System

Abstract

A system is provided.SOLUTION: A system comprising means for a user to input a preference or mood for a beverage, means for a server to receive the user's input information and search a database based thereon to select an optimal beverage recipe, means for the server to generate a preparation instruction based on the selected beverage recipe and transmit the preparation instruction to an AI machine, and means for the AI machine to prepare a beverage based on the preparation instruction and provide the beverage to 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 beverage delivery systems provide users with limited information when selecting a beverage, making it difficult to find the perfect beverage for their preferences and their mood that day. Furthermore, because they do not take into account changes in user preferences, they are unable to consistently provide new discoveries and satisfaction. Furthermore, it is difficult to provide individualized support for users who are particular about the origin or ingredients of their beverages. For these reasons, there is a demand for a system that allows users to quickly find and enjoy the perfect beverage for them. [Means for solving the problem]

[0005] This invention is a system that includes a means for a user to input their beverage preferences and mood; a means for a server to receive the user's input information and search a database based on that information to select an optimal beverage recipe; a means for the server to generate blending instructions based on the selected beverage recipe and send them to an AI machine; and a means for the AI ​​machine to blend a beverage based on the blending instructions and serve it to the user. This allows users to easily enjoy a beverage that suits their mood and preferences that day, and also allows them to make new discoveries. Furthermore, by including a means for inputting a desired origin, the system can meet the needs of users who are particular about specific origins or ingredients. Furthermore, a means for notifying the user of information about the blended beverage allows the user to obtain detailed information about the beverage provided.

[0006] "User" refers to an individual who utilizes the system to select and purchase beverages.

[0007] "Beverage" refers to liquid drinks such as beer, wine, cocktails, and coffee.

[0008] "Preferences" refers to the personal tastes and flavor preferences that a user has for beverages.

[0009] "Mood" refers to the temporary emotion or mood that influences a user's beverage selection.

[0010] "Input means" refers to an interface that allows a user to use a terminal to register information and preferences about beverages into the system.

[0011] "Server" refers to a central computer that processes information received from users, searches a database, and selects optimal drink recipes.

[0012] "Receiving" refers to the server acquiring the user's input information sent from the terminal.

[0013] "Database" refers to a collection of information, including beverage recipes and related information, stored on a server.

[0014] "Search" refers to the act of the server locating appropriate information within a database.

[0015] "Selecting" refers to the server determining the optimal drink recipe based on the user's preferences and mood.

[0016] A "recipe" is a document that describes the ingredients, their combinations, and the steps required to make a certain beverage.

[0017] "Mixing instructions" refers to instructions that the server sends to the AI ​​machine regarding the specific steps and mixing ratios required to create a beverage.

[0018] "Generating" refers to the act of the server creating a formulation instruction.

[0019] "Send" refers to the act of sending the mixing instructions generated by the server to the AI ​​machine.

[0020] "AI machine" refers to a device that automatically mixes beverages based on mixing instructions received from a server.

[0021] "Mixing" refers to the act of an AI machine following instructions to mix multiple ingredients to create a beverage.

[0022] "Serving" refers to the act of the AI ​​machine handing the finished beverage to the user.

[0023] "Place of origin" refers to the geographic location where the beverage's ingredients are produced. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] This invention is a system that provides the optimal beverage based on a user's input of their beverage preferences and mood. The system allows users to specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. Furthermore, users can select the desired region of origin. Based on this information, the system selects the optimal beverage recipe, blends it, and provides it to the user.

[0046] The specific processing flow of this system will be explained in natural language.

[0047] Step 1: Enter your user data

[0048] The device displays a question to the user.

[0049] Terminal: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10."

[0050] Terminal: "Please select your preferred origin."

[0051] The user enters information

[0052] User: Enter sourness "7", bitterness "3", richness "5", and astringency "2".

[0053] User: Enters "France" as the desired origin.

[0054] The device sends the data to the server

[0055] Terminal: Sends the entered ratio data and desired origin information to the server.

[0056] Step 2: Data processing and recipe selection

[0057] The server receives the data

[0058] Server: Receives the user's input data (acidity "7", bitterness "3", richness "5", astringency "2", origin "France").

[0059] The server searches the recipe database

[0060] Server: Searches the recipe database and lists possible drinks that match the user's preferences.

[0061] Server: Prefer French wines.

[0062] The server further narrows down the candidates

[0063] Server: Analyze the recipes that most closely match the above ratios to determine the best recipe.

[0064] Server: Choose a French Chardonnay (high acidity).

[0065] Step 3: Generate and send formulation instructions

[0066] The server generates the formulation instructions.

[0067] Server: Generates mixing instructions based on the selected recipe.

[0068] Server: Create a request for "Chardonnay (France), high acidity, low bitterness, medium body, low astringency."

[0069] The server sends compounding instructions to the AI ​​machine

[0070] Server: Sends mixing instructions to the AI ​​machine.

[0071] Step 4: Mix the drink

[0072] AI machine selects materials

[0073] AI Machine: Pick up a Chardonnay (France).

[0074] AI machine: Selects specific ingredients to enhance the sourness.

[0075] AI machines mix drinks

[0076] AI machine: Mixes selected ingredients in precise proportions.

[0077] AI machine: Adjusts the balance of acidity, bitterness, richness, and astringency.

[0078] AI machine pours drinks into containers

[0079] AI machine: Mixes and pours drinks into glasses (or other containers).

[0080] Step 5: Serve the drink

[0081] The AI ​​machine sends a completion notification to the device

[0082] AI machine: Notifies the device when the drink is ready.

[0083] The device notifies the user

[0084] Terminal: Display the message "Thank you for waiting. Your French Chardonnay is ready!"

