System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring preference data of a user; artificial intelligence means for generating a recipe based on the acquired preference data; an appliance that cooks coffee based on the generated recipe; means for collecting feedback from the user after cooking the coffee; means for analyzing the collected feedback and reflecting the feedback in the next recipe generation; and means for cooperating with a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional coffee serving systems have difficulty responding to diverse user preferences and are limited to providing a uniform product. As a result, they are unable to provide original coffee blends tailored to each user's specific preferences, which makes it difficult to improve user satisfaction. Furthermore, there is no system for effectively collecting feedback and using it to improve the product, making it difficult to achieve continuous quality improvement. The purpose of this invention is to solve the above-mentioned problems and provide coffee blends tailored to individual user preferences. [Means for solving the problem]

[0005] The present invention provides a system including: means for acquiring user preference data; artificial intelligence means for generating a recipe based on the acquired preference data; a device for preparing coffee based on the generated recipe; means for collecting feedback from the user after preparing the coffee; means for analyzing the collected feedback and reflecting it in the next recipe generation; and means for linking with the user's device. The artificial intelligence means analyzes the user preference data and the collected feedback and executes an algorithm for customizing the recipe. The system further includes means for temporarily saving the user preference data and feedback and storing them in a database. This makes it possible to provide original blends of coffee that meet each user's specific preferences and to continuously improve quality based on the feedback.

[0006] "User" refers to an individual or group of people who use the System and provide Preference Data.

[0007] "Preference data" refers to specific preference information about coffee, such as the roast level, acidity, bitterness, and aroma preferred by the user.

[0008] "Artificial Intelligence" refers to the algorithms and technologies used to analyze user preference data and feedback to generate and customize coffee recipes.

[0009] A "recipe" refers to a document that describes specific cooking steps and settings, including coffee roasting time, extraction temperature, and type of beans used.

[0010] "Cooking equipment" refers to the devices and equipment used to actually make coffee based on the generated recipe.

[0011] "Feedback" refers to the user's specific impressions and evaluations of the coffee they have sampled.

[0012] "Device" refers to the digital device (e.g., smartphone, tablet) used by a User to provide Preference Data or enter Feedback.

[0013] "Database" means a digital data management system for the temporary or permanent storage of user preference data and feedback.

[0014] "Analyzing" refers to examining the data obtained from users in detail and identifying trends and areas for improvement. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0037] Server Operation

[0038] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0039] 1. User Registration / Login:

[0040] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0041] 2. Preference Data Collection:

[0042] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0043] 3. Recipe generation:

[0044] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0045] 4. Collecting User Feedback:

[0046] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0047] 5. Feedback analysis:

[0048] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0049] Device behavior

[0050] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0051] 1. Interface display:

[0052] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0053] 2. Preference Survey Answers:

[0054] When the user answers the preference questionnaire, the terminal sends it to the server.

[0055] 3. Feedback input:

[0056] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0057] User Actions

[0058] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0059] 1. Registration and login operations:

[0060] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0061] 2. Preference input:

[0062] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0063] 3. Coffee tasting and feedback:

[0064] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0065] Specific examples

[0066] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0067] If User A, after tasting the coffee, inputs feedback such as "It's still too sour," the server receives this feedback and reflects it in the next recipe generation. This allows the server to continually provide the optimal coffee tailored to User A's preferences.

[0068] This system allows users to enjoy coffee that is optimized to their individual tastes. Furthermore, by utilizing feedback, the quality of the coffee provided will be continuously improved. The specific system configuration and operation for realizing this mechanism are the characteristics of this invention.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] User Registration

[0072] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[0073] Step 2:

[0074] Log in

[0075] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[0076] Step 3:

[0077] Collection of preference data

[0078] After logging in, the user answers a preference questionnaire displayed on the terminal. The user inputs roast level, acidity, bitterness, and aroma intensity, and then presses the send button. The terminal then sends the input preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the artificial intelligence means, which starts generating a preference profile.

[0079] Step 4:

[0080] Generating a preference profile

[0081] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[0082] Step 5:

[0083] Recipe Generation

[0084] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[0085] Step 6:

[0086] Coffee tasting and feedback

[0087] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[0088] Step 7:

[0089] Analyzing and incorporating feedback

[0090] The server inputs the received feedback data into the artificial intelligence means and performs analysis. The analysis results are set to be reflected in the next recipe generation. This allows the server to provide an original coffee blend that is closer to the user's preferences. The server saves the analysis results in a database and uses them for the next preference profile generation and recipe generation.

[0091] The above processing steps enable the system to continuously provide coffee optimized to the individual preferences of the user.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Conventional coffee blend providing systems have been unable to adequately respond to individual user preferences, resulting in inconsistent coffee quality each time. Furthermore, user feedback is not effectively reflected in the next recipe generation, making it difficult to provide the optimal coffee blend for the user. This has made improving user satisfaction a challenge.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's device, means for the user to log in to the system by entering an ID and password, means for receiving responses to a user preference questionnaire, means for saving the user's feedback data in a database, and means for generating a preference profile using an artificial intelligence model. This makes it possible to provide original coffee blends suited to the user's preferences and continuously improve the quality.

[0097] "User preference data" is information about the characteristics and flavors of coffee that a user prefers.

[0098] "Artificial intelligence means" refers to technology that analyzes and predicts based on user preference data and executes algorithms to generate optimal recipes.

[0099] A "drink preparation device" is a device that automatically prepares a drink such as coffee according to a generated recipe.

[0100] The "means for collecting feedback" is a method for users to input their evaluations and impressions into the system after tasting a beverage such as coffee.

[0101] The "means for analyzing collected feedback and reflecting it in the next recipe generation" is a method for improving the next recipe based on the feedback collected from the user to further suit the user's preferences.

[0102] "Means for linking with a user's device" refers to technology for exchanging data between the server and the terminal used by the user.

[0103] "Method of logging into a system by entering an ID and password" refers to the authentication method used by a user to access a system.

[0104] The "means for receiving responses to the user's preference questionnaire" refers to a method for transmitting data to the server when a user answers the preference questionnaire.

[0105] The "means for saving feedback data in a database" is a method for temporarily holding feedback collected from users and storing it in a database for later analysis.

[0106] "Means for generating preference profiles using artificial intelligence models" refers to a technology that utilizes AI models based on user preference data to classify user preferences in detail and create individual profiles.

[0107] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0108] The server is responsible for managing the entire system and processing data. Specifically, it is equipped with a means for acquiring user preference data. When a user answers a preference questionnaire from their device, the server receives this data and automatically stores it in a database. Furthermore, a preference profile is generated based on this preference data using a generative AI model.

[0109] Based on the generated preference profile, the server uses an AI algorithm to generate coffee recipes. This AI model analyzes the user's preference data and collected feedback and reflects it in the next recipe generation.

[0110] The generated recipe is sent to the terminal and displayed to the user. The user tastes coffee made based on this recipe. After tasting, the user inputs feedback through the terminal. This feedback data is also sent to the server and stored in the database. The server then analyzes the feedback data and reflects it in the next coffee recipe generation.

[0111] The user's terminal is a device that exchanges data with the server. When a user accesses the system for the first time, they log in by entering their ID and password. Once logged in to the system, the user can answer a preference questionnaire. The terminal sends the data the user has answered to the server. It also provides an interface for the user to enter feedback, and sends that data to the server.

[0112] As a concrete example, consider the case where User A is starting to use the system for the first time. First, he or she registers an ID and password via a terminal. Then, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like coffee with a low acidity." This data is sent to the server, where it is analyzed by the generative AI model, which then generates a coffee recipe specifically for User A.

[0113] The generated recipe includes detailed parameters such as the type of coffee bean, roasting time, and extraction temperature. This recipe information is displayed on the terminal, and User A tastes the coffee based on it. After tasting, if User A inputs feedback such as "It's still too acidic," the server receives this and reflects it in the next recipe generation.

[0114] An example prompt might be, "I like dark roasted coffee with a low acidity. Please create my own coffee blend based on this."

[0115] This system allows users to enjoy coffee that is optimized to their preferences. Furthermore, by utilizing user feedback, the quality of the coffee provided will be continuously improved, which is expected to increase user satisfaction.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] The server is responsible for user registration and login. When a user enters their ID and password from their device, the device sends this information to the server. The server receives the sent data and authenticates the user by comparing it with the database. If authentication is successful, it retrieves the user's profile data from the database and sends it back to the device. The user confirms this and login is complete.

[0119] Input: User ID and password

[0120] Output: User profile data and authentication results

[0121] Specific operation: The user enters their ID and password on their smartphone and presses the login button.

[0122] Step 2:

[0123] The server collects user preference data. When the user answers a preference questionnaire in the system, the device sends the response data to the server. The server receives the data and stores it in a database. Finally, this preference data is input into an AI model, which begins analysis and generates a preference profile.

[0124] Input: User preference survey response data

[0125] Output: User preference profile

[0126] Specific operation: The user answers the questionnaire items with answers such as "I prefer dark roast" and "I prefer a mild acidity," and the data is sent to the server.

[0127] Step 3:

[0128] The server uses AI to generate a customized coffee recipe based on the generated preference profile, including roasting time, brewing temperature, type of beans used, etc. The generated recipe is sent to the device and displayed to the user.

[0129] Input: Preference Profile

[0130] Output: Coffee recipe

[0131] How it works: The server's AI model analyzes the user's preference data and generates a recipe for a "dark roast blend with reduced acidity." The details are then sent to the device.

[0132] Step 4:

[0133] The user tastes coffee prepared based on the recipe received through the terminal. After tasting, the user inputs feedback through the terminal. The terminal sends this feedback data to the server, which stores the received data in a database.

[0134] Input: User feedback data

[0135] Output: Saved feedback data

[0136] Specific operation: The user enters feedback such as "It's still too sour" or "The bitterness is just right," and this is sent to the server.

[0137] Step 5:

[0138] The server analyzes the received feedback data. This feedback data is then input back into the AI ​​model to be reflected in the next coffee recipe generation. This adjusts the next coffee recipe to more closely match the user's preferences. The new recipe is then notified to the user again.

[0139] Input: Saved feedback data

[0140] Output: Improved coffee recipe

[0141] Specific operation: Based on the previous feedback, the AI ​​creates a new recipe called "Dark roast blend with even less acidity" and notifies the user's device.

[0142] In this way, the system continues to provide coffee blends optimized to the user's preferences.

[0143] (Application example 1)

[0144] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0145] Conventional coffee blend provision systems have difficulty generating recipes that perfectly match the user's preferences, making it difficult to provide individually optimized coffee. Furthermore, there was no system that efficiently collected user feedback and reflected it in the next recipe generation. Furthermore, in a brick-and-mortar store environment, there was a lack of a means to quickly provide custom recipes tailored to the user's preferences.

[0146] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0147] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, and means for generating recipes individually optimized for the user in conjunction with the user preference data and transmitting the recipes to devices in the brick-and-mortar store, thereby enabling individually optimized coffee blends to be provided quickly and efficiently in a brick-and-mortar environment.

[0148] - "Means for acquiring user preference data" refers to devices or software for collecting information about a user's preferred coffee.

[0149] "Artificial intelligence means" refers to machine learning algorithms and systems that generate and optimize coffee recipes based on user preference data.

[0150] "Coffee preparation equipment" means a barista machine or other preparation device for preparing coffee according to a generated recipe.

[0151] "Means for collecting feedback" refers to devices or software that allow users to obtain their impressions and ratings of the coffee they sample.

[0152] "Means for reflecting feedback in next recipe generation" refers to a system for analyzing collected feedback and adjusting the next coffee recipe to better suit the user's preferences.

[0153] "Means for linking with the user's mobile terminal" refers to the interface that allows mobile devices such as smartphones and tablets to communicate with the system and exchange data.

[0154] "Means for transmitting to devices in a physical store" refers to a communication system for transmitting the generated coffee recipe to a barista machine or cooking device installed in a physical store.

[0155] "Means for storing in a database" refers to a data storage system for saving information such as user preference data and feedback, and reusing it as needed.

[0156] The present invention is a system that provides original coffee blends tailored to the individual tastes of users. This system is built around three main components: a server, a terminal, and a user. The details and processing of each component are explained below.

[0157] Server Operation

[0158] The server is responsible for managing the entire system and processing data. It performs the following processes:

[0159] 1. User Registration / Login

[0160] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0161] 2. Collecting preference data

[0162] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into an artificial intelligence system to analyze it and generate a preference profile of the user.

[0163] 3. Recipe Generation

[0164] The server uses artificial intelligence to generate a coffee recipe based on the customer's preference profile, including the appropriate roasting time, brewing temperature, and type of beans to be used, and then transmits the recipe to a device in the physical store.

[0165] 4. Collecting User Feedback

[0166] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0167] 5. Feedback Analysis

[0168] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, thereby providing coffee that is more suited to the user's preferences.

[0169] Device behavior

[0170] The terminal is a device that exchanges data between the user and the server. It performs the following processes:

[0171] 1. Interface display

[0172] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0173] 2. Answer the preference survey

[0174] When the user answers the preference questionnaire, the terminal sends it to the server.

[0175] 3. Feedback input

[0176] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0177] User Actions

[0178] Users provide their coffee preference data and feedback through this system. The following process is performed.

[0179] 1. Registration and login operations

[0180] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0181] 2. Input your preferences

[0182] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0183] 3. Coffee tasting and feedback

[0184] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0185] Application Examples

[0186] As a concrete example, a scenario will be shown in which a new user A starts using the system.

[0187] 1. User A first registers in the system as "user_a" and logs in.

[0188] 2. User A answers a preference questionnaire on a smartphone app, providing information such as "I prefer medium roast," "low acidity," and "strong aroma."

[0189] 3. The server analyzes this information and generates an optimal coffee recipe, which is then sent to the barista machine in the store to prepare the coffee.

[0190] 4. User A tastes the coffee and provides feedback that it is still too acidic.

[0191] 5. The server analyzes the feedback and reflects it in the next recipe generation.

[0192] This allows User A to continuously enjoy coffee optimized to his or her preferences. The hardware used includes a smartphone and a barista machine, and the software used is Python, Flask / Django (server side), PostgreSQL (database), and an AI model (machine learning library, e.g., TensorFlow or PyTorch).

[0193] Example prompt for a generative AI model:

[0194] User Preferences: { "Roast": "Medium Roast", "Acidity": "Low", "Aroma": "Strong"}

[0195] Feedback: { "sour": "still high"}

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1: User registration and login

[0198] The server requests registration by the user entering their ID and password using their smartphone. The entered data is the user's account information. The server receives this data and stores it in a database. Once registration is complete, it sends a success message to the user.

[0199] When the user enters their ID and password again to request login, the server checks the database and performs authentication. If authentication is successful, the server retrieves the user profile and returns it to the terminal along with a session token. This completes the login process.

[0200] Step 2: Obtaining preference surveys

[0201] The terminal displays a preference questionnaire screen to the user, and the user inputs preference data such as "I prefer medium roast," "low acidity," and "strong aroma."

[0202] The device sends the input preference data to a server, which receives it, stores it in a database, and inputs it into an artificial intelligence model.

[0203] Step 3: Generate the recipe

[0204] The server analyzes the stored preference data using an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate a coffee recipe optimized for the user's preferences. This process includes data preprocessing, feature extraction, and the application of machine learning algorithms.

[0205] The generated recipe includes detailed parameters such as roasting time, brewing temperature, type of beans used, etc. The server stores this recipe in a database and sends it to the device in the physical store.

[0206] Step 4: Preparing and tasting the coffee

[0207] The barista machine in the physical store prepares coffee based on the recipe sent from the server, and the coffee is then served to the user exactly as the recipe was created.

[0208] The user samples this coffee and then inputs their impressions and evaluations into the terminal.

[0209] Step 5: Gather feedback

[0210] The device sends the feedback data entered by the user to the server, including specific impressions such as "It still tastes too sour."

[0211] The server stores this feedback in a database, which is used for the next recipe generation.

