results. Note that the same reference numerals are used for content that overlaps with Figure 12, and explanations are omitted. If the control unit 11 of the server 1 determines that the beer beverage proposed to the customer via the beer information output model 176 matches the beer beverage proposed last time (YES in step S108), it obtains the feedback result for the beer beverage from the history DB 174 of the large-capacity storage unit 17 based on the customer ID and beer beverage ID (step S112). The feedback result is, for example, "No. 1 (How did the beer you drank taste?): It wasn't tasty, No. 2 (What did you drink the beer with?): Snacks, No. 3 (What number beer have you drunk now?): 1st glass," etc.

[0098] The control unit 11 determines whether the feedback result for the beer beverage indicates a negative or negative (step S113). For example, if the response to feedback item No. 1 for the beer beverage is "not tasty," "not great," or "average," the control unit 11 determines that the feedback result for the beer beverage indicates a negative or negative. Conversely, if the response to feedback item No. 1 for the beer beverage is "tasty" or "very tasty," the control unit 11 determines that the feedback result for the beer beverage indicates a positive or positive.

[0099] If the control unit 11 determines that the feedback result for the beer beverage indicates a negative (YES in step S113), it executes the process in step S109 to change the beer beverage. For example, if the control unit 11 proposes a beer beverage based on similarity (e.g., cosine similarity), it identifies multiple beer beverages to be output candidates in descending order of similarity value. The control unit 11 changes the beer beverage from the identified multiple beer beverages to the next most similar beer beverage. Alternatively, the control unit 11 extracts the beer beverage ranked No. 1 in popularity from multiple beer beverages belonging to the same category as the beer beverage in question, and changes the beer beverage to the extracted beer beverage. In other words, the process of changing the beer beverage is not limited to the processing method described above, and any algorithm may be adopted. If the control unit 11 determines that the feedback result for the beer beverage indicates a positive (NO in step S113), it proceeds to the process in step S111.

[0100] Figure 20 is an explanatory diagram showing an example of a screen for receiving feedback on terminal 2. The screen includes a logo display area 14a, a beer beverage display area 14b, an item display area 14c, a suggested answer button 14d, and an item progress display area 14e. The logo display area 14a is a display area for displaying the logo of the beer beverage. The beer beverage display area 14b is a display area for displaying the name of the beer beverage and its alcohol content.

[0101] The item display area 14c is a display area for displaying the feedback items. The answer candidate button 14d is a button for selecting an answer candidate for the feedback item. Note that the answer candidate button 14d may be a button represented by numbers or icons (e.g., a star mark) instead of text. The item progress display area 14e is a display area for displaying the progress of responses for multiple feedback items. For example, the numbers of items that have been answered and are being answered may be shown with a solid line frame, and the numbers of unanswered items may be shown with a dotted line frame.

[0102] Server 1 retrieves multiple feedback items stored in the information collection DB 175 and the name (brand) and alcohol content of the beer beverage stored in the product DB 173. Server 1 transmits the retrieved multiple feedback items, the name of the beer beverage, and the alcohol content to Terminal 2. Terminal 2 receives the multiple feedback items, the name of the beer beverage, and the alcohol content transmitted from Server 1. Terminal 2 displays the received multiple feedback items in the item display field 14c and displays the name of the beer beverage and the alcohol content in the beer beverage display field 14b. The feedback items are, for example, "How did you like the taste of the beer you drank?". When Terminal 2 receives a touch operation on any of the answer candidate buttons 14d, it retrieves the corresponding answer for that item and displays the next item. For example, when Terminal 2 receives a touch operation on the answer candidate button 14d that says "Normal", it retrieves the answer "Normal". Terminal 2 displays the next item in the item display field 14c.

[0103] Terminal 2 sends the customer ID, beer beverage ID, and multiple responses selected by the response candidate button 14d to Server 1. Server 1 receives the customer ID, beer beverage ID, and multiple responses sent from Terminal 2, and stores the received multiple responses (feedback results) in the history DB 174, associating them with the customer ID and beer beverage ID.