[0085] The user receives a drink

[0086] User: Receives the finished drink and enjoys it.

[0087] These steps enable the system to quickly and accurately provide the optimal beverage tailored to the user's preferences and mood of the day. For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2", the system can provide a French Chardonnay with the optimal blend. In this way, users can enjoy beverages that bring new experiences and satisfaction to their daily lives.

[0088] The processing flow will be explained below.

[0089] Step 1:

[0090] The device displays a question to the user.

[0091] The device displays the following question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0092] Step 2:

[0093] The user enters information

[0094] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[0095] Step 3:

[0096] The device sends the data to the server

[0097] The terminal sends the ratio data entered by the user (acidity "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server.

[0098] Step 4:

[0099] The server receives the data

[0100] The server receives the user input data sent from the terminal.

[0101] Step 5:

[0102] The server searches the recipe database

[0103] The server searches the recipe database based on the user's input data, prioritizing French drinks in particular, and lists candidates that match the ratio specified by the user.

[0104] Step 6:

[0105] The server selects the best recipe

[0106] The server analyzes the recipes listed and selects the one that most closely matches the user's specified ratio (acidity: 7, bitterness: 3, richness: 5, astringency: 2). In this example, a French Chardonnay (highly acidic) is selected.

[0107] Step 7:

[0108] The server generates the formulation instructions.

[0109] The server generates specific mixing instructions based on the selected recipe, such as "Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency."

[0110] Step 8:

[0111] The server sends compounding instructions to the AI ​​machine.

[0112] The server generates mixing instructions and sends them to the AI ​​machine.

[0113] Step 9:

[0114] AI machine selects materials

[0115] The AI ​​machine receives recipe instructions from the server and selects the specified ingredients (Chardonnay (France)) and also selects specific ingredients to enhance the acidity.

[0116] Step 10:

[0117] AI machines mix drinks

[0118] The AI ​​machine selects ingredients and mixes them in precise proportions based on instructions, adjusting the balance of sourness, bitterness, richness, and astringency.

[0119] Step 11:

[0120] AI machine pours drinks into containers

[0121] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0122] Step 12:

[0123] The AI ​​machine sends a completion notification to the device

[0124] The AI ​​machine will notify the device that the drink is ready.

[0125] Step 13:

[0126] The device notifies the user

[0127] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0128] Step 14:

[0129] The user receives a drink

[0130] The user follows the notification on the terminal and receives the finished drink.

[0131] Example 1

[0132] 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."

[0133] Conventional beverage delivery systems have had difficulty responding precisely to users' preferences and moods. Furthermore, there were no systems that allowed users to input specific flavor ratios or desired origins, making it difficult to deliver beverages that met individual needs. This posed a risk of lowering user satisfaction. Furthermore, the accuracy and efficiency of beverage preparation were also issues.

[0134] 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.

[0135] In this invention, the server includes means for a user to input their beverage preferences and mood, means for a terminal to receive and display the user's input information, means for the terminal to transmit the information received to the server, means for the server to receive the user's input information and search a database based on it to select an optimal beverage recipe, means for the server to generate mixing instructions based on the selected beverage recipe and transmit them to the AI ​​machine, and means for the AI ​​machine to mix a beverage based on the mixing instructions and serve it to the user. This makes it possible to quickly and accurately provide an optimal beverage that matches the user's detailed preferences and mood.

[0136] "User" means an individual or organization that receives beverages using the system.

[0137] A "terminal" is an electronic device used by a user to enter information and view results.

[0138] A "server" is a computer system that receives user input, searches a database, selects recipes, and generates mixing instructions.

[0139] The "database" is a digital information management system that stores information about beverage recipes and various ingredients.

[0140] A "recipe" is information that indicates the combination of ingredients and how to mix them to make a specific drink.

[0141] "Mixing instructions" are specific instructions for creating a particular beverage that are generated by the server and sent to the AI ​​machine.

[0142] An "AI machine" is a device that automatically mixes beverages based on mixing instructions from a server and provides them to users.

[0143] "Preferences and moods" refers to input information regarding the ratio of sourness, bitterness, richness, and astringency that the user desires in a beverage, as well as the desired place of origin.

[0144] A "beverage" is a drinkable liquid that is dispensed for presentation to a user.

[0145] This invention is a system that inputs a user's beverage preferences and moods and provides the most suitable beverage based on that information. A specific implementation method for this system will be described below.

[0146] Hardware and Software Configuration

[0147] The system includes the following main components:

[0148] User input means: A device (e.g., tablet, smartphone) through which the user inputs the ratio of sourness, bitterness, richness, and astringency, as well as the desired origin.

[0149] Terminal: A device that receives user input and sends it to a server.

[0150] Server: A computer system that receives user input, searches and selects beverage recipes, and sends blending instructions to the AI ​​machine. This includes a database management system (e.g., MySQL®), a recipe search algorithm, and blending instruction generation software (e.g., FreeMarker).

[0151] AI machine: An automatic mixing machine (e.g., a robotic arm, a mixer with a dispenser) that mixes drinks based on mixing instructions from a server and provides them to the user.

[0152] How it works

[0153] 1. Entering user data

[0154] The user uses a terminal to input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10. They can also select the desired region of origin.

[0155] Example: A user inputs sourness "7", bitterness "3", richness "5", and astringency "2", and sets the desired origin as "France".

[0156] 2. Sending and Receiving Data

[0157] The terminal sends the input information to the server, which receives the user's input data and searches the database based on that information.

[0158] 3. Searching for and selecting recipes

[0159] The server uses a specific algorithm (e.g., SQL query or machine learning algorithm) to list drink recipes from a database that match the user's preferences. If a desired region of origin is specified, drinks from that region are searched for first.