[0212] Step 6: Analyze the feedback and incorporate it into your next recipe

[0213] The server then inputs the collected feedback data back into the AI ​​model. This data analysis allows the feedback information to be reflected in the next recipe generation. Specifically, the parameters of the next recipe are adjusted to better suit the user's preferences.

[0214] The server saves the new, adjusted recipe in its database, ready to use the next time coffee is made.

[0215] This will realize a system that allows users to continuously enjoy coffee that is optimized to their preferences.

[0216] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0217] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[0218] Server Operation

[0219] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0220] 1. User Registration / Login:

[0221] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0222] 2. Preference Data Collection:

[0223] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0224] 3. Recipe generation:

[0225] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0226] 4. Collecting User Feedback:

[0227] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0228] 5. Feedback analysis:

[0229] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0230] Emotion Engine Operation

[0231] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0232] 1. Acquiring emotion data:

[0233] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[0234] 2. Data transmission:

[0235] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[0236] Device behavior

[0237] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0238] 1. Interface display:

[0239] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0240] 2. Preference Survey Answers:

[0241] When the user answers the preference questionnaire, the terminal sends it to the server.

[0242] 3. Feedback input:

[0243] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0244] User Actions

[0245] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0246] 1. Registration and login operations:

[0247] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0248] 2. Preference input:

[0249] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0250] 3. Providing Emotion Data:

[0251] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0252] 4. Feedback Input:

[0253] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0254] Specific examples

[0255] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0256] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[0257] This system allows users to enjoy coffee that is optimized for their individual tastes and emotions. Furthermore, by combining it with an emotion engine, the quality of the coffee provided can be further improved, increasing user satisfaction. The specific system configuration and operation required to realize this mechanism are the characteristics of this invention.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] User Registration

[0261] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[0262] Step 2:

[0263] Log in

[0264] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[0265] Step 3:

[0266] Collection of preference data

[0267] After logging in, the user answers a preference questionnaire displayed on the device. The user enters roast level, acidity, bitterness, aroma intensity, etc., and presses the send button. The device then sends the entered preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the AI, which begins analysis and generates the user's preference profile.

[0268] Step 4:

[0269] Generating a preference profile

[0270] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[0271] Step 5:

[0272] Recipe Generation

[0273] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[0274] Step 6:

[0275] Coffee tasting and feedback

[0276] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[0277] Step 7:

[0278] Acquiring emotion data

[0279] When tasting coffee, the emotion engine uses a facial recognition camera and a voice recognition microphone to acquire the user's emotional data. For example, it can recognize emotions such as "happiness" and "surprise" from facial expressions, and "satisfaction" and "dissatisfaction" from voice.

[0280] Step 8:

[0281] Sending emotional data

[0282] The acquired emotion data is sent in real time from the device to the server, which receives it and stores it in a database. At the same time, the server prepares to reflect this data, along with feedback data, in the next recipe generation.

[0283] Step 9:

[0284] Analysis and reflection of feedback and emotional data

[0285] The server inputs the received feedback data and emotion data into the artificial intelligence means for analysis. The analysis results are set to be reflected in the next recipe creation. This allows the server to provide coffee that is optimized based on the user's preferences and emotions.

[0286] Through the above processing steps, the system realizes the continuous provision of coffee optimized to the individual tastes and emotions of the user.

[0287] Example 2

[0288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] Conventional recipe generation systems based on preference data were unable to take into account the user's emotional state, making it difficult to provide the perfect drink for each individual user's current emotions. Furthermore, because they did not use emotional data, the quality of feedback was limited, resulting in a problem of reduced accuracy in the next recipe generation.

[0290] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's terminal, means for acquiring user emotion data, and means for customizing the recipe based on the acquired emotion data. This makes it possible to generate more accurate customized drink recipes by comprehensively considering the user's preference data and emotion data, thereby increasing the satisfaction of each individual user.

[0291] "User preference data" is data that indicates the user's preferences regarding the type, flavor, temperature, etc. of beverages.

[0292] "Artificial intelligence means" refers to algorithms and programs that analyze user preference data and feedback and generate optimal recipes.

[0293] A "drink preparation device" is a device that automatically prepares coffee or other drinks based on a generated recipe.

[0294] "Means for collecting feedback" refers to a method or device for collecting opinions and impressions provided by users after tasting.

[0295] "Means for analyzing feedback" refers to devices or programs that process collected feedback data using artificial intelligence or other analytical methods and reflect the results in the next recipe generation.

[0296] "User terminal" means an electronic device through which a user accesses the system and inputs preference data and feedback.

[0297] "Means for acquiring emotional data" refers to sensors and analysis devices for detecting the user's psychological state from facial expressions, vocal tone, etc.

[0298] "Means for customizing recipes based on emotional data" refers to algorithms or programs that analyze the acquired emotional data and reflect the results in the recipe.

[0299] The present invention is a system that generates and provides personalized drink recipes based on user preference data and emotion data. This system is built around the main components of a server, a terminal, a user, and an emotion engine.

[0300] Overall system configuration

[0301] Server Operation

[0302] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0303] 1. User Registration / Login:

[0304] When a user enters their ID and password from the terminal, the server receives it and authenticates them by checking it against a database (e.g., MySQL). If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0305] 2. Preference Data Collection:

[0306] When a user answers a preference questionnaire, the server receives it and stores it in a database. This data is then input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates a preference profile for the user based on the results.

[0307] 3. Recipe generation:

[0308] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica).

[0309] 4. Collecting User Feedback:

[0310] After tasting, the user inputs feedback through the terminal (e.g., "It's very sour" or "It has a weak aroma"). The server receives this feedback and stores it in a database.

[0311] 5. Feedback analysis:

[0312] The server inputs the collected feedback data into the AI ​​and reflects it the next time a recipe is generated, resulting in a more accurate customized drink.

[0313] Emotion Engine Operation

[0314] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0315] 1. Acquiring emotion data:

[0316] The emotion engine uses a face recognition camera and a voice recognition microphone to capture emotion data from the user's facial expressions and voice.

[0317] 2. Data transmission:

[0318] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with the preference data, and reflects it in the next recipe generation.

[0319] Device behavior

[0320] A terminal is a device that exchanges data between a user and a server.

[0321] 1. Interface display:

[0322] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0323] 2. Preference Survey Answers:

[0324] When the user answers the preference questionnaire, the terminal sends it to the server.

[0325] 3. Feedback input:

[0326] After tasting the coffee, the user inputs feedback through the terminal and transmits it to the server.

[0327] User Actions

[0328] Users provide preference data and feedback through this system.

[0329] 1. Registration and login operations:

[0330] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0331] 2. Preference input:

[0332] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0333] 3. Providing Emotion Data:

[0334] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0335] 4. Feedback Input:

[0336] Customers are asked to taste the coffee provided and enter their feedback on the taste through the terminal.

[0337] Specific examples

[0338] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0339] When User A tastes the coffee, the emotion engine recognizes the emotion of "happiness" from his / her facial expression and sends it to the server. The server combines this emotion data with specific feedback data ("slightly acidic" and "good aroma") and reflects it in the next recipe generation. This process ensures that the next coffee provided is more optimally tailored to User A's preferences.

[0340] Example prompts to input to the generative AI model

[0341] "User A's preference data is 'prefers dark roast' and 'mild acidity', and their latest feedback is 'slightly strong acidity' and 'good aroma'. 'Delight' has been recognized as the emotional data. Based on this data, please generate the next coffee recipe that is best suited to User A."

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: User registration and login

[0344] Input: The user enters the ID and password from the terminal.

[0345] Description: The terminal sends the ID and password entered by the user to the server. The server receives this and authenticates it by checking it against a database (e.g. MySQL). If authentication is successful, it retrieves the user profile from the database and sends it back to the terminal.

[0346] Output: User authentication result (success or failure) and user profile.

[0347] Step 2: Collect preference data

[0348] Input: The user answers the preference survey.

[0349] Description: The device sends the user's preference questionnaire response data to the server. The server receives it and stores it in a database. At the same time, this data is input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates the user's preference profile based on the results.

[0350] Output: User preference profile.

[0351] Step 3: Recipe generation

[0352] Input: The generated preference profile.

[0353] Description: The server uses AI to generate a coffee recipe based on the preference profile. This recipe includes details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica). The generated recipe is sent to the beverage preparation device.

[0354] Output: A customized coffee recipe for the user.

[0355] Step 4: Obtaining emotion data

[0356] Input: Facial expressions and voice as the user samples the coffee.

[0357] Description: The emotion engine uses a face recognition camera and a voice recognition microphone to acquire user emotion data, which is then sent to the server in real time.

[0358] Output: User emotion data (e.g., emotion "joy").

[0359] Step 5: Gather feedback

[0360] Input: Feedback provided by the user after tasting (e.g., "strong acidity" or "weak aroma").

[0361] Description: The terminal displays the user's feedback in the input interface. When the user enters the feedback, it sends it to the server. The server stores the feedback data in a database.

[0362] Output: User feedback data.

[0363] Step 6: Feedback analysis

[0364] Input: Collected feedback data.

[0365] Description: The server inputs the feedback data into the generative AI model and analyzes it. The results of this analysis are reflected in the next recipe generation. The server combines the collected emotional data with the preference profile to prepare the optimal data for the next recipe generation.

[0366] Output: Parsed feedback data to be used in the next recipe generation.

[0367] Step 7: Generate the next recipe

[0368] Input: Parsed feedback data, user preference profile, emotional data.

[0369] Description: The server generates the next recipe based on the analyzed feedback data, the user's preference profile, and emotional data. In this process, the generative AI model takes into account various user data to customize the new recipe.

[0370] Output: A customized coffee recipe for the next user.

[0371] (Application example 2)

[0372] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0373] Currently, many coffee lovers face the challenge of finding a coffee that matches their tastes and emotions. In particular, there is no system that can optimize the next coffee blend based on the user's emotional state and feedback. This makes it difficult to provide personalized service that allows users to have the best coffee experience.

[0374] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data and emotion data, a device for preparing coffee based on the generated recipe, means for collecting feedback from the user after preparing the coffee, means for analyzing the collected feedback and reflecting it in generating the next recipe, and means for acquiring emotion data by using facial expression recognition and voice recognition functions in cooperation with the user's terminal. This makes it possible to provide an optimal coffee blend based on the user's preferences and emotions.

[0375] "User preference data" is information relating to the user's personal preferences, such as the taste, roast level, acidity, and aroma of coffee.

[0376] "Emotion data" is information about the emotional state obtained from the user's facial expressions and voice.

[0377] "Artificial intelligence means for generating recipes" refers to a program or system that runs an algorithm based on acquired data to generate the optimal coffee recipe for the user.

[0378] An "apparatus for preparing coffee based on a generated recipe" is a device for specifically preparing coffee according to a coffee recipe generated by artificial intelligence.

[0379] "Feedback" refers to information such as the user's impressions and evaluation of the coffee they have actually tasted, as well as areas for improvement.

[0380] The "facial expression recognition function" is a technology that uses a camera to analyze a user's facial expressions and detect their emotional state.

[0381] The "voice recognition function" is a technology that uses a microphone to analyze the user's voice and detect their emotional state.

[0382] A "user terminal" is a device through which a user inputs data or interacts with the system, including a smartphone or tablet.

[0383] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[0384] Server Operation

[0385] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0386] 1. User Registration / Login

[0387] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0388] 2. Collecting preference data

[0389] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0390] 3. Recipe Generation

[0391] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0392] 4. Collecting User Feedback

[0393] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0394] 5. Feedback Analysis

[0395] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0396] Emotion Engine Operation

[0397] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0398] 1. Acquiring Emotion Data

[0399] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[0400] 2. Data transmission

[0401] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[0402] Device behavior

[0403] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0404] 1. Interface display

[0405] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0406] 2. Answer the preference survey

[0407] When the user answers the preference questionnaire, the terminal sends it to the server.

[0408] 3. Feedback input

[0409] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0410] User Actions

[0411] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0412] 1. Registration and login operations

[0413] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0414] 2. Input your preferences

[0415] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0416] 3. Providing Emotion Data

[0417] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0418] 4. Feedback Input

[0419] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0420] Specific examples

[0421] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0422] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[0423] Prompt Sentence Examples

[0424] Design an application that allows users to select their favorite coffee. It should have a function that optimizes the next coffee recipe based on the user's preference and emotional data. Preference data includes roast level, acidity, and aroma. The smartphone used will have a facial expression recognition camera and a voice recognition microphone to collect emotional data. The collected data will be sent to a server, where it will be analyzed by AI and reflected in the next recipe. Please provide specific program code examples.

[0425] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0426] Step 1:

[0427] The user enters their ID and password using a terminal and sends them to the server. The server receives this information and authenticates them by checking it against a database. If authentication is successful, the server retrieves the user profile from the database and sends it back to the terminal. The input data is "ID" and "password," and the output data is "user profile." The server executes a database query for the authentication process.

[0428] Step 2:

[0429] The user answers a preference questionnaire from their device and sends the response data to the server. The server stores the received preference data in a database and provides it as input to the AI ​​analysis engine. The AI ​​analysis engine analyzes this data and generates a preference profile for the user. The input data is the "preference questionnaire response data" and the output data is the "preference profile." The server passes the data to the AI ​​analysis engine and waits for the results.

[0430] Step 3:

[0431] The server uses AI to generate a coffee recipe based on the generated preference profile. The generated recipe includes the appropriate roasting time, extraction temperature, type of beans used, etc. The input data is the "preference profile" and the output data is the "coffee recipe." The server inputs the profile data into the AI ​​to obtain the optimal coffee recipe.

[0432] Step 4:

[0433] After the recipe is generated, the server sends the recipe to the coffee preparation appliance. The coffee preparation appliance prepares coffee according to the received recipe. The input data is the "coffee recipe" and the output data is the "prepared coffee." The server sends the recipe data, and the coffee preparation appliance operates according to the instructions.

[0434] Step 5:

[0435] After tasting the prepared coffee, the user inputs feedback through the terminal. The terminal sends this feedback data to the server. The server stores the received feedback data in a database. The input data is "feedback" and the output data is "stored feedback data." The terminal displays a user interface and receives and sends user input.

[0436] Step 6:

[0437] The emotion engine acquires emotion data from the user's facial expressions and voice and sends it to the server in real time. The server analyzes this emotion data, combines it with preference data, and reflects it in the next recipe generation. The input data is "facial expression data" and "voice data," and the output data is "analyzed emotion data." The emotion engine uses a facial recognition camera and a voice recognition microphone to collect data in real time.

[0438] Step 7:

[0439] The server inputs the collected feedback data and emotional data into the AI, which analyzes this data and reflects it in the next recipe generation. This allows the system to provide coffee that is even more suited to the user's preferences. The input data are "feedback data" and "emotional data," and the output data is an "updated preference profile" and a "new coffee recipe." The server passes the data to the AI, which then optimizes the recipe again.

[0440] These steps allow the optimal coffee blend to be provided to each individual user in real time.

[0441] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0442] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0452] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0453] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0454] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0456] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0457] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0458] Server Operation

[0459] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0460] 1. User Registration / Login:

[0461] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0462] 2. Preference Data Collection:

[0463] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0464] 3. Recipe generation:

[0465] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0466] 4. Collecting User Feedback:

[0467] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0468] 5. Feedback analysis:

[0469] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0470] Device behavior

[0471] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0472] 1. Interface display:

[0473] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0474] 2. Preference Survey Answers:

[0475] When the user answers the preference questionnaire, the terminal sends it to the server.

[0476] 3. Feedback input:

[0477] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0478] User Actions

[0479] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0480] 1. Registration and login operations:

[0481] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0482] 2. Preference input:

[0483] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0484] 3. Coffee tasting and feedback:

[0485] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0486] Specific examples

[0487] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0488] If User A, after tasting the coffee, inputs feedback such as "It's still too sour," the server receives this feedback and reflects it in the next recipe generation. This allows the server to continually provide the optimal coffee tailored to User A's preferences.

