system

Abstract

We provide the system. [Solution] A means for acquiring user data and generating a taste profile, A means for selecting new alcoholic beverage products based on the aforementioned taste profile, A means of proposing the selected alcoholic beverages to the user, obtaining their approval, and then processing the payment, A means for delivering the aforementioned alcoholic beverages to the user, A means for obtaining feedback information from users, correcting the aforementioned taste profile, and improving the accuracy of alcohol product selection, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern consumers lead busy lives and have limited time to select alcoholic products that suit their individual preferences. Also, with the increase in online shopping, the demand for customized product offerings is growing, but there remains the problem that it is still difficult for consumers to find products that are optimal for their taste. Furthermore, a safe and efficient payment and delivery process is required, but there are limited systems that can provide these seamlessly.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring user data and generating a taste profile. Furthermore, it provides means for selecting new alcoholic beverage products based on this taste profile and proposing the selected products to the user. After the user approves the proposal, the system includes means for secure and efficient payment and delivery. In addition, the system corrects the taste profile based on feedback information obtained from the user to improve the accuracy of product selection. This realizes a system that enables the provision of alcoholic beverage products optimized for each individual user.

[0006] "User data" refers to the totality of information including a user's personal preferences, history, ratings, etc.

[0007] A "taste profile" is individual characteristic data that analyzes and visualizes a user's taste preferences and tendencies.

[0008] "Alcoholic beverages" refers to all beverages containing alcohol, including beer, wine, sake, and whiskey.

[0009] "Selection methods" refer to the process of identifying and selecting the most suitable product based on the user's profile.

[0010] "Means of suggestion" refers to a system for informing users about selected products and encouraging them to make a choice.

[0011] "Means of payment" refers to the method of processing monetary transactions for products selected by the user.

[0012] "Means of delivery" refers to the procedures for physically delivering the product selected by the user.

[0013] "Feedback information" refers to information used to record the results and impressions of a user's experience.

[0014] "To correct" means to review, improve, or adjust existing information or data.

[0015] "Selection accuracy" is an indicator that shows how appropriate the selection of products that match the user's preferences is.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [[ID=3x]] [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] An embodiment of this invention is a series of systems that propose and deliver the most suitable alcoholic beverage products through information exchange between a user, a server, and a terminal.

[0038] User: Launch the application and first create an account containing the necessary personal information. Then, answer questions about your past alcohol consumption experience and current preferences to form an initial taste profile.

[0039] Server: Receives the provided data, analyzes it using a generating AI, and generates a detailed taste profile. Based on this, it selects alcoholic beverages from the market database that are deemed most suitable for the user. It also incorporates information on new products to create a list of products with a surprise element.

[0040] Terminal: The app displays a list of selected alcoholic beverages to the user and shows the payment screen for the selected items. Payment is processed via a secure API in conjunction with the server. Once the user approves the items, the payment process is completed immediately and a confirmation message is sent.

[0041] Server: Once payment is complete, the server sends shipping instructions to the relevant logistics company and initiates the shipping process. Simultaneously, the user is provided with real-time updates on the shipping status. The server also collects feedback on the user's experience after shipping.

[0042] Specific example: If a user has a profile indicating a preference for white wine, a newly released French Chardonnay will be recommended. Once the user selects and approves the purchase, a detailed guide on the wine's optimal drinking temperature and other relevant information will be displayed within the app. After drinking, the user provides feedback on the wine, which will be used to inform future recommendations.

[0043] This system allows users to easily enjoy a personalized alcoholic beverage experience, and the entire system is continuously improved through a feedback process. This is an effective means of addressing users' specific needs and providing monthly enjoyment.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user installs the application and accesses an account creation screen that includes personal information. Here, they enter their name, email address, address, etc., to create an account.

[0047] Step 2:

[0048] The terminal sends user input information to the server. The server receives this information, stores it in a database, and processes it as initial data for forming a taste profile.

[0049] Step 3:

[0050] Users can input detailed information about their favorite types of alcohol and their ratings of alcoholic beverages they have drunk in the past within the app.

[0051] Step 4:

[0052] The server receives data on the user's taste preferences and uses a generative AI to analyze and generate a detailed taste profile of the user.

[0053] Step 5:

[0054] The server compares the generated taste profile with a market database to select the most suitable alcoholic beverage for the user. Furthermore, it incorporates new products and trends, and lists items with a surprise element.

[0055] Step 6:

[0056] The device displays a list of selected alcoholic beverages to the user. The user reviews the list within the app and selects the items they wish to purchase.

[0057] Step 7:

[0058] When a user selects to purchase, the device sends payment information to the server using a secure API. The server accepts and processes the payment.

[0059] Step 8:

[0060] Once the payment is complete, the server sends a confirmation message to the terminal to notify the user.

[0061] Step 9:

[0062] The server directly transmits the shipping information to the logistics company and initiates the shipping process. At the same time, it provides users with real-time updates on the shipping status.

[0063] Step 10:

[0064] After receiving the product, users can provide feedback on their drinking experience within the app.

[0065] Step 11:

[0066] The server receives feedback from users, analyzes it, updates their taste profiles, and uses it to improve the AI's recommendation algorithm.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] Conventional beverage product recommendation systems often fail to adequately personalize user preferences, resulting in suggested products that don't meet user expectations. Furthermore, the lack of transparency in delivery status and the inability to effectively utilize post-purchase user feedback need improvement.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means for acquiring user information and generating a preference profile, means for selecting the optimal beverage product, and means for analyzing data using a generative artificial intelligence model. This makes it possible to improve the accuracy of product recommendations based on the user's individual preferences and enhance the quality of the purchasing experience.

[0072] "User information" refers to personally identifiable information of system users, as well as data related to their preferences and consumption history.

[0073] A "preference profile" is information that shows a user's preferences for beverages, derived from their past preferences and purchase history.

[0074] "Beverage products" refers to various drinkable products offered to users, including alcoholic and non-alcoholic beverages.

[0075] A "generative artificial intelligence model" refers to AI technology that generates specific patterns or information based on large amounts of data, and is used for data analysis and automated generation.

[0076] "Data analysis" is the process of identifying and understanding useful patterns and trends based on collected information, and deriving results from them.

[0077] "Delivery status" refers to the progress and location information of an ordered product from the time it is shipped until it reaches the user.

[0078] "Personalization" refers to the process of tailoring or adjusting products and services to each user's individual preferences and needs.

[0079] This invention is a system that proposes and delivers the optimal beverage product through information exchange between the user, server, and terminal. Specific embodiments of each component are shown below.

[0080] Users launch a mobile application and enter personal and preference data. This data includes past consumption history, preferred flavors, and brand information.

[0081] The server receives data sent from the user. A generative AI model running on the server generates a preference profile based on this data. The AI ​​model runs on a cloud platform and uses a data processing pipeline built with programming languages ​​such as Python and R. This profile automatically selects the optimal beverage product that matches the user's preferences.

[0082] The device displays a list of selected beverage products in the user's app. Once the user selects a product, the device communicates with a server via a secure network protocol to confirm the purchase and process payment. The device uses the SSL / TLS protocol to ensure data encryption and secure communication.

[0083] For example, if a user prefers wine or craft beer from a particular region, the server can use this information to recommend the latest products from that region. By providing the AI ​​with specific requests, such as "a fresh white wine from France," more accurate suggestions can be made.

[0084] Example prompt: "The user wants to enjoy a pairing of red wine and cheese. Suggest a wine recommendation based on the user's preferences."

[0085] This system allows users to easily enjoy a beverage experience optimized to their preferences. Furthermore, through a feedback process, the server can continuously adjust its profile and improve the accuracy of its selections.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users launch the mobile application and create an account. Here, they enter personal information, past alcohol consumption experience, and preferences. This input data is used to generate an initial preference profile. User information is input, and the initial preference profile is sent to the server as output.

[0089] Step 2:

[0090] The server processes information received from the user and generates a preference profile using a generative AI model. The server uses the received data as input, performs data analysis using the generative AI model, and profiles the user's preferences in detail. Data based on this profile is output and used for the next process.

[0091] Step 3:

[0092] The server selects the most suitable beverage products from the market database based on the generated preference profile. The server uses this profile data as input, compares it with existing market product information, and outputs a list of beverage products that match the user's preferences.

[0093] Step 4:

[0094] The terminal displays a product list sent from the server to the user. The user reviews this list and selects the products they wish to purchase. The terminal receives the product list as input for display and provides it as output on the screen.

[0095] Step 5:

[0096] The terminal performs purchase confirmation and payment processing for the items selected by the user. The inputs used are the user's selected product information and payment information, and the output is a payment completion message. This process includes secure API communication and data protection using the SSL / TLS protocol.

[0097] Step 6:

[0098] The server issues delivery instructions to the logistics company after payment is completed. Payment information and product selection information are used as input, and the delivery instructions are sent to the logistics company as output. The server also continuously updates the user with real-time delivery status information.

[0099] Step 7:

[0100] After receiving a product, users provide feedback through the application. This user feedback is sent to the server as input and used as output to refine the user's preference profile for future purchases. This continuous feedback loop improves the accuracy of product recommendations.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] There is a need to quickly and efficiently select and recommend food products that are best suited to the individual tastes of each user. Traditional methods struggle to effectively recommend food products that reflect user preferences, and there is no established system for fully utilizing feedback to improve selection accuracy. A system that addresses these challenges is needed to enhance the user experience.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for acquiring user data and generating a taste profile; means for selecting new food products based on the taste profile; and means for proposing the selected food products to the user, obtaining their approval, and processing payment. This makes it possible to provide optimal food products based on the user's individual taste preferences.

[0106] "User data" refers to information including a user's personal preferences, past selection history, and feedback.

[0107] A "taste profile" is a dataset that represents a user's taste preferences and is useful for suggesting products based on individual tastes.

[0108] "Food products" refer to food and beverages offered to users, selected based on their taste profiles.

[0109] "Payment" refers to the process of confirming the purchase of a product selected by the user and completing the payment.

[0110] "Delivery" refers to the entire process of physically delivering the selected products to the user.

[0111] "Feedback information" refers to user feedback and evaluations provided after use, which is data used to improve the profile.

[0112] "Mobile device technology" refers to communication and application technologies that use portable devices such as smartphones and tablets.

[0113] In this embodiment of the invention, the user first launches a dedicated application using a mobile device such as a smartphone or tablet. The user enters personal information within the application and answers questions about their preferences, thereby generating an initial taste profile. This profile is sent to a server and analyzed in detail using a generative AI model.

[0114] The server selects food products from its product database that it deems most suitable for the user based on the aforementioned taste profile. It generates a product list that also takes into account newly added products to the market and information obtained from past feedback based on the user's profile. The user reviews the suggested product list on the app and selects the products they wish to purchase.

[0115] The terminal uses a payment service to securely complete the payment process for the product. Once payment is complete, the product is delivered, and the user can check its status in real time through the app. After delivery, the user experiences the food product and registers their impressions and evaluation as feedback on the app. This feedback is processed on the server and used to improve the accuracy of future selections.

[0116] For example, if a user profile is created indicating they "prefer spicy food," the AI ​​model could suggest recently released spicy Mexican dishes that match that preference. The user then orders and experiences the selected dishes, and the feedback received afterward allows for even more accurate product recommendations in the future.

[0117] When utilizing a generative AI model, a concrete example of a prompt message would be: "Based on the user's taste profile, please suggest a new spicy dish menu. The user prefers spicy food. Please also consider the feedback so that we can use it to improve future dish suggestions."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user launches the application and answers questions about their personal information and preferences. User data (personal information, preference information) is entered as input. The app collects this data to create an initial taste profile. This profile data is generated as output and sent to the server.

[0121] Step 2:

[0122] The server analyzes the received taste profile data. Using a generative AI model, the profile is further analyzed to identify individual taste tendencies. The input is taste profile data, and the output is the analyzed detailed taste profile. Based on this profile, a list of food products suitable for the user is generated.