[0160] 4. Generate mixing instructions

[0161] The server selects the optimal recipe and generates mixing instructions based on it. These mixing instructions are written out in detail using a template engine (e.g., FreeMarker).

[0162] Example: Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency.

[0163] 5. Sending mixing instructions and mixing drinks

[0164] The server generates the mixing instructions and sends them to the AI ​​machine, which selects the bottle and ingredients, mixes the drink in the correct proportions according to the instructions, and finally pours it into a container.

[0165] 6. Drink Serving and Notification

[0166] The AI ​​machine serves the blended drink to the user. It notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry for the wait. Your French Chardonnay is ready!"

[0167] Specific examples

[0168] If a user requests a "French" beverage with a ratio of "acidity 7: bitterness 3: richness 5: astringency 2," the system will provide a "French Chardonnay" with the optimal blend.

[0169] Example prompt:

[0170] User: "I'd like a wine from France with acidity 7, bitterness 3, body 5, and astringency 2."

[0171] Terminal: "Your requested beverage is being prepared. Please wait a moment."

[0172] Server: "Selecting the French Chardonnay that best suits your needs..."

[0173] AI machine: "The selected Chardonnay is blended in the appropriate proportions and poured into a container..."

[0174] Terminal: "Sorry to keep you waiting. Our French Chardonnay is ready!"

[0175] This allows users to quickly and accurately choose the perfect beverage to suit their specific preferences and moods, resulting in high levels of satisfaction.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1: Enter user data

[0178] The user operates the device

[0179] Input: Ratio of sourness, bitterness, richness, astringency, desired origin

[0180] How it works: The user enters the ratio of sourness, bitterness, richness, and astringency on the device screen on a scale of 1 to 10 and selects the desired origin.

[0181] Output: Input data (e.g., sourness "7", bitterness "3", richness "5", astringency "2", desired origin "France")

[0182] Step 2: Sending and Receiving Data

[0183] The device sends the data to the server

[0184] Input: Data entered by the user

[0185] How it works: The device organizes the data entered by the user and sends it to a server over the internet using the HTTP or HTTPS protocol.

[0186] Output: Data sent to the server

[0187] Step 3: Data processing and recipe selection

[0188] The server receives the data and starts processing it.

[0189] Input: User data sent from the terminal

[0190] How it works: The server receives user data and generates a database query, using SQL to search the recipe database for recipes that best match the user's preferences.

[0191] Output: A list of candidate drink recipes

[0192] The server narrows down the recipes

[0193] Input: A list of candidate drink recipes

[0194] How it works: Uses machine learning algorithms (e.g. KNN or cosine similarity) to select the best recipe from a list of candidates.

[0195] Output: Optimal drink recipe (e.g., French Chardonnay)

[0196] Step 4: Generate and send formulation instructions

[0197] The server generates the formulation instructions.

[0198] Input: Optimal Drink Recipe

[0199] How it works: Using a template engine (e.g., FreeMarker), specific mixing instructions are generated. The mixing instructions include the types and amounts of ingredients needed, as well as mixing procedures.

[0200] Output: Generated mixing instructions

[0201] The server sends the compounding instructions to the AI ​​machine.

[0202] Input: Generated mixing instructions

[0203] How it works: The server uses a RESTful API to send JSON-formatted formula instruction data to the AI ​​machine.

[0204] Output: Mixing instructions sent to the AI ​​machine

[0205] Step 5: Mix the drink

[0206] AI machine selects materials

[0207] Input: Mixing instructions sent from the server

[0208] How it works: The AI ​​machine retrieves the required ingredients (e.g., Chardonnay (France)) from storage based on the recipe instructions.

[0209] Output: Selected material

[0210] AI machines mix drinks

[0211] Input: Selected material

[0212] How it works: The AI ​​machine mixes ingredients in precise proportions, balancing sourness, bitterness, richness, and astringency. Reference sensors measure the amount of ingredients.

[0213] Output: Dispensed beverage

[0214] AI machine pours drinks into containers

[0215] Input: Dispensed beverage

[0216] How it works: The AI ​​machine uses a dispenser to dispense a mixed drink into a glass or other container.

[0217] Output: A drink ready to be served to the user

[0218] Step 6: Offer drinks and notify

[0219] The AI ​​machine sends a completion notification to the device

[0220] Input: Drink ready state

[0221] How it works: The AI ​​machine uses a message queuing method (e.g. RabbitMQ) to notify the device that the drink is ready.

[0222] Output: Completion notification sent to the terminal

[0223] The device notifies the user

[0224] Input: Completion notification from AI machine

[0225] What it does: The device displays a message to the user on the screen saying, "Sorry for the wait. Your French Chardonnay is ready!"

[0226] Output: The notification displayed to the user

[0227] The user receives a drink

[0228] Input: Completion notification from the terminal

[0229] How it works: The user checks the notification on their device, goes to the dispenser station, and receives a drink. Authentication is performed using an RFID tag or QR code (registered trademark).

[0230] Output: Drinks received

[0231] (Application example 1)

[0232] 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."

[0233] Conventional beverage supply systems have difficulty in quickly providing the optimal beverage according to the user's preferences and mood, and are therefore unable to provide a service that satisfies the user. In addition, there is also the problem that the beverage is manually mixed based on the user's input information in a physical store, which takes time and results in long waiting times for the user.

[0234] 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.