[0489] This system allows users to enjoy coffee that is optimized to their individual tastes. Furthermore, by utilizing feedback, the quality of the coffee provided will be continuously improved. The specific system configuration and operation for realizing this mechanism are the characteristics of this invention.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] User Registration

[0493] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[0494] Step 2:

[0495] Log in

[0496] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[0497] Step 3:

[0498] Collection of preference data

[0499] After logging in, the user answers a preference questionnaire displayed on the terminal. The user inputs roast level, acidity, bitterness, and aroma intensity, and then presses the send button. The terminal then sends the input preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the artificial intelligence means, which starts generating a preference profile.

[0500] Step 4:

[0501] Generating a preference profile

[0502] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[0503] Step 5:

[0504] Recipe Generation

[0505] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[0506] Step 6:

[0507] Coffee tasting and feedback

[0508] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[0509] Step 7:

[0510] Analyzing and incorporating feedback

[0511] The server inputs the received feedback data into the artificial intelligence means and performs analysis. The analysis results are set to be reflected in the next recipe generation. This allows the server to provide an original coffee blend that is closer to the user's preferences. The server saves the analysis results in a database and uses them for the next preference profile generation and recipe generation.

[0512] The above processing steps enable the system to continuously provide coffee optimized to the individual preferences of the user.

[0513] Example 1

[0514] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0515] Conventional coffee blend providing systems have been unable to adequately respond to individual user preferences, resulting in inconsistent coffee quality each time. Furthermore, user feedback is not effectively reflected in the next recipe generation, making it difficult to provide the optimal coffee blend for the user. This has made improving user satisfaction a challenge.

[0516] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0517] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's device, means for the user to log in to the system by entering an ID and password, means for receiving responses to a user preference questionnaire, means for saving the user's feedback data in a database, and means for generating a preference profile using an artificial intelligence model. This makes it possible to provide original coffee blends suited to the user's preferences and continuously improve the quality.

[0518] "User preference data" is information about the characteristics and flavors of coffee that a user prefers.

[0519] "Artificial intelligence means" refers to technology that analyzes and predicts based on user preference data and executes algorithms to generate optimal recipes.

[0520] A "drink preparation device" is a device that automatically prepares a drink such as coffee according to a generated recipe.

[0521] The "means for collecting feedback" is a method for users to input their evaluations and impressions into the system after tasting a beverage such as coffee.

[0522] The "means for analyzing collected feedback and reflecting it in the next recipe generation" is a method for improving the next recipe based on the feedback collected from the user to further suit the user's preferences.

[0523] "Means for linking with a user's device" refers to technology for exchanging data between the server and the terminal used by the user.

[0524] "Method of logging into a system by entering an ID and password" refers to the authentication method used by a user to access a system.

[0525] The "means for receiving responses to the user's preference questionnaire" refers to a method for transmitting data to the server when a user answers the preference questionnaire.

[0526] The "means for saving feedback data in a database" is a method for temporarily holding feedback collected from users and storing it in a database for later analysis.

[0527] "Means for generating preference profiles using artificial intelligence models" refers to a technology that utilizes AI models based on user preference data to classify user preferences in detail and create individual profiles.

[0528] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0529] The server is responsible for managing the entire system and processing data. Specifically, it is equipped with a means for acquiring user preference data. When a user answers a preference questionnaire from their device, the server receives this data and automatically stores it in a database. Furthermore, a preference profile is generated based on this preference data using a generative AI model.

[0530] Based on the generated preference profile, the server uses an AI algorithm to generate coffee recipes. This AI model analyzes the user's preference data and collected feedback and reflects it in the next recipe generation.

[0531] The generated recipe is sent to the terminal and displayed to the user. The user tastes coffee made based on this recipe. After tasting, the user inputs feedback through the terminal. This feedback data is also sent to the server and stored in the database. The server then analyzes the feedback data and reflects it in the next coffee recipe generation.

[0532] The user's terminal is a device that exchanges data with the server. When a user accesses the system for the first time, they log in by entering their ID and password. Once logged in to the system, the user can answer a preference questionnaire. The terminal sends the data the user has answered to the server. It also provides an interface for the user to enter feedback, and sends that data to the server.

[0533] As a concrete example, consider the case where User A is starting to use the system for the first time. First, he or she registers an ID and password via a terminal. Then, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like coffee with a low acidity." This data is sent to the server, where it is analyzed by the generative AI model, which then generates a coffee recipe specifically for User A.

[0534] The generated recipe includes detailed parameters such as the type of coffee bean, roasting time, and extraction temperature. This recipe information is displayed on the terminal, and User A tastes the coffee based on it. After tasting, if User A inputs feedback such as "It's still too acidic," the server receives this and reflects it in the next recipe generation.

[0535] An example prompt might be, "I like dark roasted coffee with a low acidity. Please create my own coffee blend based on this."

[0536] This system allows users to enjoy coffee that is optimized to their preferences. Furthermore, by utilizing user feedback, the quality of the coffee provided will be continuously improved, which is expected to increase user satisfaction.

[0537] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0538] Step 1:

[0539] The server is responsible for user registration and login. When a user enters their ID and password from their device, the device sends this information to the server. The server receives the sent data and authenticates the user by comparing it with the database. If authentication is successful, it retrieves the user's profile data from the database and sends it back to the device. The user confirms this and login is complete.

[0540] Input: User ID and password

[0541] Output: User profile data and authentication results

[0542] Specific operation: The user enters their ID and password on their smartphone and presses the login button.

[0543] Step 2:

[0544] The server collects user preference data. When the user answers a preference questionnaire in the system, the device sends the response data to the server. The server receives the data and stores it in a database. Finally, this preference data is input into an AI model, which begins analysis and generates a preference profile.

[0545] Input: User preference survey response data

[0546] Output: User preference profile

[0547] Specific operation: The user answers the questionnaire items with answers such as "I prefer dark roast" and "I prefer a mild acidity," and the data is sent to the server.

[0548] Step 3:

[0549] The server uses AI to generate a customized coffee recipe based on the generated preference profile, including roasting time, brewing temperature, type of beans used, etc. The generated recipe is sent to the device and displayed to the user.

[0550] Input: Preference Profile

[0551] Output: Coffee recipe

[0552] How it works: The server's AI model analyzes the user's preference data and generates a recipe for a "dark roast blend with reduced acidity." The details are then sent to the device.

[0553] Step 4:

[0554] The user tastes coffee prepared based on the recipe received through the terminal. After tasting, the user inputs feedback through the terminal. The terminal sends this feedback data to the server, which stores the received data in a database.

[0555] Input: User feedback data

[0556] Output: Saved feedback data

[0557] Specific operation: The user enters feedback such as "It's still too sour" or "The bitterness is just right," and this is sent to the server.

[0558] Step 5:

[0559] The server analyzes the received feedback data. This feedback data is then input back into the AI ​​model to be reflected in the next coffee recipe generation. This adjusts the next coffee recipe to more closely match the user's preferences. The new recipe is then notified to the user again.

[0560] Input: Saved feedback data

[0561] Output: Improved coffee recipe

[0562] Specific operation: Based on the previous feedback, the AI ​​creates a new recipe called "Dark roast blend with even less acidity" and notifies the user's device.

[0563] In this way, the system continues to provide coffee blends optimized to the user's preferences.

[0564] (Application example 1)

[0565] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] Conventional coffee blend provision systems have difficulty generating recipes that perfectly match the user's preferences, making it difficult to provide individually optimized coffee. Furthermore, there was no system that efficiently collected user feedback and reflected it in the next recipe generation. Furthermore, in a brick-and-mortar store environment, there was a lack of a means to quickly provide custom recipes tailored to the user's preferences.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0568] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, and means for generating recipes individually optimized for the user in conjunction with the user preference data and transmitting the recipes to devices in the brick-and-mortar store, thereby enabling individually optimized coffee blends to be provided quickly and efficiently in a brick-and-mortar environment.

[0569] - "Means for acquiring user preference data" refers to devices or software for collecting information about a user's preferred coffee.

[0570] "Artificial intelligence means" refers to machine learning algorithms and systems that generate and optimize coffee recipes based on user preference data.

[0571] "Coffee preparation equipment" means a barista machine or other preparation device for preparing coffee according to a generated recipe.

[0572] "Means for collecting feedback" refers to devices or software that allow users to obtain their impressions and ratings of the coffee they sample.

[0573] "Means for reflecting feedback in next recipe generation" refers to a system for analyzing collected feedback and adjusting the next coffee recipe to better suit the user's preferences.

[0574] "Means for linking with the user's mobile terminal" refers to the interface that allows mobile devices such as smartphones and tablets to communicate with the system and exchange data.

[0575] "Means for transmitting to devices in a physical store" refers to a communication system for transmitting the generated coffee recipe to a barista machine or cooking device installed in a physical store.

[0576] "Means for storing in a database" refers to a data storage system for saving information such as user preference data and feedback, and reusing it as needed.

[0577] The present invention is a system that provides original coffee blends tailored to the individual tastes of users. This system is built around three main components: a server, a terminal, and a user. The details and processing of each component are explained below.

[0578] Server Operation

[0579] The server is responsible for managing the entire system and processing data. It performs the following processes:

[0580] 1. User Registration / Login

[0581] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0582] 2. Collecting preference data

[0583] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into an artificial intelligence system to analyze it and generate a preference profile of the user.

[0584] 3. Recipe Generation

[0585] The server uses artificial intelligence to generate a coffee recipe based on the customer's preference profile, including the appropriate roasting time, brewing temperature, and type of beans to be used, and then transmits the recipe to a device in the physical store.

[0586] 4. Collecting User Feedback

[0587] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0588] 5. Feedback Analysis

[0589] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, thereby providing coffee that is more suited to the user's preferences.

[0590] Device behavior

[0591] The terminal is a device that exchanges data between the user and the server. It performs the following processes:

[0592] 1. Interface display

[0593] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0594] 2. Answer the preference survey

[0595] When the user answers the preference questionnaire, the terminal sends it to the server.

[0596] 3. Feedback input

[0597] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0598] User Actions

[0599] Users provide their coffee preference data and feedback through this system. The following process is performed.

[0600] 1. Registration and login operations

[0601] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0602] 2. Input your preferences

[0603] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0604] 3. Coffee tasting and feedback

[0605] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0606] Application Examples

[0607] As a concrete example, a scenario will be shown in which a new user A starts using the system.

[0608] 1. User A first registers in the system as "user_a" and logs in.

[0609] 2. User A answers a preference questionnaire on a smartphone app, providing information such as "I prefer medium roast," "low acidity," and "strong aroma."

[0610] 3. The server analyzes this information and generates an optimal coffee recipe, which is then sent to the barista machine in the store to prepare the coffee.

[0611] 4. User A tastes the coffee and provides feedback that it is still too acidic.

[0612] 5. The server analyzes the feedback and reflects it in the next recipe generation.

[0613] This allows User A to continuously enjoy coffee optimized to his or her preferences. The hardware used includes a smartphone and a barista machine, and the software used is Python, Flask / Django (server side), PostgreSQL (database), and an AI model (machine learning library, e.g., TensorFlow or PyTorch).

[0614] Example prompt for a generative AI model:

[0615] User Preferences: { "Roast": "Medium Roast", "Acidity": "Low", "Aroma": "Strong"}

[0616] Feedback: { "sour": "still high"}

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1: User registration and login

[0619] The server requests registration by the user entering their ID and password using their smartphone. The entered data is the user's account information. The server receives this data and stores it in a database. Once registration is complete, it sends a success message to the user.

[0620] When the user enters their ID and password again to request login, the server checks the database and performs authentication. If authentication is successful, the server retrieves the user profile and returns it to the terminal along with a session token. This completes the login process.

[0621] Step 2: Obtaining preference surveys

[0622] The terminal displays a preference questionnaire screen to the user, and the user inputs preference data such as "I prefer medium roast," "low acidity," and "strong aroma."

[0623] The device sends the input preference data to a server, which receives it, stores it in a database, and inputs it into an artificial intelligence model.

[0624] Step 3: Generate the recipe

[0625] The server analyzes the stored preference data using an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate a coffee recipe optimized for the user's preferences. This process includes data preprocessing, feature extraction, and the application of machine learning algorithms.

[0626] The generated recipe includes detailed parameters such as roasting time, brewing temperature, type of beans used, etc. The server stores this recipe in a database and sends it to the device in the physical store.

[0627] Step 4: Preparing and tasting the coffee

[0628] The barista machine in the physical store prepares coffee based on the recipe sent from the server, and the coffee is then served to the user exactly as the recipe was created.

[0629] The user samples this coffee and then inputs their impressions and evaluations into the terminal.

[0630] Step 5: Gather feedback

[0631] The device sends the feedback data entered by the user to the server, including specific impressions such as "It still tastes too sour."

[0632] The server stores this feedback in a database, which is used for the next recipe generation.

[0633] Step 6: Analyze the feedback and incorporate it into your next recipe

[0634] The server then inputs the collected feedback data back into the AI ​​model. This data analysis allows the feedback information to be reflected in the next recipe generation. Specifically, the parameters of the next recipe are adjusted to better suit the user's preferences.

[0635] The server saves the new, adjusted recipe in its database, ready to use the next time coffee is made.

[0636] This will realize a system that allows users to continuously enjoy coffee that is optimized to their preferences.

[0637] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0638] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[0639] Server Operation

[0640] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0641] 1. User Registration / Login:

[0642] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0643] 2. Preference Data Collection:

[0644] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0645] 3. Recipe generation:

[0646] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0647] 4. Collecting User Feedback:

[0648] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0649] 5. Feedback analysis:

[0650] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0651] Emotion Engine Operation

[0652] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0653] 1. Acquiring emotion data:

[0654] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[0655] 2. Data transmission:

[0656] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[0657] Device behavior

[0658] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0659] 1. Interface display:

[0660] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0661] 2. Preference Survey Answers:

[0662] When the user answers the preference questionnaire, the terminal sends it to the server.

[0663] 3. Feedback input:

[0664] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0665] User Actions

[0666] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0667] 1. Registration and login operations:

[0668] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0669] 2. Preference input:

[0670] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0671] 3. Providing Emotion Data:

[0672] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0673] 4. Feedback Input:

[0674] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0675] Specific examples

[0676] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0677] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[0678] This system allows users to enjoy coffee that is optimized for their individual tastes and emotions. Furthermore, by combining it with an emotion engine, the quality of the coffee provided can be further improved, increasing user satisfaction. The specific system configuration and operation required to realize this mechanism are the characteristics of this invention.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] User Registration

[0682] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[0683] Step 2:

[0684] Log in

[0685] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[0686] Step 3:

[0687] Collection of preference data

[0688] After logging in, the user answers a preference questionnaire displayed on the device. The user enters roast level, acidity, bitterness, aroma intensity, etc., and presses the send button. The device then sends the entered preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the AI, which begins analysis and generates the user's preference profile.

[0689] Step 4:

[0690] Generating a preference profile

[0691] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[0692] Step 5:

[0693] Recipe Generation

[0694] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[0695] Step 6:

[0696] Coffee tasting and feedback

[0697] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[0698] Step 7:

[0699] Acquiring emotion data

[0700] When tasting coffee, the emotion engine uses a facial recognition camera and a voice recognition microphone to acquire the user's emotional data. For example, it can recognize emotions such as "happiness" and "surprise" from facial expressions, and "satisfaction" and "dissatisfaction" from voice.

[0701] Step 8:

[0702] Sending emotional data

[0703] The acquired emotion data is sent in real time from the device to the server, which receives it and stores it in a database. At the same time, the server prepares to reflect this data, along with feedback data, in the next recipe generation.

[0704] Step 9:

[0705] Analysis and reflection of feedback and emotional data

[0706] The server inputs the received feedback data and emotion data into the artificial intelligence means for analysis. The analysis results are set to be reflected in the next recipe creation. This allows the server to provide coffee that is optimized based on the user's preferences and emotions.