[0123] Step 3:

[0124] The server searches the product database using a detailed taste profile and selects appropriate food products. Using a generative AI model, it generates prompt messages to select new products that match the user's preferences. The detailed taste profile is used as input, and a list of selected food products is generated as output. This list is sent to the terminal.

[0125] Step 4:

[0126] The terminal displays a list of received products to the user. The user reviews the list and selects the desired products. The selection process begins. The input is the user's selection information, and the output is the selected product data.

[0127] Step 5:

[0128] The terminal securely processes payments via a linked payment service. Inputs are selected product data and user payment information, and output is payment confirmation data. After payment is complete, the selected products proceed to the shipping process.

[0129] Step 6:

[0130] The server issues instructions to the delivery company and arranges for the selected food products to be delivered to the user. Real-time delivery status is also provided to the user. The input is payment confirmation data, and the output generates delivery instructions and delivery status information.

[0131] Step 7:

[0132] Users receive a product and provide feedback via a device after experiencing it. This feedback information is collected as data and used for future analysis. The input is user evaluation information, and the output is updated taste profile data. The feedback is processed again on the server, improving the accuracy of product selection.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a recommendation system for alcoholic beverages that combines an emotion engine that recognizes user emotions, providing a personalized experience. The following describes its specific embodiments.

[0135] Users: Through the application, they input their alcohol preferences and past purchase history to form a basic taste profile. In addition, users express their everyday emotions and their feelings when tasting products through the app's interface.

[0136] Device: When the device starts up or whenever the user responds, it uses the camera or microphone to capture the user's facial expressions and tone of voice, and sends this data to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0137] Server: The server selects the optimal alcoholic beverage by combining emotional data analyzed by the emotion engine, taste profiles, and past history. The selection reflects new product information and trends from the market, and the product recommendations are further adjusted according to the user's current emotions.

[0138] Specific example: When a user is feeling down, the emotion engine detects this emotion. The detected emotion is reflected in the system, and a product with a relaxing effect is selected, such as a wine with a specific aroma. When presented to the user, a guide about the relaxing effects and how to drink the selected product is also provided.

[0139] Feedback Collection: After sampling each product, users enter their feedback into the app. The emotion engine also captures the user's reactions during the experience, and uses these results to continuously update the taste profile and improve the accuracy of recommendations.

[0140] Thus, this system is a mechanism that develops dynamic and personalized services that respond to the user's daily life and emotional state. This makes it possible to provide experiential value tailored to the user's situation, rather than simply suggesting products.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users install the app and create an account on the login screen. Here, they enter basic personal information, as well as information about their preferred alcoholic beverages and products they have enjoyed in the past.

[0144] Step 2:

[0145] The terminal sends user input data to the server. The server receives this data and uses a generating AI to initialize the user's taste profile.

[0146] Step 3:

[0147] Through the app, users express their everyday emotions and their feelings when tasting alcoholic beverages via the camera and microphone.

[0148] Step 4:

[0149] The device captures the user's facial expressions and voice tone in real time and sends the data to an emotion engine. This engine analyzes the user's emotions and sends the results to a server.

[0150] Step 5:

[0151] The server combines the acquired emotional data and taste profiles, and uses AI to select the most suitable alcoholic beverage product at the moment. It also takes into account new product lists and market trends, and adjusts the recommendations according to the user's emotions.

[0152] Step 6:

[0153] The terminal displays a list of selected alcoholic beverages received from the server to the user. In addition, it also displays information about the characteristics of the selected products and a guide on how to consume them.

[0154] Step 7:

[0155] The user views a list of suggested products, selects the items they wish to purchase, and proceeds with the payment process.

[0156] Step 8:

[0157] The terminal sends the user's selection to the server, which securely completes the transaction via a payment API. The server confirms the completion of the payment and issues a shipping instruction to the logistics company.

[0158] Step 9:

[0159] The server tracks the delivery status in real time and periodically notifies the user of the status via the terminal.

[0160] Step 10:

[0161] After receiving a product, users input their feedback into the app based on their experience. The device also captures the user's emotions during the experience via the camera and microphone and sends them back to the emotion engine.

[0162] Step 11:

[0163] The server improves the taste profile based on new emotional data and feedback, and uses this information for future recommendations.

[0164] (Example 2)

[0165] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0166] A challenge with conventional beverage recommendation systems was their inability to provide optimal product suggestions based on individual emotional states and preferences. Furthermore, the slow pace of profile updates based on user feedback made it difficult to improve the accuracy of future recommendations.

[0167] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0168] In this invention, the server includes means for acquiring user data and generating a preference profile; means for analyzing the user's emotional state using a generation AI model and adjusting the optimal product; and means for acquiring feedback information from the user and correcting the preference profile to improve the accuracy of beverage product selection. This makes it possible to suggest individually optimized products that are in line with the user's emotions and preferences.

[0169] "User data" refers to information related to a user's preferences, past purchase history, emotional state, etc.

[0170] A "preference profile" is a dataset that shows a user's taste and preferences for beverages.

[0171] "Emotional data" refers to information about a user's emotional state, analyzed based on changes in their facial expressions and voice.

[0172] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data and reactions to evaluate emotional states and preferences.

[0173] "Beverage products" refer to the various alcoholic and non-alcoholic beverages that the system suggests to the user.

[0174] "Feedback information" refers to data including ratings and comments that users submit after trying a product.

[0175] A "prompt message" is a guide that the system presents to the user regarding their emotional state or product suggestions.

[0176] This invention is a system that recommends the optimal beverage product based on the user's emotions and preferences. The following hardware and software are used to implement the system.

[0177] The user first inputs user data, including their preferences and past purchase history, through the application. The smartphone or tablet, acting as the device, receives this information and sends data to the server to form an initial preference profile.

[0178] Furthermore, the device utilizes its built-in camera and microphone to collect user emotion data through facial expression analysis and voice recognition. This emotion data is sent to a server in real time and analyzed by a generative AI model. The generative AI model features an advanced algorithm that classifies the emotion data into categories such as "happy," "relaxed," and "depressed."

[0179] The server selects the most suitable beverage product based on analyzed sentiment data and the user's preference profile. This process also utilizes the latest market trends and new product information from external databases. Along with the selected product information, the prompt message presented to the user includes specific suggestions, such as, "Try a mild-tasting wine that suits your current mood."

[0180] After a user samples a product, they input feedback about their experience into the application. The device then collects data again and updates their preference profile. This improves the accuracy of beverage recommendations for future purchases.

[0181] This system will allow users to enjoy a more personalized experience by receiving individually optimized beverage products.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The user opens the application and enters their preferences and past purchase history. This input information is sent to the server via the terminal. The server generates a preference profile based on the received data. The main data processing involves formatting the input data and registering the profile in the database. An initial preference profile is generated as output.

[0185] Step 2:

[0186] The device collects user emotion data by capturing the user's facial expressions and voice using its built-in camera and microphone. This data is then transmitted to a server in real time. The server uses a generative AI model to analyze the user's emotional state from this data. Image processing and audio signal processing are performed during the data calculation process, and the user's emotional state is numerically evaluated. The output is the classified emotional state.

[0187] Step 3:

[0188] The server selects the optimal beverage product using analyzed sentiment data and the user's preference profile. It retrieves the latest trends and new product information from external market databases and combines this information to make product selections. The selection process includes data calculations such as linear regression and similarity calculations. The output is a list of recommended beverage products.

[0189] Step 4:

[0190] The application presents the user with the selected beverage product and its details. The terminal receives the output information from the server and presents it to the user visually. A prompt message such as "Please try a mild-tasting wine that suits your current mood" is displayed. This is used by the user to confirm their choices and decide on their next purchase action.

[0191] Step 5:

[0192] The user samples the presented product and then inputs feedback information into the application. This feedback includes satisfaction levels and emotional ratings of the product. The terminal sends this information to a server, which uses the feedback data to update the preference profile. The main data processing involves correcting the profile based on the feedback and registering new data. As output, an improved preference profile is created.

[0193] Step 6:

[0194] The terminal or server prepares the new preference profile for use at the start of the next process. The system leverages this updated data to improve the accuracy of the product selection presented next time. Data management and storage optimization are performed here. The output is highly accurate profile data available for the next recommendation process.

[0195] (Application Example 2)

[0196] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0197] Conventional alcohol product recommendation systems offer suggestions based on users' taste preferences, but they lack dynamic suggestions that take into account the user's emotional state, resulting in limited improvements to the user experience. Furthermore, there is the challenge of real-time emotion recognition and the optimization of product recommendations based on that emotion.

[0198] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0199] In this invention, the server includes means for acquiring user data and generating a preference profile; means for acquiring and analyzing facial expressions and voice to recognize the user's emotional state; and means for optimizing product suggestions based on the analyzed emotional state. This enables personalized alcoholic beverage product suggestions based on complex information, including the user's emotional state.

[0200] "User data" refers to information provided by users, such as their preferences, past purchase history, and daily emotions.

[0201] A "preference profile" is a collection of information formed based on a user's taste and preferences.

[0202] "Novel alcoholic beverage products" are alcoholic beverage products selected based on new market trends and user profiles.

[0203] "Emotional state" refers to the emotional state of the user, analyzed from their facial expressions and voice.

[0204] "Facial expression" refers to the outward display of emotions conveyed by a user's facial expressions, gestures, and other actions.

[0205] "Voice" refers to the emotional elements contained in the tone of voice and manner of speaking that a user produces.

[0206] "Analysis" is the process of breaking down information based on acquired data to obtain a specific result.

[0207] "Optimizing product recommendations" is the process of adjustment and improvement undertaken to recommend the most suitable products based on user profiles and emotional states.

[0208] "Payment settlement" refers to the process of a user paying a predetermined price for a product they have selected.

[0209] "Opinion information" refers to feedback and comments provided by users after using a product.

[0210] This system aims to recognize customers' emotional states in real time at physical stores and suggest the most suitable alcoholic beverages for them. First, users, specifically store staff, wear smart glasses, which collect customers' facial expressions and voices through cameras and microphones. This makes it possible to understand the users' emotional states.

[0211] The smart glasses integrate with emotion analysis software such as Microsoft® Azure® Emotion API and Google® Cloud Vision API, quickly analyzing collected data to assess emotional states. The analysis results are sent to a server, which optimizes product recommendations based on emotional state data and user preference profiles.

[0212] The server accesses the database and uses market trends and new product information to select the most suitable alcoholic beverages for the customer. The selected product information is displayed on the smart glasses' screen, allowing in-store staff to immediately introduce it to the customer.

[0213] For example, if the emotional engine detects tension in a customer choosing a wine in a store, the staff will be provided with information suggesting relaxing wines and encouraging them to sample them. Such personalized suggestions can enhance the customer experience.

[0214] As an example of a prompt sentence using a generative AI model, you could use a sentence like, "If a customer says they are looking for a relaxing wine, how would you suggest products?" This allows for flexible suggestions that meet the diverse needs of users.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The smart glasses on the device are activated, and the user, specifically store staff, collects facial expressions and voice input around the customer. This captures real-time facial expressions and voices, and uses the built-in camera and microphone to collect data necessary for emotion analysis. Raw image data and audio data are obtained as output.

[0218] Step 2:

[0219] The collected image and audio data are sent to a server, where emotion analysis software is used to calculate data on the user's emotional state. The input is images and audio, and the output is an analysis result indicating the user's emotional state. This provides detailed emotional information, such as whether the user is relaxed or stressed.

[0220] Step 3:

[0221] The server analyzes the emotional state and combines it with the user's preference profile. The input is the emotional state and preference profile, and the output is integrated data used for product recommendations. Here, recommended products are selected based on the user's emotions.

[0222] Step 4:

[0223] The server references market trend information and new product information from the product database to select the most suitable products. The input is integrated data, and the output is an optimized list of products. This process enables personalized product recommendations that reflect current trends.