[0235] In this invention, the server includes a means for a user to input their beverage preferences and mood, a means for the server to receive the user's input information and search a database based on the information to select an optimal beverage recipe, a means for the server to generate blending instructions based on the beverage recipe selected by the server and transmit the instructions to a remote blending device, and a means for the blending device to blend the beverage based on the blending instructions and receive the beverage at a location specified by the user. This makes it possible to quickly and accurately provide a beverage that suits the user's preferences.

[0236] A "user" is a user of the system who inputs their beverage preferences and mood and receives beverages.

[0237] "Preferences and moods" refer to the user's taste preferences for beverages and their psychological state at the time.

[0238] The "server" is a central computer system that searches a database based on information received from the user, selects the optimal drink recipe, and issues instructions.

[0239] The "database" is a repository of information that stores and manages recipe data for various beverages and can be searched as needed.

[0240] A "drink recipe" is a set of instructions outlining the ingredients, proportions, and steps needed to prepare a particular drink.

[0241] "Mixing instructions" are instructions that show a specific mixing method based on a selected beverage recipe.

[0242] A "mixing machine" is a machine that automatically mixes beverages based on mixing instructions received from a server.

[0243] An "affiliated store" is a commercial food and beverage establishment that provides beverages in cooperation with the server.

[0244] "Notification" is a communication means to inform the user that the dispensed beverage is ready.

[0245] The present invention relates to a system that provides optimal beverages based on a user's input of their beverage preferences and moods. The system is implemented using a smartphone application, a server, and a dispensing device.

[0246] Hardware and software used

[0247] Hardware:

[0248] Smartphone

[0249] server

[0250] Mixing device (AI machine)

[0251] software:

[0252] Frontend: React Native

[0253] Backend: Node.js, Express.js

[0254] Database: MySQL

[0255] Communication: Socket.IO

[0256] overview

[0257] First, the user uses a smartphone application to input their beverage preferences and feelings. Specifically, they input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and select the desired origin of the beverage. The application also uses GPS functionality to display affiliated stores near the user's current location.

[0258] The terminal sends the input data to the server, which receives it and searches a recipe database to create a list of beverage candidates that match the user's preferences.The server then selects the optimal recipe from the list and generates mixing instructions.

[0259] The generated mixing instructions are sent to the mixing device at the partner store. The mixing device automatically mixes the specified beverage based on the instructions. When mixing is complete, the server sends a notification to the terminal, prompting the user to pick up the beverage.

[0260] Specific examples

[0261] For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2" on their smartphone, the server will select the optimal French beverage recipe based on this data and send instructions to the blending device. In this case, a recipe for French Chardonnay (strong acidity) is selected.

[0262] The blending device receives instructions from the server and blends French Chardonnay. Once blending is complete, the server notifies the user via a smartphone application with a message saying, "Sorry to keep you waiting! Your French Chardonnay is ready!" The user can then pick up the beverage at the designated store based on this notification.

[0263] Prompt Sentence Examples

[0264] The user has submitted the following preference data:

[0265] Acidity: 7

[0266] Bitterness: 3

[0267] Body: 5

[0268] Astringency: 2

[0269] Preferred origin: France

[0270] Find the perfect drink recipe.

[0271] This system allows users to quickly and accurately receive beverages tailored to their preferences, providing new experiences and satisfaction.

[0272] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0273] Step 1:

[0274] The user opens the smartphone application. The user inputs the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and selects the desired origin. The application collects this input data and obtains the user's location information. The user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin and current location information) is generated.

[0275] Step 2:

[0276] The device sends the user's input data to the server. The device converts the input data into JSON format and sends it to the server using an HTTP POST request. The input data here is the user's preference information and location information, and the output is data sent to the server.

[0277] Step 3:

[0278] The server receives user input data. The server analyzes the received data and searches the database to list candidate drink recipes that match the user's preferences. The server extracts matching items from the recipe database. The input data here is the analyzed user data, and the output data is a list of matching drink recipes.

[0279] Step 4:

[0280] The server selects the optimal drink recipe. From the list of drink recipes, it analyzes and prioritizes the drinks that have the closest ratio of sourness, bitterness, richness, and astringency. It also takes into account the origin of the drink desired by the user to determine the optimal recipe. The input data is a list of recipes, and the output data is the optimal recipe.

[0281] Step 5:

[0282] The server generates mixing instructions based on the optimal beverage recipe and sends them to the mixing device at the corresponding partner store. The server then converts the mixing instructions into specific steps, formats them in a format that the mixing device can understand, and sends them. The input data here is the optimal recipe, and the output data is the mixing instructions to the store.

[0283] Step 6:

[0284] The blending machine blends the beverage based on blending instructions received from the server. The blending machine takes the specified ingredients and measures and mixes them in the correct amounts. The input data is the blending instructions and the output data is the blended beverage.

[0285] Step 7:

[0286] When the blending device has finished preparing the blended beverage, it sends a completion notification to the server. The server receives this notification and forwards it to the terminal. The input data here is the blending completion notification, and the output data is the notification to the terminal.

[0287] Step 8:

[0288] The terminal notifies the user that the blending is complete. The terminal then displays a message on the smartphone saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" and prompts the user to pick it up at the store. The input data here is the notification from the server, and the output data is the notification to the user.

[0289] This series of steps allows the user to quickly and accurately receive a beverage tailored to their preferences.

[0290] 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.

[0291] This invention combines a system that allows users to input their beverage preferences and moods and provides the optimal beverage based on those preferences with an emotion engine that recognizes the user's emotions. Users can specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. They can also select the desired region of origin. Furthermore, the emotion engine analyzes the user's emotions from their facial expressions and voice, and sends that data to a server, enabling the provision of a more personalized beverage.

[0292] Program processing flow

[0293] Entering User Data

[0294] The device displays a question to the user.