[0707] Through the above processing steps, the system realizes the continuous provision of coffee optimized to the individual tastes and emotions of the user.

[0708] Example 2

[0709] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0710] Conventional recipe generation systems based on preference data were unable to take into account the user's emotional state, making it difficult to provide the perfect drink for each individual user's current emotions. Furthermore, because they did not use emotional data, the quality of feedback was limited, resulting in a problem of reduced accuracy in the next recipe generation.

[0711] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's terminal, means for acquiring user emotion data, and means for customizing the recipe based on the acquired emotion data. This makes it possible to generate more accurate customized drink recipes by comprehensively considering the user's preference data and emotion data, thereby increasing the satisfaction of each individual user.

[0712] "User preference data" is data that indicates the user's preferences regarding the type, flavor, temperature, etc. of beverages.

[0713] "Artificial intelligence means" refers to algorithms and programs that analyze user preference data and feedback and generate optimal recipes.

[0714] A "drink preparation device" is a device that automatically prepares coffee or other drinks based on a generated recipe.

[0715] "Means for collecting feedback" refers to a method or device for collecting opinions and impressions provided by users after tasting.

[0716] "Means for analyzing feedback" refers to devices or programs that process collected feedback data using artificial intelligence or other analytical methods and reflect the results in the next recipe generation.

[0717] "User terminal" means an electronic device through which a user accesses the system and inputs preference data and feedback.

[0718] "Means for acquiring emotional data" refers to sensors and analysis devices for detecting the user's psychological state from facial expressions, vocal tone, etc.

[0719] "Means for customizing recipes based on emotional data" refers to algorithms or programs that analyze the acquired emotional data and reflect the results in the recipe.

[0720] The present invention is a system that generates and provides personalized drink recipes based on user preference data and emotion data. This system is built around the main components of a server, a terminal, a user, and an emotion engine.

[0721] Overall system configuration

[0722] Server Operation

[0723] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0724] 1. User Registration / Login:

[0725] When a user enters their ID and password from the terminal, the server receives it and authenticates them by checking it against a database (e.g., MySQL). If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0726] 2. Preference Data Collection:

[0727] When a user answers a preference questionnaire, the server receives it and stores it in a database. This data is then input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates a preference profile for the user based on the results.

[0728] 3. Recipe generation:

[0729] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica).

[0730] 4. Collecting User Feedback:

[0731] After tasting, the user inputs feedback through the terminal (e.g., "It's very sour" or "It has a weak aroma"). The server receives this feedback and stores it in a database.

[0732] 5. Feedback analysis:

[0733] The server inputs the collected feedback data into the AI ​​and reflects it the next time a recipe is generated, resulting in a more accurate customized drink.

[0734] Emotion Engine Operation

[0735] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0736] 1. Acquiring emotion data:

[0737] The emotion engine uses a face recognition camera and a voice recognition microphone to capture emotion data from the user's facial expressions and voice.

[0738] 2. Data transmission:

[0739] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with the preference data, and reflects it in the next recipe generation.

[0740] Device behavior

[0741] A terminal is a device that exchanges data between a user and a server.

[0742] 1. Interface display:

[0743] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0744] 2. Preference Survey Answers:

[0745] When the user answers the preference questionnaire, the terminal sends it to the server.

[0746] 3. Feedback input:

[0747] After tasting the coffee, the user inputs feedback through the terminal and transmits it to the server.

[0748] User Actions

[0749] Users provide preference data and feedback through this system.

[0750] 1. Registration and login operations:

[0751] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0752] 2. Preference input:

[0753] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0754] 3. Providing Emotion Data:

[0755] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0756] 4. Feedback Input:

[0757] Customers are asked to taste the coffee provided and enter their feedback on the taste through the terminal.

[0758] Specific examples

[0759] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0760] When User A tastes the coffee, the emotion engine recognizes the emotion of "happiness" from his / her facial expression and sends it to the server. The server combines this emotion data with specific feedback data ("slightly acidic" and "good aroma") and reflects it in the next recipe generation. This process ensures that the next coffee provided is more optimally tailored to User A's preferences.

[0761] Example prompts to input to the generative AI model

[0762] "User A's preference data is 'prefers dark roast' and 'mild acidity', and their latest feedback is 'slightly strong acidity' and 'good aroma'. 'Delight' has been recognized as the emotional data. Based on this data, please generate the next coffee recipe that is best suited to User A."

[0763] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0764] Step 1: User registration and login

[0765] Input: The user enters the ID and password from the terminal.

[0766] Description: The terminal sends the ID and password entered by the user to the server. The server receives this and authenticates it by checking it against a database (e.g. MySQL). If authentication is successful, it retrieves the user profile from the database and sends it back to the terminal.

[0767] Output: User authentication result (success or failure) and user profile.

[0768] Step 2: Collect preference data

[0769] Input: The user answers the preference survey.

[0770] Description: The device sends the user's preference questionnaire response data to the server. The server receives it and stores it in a database. At the same time, this data is input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates the user's preference profile based on the results.

[0771] Output: User preference profile.

[0772] Step 3: Recipe generation

[0773] Input: The generated preference profile.

[0774] Description: The server uses AI to generate a coffee recipe based on the preference profile. This recipe includes details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica). The generated recipe is sent to the beverage preparation device.

[0775] Output: A customized coffee recipe for the user.

[0776] Step 4: Obtaining emotion data

[0777] Input: Facial expressions and voice as the user samples the coffee.

[0778] Description: The emotion engine uses a face recognition camera and a voice recognition microphone to acquire user emotion data, which is then sent to the server in real time.

[0779] Output: User emotion data (e.g., emotion "joy").

[0780] Step 5: Gather feedback

[0781] Input: Feedback provided by the user after tasting (e.g., "strong acidity" or "weak aroma").

[0782] Description: The terminal displays the user's feedback in the input interface. When the user enters the feedback, it sends it to the server. The server stores the feedback data in a database.

[0783] Output: User feedback data.

[0784] Step 6: Feedback analysis

[0785] Input: Collected feedback data.

[0786] Description: The server inputs the feedback data into the generative AI model and analyzes it. The results of this analysis are reflected in the next recipe generation. The server combines the collected emotional data with the preference profile to prepare the optimal data for the next recipe generation.

[0787] Output: Parsed feedback data to be used in the next recipe generation.

[0788] Step 7: Generate the next recipe

[0789] Input: Parsed feedback data, user preference profile, emotional data.

[0790] Description: The server generates the next recipe based on the analyzed feedback data, the user's preference profile, and emotional data. In this process, the generative AI model takes into account various user data to customize the new recipe.

[0791] Output: A customized coffee recipe for the next user.

[0792] (Application example 2)

[0793] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0794] Currently, many coffee lovers face the challenge of finding a coffee that matches their tastes and emotions. In particular, there is no system that can optimize the next coffee blend based on the user's emotional state and feedback. This makes it difficult to provide personalized service that allows users to have the best coffee experience.

[0795] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data and emotion data, a device for preparing coffee based on the generated recipe, means for collecting feedback from the user after preparing the coffee, means for analyzing the collected feedback and reflecting it in generating the next recipe, and means for acquiring emotion data by using facial expression recognition and voice recognition functions in cooperation with the user's terminal. This makes it possible to provide an optimal coffee blend based on the user's preferences and emotions.

[0796] "User preference data" is information relating to the user's personal preferences, such as the taste, roast level, acidity, and aroma of coffee.

[0797] "Emotion data" is information about the emotional state obtained from the user's facial expressions and voice.

[0798] "Artificial intelligence means for generating recipes" refers to a program or system that runs an algorithm based on acquired data to generate the optimal coffee recipe for the user.

[0799] An "apparatus for preparing coffee based on a generated recipe" is a device for specifically preparing coffee according to a coffee recipe generated by artificial intelligence.

[0800] "Feedback" refers to information such as the user's impressions and evaluation of the coffee they have actually tasted, as well as areas for improvement.

[0801] The "facial expression recognition function" is a technology that uses a camera to analyze a user's facial expressions and detect their emotional state.

[0802] The "voice recognition function" is a technology that uses a microphone to analyze the user's voice and detect their emotional state.

[0803] A "user terminal" is a device through which a user inputs data or interacts with the system, including a smartphone or tablet.

[0804] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[0805] Server Operation

[0806] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0807] 1. User Registration / Login

[0808] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0809] 2. Collecting preference data

[0810] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0811] 3. Recipe Generation

[0812] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0813] 4. Collecting User Feedback

[0814] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0815] 5. Feedback Analysis

[0816] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0817] Emotion Engine Operation

[0818] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[0819] 1. Acquiring Emotion Data

[0820] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[0821] 2. Data transmission

[0822] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[0823] Device behavior

[0824] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0825] 1. Interface display

[0826] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0827] 2. Answer the preference survey

[0828] When the user answers the preference questionnaire, the terminal sends it to the server.

[0829] 3. Feedback input

[0830] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0831] User Actions

[0832] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0833] 1. Registration and login operations

[0834] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0835] 2. Input your preferences

[0836] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0837] 3. Providing Emotion Data

[0838] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[0839] 4. Feedback Input

[0840] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0841] Specific examples

[0842] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0843] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[0844] Prompt Sentence Examples

[0845] Design an application that allows users to select their favorite coffee. It should have a function that optimizes the next coffee recipe based on the user's preference and emotional data. Preference data includes roast level, acidity, and aroma. The smartphone used will have a facial expression recognition camera and a voice recognition microphone to collect emotional data. The collected data will be sent to a server, where it will be analyzed by AI and reflected in the next recipe. Please provide specific program code examples.

[0846] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0847] Step 1:

[0848] The user enters their ID and password using a terminal and sends them to the server. The server receives this information and authenticates them by checking it against a database. If authentication is successful, the server retrieves the user profile from the database and sends it back to the terminal. The input data is "ID" and "password," and the output data is "user profile." The server executes a database query for the authentication process.

[0849] Step 2:

[0850] The user answers a preference questionnaire from their device and sends the response data to the server. The server stores the received preference data in a database and provides it as input to the AI ​​analysis engine. The AI ​​analysis engine analyzes this data and generates a preference profile for the user. The input data is the "preference questionnaire response data" and the output data is the "preference profile." The server passes the data to the AI ​​analysis engine and waits for the results.

[0851] Step 3:

[0852] The server uses AI to generate a coffee recipe based on the generated preference profile. The generated recipe includes the appropriate roasting time, extraction temperature, type of beans used, etc. The input data is the "preference profile" and the output data is the "coffee recipe." The server inputs the profile data into the AI ​​to obtain the optimal coffee recipe.

[0853] Step 4:

[0854] After the recipe is generated, the server sends the recipe to the coffee preparation appliance. The coffee preparation appliance prepares coffee according to the received recipe. The input data is the "coffee recipe" and the output data is the "prepared coffee." The server sends the recipe data, and the coffee preparation appliance operates according to the instructions.

[0855] Step 5:

[0856] After tasting the prepared coffee, the user inputs feedback through the terminal. The terminal sends this feedback data to the server. The server stores the received feedback data in a database. The input data is "feedback" and the output data is "stored feedback data." The terminal displays a user interface and receives and sends user input.

[0857] Step 6:

[0858] The emotion engine acquires emotion data from the user's facial expressions and voice and sends it to the server in real time. The server analyzes this emotion data, combines it with preference data, and reflects it in the next recipe generation. The input data is "facial expression data" and "voice data," and the output data is "analyzed emotion data." The emotion engine uses a facial recognition camera and a voice recognition microphone to collect data in real time.

[0859] Step 7:

[0860] The server inputs the collected feedback data and emotional data into the AI, which analyzes this data and reflects it in the next recipe generation. This allows the system to provide coffee that is even more suited to the user's preferences. The input data are "feedback data" and "emotional data," and the output data is an "updated preference profile" and a "new coffee recipe." The server passes the data to the AI, which then optimizes the recipe again.

[0861] These steps allow the optimal coffee blend to be provided to each individual user in real time.

[0862] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0863] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0864] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0865] [Third embodiment]

[0866] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0867] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0868] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0869] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0870] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0871] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0872] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0873] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0874] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0875] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0876] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0877] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0878] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0879] Server Operation

[0880] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[0881] 1. User Registration / Login:

[0882] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[0883] 2. Preference Data Collection:

[0884] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[0885] 3. Recipe generation:

[0886] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[0887] 4. Collecting User Feedback:

[0888] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[0889] 5. Feedback analysis:

[0890] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[0891] Device behavior

[0892] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[0893] 1. Interface display:

[0894] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[0895] 2. Preference Survey Answers:

[0896] When the user answers the preference questionnaire, the terminal sends it to the server.

[0897] 3. Feedback input:

[0898] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[0899] User Actions

[0900] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[0901] 1. Registration and login operations:

[0902] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[0903] 2. Preference input:

[0904] You answer the system's preference questionnaire and provide information about your favorite coffee.

[0905] 3. Coffee tasting and feedback:

[0906] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[0907] Specific examples

[0908] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[0909] If User A, after tasting the coffee, inputs feedback such as "It's still too sour," the server receives this feedback and reflects it in the next recipe generation. This allows the server to continually provide the optimal coffee tailored to User A's preferences.

[0910] This system allows users to enjoy coffee that is optimized to their individual tastes. Furthermore, by utilizing feedback, the quality of the coffee provided will be continuously improved. The specific system configuration and operation for realizing this mechanism are the characteristics of this invention.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] User Registration

[0914] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[0915] Step 2:

[0916] Log in

[0917] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[0918] Step 3:

[0919] Collection of preference data

[0920] After logging in, the user answers a preference questionnaire displayed on the terminal. The user inputs roast level, acidity, bitterness, and aroma intensity, and then presses the send button. The terminal then sends the input preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the artificial intelligence means, which starts generating a preference profile.

[0921] Step 4:

[0922] Generating a preference profile

[0923] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[0924] Step 5:

[0925] Recipe Generation

[0926] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[0927] Step 6:

[0928] Coffee tasting and feedback

[0929] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[0930] Step 7:

[0931] Analyzing and incorporating feedback

[0932] The server inputs the received feedback data into the artificial intelligence means and performs analysis. The analysis results are set to be reflected in the next recipe generation. This allows the server to provide an original coffee blend that is closer to the user's preferences. The server saves the analysis results in a database and uses them for the next preference profile generation and recipe generation.

[0933] The above processing steps enable the system to continuously provide coffee optimized to the individual preferences of the user.

[0934] Example 1

[0935] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0936] Conventional coffee blend providing systems have been unable to adequately respond to individual user preferences, resulting in inconsistent coffee quality each time. Furthermore, user feedback is not effectively reflected in the next recipe generation, making it difficult to provide the optimal coffee blend for the user. This has made improving user satisfaction a challenge.

[0937] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0938] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's device, means for the user to log in to the system by entering an ID and password, means for receiving responses to a user preference questionnaire, means for saving the user's feedback data in a database, and means for generating a preference profile using an artificial intelligence model. This makes it possible to provide original coffee blends suited to the user's preferences and continuously improve the quality.

[0939] "User preference data" is information about the characteristics and flavors of coffee that a user prefers.

[0940] "Artificial intelligence means" refers to technology that analyzes and predicts based on user preference data and executes algorithms to generate optimal recipes.

[0941] A "drink preparation device" is a device that automatically prepares a drink such as coffee according to a generated recipe.

[0942] The "means for collecting feedback" is a method for users to input their evaluations and impressions into the system after tasting a beverage such as coffee.

[0943] The "means for analyzing collected feedback and reflecting it in the next recipe generation" is a method for improving the next recipe based on the feedback collected from the user to further suit the user's preferences.

[0944] "Means for linking with a user's device" refers to technology for exchanging data between the server and the terminal used by the user.

[0945] "Method of logging into a system by entering an ID and password" refers to the authentication method used by a user to access a system.

[0946] The "means for receiving responses to the user's preference questionnaire" refers to a method for transmitting data to the server when a user answers the preference questionnaire.