[0104] According to this embodiment, by considering the feedback results of beer beverages, it becomes possible to accurately grasp customer needs and propose appropriate beer beverages that meet those needs.

[0105] (Embodiment 3) Embodiment 3 relates to a method of changing the proposed beer beverage to one belonging to the same category as the previously proposed beer beverage, if the proposed beer beverage matches the previously proposed beer beverage. Note that explanations of content that overlaps with Embodiments 1 and 2 will be omitted.

[0106] Figure 21 is a block diagram showing an example configuration of the server 1 of Embodiment 3. Note that components that overlap with those in Figure 2 are denoted by the same reference numerals and their explanations are omitted. The large-capacity storage unit 17 stores a category classification model 177 and a category classification database 178. The category classification model 177 is used as a program module, which is part of the artificial intelligence software. It is a trained model that classifies each beer beverage into multiple categories based on learning from multiple characteristic values ​​of each beer beverage. The category classification database 178 stores classification information for categories classified through the category classification model 177.

[0107] Figure 22 is an explanatory diagram showing an example of the record layout of Category Classification DB178. Category Classification DB178 includes a Category ID column and a Product ID column. The Category ID column stores the ID of a category, which is used to uniquely identify each category. The Product ID column stores the Product ID, which is used to identify a product.

[0108] Next, we will explain the process of changing the beer beverage to be changed to a beer beverage belonging to the same category as the original beer beverage. First, if the category of the beer beverage is not classified, Server 1 uses the category classification model 177 to classify each beer beverage into one of several categories. In this embodiment, we will explain an example of category classification using an unsupervised learning algorithm. Unsupervised learning is a method in which a device learns how the input data is distributed by providing only a large amount of input data, and learns by compressing, classifying, and shaping the input data without providing corresponding supervised output data.

[0109] Specifically, Server 1 retrieves characteristic data (multiple characteristic values) for each beer beverage from Product DB 173 based on each beer beverage ID. Server 1 inputs the retrieved characteristic data for each beer beverage into Category Classification Model 177. Category Classification Model 177 accepts the characteristic data and determines the category associated with each beer beverage by clustering based on the vector representation of the accepted characteristic data. Category Classification Model 177 may include models such as k-means or random forest. Server 1 assigns a category ID to the category determined through Category Classification Model 177. Server 1 stores the assigned category IDs in Category Classification DB 178, associating them with each beer beverage ID.

[0110] Furthermore, the category classification process may be performed not only using unsupervised learning algorithms, but also using supervised learning machine learning techniques such as SVM. Alternatively, the output of the hidden layer of a neural network trained using supervised learning may be extracted and used as a clustering algorithm.

[0111] Furthermore, if a new beer beverage is received, or if a beer beverage is discontinued, Server 1 will use the category classification model 177 to reclassify each beer beverage into multiple categories. The reclassification process is the same as the classification process described above, so the explanation will be omitted.

[0112] Next, if Server 1 determines that the beer beverage suggested to the customer via the beer information output model 176 matches the previously suggested beer beverage, it extracts beer beverages belonging to the same category as the suggested beer beverage. For example, Server 1 may extract beer beverages with high similarity based on multiple characteristic values ​​from multiple beer beverages belonging to the same category as the suggested beer beverage, similar to Embodiment 1. Alternatively, Server 1 may extract beer beverages to be suggested from multiple beer beverages belonging to the same category in order of popularity ranking, price (lowest first), or date of availability (newest first). Server 1 sends the extracted beer beverages to Terminal 2 as the beer beverages to be suggested. Terminal 2 receives and displays the beer beverages sent from Server 1.

[0113] Figure 23 is a flowchart showing the processing procedure when changing to a beer beverage belonging to the same category. Note that the same reference numerals are used for content that overlaps with Figure 12 and explanations are omitted. If the control unit 11 of server 1 determines that the beer beverage proposed to the customer through the beer information output model 176 matches the previously proposed beer beverage (YES in step S108), it extracts all beer beverage IDs belonging to the same category as the proposed beer beverage (step S114). Specifically, based on the beer beverage ID, the control unit 11 obtains the category ID of the beer beverage from the category classification DB 178. Based on the obtained category ID, the control unit 11 extracts all beer beverage IDs belonging to that category from the category classification DB 178. Based on all the extracted beer beverage IDs, the control unit 11 obtains the beer beverages to be changed from the product DB 173, for example, in order of the newest start date (step S115). Note that the control unit 11 may randomly select beer beverages other than the previously proposed beer beverage from among multiple beer beverages belonging to the same category as the proposed beer beverage. Subsequently, the control unit 11 executes the process in step S109.