[0295] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency in the range of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0296] Emotion engine analyzes user emotions

[0297] The emotion engine analyzes emotions from the user's facial expressions and voice in real time and obtains emotional data.

[0298] The user enters information

[0299] The user inputs into the terminal sourness "7", bitterness "3", richness "5", and astringency "2", and also inputs "France" as the desired origin.

[0300] The emotion engine sends the emotion data to the server.

[0301] The emotion engine sends the analysis results to the server, for example, telling the server that the user is feeling "happy" or "relaxed."

[0302] The device sends the data to the server

[0303] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[0304] Data processing and recipe selection

[0305] The server receives the data

[0306] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[0307] The server searches the recipe database

[0308] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[0309] The server selects the best recipe

[0310] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. For example, if the user is feeling relaxed, the server selects a recipe that contains many ingredients that are expected to have a relaxing effect.

[0311] Generate and send formulation instructions

[0312] The server generates the formulation instructions.

[0313] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[0314] The server sends compounding instructions to the AI ​​machine.

[0315] The server generates mixing instructions and sends them to the AI ​​machine.

[0316] Drink mixing

[0317] AI machine selects materials

[0318] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) as well as specific ingredients to enhance the relaxing effect.

[0319] AI machines mix drinks

[0320] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[0321] AI machine pours drinks into containers

[0322] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0323] Drinks provided

[0324] The AI ​​machine sends a completion notification to the device

[0325] The AI ​​machine will notify the device that the drink is ready.

[0326] The device notifies the user

[0327] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0328] The user receives a drink

[0329] The user follows the notification on the terminal and receives the finished drink.

[0330] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even their emotions that day. For example, if a user requests a French beverage with a ratio of acidity (7), bitterness (3), richness (5), and astringency (2), and their emotion is "relaxation," the system can provide a French Chardonnay with ingredients that emphasize its relaxing effect. In this way, a personalized experience can be provided based on the user's individual needs.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] The device displays a question to the user.

[0334] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0335] Step 2:

[0336] Emotion engine analyzes user emotions

[0337] The emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data such as "happiness," "relaxation," and "excitement."

[0338] Step 3:

[0339] The user enters information

[0340] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[0341] Step 4:

[0342] The emotion engine sends the emotion data to the server.

[0343] The emotion engine analyzes the emotion data and sends it to the server. For example, it can tell the user that their emotion is "relaxed."

[0344] Step 5:

[0345] The device sends the data to the server

[0346] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[0347] Step 6:

[0348] The server receives the data

[0349] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[0350] Step 7:

[0351] The server searches the recipe database

[0352] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[0353] Step 8:

[0354] The server selects the best recipe

[0355] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. In this case, the server selects a recipe made with French Chardonnay, which contains ingredients expected to have a relaxing effect.

[0356] Step 9:

[0357] The server generates the formulation instructions.

[0358] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[0359] Step 10:

[0360] The server sends compounding instructions to the AI ​​machine.

[0361] The server generates mixing instructions and sends them to the AI ​​machine.

[0362] Step 11:

[0363] AI machine selects materials

[0364] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) and specific ingredients to enhance the relaxing effect.

[0365] Step 12:

[0366] AI machines mix drinks

[0367] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[0368] Step 13:

[0369] AI machine pours drinks into containers

[0370] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0371] Step 14:

[0372] The AI ​​machine sends a completion notification to the device

[0373] The AI ​​machine will notify the device that the drink is ready.

[0374] Step 15:

[0375] The device notifies the user

[0376] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0377] Step 16:

[0378] The user receives a drink

[0379] The user follows the notification on the terminal and receives the finished drink.

[0380] Example 2

[0381] 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."

[0382] Conventional beverage delivery systems have difficulty responding to a user's individual preferences or mood of the day. Furthermore, they are unable to provide the optimal beverage while taking into account the user's emotions, and thus do not achieve sufficient personalization. As a result, users may receive a dissatisfied beverage.

[0383] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input their beverage preferences and mood, a means for the server to receive the user's input information and emotion data extracted from the user's facial expression and voice, and to search a database based on the received information and select an optimal beverage recipe, a means for generating blending instructions based on the beverage recipe selected by the server and sending the instructions to the machine, and a means for notifying the user. This makes it possible to provide an optimal beverage based on the user's individual preferences, mood of the day, and emotions in real time.

[0384] "User" refers to an individual who inputs their beverage preferences and mood and uses the system to receive a beverage.

[0385] "Beverage" refers to a consumable liquid that is dispensed based on a user's preferences, mood, or even emotion.

[0386] "Preferences" refer to specific characteristics such as the ratio of sourness, bitterness, richness, and astringency that a user desires in a beverage, as well as a desired place of origin.

[0387] "Mood" refers to the temporary emotions and psychological state of a user when selecting a beverage.

[0388] The term "means" refers to various methods and devices required to configure the system.

[0389] "Server" refers to a computer device that receives user input information and emotion data, selects an optimal drink recipe based on the received information, and generates and transmits blending instructions to the machine.

[0390] "Facial expression" refers to data on the emotional state obtained from the user's facial movements and expressions.

[0391] "Voice" refers to emotional state data obtained from the user's voice.

[0392] "Emotion data" refers to information about emotions extracted from the user's facial expressions and voice.

[0393] A "database" refers to an information system that stores information such as beverage recipes and manages them in a searchable format.

[0394] "Mixing instructions" refers to instruction data for instructing the machine on specific mixing contents based on the beverage recipe selected by the server.

[0395] "Machine" refers to a device that receives mixing instructions from the server, actually mixes the beverage, and provides it to the user.

[0396] "Region" refers to information indicating the origin of the beverage desired by the user.