[0947] The "means for saving feedback data in a database" is a method for temporarily holding feedback collected from users and storing it in a database for later analysis.

[0948] "Means for generating preference profiles using artificial intelligence models" refers to a technology that utilizes AI models based on user preference data to classify user preferences in detail and create individual profiles.

[0949] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[0950] The server is responsible for managing the entire system and processing data. Specifically, it is equipped with a means for acquiring user preference data. When a user answers a preference questionnaire from their device, the server receives this data and automatically stores it in a database. Furthermore, a preference profile is generated based on this preference data using a generative AI model.

[0951] Based on the generated preference profile, the server uses an AI algorithm to generate coffee recipes. This AI model analyzes the user's preference data and collected feedback and reflects it in the next recipe generation.

[0952] The generated recipe is sent to the terminal and displayed to the user. The user tastes coffee made based on this recipe. After tasting, the user inputs feedback through the terminal. This feedback data is also sent to the server and stored in the database. The server then analyzes the feedback data and reflects it in the next coffee recipe generation.

[0953] The user's terminal is a device that exchanges data with the server. When a user accesses the system for the first time, they log in by entering their ID and password. Once logged in to the system, the user can answer a preference questionnaire. The terminal sends the data the user has answered to the server. It also provides an interface for the user to enter feedback, and sends that data to the server.

[0954] As a concrete example, consider the case where User A is starting to use the system for the first time. First, he or she registers an ID and password via a terminal. Then, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like coffee with a low acidity." This data is sent to the server, where it is analyzed by the generative AI model, which then generates a coffee recipe specifically for User A.

[0955] The generated recipe includes detailed parameters such as the type of coffee bean, roasting time, and extraction temperature. This recipe information is displayed on the terminal, and User A tastes the coffee based on it. After tasting, if User A inputs feedback such as "It's still too acidic," the server receives this and reflects it in the next recipe generation.

[0956] An example prompt might be, "I like dark roasted coffee with a low acidity. Please create my own coffee blend based on this."

[0957] This system allows users to enjoy coffee that is optimized to their preferences. Furthermore, by utilizing user feedback, the quality of the coffee provided will be continuously improved, which is expected to increase user satisfaction.

[0958] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0959] Step 1:

[0960] The server is responsible for user registration and login. When a user enters their ID and password from their device, the device sends this information to the server. The server receives the sent data and authenticates the user by comparing it with the database. If authentication is successful, it retrieves the user's profile data from the database and sends it back to the device. The user confirms this and login is complete.

[0961] Input: User ID and password

[0962] Output: User profile data and authentication results

[0963] Specific operation: The user enters their ID and password on their smartphone and presses the login button.

[0964] Step 2:

[0965] The server collects user preference data. When the user answers a preference questionnaire in the system, the device sends the response data to the server. The server receives the data and stores it in a database. Finally, this preference data is input into an AI model, which begins analysis and generates a preference profile.

[0966] Input: User preference survey response data

[0967] Output: User preference profile

[0968] Specific operation: The user answers the questionnaire items with answers such as "I prefer dark roast" and "I prefer a mild acidity," and the data is sent to the server.

[0969] Step 3:

[0970] The server uses AI to generate a customized coffee recipe based on the generated preference profile, including roasting time, brewing temperature, type of beans used, etc. The generated recipe is sent to the device and displayed to the user.

[0971] Input: Preference Profile

[0972] Output: Coffee recipe

[0973] How it works: The server's AI model analyzes the user's preference data and generates a recipe for a "dark roast blend with reduced acidity." The details are then sent to the device.

[0974] Step 4:

[0975] The user tastes coffee prepared based on the recipe received through the terminal. After tasting, the user inputs feedback through the terminal. The terminal sends this feedback data to the server, which stores the received data in a database.

[0976] Input: User feedback data

[0977] Output: Saved feedback data

[0978] Specific operation: The user enters feedback such as "It's still too sour" or "The bitterness is just right," and this is sent to the server.

[0979] Step 5:

[0980] The server analyzes the received feedback data. This feedback data is then input back into the AI ​​model to be reflected in the next coffee recipe generation. This adjusts the next coffee recipe to more closely match the user's preferences. The new recipe is then notified to the user again.

[0981] Input: Saved feedback data

[0982] Output: Improved coffee recipe

[0983] Specific operation: Based on the previous feedback, the AI ​​creates a new recipe called "Dark roast blend with even less acidity" and notifies the user's device.

[0984] In this way, the system continues to provide coffee blends optimized to the user's preferences.

[0985] (Application example 1)

[0986] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0987] Conventional coffee blend provision systems have difficulty generating recipes that perfectly match the user's preferences, making it difficult to provide individually optimized coffee. Furthermore, there was no system that efficiently collected user feedback and reflected it in the next recipe generation. Furthermore, in a brick-and-mortar store environment, there was a lack of a means to quickly provide custom recipes tailored to the user's preferences.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0989] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, and means for generating recipes individually optimized for the user in conjunction with the user preference data and transmitting the recipes to devices in the brick-and-mortar store, thereby enabling individually optimized coffee blends to be provided quickly and efficiently in a brick-and-mortar environment.

[0990] - "Means for acquiring user preference data" refers to devices or software for collecting information about a user's preferred coffee.

[0991] "Artificial intelligence means" refers to machine learning algorithms and systems that generate and optimize coffee recipes based on user preference data.

[0992] "Coffee preparation equipment" means a barista machine or other preparation device for preparing coffee according to a generated recipe.

[0993] "Means for collecting feedback" refers to devices or software that allow users to obtain their impressions and ratings of the coffee they sample.

[0994] "Means for reflecting feedback in next recipe generation" refers to a system for analyzing collected feedback and adjusting the next coffee recipe to better suit the user's preferences.

[0995] "Means for linking with the user's mobile terminal" refers to the interface that allows mobile devices such as smartphones and tablets to communicate with the system and exchange data.

[0996] "Means for transmitting to devices in a physical store" refers to a communication system for transmitting the generated coffee recipe to a barista machine or cooking device installed in a physical store.

[0997] "Means for storing in a database" refers to a data storage system for saving information such as user preference data and feedback, and reusing it as needed.

[0998] The present invention is a system that provides original coffee blends tailored to the individual tastes of users. This system is built around three main components: a server, a terminal, and a user. The details and processing of each component are explained below.

[0999] Server Operation

[1000] The server is responsible for managing the entire system and processing data. It performs the following processes:

[1001] 1. User Registration / Login

[1002] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1003] 2. Collecting preference data

[1004] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into an artificial intelligence system to analyze it and generate a preference profile of the user.

[1005] 3. Recipe Generation

[1006] The server uses artificial intelligence to generate a coffee recipe based on the customer's preference profile, including the appropriate roasting time, brewing temperature, and type of beans to be used, and then transmits the recipe to a device in the physical store.

[1007] 4. Collecting User Feedback

[1008] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1009] 5. Feedback Analysis

[1010] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, thereby providing coffee that is more suited to the user's preferences.

[1011] Device behavior

[1012] The terminal is a device that exchanges data between the user and the server. It performs the following processes:

[1013] 1. Interface display

[1014] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1015] 2. Answer the preference survey

[1016] When the user answers the preference questionnaire, the terminal sends it to the server.

[1017] 3. Feedback input

[1018] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1019] User Actions

[1020] Users provide their coffee preference data and feedback through this system. The following process is performed.

[1021] 1. Registration and login operations

[1022] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1023] 2. Input your preferences

[1024] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1025] 3. Coffee tasting and feedback

[1026] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1027] Application Examples

[1028] As a concrete example, a scenario will be shown in which a new user A starts using the system.

[1029] 1. User A first registers in the system as "user_a" and logs in.

[1030] 2. User A answers a preference questionnaire on a smartphone app, providing information such as "I prefer medium roast," "low acidity," and "strong aroma."

[1031] 3. The server analyzes this information and generates an optimal coffee recipe, which is then sent to the barista machine in the store to prepare the coffee.

[1032] 4. User A tastes the coffee and provides feedback that it is still too acidic.

[1033] 5. The server analyzes the feedback and reflects it in the next recipe generation.

[1034] This allows User A to continuously enjoy coffee optimized to his or her preferences. The hardware used includes a smartphone and a barista machine, and the software used is Python, Flask / Django (server side), PostgreSQL (database), and an AI model (machine learning library, e.g., TensorFlow or PyTorch).

[1035] Example prompt for a generative AI model:

[1036] User Preferences: { "Roast": "Medium Roast", "Acidity": "Low", "Aroma": "Strong"}

[1037] Feedback: { "sour": "still high"}

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1: User registration and login

[1040] The server requests registration by the user entering their ID and password using their smartphone. The entered data is the user's account information. The server receives this data and stores it in a database. Once registration is complete, it sends a success message to the user.

[1041] When the user enters their ID and password again to request login, the server checks the database and performs authentication. If authentication is successful, the server retrieves the user profile and returns it to the terminal along with a session token. This completes the login process.

[1042] Step 2: Obtaining preference surveys

[1043] The terminal displays a preference questionnaire screen to the user, and the user inputs preference data such as "I prefer medium roast," "low acidity," and "strong aroma."

[1044] The device sends the input preference data to a server, which receives it, stores it in a database, and inputs it into an artificial intelligence model.

[1045] Step 3: Generate the recipe

[1046] The server analyzes the stored preference data using an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate a coffee recipe optimized for the user's preferences. This process includes data preprocessing, feature extraction, and the application of machine learning algorithms.

[1047] The generated recipe includes detailed parameters such as roasting time, brewing temperature, type of beans used, etc. The server stores this recipe in a database and sends it to the device in the physical store.

[1048] Step 4: Preparing and tasting the coffee

[1049] The barista machine in the physical store prepares coffee based on the recipe sent from the server, and the coffee is then served to the user exactly as the recipe was created.

[1050] The user samples this coffee and then inputs their impressions and evaluations into the terminal.

[1051] Step 5: Gather feedback

[1052] The device sends the feedback data entered by the user to the server, including specific impressions such as "It still tastes too sour."

[1053] The server stores this feedback in a database, which is used for the next recipe generation.

[1054] Step 6: Analyze the feedback and incorporate it into your next recipe

[1055] The server then inputs the collected feedback data back into the AI ​​model. This data analysis allows the feedback information to be reflected in the next recipe generation. Specifically, the parameters of the next recipe are adjusted to better suit the user's preferences.

[1056] The server saves the new, adjusted recipe in its database, ready to use the next time coffee is made.

[1057] This will realize a system that allows users to continuously enjoy coffee that is optimized to their preferences.

[1058] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1059] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[1060] Server Operation

[1061] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1062] 1. User Registration / Login:

[1063] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1064] 2. Preference Data Collection:

[1065] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[1066] 3. Recipe generation:

[1067] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[1068] 4. Collecting User Feedback:

[1069] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1070] 5. Feedback analysis:

[1071] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[1072] Emotion Engine Operation

[1073] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1074] 1. Acquiring emotion data:

[1075] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[1076] 2. Data transmission:

[1077] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[1078] Device behavior

[1079] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[1080] 1. Interface display:

[1081] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1082] 2. Preference Survey Answers:

[1083] When the user answers the preference questionnaire, the terminal sends it to the server.

[1084] 3. Feedback input:

[1085] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1086] User Actions

[1087] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[1088] 1. Registration and login operations:

[1089] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1090] 2. Preference input:

[1091] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1092] 3. Providing Emotion Data:

[1093] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1094] 4. Feedback Input:

[1095] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1096] Specific examples

[1097] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1098] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[1099] This system allows users to enjoy coffee that is optimized for their individual tastes and emotions. Furthermore, by combining it with an emotion engine, the quality of the coffee provided can be further improved, increasing user satisfaction. The specific system configuration and operation required to realize this mechanism are the characteristics of this invention.

[1100] The processing flow will be explained below.

[1101] Step 1:

[1102] User Registration

[1103] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[1104] Step 2:

[1105] Log in

[1106] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[1107] Step 3:

[1108] Collection of preference data

[1109] After logging in, the user answers a preference questionnaire displayed on the device. The user enters roast level, acidity, bitterness, aroma intensity, etc., and presses the send button. The device then sends the entered preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the AI, which begins analysis and generates the user's preference profile.

[1110] Step 4:

[1111] Generating a preference profile

[1112] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[1113] Step 5:

[1114] Recipe Generation

[1115] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[1116] Step 6:

[1117] Coffee tasting and feedback

[1118] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[1119] Step 7:

[1120] Acquiring emotion data

[1121] When tasting coffee, the emotion engine uses a facial recognition camera and a voice recognition microphone to acquire the user's emotional data. For example, it can recognize emotions such as "happiness" and "surprise" from facial expressions, and "satisfaction" and "dissatisfaction" from voice.

[1122] Step 8:

[1123] Sending emotional data

[1124] The acquired emotion data is sent in real time from the device to the server, which receives it and stores it in a database. At the same time, the server prepares to reflect this data, along with feedback data, in the next recipe generation.

[1125] Step 9:

[1126] Analysis and reflection of feedback and emotional data

[1127] The server inputs the received feedback data and emotion data into the artificial intelligence means for analysis. The analysis results are set to be reflected in the next recipe creation. This allows the server to provide coffee that is optimized based on the user's preferences and emotions.

[1128] Through the above processing steps, the system realizes the continuous provision of coffee optimized to the individual tastes and emotions of the user.

[1129] Example 2

[1130] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1131] Conventional recipe generation systems based on preference data were unable to take into account the user's emotional state, making it difficult to provide the perfect drink for each individual user's current emotions. Furthermore, because they did not use emotional data, the quality of feedback was limited, resulting in a problem of reduced accuracy in the next recipe generation.

[1132] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's terminal, means for acquiring user emotion data, and means for customizing the recipe based on the acquired emotion data. This makes it possible to generate more accurate customized drink recipes by comprehensively considering the user's preference data and emotion data, thereby increasing the satisfaction of each individual user.

[1133] "User preference data" is data that indicates the user's preferences regarding the type, flavor, temperature, etc. of beverages.

[1134] "Artificial intelligence means" refers to algorithms and programs that analyze user preference data and feedback and generate optimal recipes.

[1135] A "drink preparation device" is a device that automatically prepares coffee or other drinks based on a generated recipe.

[1136] "Means for collecting feedback" refers to a method or device for collecting opinions and impressions provided by users after tasting.

[1137] "Means for analyzing feedback" refers to devices or programs that process collected feedback data using artificial intelligence or other analytical methods and reflect the results in the next recipe generation.

[1138] "User terminal" means an electronic device through which a user accesses the system and inputs preference data and feedback.

[1139] "Means for acquiring emotional data" refers to sensors and analysis devices for detecting the user's psychological state from facial expressions, vocal tone, etc.

[1140] "Means for customizing recipes based on emotional data" refers to algorithms or programs that analyze the acquired emotional data and reflect the results in the recipe.

[1141] The present invention is a system that generates and provides personalized drink recipes based on user preference data and emotion data. This system is built around the main components of a server, a terminal, a user, and an emotion engine.

[1142] Overall system configuration

[1143] Server Operation

[1144] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1145] 1. User Registration / Login:

[1146] When a user enters their ID and password from the terminal, the server receives it and authenticates them by checking it against a database (e.g., MySQL). If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1147] 2. Preference Data Collection:

[1148] When a user answers a preference questionnaire, the server receives it and stores it in a database. This data is then input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates a preference profile for the user based on the results.

[1149] 3. Recipe generation:

[1150] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica).

[1151] 4. Collecting User Feedback:

[1152] After tasting, the user inputs feedback through the terminal (e.g., "It's very sour" or "It has a weak aroma"). The server receives this feedback and stores it in a database.

[1153] 5. Feedback analysis:

[1154] The server inputs the collected feedback data into the AI ​​and reflects it the next time a recipe is generated, resulting in a more accurate customized drink.