[0224] Step 5:

[0225] Ultimately, the server transmits the selected product information to the smart glasses on the terminal, and the user presents it to the customer via the display. The input is an optimized list of products, and the output is visual information for staff to present to the customer. This enables immediate and efficient customer service.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] An embodiment of this invention is a series of systems that propose and deliver the most suitable alcoholic beverage products through information exchange between a user, a server, and a terminal.

[0243] User: Launch the application and first create an account containing the necessary personal information. Then, answer questions about your past alcohol consumption experience and current preferences to form an initial taste profile.

[0244] Server: Receives the provided data, analyzes it using a generating AI, and generates a detailed taste profile. Based on this, it selects alcoholic beverages from the market database that are deemed most suitable for the user. It also incorporates information on new products to create a list of products with a surprise element.

[0245] Terminal: The app displays a list of selected alcoholic beverages to the user and shows the payment screen for the selected items. Payment is processed via a secure API in conjunction with the server. Once the user approves the items, the payment process is completed immediately and a confirmation message is sent.

[0246] Server: Once payment is complete, the server sends shipping instructions to the relevant logistics company and initiates the shipping process. Simultaneously, the user is provided with real-time updates on the shipping status. The server also collects feedback on the user's experience after shipping.

[0247] Specific example: If a user has a profile indicating a preference for white wine, a newly released French Chardonnay will be recommended. Once the user selects and approves the purchase, a detailed guide on the wine's optimal drinking temperature and other relevant information will be displayed within the app. After drinking, the user provides feedback on the wine, which will be used to inform future recommendations.

[0248] This system allows users to easily enjoy a personalized alcoholic beverage experience, and the entire system is continuously improved through a feedback process. This is an effective means of addressing users' specific needs and providing monthly enjoyment.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user installs the application and accesses an account creation screen that includes personal information. Here, they enter their name, email address, address, etc., to create an account.

[0252] Step 2:

[0253] The terminal sends user input information to the server. The server receives this information, stores it in a database, and processes it as initial data for forming a taste profile.

[0254] Step 3:

[0255] Users can input detailed information about their favorite types of alcohol and their ratings of alcoholic beverages they have drunk in the past within the app.

[0256] Step 4:

[0257] The server receives data on the user's taste preferences and uses a generative AI to analyze and generate a detailed taste profile of the user.

[0258] Step 5:

[0259] The server compares the generated taste profile with a market database to select the most suitable alcoholic beverage for the user. Furthermore, it incorporates new products and trends, and lists items with a surprise element.

[0260] Step 6:

[0261] The device displays a list of selected alcoholic beverages to the user. The user reviews the list within the app and selects the items they wish to purchase.

[0262] Step 7:

[0263] When a user selects to purchase, the device sends payment information to the server using a secure API. The server accepts and processes the payment.

[0264] Step 8:

[0265] Once the payment is complete, the server sends a confirmation message to the terminal to notify the user.

[0266] Step 9:

[0267] The server directly transmits the shipping information to the logistics company and initiates the shipping process. At the same time, it provides users with real-time updates on the shipping status.

[0268] Step 10:

[0269] After receiving the product, users can provide feedback on their drinking experience within the app.

[0270] Step 11:

[0271] The server receives feedback from users, analyzes it, updates their taste profiles, and uses it to improve the AI's recommendation algorithm.

[0272] (Example 1)

[0273] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] Conventional beverage product recommendation systems often fail to adequately personalize user preferences, resulting in suggested products that don't meet user expectations. Furthermore, the lack of transparency in delivery status and the inability to effectively utilize post-purchase user feedback need improvement.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes means for acquiring user information and generating their preference profiles, means for selecting optimal beverage products, and means for analyzing data using a generative artificial intelligence model. This makes it possible to improve the accuracy of product recommendations based on individual user preferences and enhance the quality of the purchasing experience.

[0277] "User information" refers to the personal identification information of system users and data related to their preferences and consumption history.

[0278] "Preference profile" refers to information indicating the user's preference trends for beverages, derived from the user's past preferences and purchase history.

[0279] "Beverage product" refers to various drinkable commodities provided to users, including alcoholic beverages and non-alcoholic beverages.

[0280] "Generative artificial intelligence model" refers to AI technology that generates specific patterns and information based on a large amount of data and is used for data analysis and automatic generation.

[0281] "Data analysis" is the process of identifying, understanding useful patterns and trends based on the collected information, and deriving results.

[0282] "Delivery status" refers to the progress status and location information from when the ordered product is shipped until it reaches the user.

[0283] "Personalize" means to perform operations and adjustments to specialize products and services according to the individual preferences and needs of each user.

[0284] This invention is a system that proposes and delivers optimal beverage products through information exchange among users, servers, and terminals. Specific embodiments by each component are shown below.

[0285] The user launches the mobile application and enters data related to personal information and preferences. This data includes past consumption history, favorite flavors, and brand-related information.

[0286] The server receives the data sent by the user. The generative AI model operating on the server generates a preference profile based on this data. The AI model operates on a cloud platform and uses a data processing pipeline built with programming languages such as Python and R. Based on this profile, the optimal beverage products that match the user's preferences are automatically selected.

[0287] The terminal displays the list of selected beverage products in the user's app. When the user selects a product, the terminal communicates with the server via a secure network protocol to perform purchase confirmation and payment processing. The terminal uses the SSL / TLS protocol to ensure data encryption and secure communication.

[0288] As a specific example, if the user prefers wines or craft beers from a specific region, the server uses this information to recommend the latest products from that region. For example, by providing the AI with a detailed specification such as "fresh white wine from France" as a prompt sentence, more accurate recommendations can be made.

[0289] Example of a prompt sentence: "The user wants to enjoy the marriage of red wine and cheese. Recommend a wine based on the user's preferences."

[0290] With this system, the user can easily enjoy a beverage experience optimized for their preferences. Also, through the feedback process, the server can continuously adjust the profile and improve the accuracy of selection.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] Users launch the mobile application and create an account. Here, they enter personal information, past alcohol consumption experience, and preferences. This input data is used to generate an initial preference profile. User information is input, and the initial preference profile is sent to the server as output.

[0294] Step 2:

[0295] The server processes information received from the user and generates a preference profile using a generative AI model. The server uses the received data as input, performs data analysis using the generative AI model, and profiles the user's preferences in detail. Data based on this profile is output and used for the next process.

[0296] Step 3:

[0297] The server selects the most suitable beverage products from the market database based on the generated preference profile. The server uses this profile data as input, compares it with existing market product information, and outputs a list of beverage products that match the user's preferences.

[0298] Step 4:

[0299] The terminal displays a product list sent from the server to the user. The user reviews this list and selects the products they wish to purchase. The terminal receives the product list as input for display and provides it as output on the screen.

[0300] Step 5:

[0301] The terminal performs purchase confirmation and payment processing for the items selected by the user. The inputs used are the user's selected product information and payment information, and the output is a payment completion message. This process includes secure API communication and data protection using the SSL / TLS protocol.

[0302] Step 6:

[0303] After the payment is completed, the server issues a delivery instruction to the logistics company. As input, payment information and product selection information are used, and as output, the delivery instruction is sent to the logistics company. Also, the server continuously updates the delivery status information to the user in real time.

[0304] Step 7:

[0305] After the user receives the product, the user provides feedback on the product through the application. The user's feedback is sent to the server as input and is used as output for adjusting the next preference profile. This continuous feedback loop improves the accuracy of product recommendations.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] There is a need to quickly and efficiently select and propose food products that are optimal for the taste of individual users. With conventional methods, it is difficult to effectively propose food products that reflect the user's preferences, and there is also no established method for fully utilizing feedback to improve the selection accuracy. To improve the user experience, a system that solves these problems is needed.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for acquiring user data and generating a taste profile; means for selecting new food products based on the taste profile; and means for proposing the selected food products to the user, obtaining their approval, and processing payment. This makes it possible to provide optimal food products based on the user's individual taste preferences.

[0311] "User data" refers to information including a user's personal preferences, past selection history, and feedback.

[0312] A "taste profile" is a dataset that represents a user's taste preferences and is useful for suggesting products based on individual tastes.

[0313] "Food products" refer to food and beverages offered to users, selected based on their taste profiles.

[0314] "Payment" refers to the process of confirming the purchase of a product selected by the user and completing the payment.

[0315] "Delivery" refers to the entire process of physically delivering the selected products to the user.

[0316] "Feedback information" refers to user feedback and evaluations provided after use, which is data used to improve the profile.

[0317] "Mobile device technology" refers to communication and application technologies that use portable devices such as smartphones and tablets.

[0318] In this embodiment of the invention, the user first launches a dedicated application using a mobile device such as a smartphone or tablet. The user enters personal information within the application and answers questions about their preferences, thereby generating an initial taste profile. This profile is sent to a server and analyzed in detail using a generative AI model.

[0319] The server selects food products from its product database that it deems most suitable for the user based on the aforementioned taste profile. It generates a product list that also takes into account newly added products to the market and information obtained from past feedback based on the user's profile. The user reviews the suggested product list on the app and selects the products they wish to purchase.

[0320] The terminal uses a payment service to securely complete the payment process for the product. Once payment is complete, the product is delivered, and the user can check its status in real time through the app. After delivery, the user experiences the food product and registers their impressions and evaluation as feedback on the app. This feedback is processed on the server and used to improve the accuracy of future selections.

[0321] For example, if a user profile is created indicating they "prefer spicy food," the AI ​​model could suggest recently released spicy Mexican dishes that match that preference. The user then orders and experiences the selected dishes, and the feedback received afterward allows for even more accurate product recommendations in the future.

[0322] When utilizing a generative AI model, a concrete example of a prompt message would be: "Based on the user's taste profile, please suggest a new spicy dish menu. The user prefers spicy food. Please also consider the feedback so that we can use it to improve future dish suggestions."

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The user launches the application and answers questions about their personal information and preferences. User data (personal information, preference information) is entered as input. The app collects this data to create an initial taste profile. This profile data is generated as output and sent to the server.

[0326] Step 2:

[0327] The server analyzes the received taste profile data. Using a generative AI model, the profile is further analyzed to identify individual taste tendencies. The input is taste profile data, and the output is the analyzed detailed taste profile. Based on this profile, a list of food products suitable for the user is generated.

[0328] Step 3:

[0329] The server searches the product database using a detailed taste profile and selects appropriate food products. Using a generative AI model, it generates prompt messages to select new products that match the user's preferences. The detailed taste profile is used as input, and a list of selected food products is generated as output. This list is sent to the terminal.

[0330] Step 4:

[0331] The terminal displays a list of received products to the user. The user reviews the list and selects the desired products. The selection process begins. The input is the user's selection information, and the output is the selected product data.

[0332] Step 5:

[0333] The terminal securely processes payments via a linked payment service. Inputs are selected product data and user payment information, and output is payment confirmation data. After payment is complete, the selected products proceed to the shipping process.

[0334] Step 6:

[0335] The server issues instructions to the delivery company and arranges for the selected food products to be delivered to the user. Real-time delivery status is also provided to the user. The input is payment confirmation data, and the output generates delivery instructions and delivery status information.

[0336] Step 7:

[0337] Users receive a product and provide feedback via a device after experiencing it. This feedback information is collected as data and used for future analysis. The input is user evaluation information, and the output is updated taste profile data. The feedback is processed again on the server, improving the accuracy of product selection.

[0338] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0339] This invention is a recommendation system for alcoholic beverages that combines an emotion engine that recognizes user emotions, providing a personalized experience. The following describes its specific embodiments.

[0340] Users: Through the application, they input their alcohol preferences and past purchase history to form a basic taste profile. In addition, users express their everyday emotions and their feelings when tasting products through the app's interface.