[0397] "Notification" refers to a communication means for informing a user about a dispensed beverage.

[0398] This invention is a system that provides the optimal beverage based on the user's input of their beverage preferences, mood, and even emotions. This system also utilizes an emotion engine that recognizes emotions from the user's facial expressions and voice, thereby providing a more personalized beverage.

[0399] A terminal is used as a way for users to input their preferences and feelings about beverages. This terminal is equipped with a user interface and displays an input screen for inputting the ratios of sourness, bitterness, richness, and astringency. Users can also select their desired origin.

[0400] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, and processes this data in real time to analyze their emotions. Specifically, it uses image recognition and voice analysis technologies to obtain the user's emotional data. For example, it uses facial recognition technology to detect smiling and sad expressions, and voice analysis technology to evaluate the tone and speed of the voice.

[0401] When a user inputs the necessary information into the device, this data is sent to the server. The server receives the user's input data and emotion data sent from the device and emotion engine. It then searches the database based on this data and runs an algorithm to select the optimal drink recipe. This search process prioritizes drinks from specific regions and lists candidates that match the ratios and emotions specified by the user.

[0402] After the server selects the optimal recipe, it generates mixing instructions based on this recipe. For example, specific mixing instructions such as "Chardonnay (France), strong acidity, relaxing effect" are created. These mixing instructions are then sent to the AI ​​machine.

[0403] The AI ​​machine selects the specified ingredients based on the mixing instructions received from the server. During this process, it automatically selects ingredients from its inventory and also selects additional ingredients to complement specific ingredients. The AI ​​machine then mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. Ingredients intended for relaxation effects can also be added.

[0404] Once the drink is mixed, the AI ​​machine pours it into a glass (or other container), with the pouring program controlling the amount to be dispensed.

[0405] Finally, the AI ​​machine notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The user confirms the notification and collects the finished drink from the designated pick-up location.

[0406] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even real-time emotions. For example, if a user enters the following prompt, the system will automatically begin the process of blending a beverage:

[0407] Example prompt sentence:

[0408] "My mood is relaxation. I like French drinks and I like the balance of acidity 7, bitterness 3, body 5 and astringency 2."

[0409] Based on this prompt, the system will provide the best French Chardonnay that matches the user's tastes and feelings.

[0410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0411] Step 1:

[0412] The device displays the input screen.

[0413] The terminal displays the question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency in a range from 1 to 10." It also displays the option, "Please select a preferred origin, if any." The input format consists of a numeric input field and a drop-down list for selecting origin. For example, the input data can be sourness "7," bitterness "3," richness "5," astringency "2," and the desired origin "France."

[0414] Step 2:

[0415] Emotion engine analyzes emotions

[0416] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, then processes this data in real time to analyze their emotions. The emotion engine performs facial recognition and voice analysis to distinguish between smiling and sad expressions, as well as the tone and speed of the voice. This data processing uses the captured images and voice as input data and outputs emotions such as "happy" or "relaxed."

[0417] Step 3:

[0418] The user enters information

[0419] The user inputs the values ​​for sourness (7), bitterness (3), richness (5), and astringency (2) on the terminal screen, and selects "France" as the desired origin. After completing the input, the user presses the "Send" button, which sends the data to the server. The input data includes the user's preferred ratio and origin information.

[0420] Step 4:

[0421] The emotion engine sends the emotion data to the server.

[0422] The emotion engine sends the emotion data obtained as a result of its analysis, such as "happy" or "relaxed," to the server. Specifically, the emotion data is sent to the server in JSON format. The emotion engine's analysis results are the input data, and the emotion information received by the server is the output data.

[0423] Step 5:

[0424] The device sends user data to the server

[0425] The terminal sends the ratio data entered by the user (sourness "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server. This data is also sent in JSON format. The information entered by the user is obtained as input data, and the preference information received by the server is obtained as output data.

[0426] Step 6:

[0427] The server receives the data

[0428] The server receives the user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin) and emotion data sent from the device and emotion engine. Various information from the device and emotion engine is obtained as input data, and data for searching in the internal database is obtained as output data.

[0429] Step 7:

[0430] The server searches the recipe database

[0431] Based on the data received by the server, a database search engine is used to query the recipe database. Candidates that match the user's specified ratios, desired origin, and emotional information are extracted. SQL queries and other data calculations are executed. The search query is used as input data, and the extracted recipe candidates are used as output data.

[0432] Step 8:

[0433] The server selects the best recipe

[0434] The server uses an algorithm to select the optimal recipe from the search results. For example, if a user is looking for a relaxing effect, it will identify recipes that contain that ingredient. The selection process is carried out through algorithmic processing. The extracted recipe candidates are obtained as input data, and the optimal recipe is obtained as output data.

[0435] Step 9:

[0436] The server generates the formulation instructions.

[0437] The server creates blending instructions based on the selected recipe. For example, it generates blending instructions such as "Chardonnay (France), strong acidity, relaxing effect." Blending instruction data is created as data processing. The selected recipe is obtained as input data, and the generated blending instructions are obtained as output data.

[0438] Step 10:

[0439] The server sends compounding instructions to the AI ​​machine.

[0440] The server sends the generated compounding instructions to the AI ​​machine via the network. This is usually done through an API. The generated compounding instructions are the input data, and the instruction information received by the AI ​​machine is the output data.

[0441] Step 11:

[0442] AI machine selects materials

[0443] Based on the compounding instructions received from the server, the AI ​​machine automatically selects the specified materials from the warehouse. Based on the material selection logic, the necessary materials are identified and retrieved. The instruction information is obtained as input data, and the selected materials are output data.