[1155] Emotion Engine Operation

[1156] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1157] 1. Acquiring emotion data:

[1158] The emotion engine uses a face recognition camera and a voice recognition microphone to capture emotion data from the user's facial expressions and voice.

[1159] 2. Data transmission:

[1160] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with the preference data, and reflects it in the next recipe generation.

[1161] Device behavior

[1162] A terminal is a device that exchanges data between a user and a server.

[1163] 1. Interface display:

[1164] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1165] 2. Preference Survey Answers:

[1166] When the user answers the preference questionnaire, the terminal sends it to the server.

[1167] 3. Feedback input:

[1168] After tasting the coffee, the user inputs feedback through the terminal and transmits it to the server.

[1169] User Actions

[1170] Users provide preference data and feedback through this system.

[1171] 1. Registration and login operations:

[1172] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1173] 2. Preference input:

[1174] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1175] 3. Providing Emotion Data:

[1176] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1177] 4. Feedback Input:

[1178] Customers are asked to taste the coffee provided and enter their feedback on the taste through the terminal.

[1179] Specific examples

[1180] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1181] When User A tastes the coffee, the emotion engine recognizes the emotion of "happiness" from his / her facial expression and sends it to the server. The server combines this emotion data with specific feedback data ("slightly acidic" and "good aroma") and reflects it in the next recipe generation. This process ensures that the next coffee provided is more optimally tailored to User A's preferences.

[1182] Example prompts to input to the generative AI model

[1183] "User A's preference data is 'prefers dark roast' and 'mild acidity', and their latest feedback is 'slightly strong acidity' and 'good aroma'. 'Delight' has been recognized as the emotional data. Based on this data, please generate the next coffee recipe that is best suited to User A."

[1184] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1185] Step 1: User registration and login

[1186] Input: The user enters the ID and password from the terminal.

[1187] Description: The terminal sends the ID and password entered by the user to the server. The server receives this and authenticates it by checking it against a database (e.g. MySQL). If authentication is successful, it retrieves the user profile from the database and sends it back to the terminal.

[1188] Output: User authentication result (success or failure) and user profile.

[1189] Step 2: Collect preference data

[1190] Input: The user answers the preference survey.

[1191] Description: The device sends the user's preference questionnaire response data to the server. The server receives it and stores it in a database. At the same time, this data is input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates the user's preference profile based on the results.

[1192] Output: User preference profile.

[1193] Step 3: Recipe generation

[1194] Input: The generated preference profile.

[1195] Description: The server uses AI to generate a coffee recipe based on the preference profile. This recipe includes details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica). The generated recipe is sent to the beverage preparation device.

[1196] Output: A customized coffee recipe for the user.

[1197] Step 4: Obtaining emotion data

[1198] Input: Facial expressions and voice as the user samples the coffee.

[1199] Description: The emotion engine uses a face recognition camera and a voice recognition microphone to acquire user emotion data, which is then sent to the server in real time.

[1200] Output: User emotion data (e.g., emotion "joy").

[1201] Step 5: Gather feedback

[1202] Input: Feedback provided by the user after tasting (e.g., "strong acidity" or "weak aroma").

[1203] Description: The terminal displays the user's feedback in the input interface. When the user enters the feedback, it sends it to the server. The server stores the feedback data in a database.

[1204] Output: User feedback data.

[1205] Step 6: Feedback analysis

[1206] Input: Collected feedback data.

[1207] Description: The server inputs the feedback data into the generative AI model and analyzes it. The results of this analysis are reflected in the next recipe generation. The server combines the collected emotional data with the preference profile to prepare the optimal data for the next recipe generation.

[1208] Output: Parsed feedback data to be used in the next recipe generation.

[1209] Step 7: Generate the next recipe

[1210] Input: Parsed feedback data, user preference profile, emotional data.

[1211] Description: The server generates the next recipe based on the analyzed feedback data, the user's preference profile, and emotional data. In this process, the generative AI model takes into account various user data to customize the new recipe.

[1212] Output: A customized coffee recipe for the next user.

[1213] (Application example 2)

[1214] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1215] Currently, many coffee lovers face the challenge of finding a coffee that matches their tastes and emotions. In particular, there is no system that can optimize the next coffee blend based on the user's emotional state and feedback. This makes it difficult to provide personalized service that allows users to have the best coffee experience.

[1216] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data and emotion data, a device for preparing coffee based on the generated recipe, means for collecting feedback from the user after preparing the coffee, means for analyzing the collected feedback and reflecting it in generating the next recipe, and means for acquiring emotion data by using facial expression recognition and voice recognition functions in cooperation with the user's terminal. This makes it possible to provide an optimal coffee blend based on the user's preferences and emotions.

[1217] "User preference data" is information relating to the user's personal preferences, such as the taste, roast level, acidity, and aroma of coffee.

[1218] "Emotion data" is information about the emotional state obtained from the user's facial expressions and voice.

[1219] "Artificial intelligence means for generating recipes" refers to a program or system that runs an algorithm based on acquired data to generate the optimal coffee recipe for the user.

[1220] An "apparatus for preparing coffee based on a generated recipe" is a device for specifically preparing coffee according to a coffee recipe generated by artificial intelligence.

[1221] "Feedback" refers to information such as the user's impressions and evaluation of the coffee they have actually tasted, as well as areas for improvement.

[1222] The "facial expression recognition function" is a technology that uses a camera to analyze a user's facial expressions and detect their emotional state.

[1223] The "voice recognition function" is a technology that uses a microphone to analyze the user's voice and detect their emotional state.

[1224] A "user terminal" is a device through which a user inputs data or interacts with the system, including a smartphone or tablet.

[1225] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[1226] Server Operation

[1227] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1228] 1. User Registration / Login

[1229] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1230] 2. Collecting preference data

[1231] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[1232] 3. Recipe Generation

[1233] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[1234] 4. Collecting User Feedback

[1235] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1236] 5. Feedback Analysis

[1237] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[1238] Emotion Engine Operation

[1239] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1240] 1. Acquiring Emotion Data

[1241] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[1242] 2. Data transmission

[1243] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[1244] Device behavior

[1245] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[1246] 1. Interface display

[1247] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1248] 2. Answer the preference survey

[1249] When the user answers the preference questionnaire, the terminal sends it to the server.

[1250] 3. Feedback input

[1251] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1252] User Actions

[1253] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[1254] 1. Registration and login operations

[1255] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1256] 2. Input your preferences

[1257] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1258] 3. Providing Emotion Data

[1259] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1260] 4. Feedback Input

[1261] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1262] Specific examples

[1263] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1264] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[1265] Prompt Sentence Examples

[1266] Design an application that allows users to select their favorite coffee. It should have a function that optimizes the next coffee recipe based on the user's preference and emotional data. Preference data includes roast level, acidity, and aroma. The smartphone used will have a facial expression recognition camera and a voice recognition microphone to collect emotional data. The collected data will be sent to a server, where it will be analyzed by AI and reflected in the next recipe. Please provide specific program code examples.

[1267] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1268] Step 1:

[1269] The user enters their ID and password using a terminal and sends them to the server. The server receives this information and authenticates them by checking it against a database. If authentication is successful, the server retrieves the user profile from the database and sends it back to the terminal. The input data is "ID" and "password," and the output data is "user profile." The server executes a database query for the authentication process.

[1270] Step 2:

[1271] The user answers a preference questionnaire from their device and sends the response data to the server. The server stores the received preference data in a database and provides it as input to the AI ​​analysis engine. The AI ​​analysis engine analyzes this data and generates a preference profile for the user. The input data is the "preference questionnaire response data" and the output data is the "preference profile." The server passes the data to the AI ​​analysis engine and waits for the results.

[1272] Step 3:

[1273] The server uses AI to generate a coffee recipe based on the generated preference profile. The generated recipe includes the appropriate roasting time, extraction temperature, type of beans used, etc. The input data is the "preference profile" and the output data is the "coffee recipe." The server inputs the profile data into the AI ​​to obtain the optimal coffee recipe.

[1274] Step 4:

[1275] After the recipe is generated, the server sends the recipe to the coffee preparation appliance. The coffee preparation appliance prepares coffee according to the received recipe. The input data is the "coffee recipe" and the output data is the "prepared coffee." The server sends the recipe data, and the coffee preparation appliance operates according to the instructions.

[1276] Step 5:

[1277] After tasting the prepared coffee, the user inputs feedback through the terminal. The terminal sends this feedback data to the server. The server stores the received feedback data in a database. The input data is "feedback" and the output data is "stored feedback data." The terminal displays a user interface and receives and sends user input.

[1278] Step 6:

[1279] The emotion engine acquires emotion data from the user's facial expressions and voice and sends it to the server in real time. The server analyzes this emotion data, combines it with preference data, and reflects it in the next recipe generation. The input data is "facial expression data" and "voice data," and the output data is "analyzed emotion data." The emotion engine uses a facial recognition camera and a voice recognition microphone to collect data in real time.

[1280] Step 7:

[1281] The server inputs the collected feedback data and emotional data into the AI, which analyzes this data and reflects it in the next recipe generation. This allows the system to provide coffee that is even more suited to the user's preferences. The input data are "feedback data" and "emotional data," and the output data is an "updated preference profile" and a "new coffee recipe." The server passes the data to the AI, which then optimizes the recipe again.

[1282] These steps allow the optimal coffee blend to be provided to each individual user in real time.

[1283] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1284] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1285] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1286] [Fourth embodiment]

[1287] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1288] 7, a 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.

[1289] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1290] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1291] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1292] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1293] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1294] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1295] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1296] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1297] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1298] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1299] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1300] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[1301] Server Operation

[1302] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1303] 1. User Registration / Login:

[1304] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1305] 2. Preference Data Collection:

[1306] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[1307] 3. Recipe generation:

[1308] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[1309] 4. Collecting User Feedback:

[1310] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1311] 5. Feedback analysis:

[1312] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[1313] Device behavior

[1314] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[1315] 1. Interface display:

[1316] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1317] 2. Preference Survey Answers:

[1318] When the user answers the preference questionnaire, the terminal sends it to the server.

[1319] 3. Feedback input:

[1320] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1321] User Actions

[1322] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[1323] 1. Registration and login operations:

[1324] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1325] 2. Preference input:

[1326] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1327] 3. Coffee tasting and feedback:

[1328] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1329] Specific examples

[1330] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1331] If User A, after tasting the coffee, inputs feedback such as "It's still too sour," the server receives this feedback and reflects it in the next recipe generation. This allows the server to continually provide the optimal coffee tailored to User A's preferences.

[1332] This system allows users to enjoy coffee that is optimized to their individual tastes. Furthermore, by utilizing feedback, the quality of the coffee provided will be continuously improved. The specific system configuration and operation for realizing this mechanism are the characteristics of this invention.

[1333] The processing flow will be explained below.

[1334] Step 1:

[1335] User Registration

[1336] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[1337] Step 2:

[1338] Log in

[1339] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[1340] Step 3:

[1341] Collection of preference data

[1342] After logging in, the user answers a preference questionnaire displayed on the terminal. The user inputs roast level, acidity, bitterness, and aroma intensity, and then presses the send button. The terminal then sends the input preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the artificial intelligence means, which starts generating a preference profile.

[1343] Step 4:

[1344] Generating a preference profile

[1345] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[1346] Step 5:

[1347] Recipe Generation

[1348] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[1349] Step 6:

[1350] Coffee tasting and feedback

[1351] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[1352] Step 7:

[1353] Analyzing and incorporating feedback

[1354] The server inputs the received feedback data into the artificial intelligence means and performs analysis. The analysis results are set to be reflected in the next recipe generation. This allows the server to provide an original coffee blend that is closer to the user's preferences. The server saves the analysis results in a database and uses them for the next preference profile generation and recipe generation.

[1355] The above processing steps enable the system to continuously provide coffee optimized to the individual preferences of the user.

[1356] Example 1

[1357] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] Conventional coffee blend providing systems have been unable to adequately respond to individual user preferences, resulting in inconsistent coffee quality each time. Furthermore, user feedback is not effectively reflected in the next recipe generation, making it difficult to provide the optimal coffee blend for the user. This has made improving user satisfaction a challenge.

[1359] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1360] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's device, means for the user to log in to the system by entering an ID and password, means for receiving responses to a user preference questionnaire, means for saving the user's feedback data in a database, and means for generating a preference profile using an artificial intelligence model. This makes it possible to provide original coffee blends suited to the user's preferences and continuously improve the quality.

[1361] "User preference data" is information about the characteristics and flavors of coffee that a user prefers.

[1362] "Artificial intelligence means" refers to technology that analyzes and predicts based on user preference data and executes algorithms to generate optimal recipes.

[1363] A "drink preparation device" is a device that automatically prepares a drink such as coffee according to a generated recipe.

[1364] The "means for collecting feedback" is a method for users to input their evaluations and impressions into the system after tasting a beverage such as coffee.

[1365] The "means for analyzing collected feedback and reflecting it in the next recipe generation" is a method for improving the next recipe based on the feedback collected from the user to further suit the user's preferences.

[1366] "Means for linking with a user's device" refers to technology for exchanging data between the server and the terminal used by the user.

[1367] "Method of logging into a system by entering an ID and password" refers to the authentication method used by a user to access a system.

[1368] The "means for receiving responses to the user's preference questionnaire" refers to a method for transmitting data to the server when a user answers the preference questionnaire.

[1369] The "means for saving feedback data in a database" is a method for temporarily holding feedback collected from users and storing it in a database for later analysis.

[1370] "Means for generating preference profiles using artificial intelligence models" refers to a technology that utilizes AI models based on user preference data to classify user preferences in detail and create individual profiles.

[1371] The present invention is a system that provides original coffee blends tailored to individual user preferences. The system is built around three main components: a server, a terminal, and a user.

[1372] The server is responsible for managing the entire system and processing data. Specifically, it is equipped with a means for acquiring user preference data. When a user answers a preference questionnaire from their device, the server receives this data and automatically stores it in a database. Furthermore, a preference profile is generated based on this preference data using a generative AI model.

[1373] Based on the generated preference profile, the server uses an AI algorithm to generate coffee recipes. This AI model analyzes the user's preference data and collected feedback and reflects it in the next recipe generation.

[1374] The generated recipe is sent to the terminal and displayed to the user. The user tastes coffee made based on this recipe. After tasting, the user inputs feedback through the terminal. This feedback data is also sent to the server and stored in the database. The server then analyzes the feedback data and reflects it in the next coffee recipe generation.

[1375] The user's terminal is a device that exchanges data with the server. When a user accesses the system for the first time, they log in by entering their ID and password. Once logged in to the system, the user can answer a preference questionnaire. The terminal sends the data the user has answered to the server. It also provides an interface for the user to enter feedback, and sends that data to the server.

[1376] As a concrete example, consider the case where User A is starting to use the system for the first time. First, he or she registers an ID and password via a terminal. Then, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like coffee with a low acidity." This data is sent to the server, where it is analyzed by the generative AI model, which then generates a coffee recipe specifically for User A.

[1377] The generated recipe includes detailed parameters such as the type of coffee bean, roasting time, and extraction temperature. This recipe information is displayed on the terminal, and User A tastes the coffee based on it. After tasting, if User A inputs feedback such as "It's still too acidic," the server receives this and reflects it in the next recipe generation.

[1378] An example prompt might be, "I like dark roasted coffee with a low acidity. Please create my own coffee blend based on this."

[1379] This system allows users to enjoy coffee that is optimized to their preferences. Furthermore, by utilizing user feedback, the quality of the coffee provided will be continuously improved, which is expected to increase user satisfaction.

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Step 1:

[1382] The server is responsible for user registration and login. When a user enters their ID and password from their device, the device sends this information to the server. The server receives the sent data and authenticates the user by comparing it with the database. If authentication is successful, it retrieves the user's profile data from the database and sends it back to the device. The user confirms this and login is complete.

[1383] Input: User ID and password

[1384] Output: User profile data and authentication results

[1385] Specific operation: The user enters their ID and password on their smartphone and presses the login button.