[0114] According to this embodiment, if the beer beverage suggested to the customer through the beer information output model 176 matches the beer beverage suggested previously, it becomes possible to change the beer beverage to one belonging to the same category as the previously suggested beer beverage.

[0115] According to this embodiment, it becomes possible to propose the optimal beer beverage tailored to the customer's preferences.

[0116] (Embodiment 4) Embodiment 4 relates to a configuration in which the beer information output model 176 is updated using customer feedback results. Customer feedback results include responses to multiple feedback items, etc. Note that explanations of content that overlaps with Embodiments 1 to 3 will be omitted.

[0117] If the beer beverage suggested to the customer through the beer information output model 176 does not match the previously suggested beer beverage, the original beer beverage (the one before the change) is suggested to the customer without modification. Conversely, if the beer beverage suggested to the customer through the beer information output model 176 matches the previously suggested beer beverage, a different beer beverage (the changed beer beverage) is suggested to the customer. As a result, the feedback results include feedback results for both the original beer beverage and the changed beer beverage. Either feedback result includes information such as the customer's preference for the beer beverage and the number of glasses consumed.

[0118] The feedback results stored in the history DB 174 are used to retrain the beer information output model 176. Specifically, Server 1 creates first training data based on the feedback results for the beer beverage before the change. The first training data includes customer attributes and answers to several questions, beer information about the beer beverage before the change, and customer feedback on the beer information. Server 1 creates second training data based on the feedback results for the beer beverage after the change. The second training data includes customer attributes and answers to several questions, beer information about the beer beverage after the change, and customer feedback on the beer information.

[0119] Server 1 uses the customer attributes included in the created first and second training data, along with the answers to multiple questions, as input data for retraining (training), and updates the beer information output model 176 by learning the proposed beer beverages as the correct values ​​for retraining.

[0120] In this case, it is preferable for Server 1 to retrain based on whether or not the suggested beer beverage was ordered, the answers to multiple items in the feedback entered after the order, etc. For example, if the customer does not order the suggested beer beverage, Server 1 modifies the characteristic value of the suggested beer beverage (for example, by changing it to the characteristic value of a different beer beverage ordered by the customer) and updates the beer information output model 176 with the modified characteristic value as the correct value for retraining. Also, if a beer beverage displayed in the candidate field 75 instead of the beer display field 71 is ordered, Server 1 modifies the characteristic value of the suggested beer beverage (for example, by changing it to the characteristic value of a candidate beer beverage) and updates the beer information output model 176 with the modified characteristic value as the correct value for retraining. Furthermore, for example, if the customer's feedback result is negative regarding the beer beverage the customer drank, Server 1 modifies the characteristic value of the suggested beer beverage (for example, by changing it to the characteristic value of a beer beverage corresponding to a positive response to the feedback) and updates the beer information output model 176 with the modified characteristic value as the correct value for retraining.

[0121] The above retraining method is merely an example, and this embodiment is not limited thereto. For example, if the feedback result includes a question asking about the beer beverage requested by the user, Server 1 may change the characteristic values ​​of the suggested beer beverage (e.g., change the alcohol content) according to the answer to that question (e.g., "I would prefer something with a higher alcohol content"), and update the beer information output model 176 using the changed characteristic values ​​as the correct values ​​for retraining. In other words, the correct values ​​of the beer information (characteristic values) used for retraining may be changed according to the content of the answers to multiple items in the feedback.

[0122] In this way, the accuracy of proposals can be improved through the operation of this system, based on customer feedback and other factors.

[0123] Figure 24 is a flowchart showing the procedure for updating the beer information output model 176. Based on Figure 24, the process of updating the beer information output model 176 by retraining will be explained.