[0444] Step 12:

[0445] AI machines mix drinks

[0446] The AI ​​machine mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. The ingredients are mixed and the blend is carried out according to the program. The selected ingredients are the input data, and the blended beverage is the output data.

[0447] Step 13:

[0448] AI machine pours drinks into containers

[0449] Once the AI ​​machine has mixed the drink, it automatically pours it into a glass (or other container). The pouring method is also controlled by the program, providing the appropriate amount. The input data is the mixed drink, and the output data is the drink poured into the container.

[0450] Step 14:

[0451] The AI ​​machine sends a completion notification to the device

[0452] The AI ​​machine notifies the server that the drink is ready and transfers that information to the device. The input data is information about the completion of mixing, and the output data is information about the notification sent to the device.

[0453] Step 15:

[0454] The device notifies the user

[0455] The terminal displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The notification message is displayed on the screen. The input data is the completion notification, and the output data is the message to be displayed to the user.

[0456] Step 16:

[0457] The user receives a drink

[0458] The user confirms the notification from the device and collects the completed drink from the designated pick-up location. The process is completed when the user picks up the drink at the pick-up location. The notification to the user is obtained as input data, and the drink provided to the user is obtained as output data.

[0459] (Application example 2)

[0460] 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."

[0461] Conventional user beverage selection systems provide beverages based on the user's preferences and mood, but do not consider the user's emotional state. As a result, the beverage provided may not necessarily be optimal for the user's current emotion or mood. To improve customer experience, especially in cafes and beverage stores, there is a need to analyze the user's emotional state in real time and provide personalized beverages based on that analysis.

[0462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0463] In this invention, the server includes: means for a user to input their preferences and mood regarding beverages; means for an emotion engine to analyze emotions from the user's facial expressions and voice and acquire emotion data; means for the server to receive the user's input information and emotion data and search a database based on the information and emotion data to select an optimal beverage recipe; means for the server to generate blending instructions based on the beverage recipe selected and send them to the blending device; means for the blending device to blend a beverage based on the blending instructions and provide it to the user; and means for the terminal used by the user to input information to send the emotion engine's analysis results to the server. This makes it possible to provide an optimal beverage according to the user's current emotional state and preferences.

[0464] "A means for users to input their preferences and mood regarding beverages" refers to an interface that allows users to input the characteristics of beverages (sourness, bitterness, richness, astringency) and moods (relaxed, happy, etc.) using specific numerical values ​​or text.

[0465] "Means for the emotion engine to analyze emotions from the user's facial expressions and voice and obtain emotional data" refers to a system that uses sensors and cameras to obtain the user's real-time facial expressions and voice, and analyzes that data to identify the user's emotional state.

[0466] "Means for the server to receive the user's input information and emotional data, and search the database based on that to select the optimal drink recipe" refers to the process in which the server receives the preference and mood information entered by the user, as well as the emotional data obtained by the emotion engine, and searches the drink recipe database based on that to select the optimal drink recipe.

[0467] The "means for generating mixing instructions based on a beverage recipe selected by the server and transmitting them to the mixing device" refers to a mechanism for generating specific mixing instructions based on a selected beverage recipe and transmitting these instructions to the mixing device.

[0468] The "means for the blending device to blend a beverage based on blending instructions and provide it to a user" refers to a device and process that mixes specified ingredients based on blending instructions sent from a server to create a beverage and provides it to a user.

[0469] "Means for the terminal to transmit the emotion engine's analysis results to the server when the user inputs information" refers to the process in which the user inputs input data into the terminal and the terminal transmits emotion data, which is the emotion engine's analysis result, to the server.

[0470] MODE FOR CARRYING OUT THE INVENTION

[0471] This invention is a system that combines a system that allows a user to input their beverage preferences and moods and provides the optimal beverage based on the input, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[0472] Hardware and Software

[0473] This system uses the following main hardware and software:

[0474] User device: Smartphone, tablet, etc. (with camera and microphone)

[0475] Emotion engine: Software for emotion analysis (e.g., Microsoft® Azure® Face API, Google® Cloud Speech-to-Text)

[0476] Server: AWS Lambda or similar cloud service

[0477] Database: A database that stores drink recipes (e.g., Amazon RDS)

[0478] Mixing device: Robot barista (e.g., SoftBank Robotics' Pepper, CafeX)

[0479] System Operation

[0480] The user terminal collects the user's input information and emotion data and sends it to the server. The server searches the database, selects the optimal drink recipe, and sends mixing instructions to the mixing device. The mixing device mixes the drink based on the mixing instructions and serves it to the user.

[0481] User data input and sentiment analysis

[0482] The user inputs the ratio of sourness, bitterness, richness, and astringency of the beverage, as well as the desired origin, through a smartphone app. Using the app's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, if a user inputs "sourness 7, bitterness 3, richness 5, astringency 2" and the desired origin is "France," the emotion engine may recognize the emotion "relaxed."

[0483] Data processing and recipe selection

[0484] The device sends input data and emotional data from the user to the server. The server receives this data and searches the database to select the optimal drink recipe. Since the user is in a relaxed state, a recipe containing ingredients with a relaxing effect is selected.

[0485] Mixing instructions and beverage preparation

[0486] The server generates mixing instructions based on the selected drink recipe and sends them to the mixing device. The mixing device receives instructions from the server, selects the specified ingredients, and mixes them in the correct ratios to prepare the drink. For example, a drink can be prepared based on specific instructions such as "Chardonnay (France), strong acidity, and a relaxing effect."

[0487] Drink offering and notification

[0488] When the blending is complete, the blending device sends the information to the terminal, which then notifies the user, "Your French Chardonnay is ready!" The user follows the notification and receives the finished drink.