[0341] Device: When the device starts up or whenever the user responds, it uses the camera or microphone to capture the user's facial expressions and tone of voice, and sends this data to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0342] Server: The server selects the optimal alcoholic beverage by combining emotional data analyzed by the emotion engine, taste profiles, and past history. The selection reflects new product information and trends from the market, and the product recommendations are further adjusted according to the user's current emotions.

[0343] Specific example: When a user is feeling down, the emotion engine detects this emotion. The detected emotion is reflected in the system, and a product with a relaxing effect is selected, such as a wine with a specific aroma. When presented to the user, a guide about the relaxing effects and how to drink the selected product is also provided.

[0344] Feedback Collection: After sampling each product, users enter their feedback into the app. The emotion engine also captures the user's reactions during the experience, and uses these results to continuously update the taste profile and improve the accuracy of recommendations.

[0345] Thus, this system is a mechanism that develops dynamic and personalized services that respond to the user's daily life and emotional state. This makes it possible to provide experiential value tailored to the user's situation, rather than simply suggesting products.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] Users install the app and create an account on the login screen. Here, they enter basic personal information, as well as information about their preferred alcoholic beverages and products they have enjoyed in the past.

[0349] Step 2:

[0350] The terminal sends user input data to the server. The server receives this data and uses a generating AI to initialize the user's taste profile.

[0351] Step 3:

[0352] Through the app, users express their everyday emotions and their feelings when tasting alcoholic beverages via the camera and microphone.

[0353] Step 4:

[0354] The device captures the user's facial expressions and voice tone in real time and sends the data to an emotion engine. This engine analyzes the user's emotions and sends the results to a server.

[0355] Step 5:

[0356] The server combines the acquired emotional data and taste profiles, and uses AI to select the most suitable alcoholic beverage product at the moment. It also takes into account new product lists and market trends, and adjusts the recommendations according to the user's emotions.

[0357] Step 6:

[0358] The terminal displays a list of selected alcoholic beverages received from the server to the user. In addition, it also displays information about the characteristics of the selected products and a guide on how to consume them.

[0359] Step 7:

[0360] The user views a list of suggested products, selects the items they wish to purchase, and proceeds with the payment process.

[0361] Step 8:

[0362] The terminal sends the user's selection to the server, which securely completes the transaction via a payment API. The server confirms the completion of the payment and issues a shipping instruction to the logistics company.

[0363] Step 9:

[0364] The server tracks the delivery status in real time and periodically notifies the user of the status via the terminal.

[0365] Step 10:

[0366] After receiving a product, users input their feedback into the app based on their experience. The device also captures the user's emotions during the experience via the camera and microphone and sends them back to the emotion engine.

[0367] Step 11:

[0368] The server improves the taste profile based on new emotional data and feedback, and uses this information for future recommendations.

[0369] (Example 2)

[0370] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0371] A challenge with conventional beverage recommendation systems was their inability to provide optimal product suggestions based on individual emotional states and preferences. Furthermore, the slow pace of profile updates based on user feedback made it difficult to improve the accuracy of future recommendations.

[0372] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0373] In this invention, the server includes means for acquiring user data and generating a preference profile; means for analyzing the user's emotional state using a generation AI model and adjusting the optimal product; and means for acquiring feedback information from the user and correcting the preference profile to improve the accuracy of beverage product selection. This makes it possible to suggest individually optimized products that are in line with the user's emotions and preferences.

[0374] "User data" refers to information related to a user's preferences, past purchase history, emotional state, etc.

[0375] A "preference profile" is a dataset that shows a user's taste and preferences for beverages.

[0376] "Emotional data" refers to information about a user's emotional state, analyzed based on changes in their facial expressions and voice.

[0377] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data and reactions to evaluate emotional states and preferences.

[0378] "Beverage products" refer to the various alcoholic and non-alcoholic beverages that the system suggests to the user.

[0379] "Feedback information" refers to data including ratings and comments that users submit after trying a product.

[0380] A "prompt message" is a guide that the system presents to the user regarding their emotional state or product suggestions.

[0381] This invention is a system that recommends the optimal beverage product based on the user's emotions and preferences. The following hardware and software are used to implement the system.

[0382] The user first inputs user data, including their preferences and past purchase history, through the application. The smartphone or tablet, acting as the device, receives this information and sends data to the server to form an initial preference profile.

[0383] Furthermore, the device utilizes its built-in camera and microphone to collect user emotion data through facial expression analysis and voice recognition. This emotion data is sent to a server in real time and analyzed by a generative AI model. The generative AI model features an advanced algorithm that classifies the emotion data into categories such as "happy," "relaxed," and "depressed."

[0384] The server selects the most suitable beverage product based on analyzed sentiment data and the user's preference profile. This process also utilizes the latest market trends and new product information from external databases. Along with the selected product information, the prompt message presented to the user includes specific suggestions, such as, "Try a mild-tasting wine that suits your current mood."

[0385] After a user samples a product, they input feedback about their experience into the application. The device then collects data again and updates their preference profile. This improves the accuracy of beverage recommendations for future purchases.

[0386] This system will allow users to enjoy a more personalized experience by receiving individually optimized beverage products.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The user opens the application and enters their preferences and past purchase history. This input information is sent to the server via the terminal. The server generates a preference profile based on the received data. The main data processing involves formatting the input data and registering the profile in the database. An initial preference profile is generated as output.

[0390] Step 2:

[0391] The device collects user emotion data by capturing the user's facial expressions and voice using its built-in camera and microphone. This data is then transmitted to a server in real time. The server uses a generative AI model to analyze the user's emotional state from this data. Image processing and audio signal processing are performed during the data calculation process, and the user's emotional state is numerically evaluated. The output is the classified emotional state.

[0392] Step 3:

[0393] The server selects the optimal beverage product using analyzed sentiment data and the user's preference profile. It retrieves the latest trends and new product information from external market databases and combines this information to make product selections. The selection process includes data calculations such as linear regression and similarity calculations. The output is a list of recommended beverage products.

[0394] Step 4:

[0395] The application presents the user with the selected beverage product and its details. The terminal receives the output information from the server and presents it to the user visually. A prompt message such as "Please try a mild-tasting wine that suits your current mood" is displayed. This is used by the user to confirm their choices and decide on their next purchase action.

[0396] Step 5:

[0397] The user samples the presented product and then inputs feedback information into the application. This feedback includes satisfaction levels and emotional ratings of the product. The terminal sends this information to a server, which uses the feedback data to update the preference profile. The main data processing involves correcting the profile based on the feedback and registering new data. As output, an improved preference profile is created.

[0398] Step 6:

[0399] The terminal or server prepares the new preference profile for use at the start of the next process. The system leverages this updated data to improve the accuracy of the product selection presented next time. Data management and storage optimization are performed here. The output is highly accurate profile data available for the next recommendation process.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] Conventional alcohol product recommendation systems offer suggestions based on users' taste preferences, but they lack dynamic suggestions that take into account the user's emotional state, resulting in limited improvements to the user experience. Furthermore, there is the challenge of real-time emotion recognition and the optimization of product recommendations based on that emotion.

[0403] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0404] In this invention, the server includes means for acquiring user data and generating a preference profile; means for acquiring and analyzing facial expressions and voice to recognize the user's emotional state; and means for optimizing product suggestions based on the analyzed emotional state. This enables personalized alcoholic beverage product suggestions based on complex information, including the user's emotional state.

[0405] "User data" refers to information provided by users, such as their preferences, past purchase history, and daily emotions.

[0406] A "preference profile" is a collection of information formed based on a user's taste and preferences.

[0407] "Novel alcoholic beverage products" are alcoholic beverage products selected based on new market trends and user profiles.

[0408] "Emotional state" refers to the emotional state of the user, analyzed from their facial expressions and voice.

[0409] "Facial expression" refers to the outward display of emotions conveyed by a user's facial expressions, gestures, and other actions.

[0410] "Voice" refers to the emotional elements contained in the tone of voice and manner of speaking that a user produces.

[0411] "Analysis" is the process of breaking down information based on acquired data to obtain a specific result.

[0412] "Optimizing product recommendations" is the process of adjustment and improvement undertaken to recommend the most suitable products based on user profiles and emotional states.

[0413] "Payment settlement" refers to the process of a user paying a predetermined price for a product they have selected.

[0414] "Opinion information" refers to feedback and comments provided by users after using a product.

[0415] This system aims to recognize customers' emotional states in real time at physical stores and suggest the most suitable alcoholic beverages for them. First, users, specifically store staff, wear smart glasses, which collect customers' facial expressions and voices through cameras and microphones. This makes it possible to understand the users' emotional states.

[0416] The smart glasses integrate with emotion analysis software such as Microsoft Azure Emotion API and Google Cloud Vision API to quickly analyze collected data and assess emotional states. The analysis results are sent to a server, which optimizes product recommendations based on emotional state data and user preference profiles.

[0417] The server accesses the database and uses market trends and new product information to select the most suitable alcoholic beverages for the customer. The selected product information is displayed on the smart glasses' screen, allowing in-store staff to immediately introduce it to the customer.

[0418] For example, if the emotional engine detects tension in a customer choosing a wine in a store, the staff will be provided with information suggesting relaxing wines and encouraging them to sample them. Such personalized suggestions can enhance the customer experience.

[0419] As an example of a prompt sentence using a generative AI model, you could use a sentence like, "If a customer says they are looking for a relaxing wine, how would you suggest products?" This allows for flexible suggestions that meet the diverse needs of users.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The smart glasses on the device are activated, and the user, specifically store staff, collects facial expressions and voice input around the customer. This captures real-time facial expressions and voices, and uses the built-in camera and microphone to collect data necessary for emotion analysis. Raw image data and audio data are obtained as output.

[0423] Step 2:

[0424] The collected image and audio data are sent to a server, where emotion analysis software is used to calculate data on the user's emotional state. The input is images and audio, and the output is an analysis result indicating the user's emotional state. This provides detailed emotional information, such as whether the user is relaxed or stressed.

[0425] Step 3:

[0426] The server analyzes the emotional state and combines it with the user's preference profile. The input is the emotional state and preference profile, and the output is integrated data used for product recommendations. Here, recommended products are selected based on the user's emotions.

[0427] Step 4:

[0428] The server references market trend information and new product information from the product database to select the most suitable products. The input is integrated data, and the output is an optimized list of products. This process enables personalized product recommendations that reflect current trends.

[0429] Step 5:

[0430] Ultimately, the server transmits the selected product information to the smart glasses on the terminal, and the user presents it to the customer via the display. The input is an optimized list of products, and the output is visual information for staff to present to the customer. This enables immediate and efficient customer service.

[0431] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0432] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0433] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0437] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0438] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0439] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0440] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0441] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0442] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0443] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0444] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0445] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0446] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0447] An embodiment of this invention is a series of systems that propose and deliver the most suitable alcoholic beverage products through information exchange between a user, a server, and a terminal.

[0448] User: Launch the application and first create an account containing the necessary personal information. Then, answer questions about your past alcohol consumption experience and current preferences to form an initial taste profile.

[0449] Server: Receives the provided data, analyzes it using a generating AI, and generates a detailed taste profile. Based on this, it selects alcoholic beverages from the market database that are deemed most suitable for the user. It also incorporates information on new products to create a list of products with a surprise element.

[0450] Terminal: The app displays a list of selected alcoholic beverages to the user and shows the payment screen for the selected items. Payment is processed via a secure API in conjunction with the server. Once the user approves the items, the payment process is completed immediately and a confirmation message is sent.

[0451] Server: Once payment is complete, the server sends shipping instructions to the relevant logistics company and initiates the shipping process. Simultaneously, the user is provided with real-time updates on the shipping status. The server also collects feedback on the user's experience after shipping.

[0452] Specific example: If a user has a profile indicating a preference for white wine, a newly released French Chardonnay will be recommended. Once the user selects and approves the purchase, a detailed guide on the wine's optimal drinking temperature and other relevant information will be displayed within the app. After drinking, the user provides feedback on the wine, which will be used to inform future recommendations.