[0124] The control unit 11 of server 1 obtains feedback results for retraining from the history DB 174, etc. (step S131). The control unit 11 creates first training data for retraining for the beer beverage before the change (step S132). The first training data includes customer attributes, answers to multiple questions, characteristic values ​​of the beer beverage before the change, etc., as well as whether or not the beer beverage before the change was ordered, answers to multiple items of feedback entered after the order, etc. The control unit 11 creates second training data for retraining for the beer beverage after the change (step S133). The second training data includes customer attributes, answers to multiple questions, characteristic values ​​of the beer beverage after the change, etc., as well as whether or not the beer beverage after the change was ordered, answers to multiple items of feedback entered after the order, etc.

[0125] The control unit 11 updates the beer information output model 176 based on the first and second training data for retraining (step S134). For example, if the customer does not order the suggested beer beverage, the control unit 11 updates the parameters of the beer information output model 176 so that the loss function increases. Also, for example, if the feedback on the suggested beer beverage is positive or negative, the control unit 11 updates the parameters of the beer information output model 176 so that the loss function decreases or increases. The control unit 11 then completes the series of processes.

[0126] According to this embodiment, the estimation accuracy of the beer information output model 176 can be improved by retraining the beer information output model 176 using customer feedback results.

[0127] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the claims, not in the sense described above, and all modifications are intended to be in the sense and scope equivalent to the claims. [Explanation of Symbols]

[0128] 1. Information processing device (server) 11 Control Unit 12 Storage section 13 Communications Department 14 Input section 15 Display 16 Reading Unit 17 Mass storage 171 Customer DB 172 Store Database 173 Product DB 174 History DB 175 Information Gathering Database 176 Beer Information Output Model 177 Category Classification Models 178 Category Classification Database 1a Portable storage medium 1b Semiconductor memory 1P Control Program 2. Information processing terminal (terminal) 21 Control Unit 22 Memory section 23 Communications Department 24 Input section 25 Display section 2P control program

Claims

1. Obtain customer attributes, We obtained answers to several questions from the aforementioned customer, A learning model that outputs beer information regarding beer, low-malt beer, new genre beer, or beer-flavored beverages to be suggested when customer attributes and multiple responses are input, outputs beer information that includes characteristic values ​​of at least two of the following for the beer beverage to be suggested when customer attributes and multiple responses are input: alcohol content, bitterness value, color, and ethyl acetate. Based on the beer information, which consists of characteristic values ​​including at least two of the outputted alcohol content, bitterness value, color, and ethyl acetate, the history of the proposed beer beverage is stored in association with the customer. When displaying a screen that includes a first display field for displaying beer information relating to the previously proposed beer beverage, a second display field for displaying beer information relating to a modified beer beverage different from the previously proposed beer beverage, and a third display field for displaying beer information relating to a candidate beer beverage that is different from the modified beer beverage, The aforementioned screen is displayed when the beer beverage suggested to the customer, based on the history, matches the beer beverage suggested previously. The first display field shows the previously suggested beer beverage, and the second display field shows the changed beer beverage. An information processing method in which a computer performs the processing.

2. The aforementioned learning model outputs multiple characteristic values ​​that represent the characteristics of beer beverages, The system references a memory unit that stores multiple characteristic values ​​associated with beer beverages, and extracts beer beverages that have a high similarity to the multiple characteristic values ​​output from the learning model. The information processing method according to claim 1.

3. If the beer beverage suggested to the customer through the aforementioned learning model matches the previously suggested beer beverage, the system will change it to the next most similar beer beverage. The information processing method according to claim 2.

4. If the beer beverage suggested to the customer through the aforementioned learning model matches the previously suggested beer beverage, and the feedback result for the said beer beverage is negative, then a different beer beverage will be selected. The information processing method according to any one of claims 1 to 3.

5. Each beer beverage is classified into multiple categories based on unsupervised learning using multiple characteristic values ​​of each beer beverage. If the beer beverage suggested to the customer through the aforementioned learning model matches the beer beverage suggested previously, the system extracts beer beverages belonging to the same category as the previously suggested beer beverage. The information processing method according to any one of claims 1 to 4.