[1386] Step 2:

[1387] The server collects user preference data. When the user answers a preference questionnaire in the system, the device sends the response data to the server. The server receives the data and stores it in a database. Finally, this preference data is input into an AI model, which begins analysis and generates a preference profile.

[1388] Input: User preference survey response data

[1389] Output: User preference profile

[1390] Specific operation: The user answers the questionnaire items with answers such as "I prefer dark roast" and "I prefer a mild acidity," and the data is sent to the server.

[1391] Step 3:

[1392] The server uses AI to generate a customized coffee recipe based on the generated preference profile, including roasting time, brewing temperature, type of beans used, etc. The generated recipe is sent to the device and displayed to the user.

[1393] Input: Preference Profile

[1394] Output: Coffee recipe

[1395] How it works: The server's AI model analyzes the user's preference data and generates a recipe for a "dark roast blend with reduced acidity." The details are then sent to the device.

[1396] Step 4:

[1397] The user tastes coffee prepared based on the recipe received through the terminal. After tasting, the user inputs feedback through the terminal. The terminal sends this feedback data to the server, which stores the received data in a database.

[1398] Input: User feedback data

[1399] Output: Saved feedback data

[1400] Specific operation: The user enters feedback such as "It's still too sour" or "The bitterness is just right," and this is sent to the server.

[1401] Step 5:

[1402] The server analyzes the received feedback data. This feedback data is then input back into the AI ​​model to be reflected in the next coffee recipe generation. This adjusts the next coffee recipe to more closely match the user's preferences. The new recipe is then notified to the user again.

[1403] Input: Saved feedback data

[1404] Output: Improved coffee recipe

[1405] Specific operation: Based on the previous feedback, the AI ​​creates a new recipe called "Dark roast blend with even less acidity" and notifies the user's device.

[1406] In this way, the system continues to provide coffee blends optimized to the user's preferences.

[1407] (Application example 1)

[1408] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1409] Conventional coffee blend provision systems have difficulty generating recipes that perfectly match the user's preferences, making it difficult to provide individually optimized coffee. Furthermore, there was no system that efficiently collected user feedback and reflected it in the next recipe generation. Furthermore, in a brick-and-mortar store environment, there was a lack of a means to quickly provide custom recipes tailored to the user's preferences.

[1410] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1411] In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating recipes based on the acquired preference data, and means for generating recipes individually optimized for the user in conjunction with the user preference data and transmitting the recipes to devices in the brick-and-mortar store, thereby enabling individually optimized coffee blends to be provided quickly and efficiently in a brick-and-mortar environment.

[1412] - "Means for acquiring user preference data" refers to devices or software for collecting information about a user's preferred coffee.

[1413] "Artificial intelligence means" refers to machine learning algorithms and systems that generate and optimize coffee recipes based on user preference data.

[1414] "Coffee preparation equipment" means a barista machine or other preparation device for preparing coffee according to a generated recipe.

[1415] "Means for collecting feedback" refers to devices or software that allow users to obtain their impressions and ratings of the coffee they sample.

[1416] "Means for reflecting feedback in next recipe generation" refers to a system for analyzing collected feedback and adjusting the next coffee recipe to better suit the user's preferences.

[1417] "Means for linking with the user's mobile terminal" refers to the interface that allows mobile devices such as smartphones and tablets to communicate with the system and exchange data.

[1418] "Means for transmitting to devices in a physical store" refers to a communication system for transmitting the generated coffee recipe to a barista machine or cooking device installed in a physical store.

[1419] "Means for storing in a database" refers to a data storage system for saving information such as user preference data and feedback, and reusing it as needed.

[1420] The present invention is a system that provides original coffee blends tailored to the individual tastes of users. This system is built around three main components: a server, a terminal, and a user. The details and processing of each component are explained below.

[1421] Server Operation

[1422] The server is responsible for managing the entire system and processing data. It performs the following processes:

[1423] 1. User Registration / Login

[1424] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1425] 2. Collecting preference data

[1426] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into an artificial intelligence system to analyze it and generate a preference profile of the user.

[1427] 3. Recipe Generation

[1428] The server uses artificial intelligence to generate a coffee recipe based on the customer's preference profile, including the appropriate roasting time, brewing temperature, and type of beans to be used, and then transmits the recipe to a device in the physical store.

[1429] 4. Collecting User Feedback

[1430] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1431] 5. Feedback Analysis

[1432] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, thereby providing coffee that is more suited to the user's preferences.

[1433] Device behavior

[1434] The terminal is a device that exchanges data between the user and the server. It performs the following processes:

[1435] 1. Interface display

[1436] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1437] 2. Answer the preference survey

[1438] When the user answers the preference questionnaire, the terminal sends it to the server.

[1439] 3. Feedback input

[1440] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1441] User Actions

[1442] Users provide their coffee preference data and feedback through this system. The following process is performed.

[1443] 1. Registration and login operations

[1444] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1445] 2. Input your preferences

[1446] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1447] 3. Coffee tasting and feedback

[1448] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1449] Application Examples

[1450] As a concrete example, a scenario will be shown in which a new user A starts using the system.

[1451] 1. User A first registers in the system as "user_a" and logs in.

[1452] 2. User A answers a preference questionnaire on a smartphone app, providing information such as "I prefer medium roast," "low acidity," and "strong aroma."

[1453] 3. The server analyzes this information and generates an optimal coffee recipe, which is then sent to the barista machine in the store to prepare the coffee.

[1454] 4. User A tastes the coffee and provides feedback that it is still too acidic.

[1455] 5. The server analyzes the feedback and reflects it in the next recipe generation.

[1456] This allows User A to continuously enjoy coffee optimized to his or her preferences. The hardware used includes a smartphone and a barista machine, and the software used is Python, Flask / Django (server side), PostgreSQL (database), and an AI model (machine learning library, e.g., TensorFlow or PyTorch).

[1457] Example prompt for a generative AI model:

[1458] User Preferences: { "Roast": "Medium Roast", "Acidity": "Low", "Aroma": "Strong"}

[1459] Feedback: { "sour": "still high"}

[1460] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1461] Step 1: User registration and login

[1462] The server requests registration by the user entering their ID and password using their smartphone. The entered data is the user's account information. The server receives this data and stores it in a database. Once registration is complete, it sends a success message to the user.

[1463] When the user enters their ID and password again to request login, the server checks the database and performs authentication. If authentication is successful, the server retrieves the user profile and returns it to the terminal along with a session token. This completes the login process.

[1464] Step 2: Obtaining preference surveys

[1465] The terminal displays a preference questionnaire screen to the user, and the user inputs preference data such as "I prefer medium roast," "low acidity," and "strong aroma."

[1466] The device sends the input preference data to a server, which receives it, stores it in a database, and inputs it into an artificial intelligence model.

[1467] Step 3: Generate the recipe

[1468] The server analyzes the stored preference data using an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate a coffee recipe optimized for the user's preferences. This process includes data preprocessing, feature extraction, and the application of machine learning algorithms.

[1469] The generated recipe includes detailed parameters such as roasting time, brewing temperature, type of beans used, etc. The server stores this recipe in a database and sends it to the device in the physical store.

[1470] Step 4: Preparing and tasting the coffee

[1471] The barista machine in the physical store prepares coffee based on the recipe sent from the server, and the coffee is then served to the user exactly as the recipe was created.

[1472] The user samples this coffee and then inputs their impressions and evaluations into the terminal.

[1473] Step 5: Gather feedback

[1474] The device sends the feedback data entered by the user to the server, including specific impressions such as "It still tastes too sour."

[1475] The server stores this feedback in a database, which is used for the next recipe generation.

[1476] Step 6: Analyze the feedback and incorporate it into your next recipe

[1477] The server then inputs the collected feedback data back into the AI ​​model. This data analysis allows the feedback information to be reflected in the next recipe generation. Specifically, the parameters of the next recipe are adjusted to better suit the user's preferences.

[1478] The server saves the new, adjusted recipe in its database, ready to use the next time coffee is made.

[1479] This will realize a system that allows users to continuously enjoy coffee that is optimized to their preferences.

[1480] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1481] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[1482] Server Operation

[1483] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1484] 1. User Registration / Login:

[1485] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1486] 2. Preference Data Collection:

[1487] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[1488] 3. Recipe generation:

[1489] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[1490] 4. Collecting User Feedback:

[1491] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1492] 5. Feedback analysis:

[1493] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[1494] Emotion Engine Operation

[1495] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1496] 1. Acquiring emotion data:

[1497] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[1498] 2. Data transmission:

[1499] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[1500] Device behavior

[1501] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[1502] 1. Interface display:

[1503] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1504] 2. Preference Survey Answers:

[1505] When the user answers the preference questionnaire, the terminal sends it to the server.

[1506] 3. Feedback input:

[1507] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1508] User Actions

[1509] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[1510] 1. Registration and login operations:

[1511] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1512] 2. Preference input:

[1513] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1514] 3. Providing Emotion Data:

[1515] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1516] 4. Feedback Input:

[1517] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1518] Specific examples

[1519] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1520] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[1521] This system allows users to enjoy coffee that is optimized for their individual tastes and emotions. Furthermore, by combining it with an emotion engine, the quality of the coffee provided can be further improved, increasing user satisfaction. The specific system configuration and operation required to realize this mechanism are the characteristics of this invention.

[1522] The processing flow will be explained below.

[1523] Step 1:

[1524] User Registration

[1525] The user enters their user ID, password, and basic profile information on the terminal and presses the registration button. The terminal sends the entered data to the server. The server stores the received data in a database, generates a message indicating whether registration was successful or not, and sends it back to the terminal. The user checks the message displayed on the terminal.

[1526] Step 2:

[1527] Log in

[1528] The user enters their user ID and password on the terminal and presses the login button. The terminal sends the entered data to the server. The server compares the entered data with the database and performs authentication. If authentication is successful, the user profile data is obtained and sent back to the terminal. The user confirms the message displayed on the terminal and login is complete.

[1529] Step 3:

[1530] Collection of preference data

[1531] After logging in, the user answers a preference questionnaire displayed on the device. The user enters roast level, acidity, bitterness, aroma intensity, etc., and presses the send button. The device then sends the entered preference data to the server. The server temporarily saves the received data and stores it in a database. At the same time, this data is input into the AI, which begins analysis and generates the user's preference profile.

[1532] Step 4:

[1533] Generating a preference profile

[1534] The server uses artificial intelligence to analyze the received preference data and generate a user profile, which includes information such as the user's preferred roast level, acidity, bitterness, and aroma. The generated profile is stored in a database and used in the next step.

[1535] Step 5:

[1536] Recipe Generation

[1537] The server uses artificial intelligence means to generate a coffee recipe based on the generated preference profile, including the appropriate roasting time, extraction temperature, and type of beans to be used. The generated recipe is then sent from the server to the barista machine, which prepares the coffee based on the received recipe.

[1538] Step 6:

[1539] Coffee tasting and feedback

[1540] The user tastes coffee prepared based on the generated coffee recipe. After tasting, the user inputs feedback about the taste through the terminal. For example, the user inputs specific impressions such as "too acidic" or "not bitter enough" and presses the send button. The terminal sends the input feedback data to the server. The server stores the received data in a database.

[1541] Step 7:

[1542] Acquiring emotion data

[1543] When tasting coffee, the emotion engine uses a facial recognition camera and a voice recognition microphone to acquire the user's emotional data. For example, it can recognize emotions such as "happiness" and "surprise" from facial expressions, and "satisfaction" and "dissatisfaction" from voice.

[1544] Step 8:

[1545] Sending emotional data

[1546] The acquired emotion data is sent in real time from the device to the server, which receives it and stores it in a database. At the same time, the server prepares to reflect this data, along with feedback data, in the next recipe generation.

[1547] Step 9:

[1548] Analysis and reflection of feedback and emotional data

[1549] The server inputs the received feedback data and emotion data into the artificial intelligence means for analysis. The analysis results are set to be reflected in the next recipe creation. This allows the server to provide coffee that is optimized based on the user's preferences and emotions.

[1550] Through the above processing steps, the system realizes the continuous provision of coffee optimized to the individual tastes and emotions of the user.

[1551] Example 2

[1552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1553] Conventional recipe generation systems based on preference data were unable to take into account the user's emotional state, making it difficult to provide the perfect drink for each individual user's current emotions. Furthermore, because they did not use emotional data, the quality of feedback was limited, resulting in a problem of reduced accuracy in the next recipe generation.

[1554] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data, a device for preparing a beverage based on the generated recipe, means for collecting feedback from the user after preparing the beverage, means for analyzing the collected feedback and reflecting it in the next recipe generation, means for linking with the user's terminal, means for acquiring user emotion data, and means for customizing the recipe based on the acquired emotion data. This makes it possible to generate more accurate customized drink recipes by comprehensively considering the user's preference data and emotion data, thereby increasing the satisfaction of each individual user.

[1555] "User preference data" is data that indicates the user's preferences regarding the type, flavor, temperature, etc. of beverages.

[1556] "Artificial intelligence means" refers to algorithms and programs that analyze user preference data and feedback and generate optimal recipes.

[1557] A "drink preparation device" is a device that automatically prepares coffee or other drinks based on a generated recipe.

[1558] "Means for collecting feedback" refers to a method or device for collecting opinions and impressions provided by users after tasting.

[1559] "Means for analyzing feedback" refers to devices or programs that process collected feedback data using artificial intelligence or other analytical methods and reflect the results in the next recipe generation.

[1560] "User terminal" means an electronic device through which a user accesses the system and inputs preference data and feedback.

[1561] "Means for acquiring emotional data" refers to sensors and analysis devices for detecting the user's psychological state from facial expressions, vocal tone, etc.

[1562] "Means for customizing recipes based on emotional data" refers to algorithms or programs that analyze the acquired emotional data and reflect the results in the recipe.

[1563] The present invention is a system that generates and provides personalized drink recipes based on user preference data and emotion data. This system is built around the main components of a server, a terminal, a user, and an emotion engine.

[1564] Overall system configuration

[1565] Server Operation

[1566] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1567] 1. User Registration / Login:

[1568] When a user enters their ID and password from the terminal, the server receives it and authenticates them by checking it against a database (e.g., MySQL). If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1569] 2. Preference Data Collection:

[1570] When a user answers a preference questionnaire, the server receives it and stores it in a database. This data is then input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates a preference profile for the user based on the results.

[1571] 3. Recipe generation:

[1572] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica).

[1573] 4. Collecting User Feedback:

[1574] After tasting, the user inputs feedback through the terminal (e.g., "It's very sour" or "It has a weak aroma"). The server receives this feedback and stores it in a database.

[1575] 5. Feedback analysis:

[1576] The server inputs the collected feedback data into the AI ​​and reflects it the next time a recipe is generated, resulting in a more accurate customized drink.

[1577] Emotion Engine Operation

[1578] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1579] 1. Acquiring emotion data:

[1580] The emotion engine uses a face recognition camera and a voice recognition microphone to capture emotion data from the user's facial expressions and voice.

[1581] 2. Data transmission:

[1582] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with the preference data, and reflects it in the next recipe generation.

[1583] Device behavior

[1584] A terminal is a device that exchanges data between a user and a server.

[1585] 1. Interface display:

[1586] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1587] 2. Preference Survey Answers:

[1588] When the user answers the preference questionnaire, the terminal sends it to the server.

[1589] 3. Feedback input:

[1590] After tasting the coffee, the user inputs feedback through the terminal and transmits it to the server.

[1591] User Actions

[1592] Users provide preference data and feedback through this system.

[1593] 1. Registration and login operations:

[1594] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1595] 2. Preference input:

[1596] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1597] 3. Providing Emotion Data:

[1598] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1599] 4. Feedback Input:

[1600] Customers are asked to taste the coffee provided and enter their feedback on the taste through the terminal.