[0453] This system allows users to easily enjoy a personalized alcoholic beverage experience, and the entire system is continuously improved through a feedback process. This is an effective means of addressing users' specific needs and providing monthly enjoyment.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The user installs the application and accesses an account creation screen that includes personal information. Here, they enter their name, email address, address, etc., to create an account.

[0457] Step 2:

[0458] The terminal sends user input information to the server. The server receives this information, stores it in a database, and processes it as initial data for forming a taste profile.

[0459] Step 3:

[0460] Users can input detailed information about their favorite types of alcohol and their ratings of alcoholic beverages they have drunk in the past within the app.

[0461] Step 4:

[0462] The server receives data on the user's taste preferences and uses a generative AI to analyze and generate a detailed taste profile of the user.

[0463] Step 5:

[0464] The server compares the generated taste profile with a market database to select the most suitable alcoholic beverage for the user. Furthermore, it incorporates new products and trends, and lists items with a surprise element.

[0465] Step 6:

[0466] The device displays a list of selected alcoholic beverages to the user. The user reviews the list within the app and selects the items they wish to purchase.

[0467] Step 7:

[0468] When a user selects to purchase, the device sends payment information to the server using a secure API. The server accepts and processes the payment.

[0469] Step 8:

[0470] Once the payment is complete, the server sends a confirmation message to the terminal to notify the user.

[0471] Step 9:

[0472] The server directly transmits the shipping information to the logistics company and initiates the shipping process. At the same time, it provides users with real-time updates on the shipping status.

[0473] Step 10:

[0474] After receiving the product, users can provide feedback on their drinking experience within the app.

[0475] Step 11:

[0476] The server receives feedback from users, analyzes it, updates their taste profiles, and uses it to improve the AI's recommendation algorithm.

[0477] (Example 1)

[0478] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] Conventional beverage product recommendation systems often fail to adequately personalize user preferences, resulting in suggested products that don't meet user expectations. Furthermore, the lack of transparency in delivery status and the inability to effectively utilize post-purchase user feedback need improvement.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0481] In this invention, the server includes means for acquiring user information and generating a preference profile, means for selecting the optimal beverage product, and means for analyzing data using a generative artificial intelligence model. This makes it possible to improve the accuracy of product recommendations based on the user's individual preferences and enhance the quality of the purchasing experience.

[0482] "User information" refers to personally identifiable information of system users, as well as data related to their preferences and consumption history.

[0483] A "preference profile" is information that shows a user's preferences for beverages, derived from their past preferences and purchase history.

[0484] "Beverage products" refers to various drinkable products offered to users, including alcoholic and non-alcoholic beverages.

[0485] A "generative artificial intelligence model" refers to AI technology that generates specific patterns or information based on large amounts of data, and is used for data analysis and automated generation.

[0486] "Data analysis" is the process of identifying and understanding useful patterns and trends based on collected information, and deriving results from them.

[0487] "Delivery status" refers to the progress and location information of an ordered product from the time it is shipped until it reaches the user.

[0488] "Personalization" refers to the process of tailoring or adjusting products and services to each user's individual preferences and needs.

[0489] This invention is a system that proposes and delivers the optimal beverage product through information exchange between the user, server, and terminal. Specific embodiments of each component are shown below.

[0490] Users launch a mobile application and enter personal and preference data. This data includes past consumption history, preferred flavors, and brand information.

[0491] The server receives data sent from the user. A generative AI model running on the server generates a preference profile based on this data. The AI ​​model runs on a cloud platform and uses a data processing pipeline built with programming languages ​​such as Python and R. This profile automatically selects the optimal beverage product that matches the user's preferences.

[0492] The device displays a list of selected beverage products in the user's app. Once the user selects a product, the device communicates with a server via a secure network protocol to confirm the purchase and process payment. The device uses the SSL / TLS protocol to ensure data encryption and secure communication.

[0493] For example, if a user prefers wine or craft beer from a particular region, the server can use this information to recommend the latest products from that region. By providing the AI ​​with specific requests, such as "a fresh white wine from France," more accurate suggestions can be made.

[0494] Example prompt: "The user wants to enjoy a pairing of red wine and cheese. Suggest a wine recommendation based on the user's preferences."

[0495] This system allows users to easily enjoy a beverage experience optimized to their preferences. Furthermore, through a feedback process, the server can continuously adjust its profile and improve the accuracy of its selections.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] Users launch the mobile application and create an account. Here, they enter personal information, past alcohol consumption experience, and preferences. This input data is used to generate an initial preference profile. User information is input, and the initial preference profile is sent to the server as output.

[0499] Step 2:

[0500] The server processes information received from the user and generates a preference profile using a generative AI model. The server uses the received data as input, performs data analysis using the generative AI model, and profiles the user's preferences in detail. Data based on this profile is output and used for the next process.

[0501] Step 3:

[0502] The server selects the most suitable beverage products from the market database based on the generated preference profile. The server takes this profile data as input, compares it with existing market product information, and outputs a list of beverage products that match the user's preferences.

[0503] Step 4:

[0504] The terminal displays a product list sent from the server to the user. The user reviews this list and selects the products they wish to purchase. The terminal receives the product list as input for display and provides it as output on the screen.

[0505] Step 5:

[0506] The terminal performs purchase confirmation and payment processing for the items selected by the user. The inputs used are the user's selected product information and payment information, and the output is a payment completion message. This process includes secure API communication and data protection using the SSL / TLS protocol.

[0507] Step 6:

[0508] The server issues delivery instructions to the logistics company after payment is completed. Payment information and product selection information are used as input, and the delivery instructions are sent to the logistics company as output. The server also continuously updates the user with real-time delivery status information.

[0509] Step 7:

[0510] After receiving a product, users provide feedback through the application. This user feedback is sent to the server as input and used as output to refine the user's preference profile for future purchases. This continuous feedback loop improves the accuracy of product recommendations.

[0511] (Application Example 1)

[0512] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] There is a need to quickly and efficiently select and recommend food products that are best suited to the individual tastes of each user. Traditional methods struggle to effectively recommend food products that reflect user preferences, and there is no established system for fully utilizing feedback to improve selection accuracy. A system that addresses these challenges is needed to enhance the user experience.

[0514] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0515] In this invention, the server includes means for acquiring user data and generating a taste profile; means for selecting new food products based on the taste profile; and means for proposing the selected food products to the user, obtaining their approval, and processing payment. This makes it possible to provide optimal food products based on the user's individual taste preferences.

[0516] "User data" refers to information including a user's personal preferences, past selection history, and feedback.

[0517] A "taste profile" is a dataset that represents a user's taste preferences and is useful for suggesting products based on individual tastes.

[0518] "Food products" refer to food and beverages offered to users, selected based on their taste profiles.

[0519] "Payment" refers to the process of confirming the purchase of a product selected by the user and completing the payment.

[0520] "Delivery" refers to the entire process of physically delivering the selected products to the user.

[0521] "Feedback information" refers to user feedback and evaluations provided after use, which is data used to improve the profile.

[0522] "Mobile device technology" refers to communication and application technologies that use portable devices such as smartphones and tablets.

[0523] In this embodiment of the invention, the user first launches a dedicated application using a mobile device such as a smartphone or tablet. The user enters personal information within the application and answers questions about their preferences, thereby generating an initial taste profile. This profile is sent to a server and analyzed in detail using a generative AI model.

[0524] The server selects food products from its product database that it deems most suitable for the user based on the aforementioned taste profile. It generates a product list that also takes into account newly added products to the market and information obtained from past feedback based on the user's profile. The user reviews the suggested product list on the app and selects the products they wish to purchase.

[0525] The terminal uses a payment service to securely complete the payment process for the product. Once payment is complete, the product is delivered, and the user can check its status in real time through the app. After delivery, the user experiences the food product and registers their impressions and evaluation as feedback on the app. This feedback is processed on the server and used to improve the accuracy of future selections.

[0526] For example, if a user profile is created indicating they "prefer spicy food," the AI ​​model could suggest recently released spicy Mexican dishes that match that preference. The user then orders and experiences the selected dishes, and the feedback received afterward allows for even more accurate product recommendations in the future.

[0527] When utilizing a generative AI model, a concrete example of a prompt message would be: "Based on the user's taste profile, please suggest a new spicy dish menu. The user prefers spicy food. Please also consider the feedback so that we can use it to improve future dish suggestions."

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The user launches the application and answers questions about their personal information and preferences. User data (personal information, preference information) is entered as input. The app collects this data to create an initial taste profile. This profile data is generated as output and sent to the server.

[0531] Step 2:

[0532] The server analyzes the received taste profile data. Using a generative AI model, the profile is further analyzed to identify individual taste tendencies. The input is taste profile data, and the output is the analyzed detailed taste profile. Based on this profile, a list of food products suitable for the user is generated.

[0533] Step 3:

[0534] The server searches the product database using a detailed taste profile and selects appropriate food products. Using a generative AI model, it generates prompt messages to select new products that match the user's preferences. The detailed taste profile is used as input, and a list of selected food products is generated as output. This list is sent to the terminal.

[0535] Step 4:

[0536] The terminal displays a list of received products to the user. The user reviews the list and selects the desired products. The selection process begins. The input is the user's selection information, and the output is the selected product data.

[0537] Step 5:

[0538] The terminal securely processes payments via a linked payment service. Inputs are selected product data and user payment information, and output is payment confirmation data. After payment is complete, the selected products proceed to the shipping process.

[0539] Step 6:

[0540] The server issues instructions to the delivery company and arranges for the selected food products to be delivered to the user. Real-time delivery status is also provided to the user. The input is payment confirmation data, and the output generates delivery instructions and delivery status information.

[0541] Step 7:

[0542] Users receive a product and provide feedback via a device after experiencing it. This feedback information is collected as data and used for future analysis. The input is user evaluation information, and the output is updated taste profile data. The feedback is processed again on the server, improving the accuracy of product selection.

[0543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0544] This invention is a recommendation system for alcoholic beverages that combines an emotion engine that recognizes user emotions, providing a personalized experience. The following describes its specific embodiments.

[0545] Users: Through the application, they input their alcohol preferences and past purchase history to form a basic taste profile. In addition, users express their everyday emotions and their feelings when tasting products through the app's interface.

[0546] Device: When the device starts up or whenever the user responds, it uses the camera or microphone to capture the user's facial expressions and tone of voice, and sends this data to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0547] Server: The server selects the optimal alcoholic beverage by combining emotional data analyzed by the emotion engine, taste profiles, and past history. The selection reflects new product information and trends from the market, and the product recommendations are further adjusted according to the user's current emotions.

[0548] Specific example: When a user is feeling down, the emotion engine detects this emotion. The detected emotion is reflected in the system, and a product with a relaxing effect is selected, such as a wine with a specific aroma. When presented to the user, a guide about the relaxing effects and how to drink the selected product is also provided.

[0549] Feedback Collection: After sampling each product, users enter their feedback into the app. The emotion engine also captures the user's reactions during the experience, and uses these results to continuously update the taste profile and improve the accuracy of recommendations.

[0550] Thus, this system is a mechanism that develops dynamic and personalized services that respond to the user's daily life and emotional state. This makes it possible to provide experiential value tailored to the user's situation, rather than simply suggesting products.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] Users install the app and create an account on the login screen. Here, they enter basic personal information, as well as information about their preferred alcoholic beverages and products they have enjoyed in the past.

[0554] Step 2:

[0555] The terminal sends user input data to the server. The server receives this data and uses a generating AI to initialize the user's taste profile.

[0556] Step 3:

[0557] Through the app, users express their everyday emotions and their feelings when tasting alcoholic beverages via the camera and microphone.

[0558] Step 4:

[0559] The device captures the user's facial expressions and voice tone in real time and sends the data to an emotion engine. This engine analyzes the user's emotions and sends the results to a server.