6. Obtain customer attributes, We obtained answers to several questions from the aforementioned customer, A learning model that outputs beer information regarding beer, low-malt beer, new genre beer, or beer-flavored beverages to be suggested when customer attributes and multiple responses are input, outputs beer information that includes characteristic values ​​of at least two of the following for the beer beverage to be suggested when customer attributes and multiple responses are input: alcohol content, bitterness value, color, and ethyl acetate. Based on the beer information, which consists of characteristic values ​​including at least two of the outputted alcohol content, bitterness value, color, and ethyl acetate, the history of the proposed beer beverage is stored in association with the customer. When displaying a screen that includes a first display field for displaying beer information relating to the previously proposed beer beverage, a second display field for displaying beer information relating to a modified beer beverage different from the previously proposed beer beverage, and a third display field for displaying beer information relating to a candidate beer beverage that is different from the modified beer beverage, The aforementioned screen is displayed when the beer beverage suggested to the customer, based on the history, matches the beer beverage suggested previously. The first display field shows the previously suggested beer beverage, and the second display field shows the changed beer beverage. A program that instructs a computer to perform a process.

7. A first acquisition unit that acquires customer attributes, A second acquisition unit that obtains answers to multiple questions from the aforementioned customer, A learning model that outputs beer information regarding beer, low-malt beer, new genre beer, or beer-flavored beverages to be suggested when customer attributes and multiple responses are input, includes an output unit that outputs beer information which is a characteristic value including at least two of the following for the beer beverage to be suggested when customer attributes and multiple responses are input: A storage unit that stores a history of beer beverages proposed based on beer information, which is a characteristic value including at least two of the outputted alcohol content, bitterness value, color, and ethyl acetate, in association with the customer, When displaying a screen that includes a first display field for displaying beer information relating to the previously proposed beer beverage, a second display field for displaying beer information relating to a modified beer beverage different from the previously proposed beer beverage, and a third display field for displaying beer information relating to a candidate beer beverage that is different from the modified beer beverage, The aforementioned screen is displayed when the beer beverage suggested to the customer based on the history matches the beer beverage suggested previously, and the first display field displays the previously suggested beer beverage, and the second display field displays the changed beer beverage. An information processing device characterized by comprising:

8. By inputting customer attributes and answers to multiple questions into a learning model that outputs beer information, which is a characteristic value including at least two of the following: alcohol content, bitterness value, color, and ethyl acetate, if the beer beverage, including beer, sparkling wine, or beer-flavored beverage, suggested based on the outputted beer information does not match the previously suggested beer beverage stored in the history of the beer beverage, first training data is obtained, which includes beer information about the beer beverage suggested based on the beer information and the customer's feedback on the suggested beer beverage. If the beer beverage suggested based on the customer's attributes and answers to multiple questions, and the beer information output from the learning model, matches the previously suggested beer beverage stored in the beer beverage history, then second training data is acquired, which includes beer information about a modified beer beverage different from the previously suggested beer beverage, and the customer's feedback on the modified beer beverage. The learning model is generated using the first and second training data. A method for generating a learning model that allows a computer to perform a process.

9. Obtain customer attributes, We obtained answers to several questions from the aforementioned customer, A learning model that outputs beer information regarding beer, low-malt beer, new genre beer, or beer-flavored beverages should be suggested when the acquired customer attributes and multiple responses are input, outputs beer information regarding beer beverages that should be suggested when the customer attributes and multiple responses are input, Based on the outputted beer information, the history of the beer beverages suggested is stored in association with the customer. A character that provides guidance to customers is displayed on the screen. If the beer beverage suggested to the customer through the learning model matches the beer beverage suggested previously based on the history, the beer beverage suggested to the customer will be changed to a different beer beverage from the one previously suggested. When the beer beverage is changed to a different one, the character's comments and expressions are changed to a second comment and expression that are different from the first comment and first expression used when informing the customer about the beer beverage suggested to the customer through the learning model. An information processing method in which a computer performs the processing.

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