[1601] Specific examples

[1602] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1603] When User A tastes the coffee, the emotion engine recognizes the emotion of "happiness" from his / her facial expression and sends it to the server. The server combines this emotion data with specific feedback data ("slightly acidic" and "good aroma") and reflects it in the next recipe generation. This process ensures that the next coffee provided is more optimally tailored to User A's preferences.

[1604] Example prompts to input to the generative AI model

[1605] "User A's preference data is 'prefers dark roast' and 'mild acidity', and their latest feedback is 'slightly strong acidity' and 'good aroma'. 'Delight' has been recognized as the emotional data. Based on this data, please generate the next coffee recipe that is best suited to User A."

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Step 1: User registration and login

[1608] Input: The user enters the ID and password from the terminal.

[1609] Description: The terminal sends the ID and password entered by the user to the server. The server receives this and authenticates it by checking it against a database (e.g. MySQL). If authentication is successful, it retrieves the user profile from the database and sends it back to the terminal.

[1610] Output: User authentication result (success or failure) and user profile.

[1611] Step 2: Collect preference data

[1612] Input: The user answers the preference survey.

[1613] Description: The device sends the user's preference questionnaire response data to the server. The server receives it and stores it in a database. At the same time, this data is input into a generative AI model (e.g., TensorFlow) to begin analysis. The server then generates the user's preference profile based on the results.

[1614] Output: User preference profile.

[1615] Step 3: Recipe generation

[1616] Input: The generated preference profile.

[1617] Description: The server uses AI to generate a coffee recipe based on the preference profile. This recipe includes details such as roast time (e.g., 20 minutes), brew temperature (e.g., 90°C), and the type of beans used (e.g., Arabica). The generated recipe is sent to the beverage preparation device.

[1618] Output: A customized coffee recipe for the user.

[1619] Step 4: Obtaining emotion data

[1620] Input: Facial expressions and voice as the user samples the coffee.

[1621] Description: The emotion engine uses a face recognition camera and a voice recognition microphone to acquire user emotion data, which is then sent to the server in real time.

[1622] Output: User emotion data (e.g., emotion "joy").

[1623] Step 5: Gather feedback

[1624] Input: Feedback provided by the user after tasting (e.g., "strong acidity" or "weak aroma").

[1625] Description: The terminal displays the user's feedback in the input interface. When the user enters the feedback, it sends it to the server. The server stores the feedback data in a database.

[1626] Output: User feedback data.

[1627] Step 6: Feedback analysis

[1628] Input: Collected feedback data.

[1629] Description: The server inputs the feedback data into the generative AI model and analyzes it. The results of this analysis are reflected in the next recipe generation. The server combines the collected emotional data with the preference profile to prepare the optimal data for the next recipe generation.

[1630] Output: Parsed feedback data to be used in the next recipe generation.

[1631] Step 7: Generate the next recipe

[1632] Input: Parsed feedback data, user preference profile, emotional data.

[1633] Description: The server generates the next recipe based on the analyzed feedback data, the user's preference profile, and emotional data. In this process, the generative AI model takes into account various user data to customize the new recipe.

[1634] Output: A customized coffee recipe for the next user.

[1635] (Application example 2)

[1636] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1637] Currently, many coffee lovers face the challenge of finding a coffee that matches their tastes and emotions. In particular, there is no system that can optimize the next coffee blend based on the user's emotional state and feedback. This makes it difficult to provide personalized service that allows users to have the best coffee experience.

[1638] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user preference data, artificial intelligence means for generating a recipe based on the acquired preference data and emotion data, a device for preparing coffee based on the generated recipe, means for collecting feedback from the user after preparing the coffee, means for analyzing the collected feedback and reflecting it in generating the next recipe, and means for acquiring emotion data by using facial expression recognition and voice recognition functions in cooperation with the user's terminal. This makes it possible to provide an optimal coffee blend based on the user's preferences and emotions.

[1639] "User preference data" is information relating to the user's personal preferences, such as the taste, roast level, acidity, and aroma of coffee.

[1640] "Emotion data" is information about the emotional state obtained from the user's facial expressions and voice.

[1641] "Artificial intelligence means for generating recipes" refers to a program or system that runs an algorithm based on acquired data to generate the optimal coffee recipe for the user.

[1642] An "apparatus for preparing coffee based on a generated recipe" is a device for specifically preparing coffee according to a coffee recipe generated by artificial intelligence.

[1643] "Feedback" refers to information such as the user's impressions and evaluation of the coffee they have actually tasted, as well as areas for improvement.

[1644] The "facial expression recognition function" is a technology that uses a camera to analyze a user's facial expressions and detect their emotional state.

[1645] The "voice recognition function" is a technology that uses a microphone to analyze the user's voice and detect their emotional state.

[1646] A "user terminal" is a device through which a user inputs data or interacts with the system, including a smartphone or tablet.

[1647] This invention is a system that provides personalized coffee blends based on user preference and emotion data. The system is built around four main components: a server, a terminal, a user, and an emotion engine.

[1648] Server Operation

[1649] The server is responsible for managing the entire system and processing data. Its specific operations are as follows:

[1650] 1. User Registration / Login

[1651] When a user enters their ID and password from the terminal, the server receives them and authenticates them by checking them against the database. If authentication is successful, the server retrieves the user profile and returns it to the terminal.

[1652] 2. Collecting preference data

[1653] When a user answers the preference questionnaire, the server receives it and stores it in a database. At the same time, this data is input into the AI ​​to begin analysis and generate a preference profile of the user.

[1654] 3. Recipe Generation

[1655] Based on the generated preference profile, the server uses AI to generate a coffee recipe, including the appropriate roasting time, extraction temperature, and type of beans to use.

[1656] 4. Collecting User Feedback

[1657] After tasting the coffee, the user inputs feedback through the terminal, which is received by the server and stored in a database.

[1658] 5. Feedback Analysis

[1659] The server inputs the collected feedback data into the AI ​​and processes it so that it is reflected in the next recipe generation, allowing the system to provide coffee that is more suited to the user's preferences.

[1660] Emotion Engine Operation

[1661] The emotion engine is a key component for recognizing user emotion data and reflecting it in recipe generation.

[1662] 1. Acquiring Emotion Data

[1663] The emotion engine captures emotion data from the user's facial expressions and voice using a facial recognition camera and a voice recognition microphone.

[1664] 2. Data transmission

[1665] The acquired emotion data is sent in real time to the server, which analyzes it, combines it with preference data, and reflects it in the next recipe generation.

[1666] Device behavior

[1667] The terminal is a device that exchanges data between the user and the server. The specific operation is as follows.

[1668] 1. Interface display

[1669] Based on the data received from the server, it displays an interface for the user to operate, including registration screens, preference survey screens, feedback forms, etc.

[1670] 2. Answer the preference survey

[1671] When the user answers the preference questionnaire, the terminal sends it to the server.

[1672] 3. Feedback input

[1673] After tasting the coffee, the user inputs feedback through the terminal and sends it to the server.

[1674] User Actions

[1675] Users provide their coffee preference data and feedback through this system. The specific operation is as follows.

[1676] 1. Registration and login operations

[1677] When using the service for the first time, you will need to register by entering your ID and password, then log in to access the system.

[1678] 2. Input your preferences

[1679] You answer the system's preference questionnaire and provide information about your favorite coffee.

[1680] 3. Providing Emotion Data

[1681] When tasting coffee, emotional data such as facial expressions and voice are automatically collected by the emotion engine.

[1682] 4. Feedback Input

[1683] The user samples the coffee provided and enters their feedback on the taste via a terminal, including specific impressions such as "too acidic" or "weak aroma."

[1684] Specific examples

[1685] For example, when User A starts using the system for the first time, he or she first registers an ID and password via a terminal. After that, he or she answers a preference questionnaire, providing information such as "I prefer dark roasts" and "I like less acidity." This data is sent to the server, where AI analyzes it and generates a coffee recipe specifically for User A. The generated recipe is then sent to the barista machine, which prepares the coffee using the appropriate parameters.

[1686] When User A tastes the coffee, if the emotion engine recognizes the emotion of "joy" from the facial expression, that data is sent to the server and reflected in the next recipe generation along with the feedback data. In this way, feedback including the emotion data is reflected in the next recipe, providing the optimal coffee that is closer to User A's preferences.

[1687] Prompt Sentence Examples

[1688] Design an application that allows users to select their favorite coffee. It should have a function that optimizes the next coffee recipe based on the user's preference and emotional data. Preference data includes roast level, acidity, and aroma. The smartphone used will have a facial expression recognition camera and a voice recognition microphone to collect emotional data. The collected data will be sent to a server, where it will be analyzed by AI and reflected in the next recipe. Please provide specific program code examples.

[1689] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1690] Step 1:

[1691] The user enters their ID and password using a terminal and sends them to the server. The server receives this information and authenticates them by checking it against a database. If authentication is successful, the server retrieves the user profile from the database and sends it back to the terminal. The input data is "ID" and "password," and the output data is "user profile." The server executes a database query for the authentication process.

[1692] Step 2:

[1693] The user answers a preference questionnaire from their device and sends the response data to the server. The server stores the received preference data in a database and provides it as input to the AI ​​analysis engine. The AI ​​analysis engine analyzes this data and generates a preference profile for the user. The input data is the "preference questionnaire response data" and the output data is the "preference profile." The server passes the data to the AI ​​analysis engine and waits for the results.

[1694] Step 3:

[1695] The server uses AI to generate a coffee recipe based on the generated preference profile. The generated recipe includes the appropriate roasting time, extraction temperature, type of beans used, etc. The input data is the "preference profile" and the output data is the "coffee recipe." The server inputs the profile data into the AI ​​to obtain the optimal coffee recipe.

[1696] Step 4:

[1697] After the recipe is generated, the server sends the recipe to the coffee preparation appliance. The coffee preparation appliance prepares coffee according to the received recipe. The input data is the "coffee recipe" and the output data is the "prepared coffee." The server sends the recipe data, and the coffee preparation appliance operates according to the instructions.

[1698] Step 5:

[1699] After tasting the prepared coffee, the user inputs feedback through the terminal. The terminal sends this feedback data to the server. The server stores the received feedback data in a database. The input data is "feedback" and the output data is "stored feedback data." The terminal displays a user interface and receives and sends user input.

[1700] Step 6:

[1701] The emotion engine acquires emotion data from the user's facial expressions and voice and sends it to the server in real time. The server analyzes this emotion data, combines it with preference data, and reflects it in the next recipe generation. The input data is "facial expression data" and "voice data," and the output data is "analyzed emotion data." The emotion engine uses a facial recognition camera and a voice recognition microphone to collect data in real time.

[1702] Step 7:

[1703] The server inputs the collected feedback data and emotional data into the AI, which analyzes this data and reflects it in the next recipe generation. This allows the system to provide coffee that is even more suited to the user's preferences. The input data are "feedback data" and "emotional data," and the output data is an "updated preference profile" and a "new coffee recipe." The server passes the data to the AI, which then optimizes the recipe again.

[1704] These steps allow the optimal coffee blend to be provided to each individual user in real time.

[1705] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1706] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1707] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1708] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1709] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1710] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1711] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1712] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1713] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1714] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1715] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1716] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1717] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1718] 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.

[1719] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1720] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1721] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1722] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1723] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1724] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1725] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1726] The following is further disclosed regarding the above embodiment.

[1727] (Claim 1)

[1728] means for acquiring user preference data;

[1729] an artificial intelligence means for generating recipes based on the acquired preference data;

[1730] a device for preparing coffee based on the generated recipe;

[1731] means for collecting feedback from a user after preparing said coffee;

[1732] A means to analyze the collected feedback and reflect it in the next recipe generation,

[1733] A means for linking with a user's terminal;

[1734] A system including:

[1735] (Claim 2)

[1736] 10. The system of claim 1, wherein the artificial intelligence means analyzes user preference data and collected feedback and executes algorithms to customize recipes.

[1737] (Claim 3)

[1738] 10. The system of claim 1, further comprising means for temporarily saving and storing the user preference data and feedback in a database.

[1739] "Example 1"

[1740] (Claim 1)

[1741] means for acquiring user preference data;

[1742] an artificial intelligence means for generating recipes based on the acquired preference data;

[1743] a device for preparing a beverage based on the generated recipe;

[1744] means for collecting feedback from a user after preparing said beverage;

[1745] A means to analyze the collected feedback and reflect it in the next recipe generation,

[1746] a means for interfacing with a user's device;

[1747] A means for users to log in to the system by entering their ID and password,

[1748] means for receiving responses to a user preference questionnaire;

[1749] means for storing user feedback data in a database;

[1750] means for generating a preference profile using an artificial intelligence model;

[1751] A system including:

[1752] (Claim 2)

[1753] 10. The system of claim 1, wherein the artificial intelligence means analyzes user preference data and collected feedback and executes algorithms to customize recipes.

[1754] (Claim 3)

[1755] 10. The system of claim 1, further comprising means for temporarily saving and storing the user preference data and feedback in a database.

[1756] "Application Example 1"

[1757] (Claim 1)

[1758] means for acquiring user preference data;

[1759] an artificial intelligence means for generating recipes based on the acquired preference data;

[1760] a device for preparing coffee based on the generated recipe;

[1761] means for collecting feedback from a user after preparing said coffee;

[1762] A means to analyze the collected feedback and reflect it in the next recipe generation,

[1763] means for associating with a user's mobile terminal;

[1764] A means for generating a recipe individually optimized for the user in cooperation with the user's preference data and transmitting the recipe to a device in the physical store;

[1765] A system including:

[1766] (Claim 2)

[1767] 2. The system of claim 1, wherein the artificial intelligence means includes means for analyzing user preference data and collected feedback and executing an algorithm for customizing recipes, and communication means for transmitting the generated recipes to devices in the physical store.

[1768] (Claim 3)

[1769] 10. The system of claim 1, further comprising means for temporarily saving and storing the user preference data and feedback in a database.

[1770] "Example 2: Combining Emotion Engines"

[1771] (Claim 1)

[1772] means for acquiring user preference data;

[1773] an artificial intelligence means for generating recipes based on the acquired preference data;

[1774] a device for preparing a beverage based on the generated recipe;

[1775] means for collecting feedback from a user after preparing said beverage;

[1776] A means to analyze the collected feedback and reflect it in the next recipe generation,

[1777] A means for linking with a user's terminal;

[1778] A means for acquiring user emotion data;

[1779] A means for customizing recipes based on the acquired emotional data;

[1780] A system including:

[1781] (Claim 2)

[1782] 2. The system of claim 1, wherein the artificial intelligence means analyzes user preference data, collected feedback and acquired sentiment data and executes algorithms to customize recipes.

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for temporarily saving and storing the user preference data, feedback and emotion data in a database.

[1785] "Application example 2 when combining emotion engines"

[1786] (Claim 1)

[1787] means for acquiring user preference data;

[1788] an artificial intelligence means for generating recipes based on the acquired preference data and emotion data;

[1789] a device for preparing coffee based on the generated recipe;

[1790] means for collecting feedback from a user after preparing said coffee;

[1791] A means to analyze the collected feedback and reflect it in the next recipe generation,

[1792] A means for acquiring emotion data by using a facial expression recognition function and a voice recognition function in cooperation with a user's terminal;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, wherein the artificial intelligence means analyzes user preference data and collected emotion data and feedback and executes algorithms to customize recipes.

[1796] (Claim 3)

[1797] 10. The system of claim 1, further comprising means for temporarily saving and storing the user preference data, emotion data and feedback in a database. [Explanation of symbols]

[1798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring user preference data; an artificial intelligence means for generating recipes based on the acquired preference data; a device for preparing coffee based on the generated recipe; means for collecting feedback from a user after preparing said coffee; A means to analyze the collected feedback and reflect it in the next recipe generation, A means for linking with a user's terminal; A system including:

2. 10. The system of claim 1, wherein the artificial intelligence means analyzes user preference data and collected feedback and executes algorithms to customize recipes.

3. 10. The system of claim 1, further comprising means for temporarily saving and storing said user preference data and feedback in a database.

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