[0560] Step 5:

[0561] The server combines the acquired emotional data and taste profiles, and uses AI to select the most suitable alcoholic beverage product at the moment. It also takes into account new product lists and market trends, and adjusts the recommendations according to the user's emotions.

[0562] Step 6:

[0563] The terminal displays a list of selected alcoholic beverages received from the server to the user. In addition, it also displays information about the characteristics of the selected products and a guide on how to consume them.

[0564] Step 7:

[0565] The user views a list of suggested products, selects the items they wish to purchase, and proceeds with the payment process.

[0566] Step 8:

[0567] The terminal sends the user's selection to the server, which securely completes the transaction via a payment API. The server confirms the completion of the payment and issues a shipping instruction to the logistics company.

[0568] Step 9:

[0569] The server tracks the delivery status in real time and periodically notifies the user of the status via the terminal.

[0570] Step 10:

[0571] After receiving a product, users input their feedback into the app based on their experience. The device also captures the user's emotions during the experience via the camera and microphone and sends them back to the emotion engine.

[0572] Step 11:

[0573] The server improves the taste profile based on new emotional data and feedback, and uses this information for future recommendations.

[0574] (Example 2)

[0575] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0576] A challenge with conventional beverage recommendation systems was their inability to provide optimal product suggestions based on individual emotional states and preferences. Furthermore, the slow pace of profile updates based on user feedback made it difficult to improve the accuracy of future recommendations.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0578] In this invention, the server includes means for acquiring user data and generating a preference profile; means for analyzing the user's emotional state using a generation AI model and adjusting the optimal product; and means for acquiring feedback information from the user and correcting the preference profile to improve the accuracy of beverage product selection. This makes it possible to provide individually optimized product suggestions that are tailored to the user's emotions and preferences.

[0579] "User data" refers to information related to a user's preferences, past purchase history, emotional state, etc.

[0580] A "preference profile" is a dataset that shows a user's taste and preferences for beverages.

[0581] "Emotional data" refers to information about a user's emotional state, analyzed based on changes in their facial expressions and voice.

[0582] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data and reactions to evaluate emotional states and preferences.

[0583] "Beverage products" refer to the various alcoholic and non-alcoholic beverages that the system suggests to the user.

[0584] "Feedback information" refers to data including ratings and comments that users submit after trying a product.

[0585] A "prompt message" is a guide that the system presents to the user regarding their emotional state or product suggestions.

[0586] This invention is a system that recommends the optimal beverage product based on the user's emotions and preferences. The following hardware and software are used to implement the system.

[0587] The user first inputs user data, including their preferences and past purchase history, through the application. The smartphone or tablet, acting as the device, receives this information and sends it to the server to form an initial preference profile.

[0588] Furthermore, the device utilizes its built-in camera and microphone to collect user emotion data through facial expression analysis and voice recognition. This emotion data is sent to a server in real time and analyzed by a generative AI model. The generative AI model features an advanced algorithm that classifies the emotion data into categories such as "happy," "relaxed," and "depressed."

[0589] The server selects the most suitable beverage product based on analyzed sentiment data and the user's preference profile. This process also utilizes the latest market trends and new product information from external databases. Along with the selected product information, the prompt message presented to the user includes specific suggestions, such as, "Try a mild-tasting wine that suits your current mood."

[0590] After a user samples a product, they input feedback about their experience into the application. The device then collects data again and updates their preference profile. This improves the accuracy of beverage recommendations for future purchases.

[0591] This system will allow users to enjoy a more personalized experience by receiving individually optimized beverage products.

[0592] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0593] Step 1:

[0594] The user opens the application and enters their preferences and past purchase history. This input information is sent to the server via the terminal. The server generates a preference profile based on the received data. The main data processing involves formatting the input data and registering the profile in the database. An initial preference profile is generated as output.

[0595] Step 2:

[0596] The device collects user emotion data by capturing the user's facial expressions and voice using its built-in camera and microphone. This data is then transmitted to a server in real time. The server uses a generative AI model to analyze the user's emotional state from this data. Image processing and audio signal processing are performed during the data calculation process, and the user's emotional state is numerically evaluated. The output is the classified emotional state.

[0597] Step 3:

[0598] The server selects the optimal beverage product using analyzed sentiment data and the user's preference profile. It retrieves the latest trends and new product information from external market databases and combines this information to make product selections. The selection process includes data calculations such as linear regression and similarity calculations. The output is a list of recommended beverage products.

[0599] Step 4:

[0600] The application presents the user with the selected beverage product and its details. The terminal receives the output information from the server and presents it to the user visually. A prompt message such as "Please try a mild-tasting wine that suits your current mood" is displayed. This is used by the user to confirm their choices and decide on their next purchase action.

[0601] Step 5:

[0602] The user samples the presented product and then inputs feedback information into the application. This feedback includes satisfaction levels and emotional ratings of the product. The terminal sends this information to a server, which uses the feedback data to update the preference profile. The main data processing involves correcting the profile based on the feedback and registering new data. As output, an improved preference profile is created.

[0603] Step 6:

[0604] The terminal or server prepares the new preference profile for use at the start of the next process. The system leverages this updated data to improve the accuracy of the product selection presented next time. Data management and storage optimization are performed here. The output is highly accurate profile data available for the next recommendation process.

[0605] (Application Example 2)

[0606] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0607] Conventional alcohol product recommendation systems offer suggestions based on users' taste preferences, but they lack dynamic suggestions that take into account the user's emotional state, resulting in limited improvements to the user experience. Furthermore, there is the challenge of real-time emotion recognition and the optimization of product recommendations based on that emotion.

[0608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0609] In this invention, the server includes means for acquiring user data and generating a preference profile; means for acquiring and analyzing facial expressions and voice to recognize the user's emotional state; and means for optimizing product suggestions based on the analyzed emotional state. This enables personalized alcoholic beverage product suggestions based on complex information, including the user's emotional state.

[0610] "User data" refers to information provided by users, such as their preferences, past purchase history, and daily emotions.

[0611] A "preference profile" is a collection of information formed based on a user's taste and preferences.

[0612] "Novel alcoholic beverage products" are alcoholic beverage products selected based on new market trends and user profiles.

[0613] "Emotional state" refers to the emotional state of the user, analyzed from their facial expressions and voice.

[0614] "Facial expression" refers to the outward display of emotions conveyed by a user's facial expressions, gestures, and other actions.

[0615] "Voice" refers to the emotional elements contained in the tone of voice and manner of speaking that a user produces.

[0616] "Analysis" is the process of breaking down information based on acquired data to obtain a specific result.

[0617] "Optimizing product recommendations" is the process of adjustment and improvement undertaken to recommend the most suitable products based on user profiles and emotional states.

[0618] "Payment settlement" refers to the process of a user paying a predetermined price for a product they have selected.

[0619] "Opinion information" refers to feedback and comments provided by users after using a product.

[0620] This system aims to recognize customers' emotional states in real time at physical stores and suggest the most suitable alcoholic beverages for them. First, users, specifically store staff, wear smart glasses, which collect customers' facial expressions and voices through cameras and microphones. This makes it possible to understand the users' emotional states.

[0621] The smart glasses integrate with emotion analysis software such as Microsoft Azure Emotion API and Google Cloud Vision API to quickly analyze collected data and assess emotional states. The analysis results are sent to a server, which optimizes product recommendations based on emotional state data and user preference profiles.

[0622] The server accesses the database and uses market trends and new product information to select the most suitable alcoholic beverages for the customer. The selected product information is displayed on the smart glasses' screen, allowing in-store staff to immediately introduce it to the customer.

[0623] For example, if the emotional engine detects tension in a customer choosing a wine in a store, the staff will be provided with information suggesting relaxing wines and encouraging them to sample them. Such personalized suggestions can enhance the customer experience.

[0624] As an example of a prompt sentence using a generative AI model, you could use a sentence like, "If a customer says they are looking for a relaxing wine, how would you suggest products?" This allows for flexible suggestions that meet the diverse needs of users.

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The smart glasses on the device are activated, and the user, specifically store staff, collects facial expressions and voice input around the customer. This captures real-time facial expressions and voices, and uses the built-in camera and microphone to collect data necessary for emotion analysis. Raw image data and audio data are obtained as output.

[0628] Step 2:

[0629] The collected image and audio data are sent to a server, where emotion analysis software is used to calculate emotional state data. The input is images and audio, and the output is an analysis result indicating the user's emotional state. This provides detailed emotional information, such as whether the user is relaxed or stressed.

[0630] Step 3:

[0631] The server analyzes the emotional state and combines it with the user's preference profile. The input is the emotional state and preference profile, and the output is integrated data used for product recommendations. Here, recommended products are selected based on the user's emotions.

[0632] Step 4:

[0633] The server references market trend information and new product information from the product database to select the most suitable products. The input is integrated data, and the output is an optimized list of products. This process enables personalized product recommendations that reflect current trends.

[0634] Step 5:

[0635] Ultimately, the server transmits the selected product information to the smart glasses on the terminal, and the user presents it to the customer via the display. The input is an optimized list of products, and the output is visual information for staff to present to the customer. This enables immediate and efficient customer service.

[0636] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0642] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0643] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0644] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0645] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0646] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0647] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0648] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0649] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0650] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0651] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0652] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] An embodiment of this invention is a series of systems that propose and deliver the most suitable alcoholic beverage products through information exchange between a user, a server, and a terminal.

[0654] User: Launch the application and first create an account containing the necessary personal information. Then, answer questions about your past alcohol consumption experience and current preferences to form an initial taste profile.

[0655] Server: Receives the provided data, analyzes it using a generating AI, and generates a detailed taste profile. Based on this, it selects alcoholic beverages from the market database that are deemed most suitable for the user. It also incorporates information on new products to create a list of products with a surprise element.

[0656] Terminal: The app displays a list of selected alcoholic beverages to the user and shows the payment screen for the selected items. Payment is processed via a secure API in conjunction with the server. Once the user approves the items, the payment process is completed immediately and a confirmation message is sent.

[0657] Server: Once payment is complete, the server sends shipping instructions to the relevant logistics company and initiates the shipping process. Simultaneously, the user is provided with real-time updates on the shipping status. The server also collects feedback on the user's experience after shipping.

[0658] Specific example: If a user has a profile indicating a preference for white wine, a newly released French Chardonnay will be recommended. Once the user selects and approves the purchase, a detailed guide on the wine's optimal drinking temperature and other relevant information will be displayed within the app. After drinking, the user provides feedback on the wine, which will be used to inform future recommendations.

[0659] This system allows users to easily enjoy a personalized alcoholic beverage experience, and the entire system is continuously improved through a feedback process. This is an effective means of addressing users' specific needs and providing monthly enjoyment.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The user installs the application and accesses an account creation screen that includes personal information. Here, they enter their name, email address, address, etc., to create an account.

[0663] Step 2:

[0664] The terminal sends user input information to the server. The server receives this information, stores it in a database, and processes it as initial data for forming a taste profile.

[0665] Step 3:

[0666] Users can input detailed information about their favorite types of alcohol and their ratings of alcoholic beverages they have drunk in the past within the app.

[0667] Step 4:

[0668] The server receives data on the user's taste preferences and uses a generative AI to analyze and generate a detailed taste profile of the user.

[0669] Step 5:

[0670] The server compares the generated taste profile with a market database to select the most suitable alcoholic beverage for the user. Furthermore, it incorporates new products and trends, and lists items with a surprise element.

[0671] Step 6:

[0672] The device displays a list of selected alcoholic beverages to the user. The user reviews the list within the app and selects the items they wish to purchase.

[0673] Step 7:

[0674] When a user selects to purchase, the device sends payment information to the server using a secure API. The server accepts and processes the payment.

[0675] Step 8:

[0676] Once the payment is complete, the server sends a confirmation message to the terminal to notify the user.

[0677] Step 9:

[0678] The server directly transmits the shipping information to the logistics company and initiates the shipping process. At the same time, it provides users with real-time updates on the shipping status.

[0679] Step 10:

[0680] After receiving the product, users can provide feedback on their drinking experience within the app.

[0681] Step 11:

[0682] The server receives feedback from users, analyzes it, updates their taste profiles, and uses it to improve the AI's recommendation algorithm.

[0683] (Example 1)

[0684] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Conventional beverage product recommendation systems often fail to adequately personalize user preferences, resulting in suggested products that don't meet user expectations. Furthermore, the lack of transparency in delivery status and the inability to effectively utilize post-purchase user feedback need improvement.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0687] In this invention, the server includes means for acquiring user information and generating a preference profile, means for selecting the optimal beverage product, and means for analyzing data using a generative artificial intelligence model. This makes it possible to improve the accuracy of product recommendations based on the user's individual preferences and enhance the quality of the purchasing experience.

[0688] "User information" refers to personally identifiable information of system users, as well as data related to their preferences and consumption history.

[0689] A "preference profile" is information that shows a user's preferences for beverages, derived from their past preferences and purchase history.

[0690] "Beverage products" refers to various drinkable products offered to users, including alcoholic and non-alcoholic beverages.

[0691] A "generative artificial intelligence model" refers to AI technology that generates specific patterns or information based on large amounts of data, and is used for data analysis and automated generation.

[0692] "Data analysis" is the process of identifying and understanding useful patterns and trends based on collected information, and deriving results from them.

[0693] "Delivery status" refers to the progress and location information of an ordered product from the time it is shipped until it reaches the user.

[0694] "Personalization" refers to the process of tailoring or adjusting products and services to each user's individual preferences and needs.

[0695] This invention is a system that proposes and delivers the optimal beverage product through information exchange between the user, server, and terminal. Specific embodiments of each component are shown below.

[0696] Users launch a mobile application and enter personal and preference data. This data includes past consumption history, preferred flavors, and brand information.

[0697] The server receives data sent from the user. A generative AI model running on the server generates a preference profile based on this data. The AI ​​model runs on a cloud platform and uses a data processing pipeline built with programming languages ​​such as Python and R. This profile automatically selects the optimal beverage product that matches the user's preferences.

[0698] The device displays a list of selected beverage products in the user's app. Once the user selects a product, the device communicates with a server via a secure network protocol to confirm the purchase and process payment. The device uses the SSL / TLS protocol to ensure data encryption and secure communication.

[0699] For example, if a user prefers wine or craft beer from a particular region, the server can use this information to recommend the latest products from that region. By providing the AI ​​with specific requests, such as "a fresh white wine from France," more accurate suggestions can be made.

[0700] Example prompt: "The user wants to enjoy a pairing of red wine and cheese. Suggest a wine recommendation based on the user's preferences."

[0701] This system allows users to easily enjoy a beverage experience optimized to their preferences. Furthermore, through a feedback process, the server can continuously adjust its profile and improve the accuracy of its selections.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] Users launch the mobile application and create an account. Here, they enter personal information, past alcohol consumption experience, and preferences. This input data is used to generate an initial preference profile. User information is input, and the initial preference profile is sent to the server as output.

[0705] Step 2:

[0706] The server processes information received from the user and generates a preference profile using a generative AI model. The server uses the received data as input, performs data analysis using the generative AI model, and profiles the user's preferences in detail. Data based on this profile is output and used for the next process.

[0707] Step 3:

[0708] The server selects the most suitable beverage products from the market database based on the generated preference profile. The server takes this profile data as input, compares it with existing market product information, and outputs a list of beverage products that match the user's preferences.

[0709] Step 4:

[0710] The terminal displays a product list sent from the server to the user. The user reviews this list and selects the products they wish to purchase. The terminal receives the product list as input for display and provides it as output on the screen.

[0711] Step 5:

[0712] The terminal performs purchase confirmation and payment processing for the items selected by the user. The inputs used are the user's selected product information and payment information, and the output is a payment completion message. This process includes secure API communication and data protection using the SSL / TLS protocol.

[0713] Step 6:

[0714] The server issues delivery instructions to the logistics company after payment is completed. Payment information and product selection information are used as input, and the delivery instructions are sent to the logistics company as output. The server also continuously updates the user with real-time delivery status information.

[0715] Step 7:

[0716] After receiving a product, users provide feedback through the application. This user feedback is sent to the server as input and used as output to refine the user's preference profile for future purchases. This continuous feedback loop improves the accuracy of product recommendations.

[0717] (Application Example 1)

[0718] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] There is a need to quickly and efficiently select and recommend food products that are best suited to the individual tastes of each user. Traditional methods struggle to effectively recommend food products that reflect user preferences, and there is no established system for fully utilizing feedback to improve selection accuracy. A system that addresses these challenges is needed to enhance the user experience.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0721] In this invention, the server includes means for acquiring user data and generating a taste profile; means for selecting new food products based on the taste profile; and means for proposing the selected food products to the user, obtaining their approval, and processing payment. This makes it possible to provide optimal food products based on the user's individual taste preferences.

[0722] "User data" refers to information including a user's personal preferences, past selection history, and feedback.

[0723] A "taste profile" is a dataset that represents a user's taste preferences and is useful for suggesting products based on individual tastes.

[0724] "Food products" refer to food and beverages offered to users, selected based on their taste profiles.

[0725] "Payment" refers to the process of confirming the purchase of a product selected by the user and completing the payment.

[0726] "Delivery" refers to the entire process of physically delivering the selected products to the user.

[0727] "Feedback information" refers to user feedback and evaluations provided after use, which is data used to improve the profile.

[0728] "Mobile device technology" refers to communication and application technologies that use portable devices such as smartphones and tablets.

[0729] In this embodiment of the invention, the user first launches a dedicated application using a mobile device such as a smartphone or tablet. The user enters personal information within the application and answers questions about their preferences, thereby generating an initial taste profile. This profile is sent to a server and analyzed in detail using a generative AI model.

[0730] The server selects food products from its product database that it deems most suitable for the user based on the aforementioned taste profile. It generates a product list that also takes into account newly added products to the market and information obtained from past feedback based on the user's profile. The user reviews the suggested product list on the app and selects the products they wish to purchase.

[0731] The terminal uses a payment service to securely complete the payment process for the product. Once payment is complete, the product is delivered, and the user can check its status in real time through the app. After delivery, the user experiences the food product and registers their impressions and evaluation as feedback on the app. This feedback is processed on the server and used to improve the accuracy of future selections.

[0732] For example, if a user profile is created indicating they "prefer spicy food," the AI ​​model could suggest recently released spicy Mexican dishes that match that preference. The user then orders and experiences the selected dishes, and the feedback received afterward allows for even more accurate product recommendations in the future.

[0733] When utilizing a generative AI model, a concrete example of a prompt message would be: "Based on the user's taste profile, please suggest a new spicy dish menu. The user prefers spicy food. Please also consider the feedback so that we can use it to improve future dish suggestions."

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The user launches the application and answers questions about their personal information and preferences. User data (personal information, preference information) is entered as input. The app collects this data to create an initial taste profile. This profile data is generated as output and sent to the server.

[0737] Step 2:

[0738] The server analyzes the received taste profile data. Using a generative AI model, the profile is further analyzed to identify individual taste tendencies. The input is taste profile data, and the output is the analyzed detailed taste profile. Based on this profile, a list of food products suitable for the user is generated.

[0739] Step 3:

[0740] The server searches the product database using a detailed taste profile and selects appropriate food products. Using a generative AI model, it generates prompt messages to select new products that match the user's preferences. The detailed taste profile is used as input, and a list of selected food products is generated as output. This list is sent to the terminal.

[0741] Step 4:

[0742] The terminal displays a list of received products to the user. The user reviews the list and selects the desired products. The selection process begins. The input is the user's selection information, and the output is the selected product data.

[0743] Step 5:

[0744] The terminal securely processes payments via a linked payment service. Inputs are selected product data and user payment information, and output is payment confirmation data. After payment is complete, the selected products proceed to the shipping process.

[0745] Step 6:

[0746] The server issues instructions to the delivery company and arranges for the selected food products to be delivered to the user. Real-time delivery status is also provided to the user. The input is payment confirmation data, and the output generates delivery instructions and delivery status information.

[0747] Step 7:

[0748] Users receive a product and provide feedback via a device after experiencing it. This feedback information is collected as data and used for future analysis. The input is user evaluation information, and the output is updated taste profile data. The feedback is processed again on the server, improving the accuracy of product selection.

[0749] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0750] This invention is a recommendation system for alcoholic beverages that combines an emotion engine that recognizes user emotions, providing a personalized experience. The following describes its specific embodiments.

[0751] Users: Through the application, they input their alcohol preferences and past purchase history to form a basic taste profile. In addition, users express their everyday emotions and their feelings when tasting products through the app's interface.

[0752] Device: When the device starts up or whenever the user responds, it uses the camera or microphone to capture the user's facial expressions and tone of voice, and sends this data to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0753] Server: The server selects the optimal alcoholic beverage by combining emotional data analyzed by the emotion engine, taste profiles, and past history. The selection reflects new product information and trends from the market, and the product recommendations are further adjusted according to the user's current emotions.

[0754] Specific example: When a user is feeling down, the emotion engine detects this emotion. The detected emotion is reflected in the system, and a product with a relaxing effect is selected, such as a wine with a specific aroma. When presented to the user, a guide about the relaxing effects and how to drink the selected product is also provided.

[0755] Feedback Collection: After sampling each product, users enter their feedback into the app. The emotion engine also captures the user's reactions during the experience, and uses these results to continuously update the taste profile and improve the accuracy of recommendations.

[0756] Thus, this system is a mechanism that develops dynamic and personalized services that respond to the user's daily life and emotional state. This makes it possible to provide experiential value tailored to the user's situation, rather than simply suggesting products.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] Users install the app and create an account on the login screen. Here, they enter basic personal information, as well as information about their preferred alcoholic beverages and products they have enjoyed in the past.

[0760] Step 2:

[0761] The terminal sends user input data to the server. The server receives this data and uses a generating AI to initialize the user's taste profile.

[0762] Step 3:

[0763] Through the app, users express their everyday emotions and their feelings when tasting alcoholic beverages via the camera and microphone.

[0764] Step 4:

[0765] The device captures the user's facial expressions and voice tone in real time and sends the data to an emotion engine. This engine analyzes the user's emotions and sends the results to a server.

[0766] Step 5:

[0767] The server combines the acquired emotional data and taste profiles, and uses AI to select the most suitable alcoholic beverage product at the moment. It also takes into account new product lists and market trends, and adjusts the recommendations according to the user's emotions.

[0768] Step 6:

[0769] The terminal displays a list of selected alcoholic beverages received from the server to the user. In addition, it also displays information about the characteristics of the selected products and a guide on how to consume them.

[0770] Step 7:

[0771] The user views a list of suggested products, selects the items they wish to purchase, and proceeds with the payment process.

[0772] Step 8:

[0773] The terminal sends the user's selection to the server, which securely completes the transaction via a payment API. The server confirms the completion of the payment and issues a shipping instruction to the logistics company.

[0774] Step 9:

[0775] The server tracks the delivery status in real time and periodically notifies the user of the status via the terminal.

[0776] Step 10:

[0777] After receiving a product, users input their feedback into the app based on their experience. The device also captures the user's emotions during the experience via the camera and microphone and sends them back to the emotion engine.

[0778] Step 11:

[0779] The server improves the taste profile based on new emotional data and feedback, and uses this information for future recommendations.

[0780] (Example 2)

[0781] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] A challenge with conventional beverage recommendation systems was their inability to provide optimal product suggestions based on individual emotional states and preferences. Furthermore, the slow pace of profile updates based on user feedback made it difficult to improve the accuracy of future recommendations.

[0783] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0784] In this invention, the server includes means for acquiring user data and generating a preference profile; means for analyzing the user's emotional state using a generation AI model and adjusting the optimal product; and means for acquiring feedback information from the user and correcting the preference profile to improve the accuracy of beverage product selection. This makes it possible to provide individually optimized product suggestions that are tailored to the user's emotions and preferences.

[0785] "User data" refers to information related to a user's preferences, past purchase history, emotional state, etc.

[0786] A "preference profile" is a dataset that shows a user's taste and preferences for beverages.

[0787] "Emotional data" refers to information about a user's emotional state, analyzed based on changes in their facial expressions and voice.

[0788] A "generative AI model" is an artificial intelligence algorithm that analyzes user input data and reactions to evaluate emotional states and preferences.

[0789] "Beverage products" refer to the various alcoholic and non-alcoholic beverages that the system suggests to the user.

[0790] "Feedback information" refers to data including ratings and comments that users submit after trying a product.

[0791] A "prompt message" is a guide that the system presents to the user regarding their emotional state or product suggestions.

[0792] This invention is a system that recommends the optimal beverage product based on the user's emotions and preferences. The following hardware and software are used to implement the system.

[0793] The user first inputs user data, including their preferences and past purchase history, through the application. The smartphone or tablet, acting as the device, receives this information and sends it to the server to form an initial preference profile.

[0794] Furthermore, the device utilizes its built-in camera and microphone to collect user emotion data through facial expression analysis and voice recognition. This emotion data is sent to a server in real time and analyzed by a generative AI model. The generative AI model features an advanced algorithm that classifies the emotion data into categories such as "happy," "relaxed," and "depressed."

[0795] The server selects the most suitable beverage product based on analyzed sentiment data and the user's preference profile. This process also utilizes the latest market trends and new product information from external databases. Along with the selected product information, the prompt message presented to the user includes specific suggestions, such as, "Try a mild-tasting wine that suits your current mood."

[0796] After a user samples a product, they input feedback about their experience into the application. The device then collects data again and updates their preference profile. This improves the accuracy of beverage recommendations for future purchases.

[0797] This system will allow users to enjoy a more personalized experience by receiving individually optimized beverage products.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The user opens the application and enters their preferences and past purchase history. This input information is sent to the server via the terminal. The server generates a preference profile based on the received data. The main data processing involves formatting the input data and registering the profile in the database. An initial preference profile is generated as output.

[0801] Step 2:

[0802] The device collects user emotion data by capturing the user's facial expressions and voice using its built-in camera and microphone. This data is then transmitted to a server in real time. The server uses a generative AI model to analyze the user's emotional state from this data. Image processing and audio signal processing are performed during the data calculation process, and the user's emotional state is numerically evaluated. The output is the classified emotional state.

[0803] Step 3:

[0804] The server selects the optimal beverage product using analyzed sentiment data and the user's preference profile. It retrieves the latest trends and new product information from external market databases and combines this information to make product selections. The selection process includes data calculations such as linear regression and similarity calculations. The output is a list of recommended beverage products.

[0805] Step 4:

[0806] The application presents the user with the selected beverage product and its details. The terminal receives the output information from the server and presents it to the user visually. A prompt message such as "Please try a mild-tasting wine that suits your current mood" is displayed. This is used by the user to confirm their choices and decide on their next purchase action.

[0807] Step 5:

[0808] The user samples the presented product and then inputs feedback information into the application. This feedback includes satisfaction levels and emotional ratings of the product. The terminal sends this information to a server, which uses the feedback data to update the preference profile. The main data processing involves correcting the profile based on the feedback and registering new data. As output, an improved preference profile is created.

[0809] Step 6:

[0810] The terminal or server prepares the new preference profile for use at the start of the next process. The system leverages this updated data to improve the accuracy of the product selection presented next time. Data management and storage optimization are performed here. The output is highly accurate profile data available for the next recommendation process.

[0811] (Application Example 2)

[0812] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0813] Conventional alcohol product recommendation systems offer suggestions based on users' taste preferences, but they lack dynamic suggestions that take into account the user's emotional state, resulting in limited improvements to the user experience. Furthermore, there is the challenge of real-time emotion recognition and the optimization of product recommendations based on that emotion.

[0814] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0815] In this invention, the server includes means for acquiring user data and generating a preference profile; means for acquiring and analyzing facial expressions and voice to recognize the user's emotional state; and means for optimizing product suggestions based on the analyzed emotional state. This enables personalized alcoholic beverage product suggestions based on complex information, including the user's emotional state.

[0816] "User data" refers to information provided by users, such as their preferences, past purchase history, and daily emotions.

[0817] A "preference profile" is a collection of information formed based on a user's taste and preferences.

[0818] "Novel alcoholic beverage products" are alcoholic beverage products selected based on new market trends and user profiles.

[0819] "Emotional state" refers to the emotional state of the user, analyzed from their facial expressions and voice.

[0820] "Facial expression" refers to the outward display of emotions conveyed by a user's facial expressions, gestures, and other actions.

[0821] "Voice" refers to the emotional elements contained in the tone of voice and manner of speaking that a user produces.

[0822] "Analysis" is the process of breaking down information based on acquired data to obtain a specific result.

[0823] "Optimizing product recommendations" is the process of adjustment and improvement undertaken to recommend the most suitable products based on user profiles and emotional states.

[0824] "Payment settlement" refers to the process of a user paying a predetermined price for a product they have selected.

[0825] "Opinion information" refers to feedback and comments provided by users after using a product.

[0826] This system aims to recognize customers' emotional states in real time at physical stores and suggest the most suitable alcoholic beverages for them. First, users, specifically store staff, wear smart glasses, which collect customers' facial expressions and voices through cameras and microphones. This makes it possible to understand the users' emotional states.

[0827] The smart glasses integrate with emotion analysis software such as Microsoft Azure Emotion API and Google Cloud Vision API to quickly analyze collected data and assess emotional states. The analysis results are sent to a server, which optimizes product recommendations based on emotional state data and user preference profiles.

[0828] The server accesses the database and uses market trends and new product information to select the most suitable alcoholic beverages for the customer. The selected product information is displayed on the smart glasses' screen, allowing in-store staff to immediately introduce it to the customer.

[0829] For example, if the emotional engine detects tension in a customer choosing a wine in a store, the staff will be provided with information suggesting relaxing wines and encouraging them to sample them. Such personalized suggestions can enhance the customer experience.

[0830] As an example of a prompt sentence using a generative AI model, you could use a sentence like, "If a customer says they are looking for a relaxing wine, how would you suggest products?" This allows for flexible suggestions that meet the diverse needs of users.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] The smart glasses on the device are activated, and the user, specifically store staff, collects facial expressions and voice input around the customer. This captures real-time facial expressions and voices, and uses the built-in camera and microphone to collect data necessary for emotion analysis. Raw image data and audio data are obtained as output.

[0834] Step 2:

[0835] The collected image and audio data are sent to a server, where emotion analysis software is used to calculate emotional state data. The input is images and audio, and the output is an analysis result indicating the user's emotional state. This provides detailed emotional information, such as whether the user is relaxed or stressed.

[0836] Step 3:

[0837] The server analyzes the emotional state and combines it with the user's preference profile. The input is the emotional state and preference profile, and the output is integrated data used for product recommendations. Here, recommended products are selected based on the user's emotions.

[0838] Step 4:

[0839] The server references market trend information and new product information from the product database to select the most suitable products. The input is integrated data, and the output is an optimized list of products. This process enables personalized product recommendations that reflect current trends.

[0840] Step 5:

[0841] Ultimately, the server transmits the selected product information to the smart glasses on the terminal, and the user presents it to the customer via the display. The input is an optimized list of products, and the output is visual information for staff to present to the customer. This enables immediate and efficient customer service.

[0842] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0843] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0844] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0845] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0846] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0847] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0848] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0849] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0850] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0851] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0852] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0853] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0854] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0855] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0856] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0857] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0858] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0859] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0860] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0861] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0862] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] A means for acquiring user data and generating a taste profile,

[0866] A means for selecting new alcoholic beverage products based on the aforementioned taste profile,

[0867] A means of proposing the selected alcoholic beverages to the user, obtaining their approval, and then processing the payment,

[0868] A means for delivering the aforementioned alcoholic beverages to the user,

[0869] A means for obtaining feedback information from users, correcting the aforementioned taste profile, and improving the accuracy of alcohol product selection,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising means for generating detailed information and a guide on how to consume alcoholic beverages provided to the user.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising means for providing a tasting experience of alcoholic beverages using virtual reality technology.

[0875] "Example 1"

[0876] (Claim 1)

[0877] A means for acquiring user information and generating a preference profile,

[0878] A means for selecting the optimal beverage product based on the aforementioned preference profile,

[0879] A means of presenting the selected beverage product to the user, obtaining their approval, and executing payment,

[0880] Means for issuing instructions to deliver the beverage product to the user,

[0881] A means for obtaining feedback information from users, correcting the aforementioned preference profile, and improving the accuracy of beverage product selection,

[0882] A means of updating the delivery status in real time,

[0883] A method for analyzing data using a generative artificial intelligence model,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, which generates detailed information and usage guides regarding beverage products provided to users.

[0887] (Claim 3)

[0888] The system according to claim 1, which provides a beverage product tasting experience using virtual reality technology.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] A means for acquiring user data and generating a taste profile,

[0892] A means for selecting a new food product based on the aforementioned taste profile,

[0893] A means of proposing the selected food products to the user, obtaining their approval, and processing the payment,

[0894] A means for delivering the aforementioned food products to the user,

[0895] A means for obtaining feedback information from users, correcting the aforementioned taste profile, and improving the accuracy of food product selection,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising means for generating detailed information about food products provided to the user and a guide on how to use food and beverages.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising means for providing a food product experience using mobile device technology.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A means for acquiring user data and generating a preference profile,

[0904] A means for selecting a new beverage product based on the aforementioned preference profile and emotional data,

[0905] A method for analyzing the user's emotional state using a generative AI model and adjusting the optimal product accordingly,

[0906] A means of proposing the selected beverage product to the user, obtaining their approval, and processing the payment,

[0907] A means for delivering the aforementioned beverage product to the user,

[0908] A means for obtaining feedback information from users, correcting the preference profile, and improving the accuracy of beverage product selection,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising means for generating detailed information and a consumption guide regarding beverage products provided to the user.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising means for providing a beverage product tasting experience using virtual reality technology.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A means for acquiring user data and generating a preference profile,

[0917] A means for selecting new alcoholic beverage products based on the aforementioned preference profile,

[0918] In order to recognize the user's emotional state, a means is provided to acquire and analyze facial expressions and voice.

[0919] A means for optimizing product suggestions based on the analyzed emotional state,

[0920] A means of proposing the selected alcoholic beverages to the user, obtaining their approval, and then processing the payment,

[0921] A means for delivering the aforementioned alcoholic beverages to the user,

[0922] A means for obtaining opinion information from users, correcting the aforementioned preference profile, and improving the accuracy of alcohol product selection,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, comprising means for generating detailed information and usage guides regarding alcoholic beverage products provided to users.

[0926] (Claim 3)

[0927] The system according to claim 1, comprising means for providing an alcohol product evaluation experience using visual technology. [Explanation of Symbols]

[0928] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring user data and generating a taste profile, A means for selecting new alcoholic beverage products based on the aforementioned taste profile, A means of proposing the selected alcoholic beverages to the user, obtaining their approval, and then processing the payment, A means for delivering the aforementioned alcoholic beverages to the user, A means for obtaining feedback information from users, correcting the aforementioned taste profile, and improving the accuracy of alcohol product selection, A system that includes this.

2. The system according to claim 1, comprising means for generating detailed information and a guide on how to consume alcoholic beverages provided to the user.

3. The system according to claim 1, comprising means for providing a tasting experience of alcoholic beverages using virtual reality technology.

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