[0489] Prompt Sentence Examples

[0490] Here are some example prompts for using a generative AI model:

[0491] Build an algorithm to select the recipe that provides the optimal drink based on the user's preferred ratio of sourness, bitterness, richness, and astringency, as well as emotional data. Explain this using the example where the user's current emotional state is "Relaxed," the preferred ratios are sourness "7," bitterness "3," richness "5," astringency "2," and the origin is "France."

[0492] The present invention makes it possible to provide the user with an optimal beverage according to their current emotional state and preferences.

[0493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0494] Step 1:

[0495] User Input

[0496] The user device displays questions about the beverage to the user. The user uses the smartphone interface to input "acidity 7, bitterness 3, richness 5, astringency 2" and selects "France" as the desired origin. Once the user has completed the input, the device temporarily stores the data.

[0497] Input: User's desired ratio of sourness, bitterness, richness, and astringency of the beverage, as well as desired origin information

[0498] Output: User input data stored on the device

[0499] Step 2:

[0500] sentiment analysis

[0501] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time. For example, Microsoft Azure Face API detects the emotion "relaxed" from the user's facial expressions, and Google Cloud Speech-to-Text analyzes emotions from the voice. The emotion engine then sends the acquired emotional data to the device.

[0502] Input: User's facial and voice data

[0503] Output: Emotion data analyzed by the emotion engine (e.g., "Relaxed")

[0504] Step 3:

[0505] Data transmission

[0506] The device transmits the user's input data and emotion data to a server via an internet connection.

[0507] Input: User input data (sourness 7, bitterness 3, richness 5, astringency 2, desired origin: France) and emotional data (relaxed)

[0508] Output: The user's dataset that is sent to the server

[0509] Step 4:

[0510] Database search

[0511] The server searches a database of beverage recipes based on the received user and emotion data. For example, it uses Amazon RDS to find a list of French beverage recipes from the database that best fit the user's preferences and emotions.

[0512] Input: Your dataset

[0513] Output: A shortlist of optimal drink recipes

[0514] Step 5:

[0515] Recipe Selection

[0516] The server selects the drink recipe that best suits the user's taste and emotions from the list of drink recipes. For example, it selects a recipe such as "Chardonnay (France), strong acidity, relaxing effect."

[0517] Input: List of drink recipe candidates

[0518] Output: Selected optimal drink recipe

[0519] Step 6:

[0520] Mixing instructions generation

[0521] The server generates specific mixing instructions based on the selected drink recipe, including the ingredients and their proportions. For example, the server might generate instructions such as "Mix Chardonnay (France) with a high acidity and add a relaxing ingredient."

[0522] Input: Selected drink recipe

[0523] Output: Mixing instructions

[0524] Step 7:

[0525] Sending mixing instructions

[0526] The server sends the generated mixing instructions to the mixing device (robot barista), and this communication is also carried out via the Internet.

[0527] Input: Mixing instructions

[0528] Output: Mixing instructions sent to the mixer

[0529] Step 8:

[0530] Beverage blending

[0531] The blending device receives the blending instructions and blends the beverage in proportion to the specified ingredients, for example, using Chardonnay (France) to create a more acidic drink and adding a relaxing ingredient.

[0532] Input: Mixing instructions

[0533] Output: Finished beverage

[0534] Step 9:

[0535] Completion notification

[0536] The dispensing device notifies the terminal that the beverage is ready, and the terminal displays the message "Your French Chardonnay is ready!" to the user.

[0537] Input: Completion notification from compounding device

[0538] Output: A notification message to the user

[0539] Step 10:

[0540] Receiving beverages

[0541] The user follows the notification on the terminal and receives the finished drink.

[0542] Input: Notification message

[0543] Output: The finished beverage received by the user

[0544] This allows users to easily enjoy personalized beverages that match their mood and preferences that day.

[0545] 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.

[0546] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0547] 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.

[0548] [Second embodiment]

[0549] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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. 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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."

[0561] This invention is a system that provides the optimal beverage based on a user's input of their beverage preferences and mood. The system allows users to specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. Furthermore, users can select the desired region of origin. Based on this information, the system selects the optimal beverage recipe, blends it, and provides it to the user.

[0562] The specific processing flow of this system will be explained in natural language.

[0563] Step 1: Enter your user data

[0564] The device displays a question to the user.

[0565] Terminal: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10."

[0566] Terminal: "Please select your preferred origin."

[0567] The user enters information

[0568] User: Enter sourness "7", bitterness "3", richness "5", and astringency "2".

[0569] User: Enters "France" as the desired origin.

[0570] The device sends the data to the server

[0571] Terminal: Sends the entered ratio data and desired origin information to the server.

[0572] Step 2: Data processing and recipe selection

[0573] The server receives the data

[0574] Server: Receives the user's input data (acidity "7", bitterness "3", richness "5", astringency "2", origin "France").

[0575] The server searches the recipe database

[0576] Server: Searches the recipe database and lists possible drinks that match the user's preferences.

[0577] Server: Prefer French wines.

[0578] The server further narrows down the candidates

[0579] Server: Analyze the recipes that most closely match the above ratios to determine the best recipe.

[0580] Server: Choose a French Chardonnay (high acidity).

[0581] Step 3: Generate and send formulation instructions

[0582] The server generates the formulation instructions.

[0583] Server: Generates mixing instructions based on the selected recipe.

[0584] Server: Create a request for "Chardonnay (France), high acidity, low bitterness, medium body, low astringency."

[0585] The server sends compounding instructions to the AI ​​machine

[0586] Server: Sends mixing instructions to the AI ​​machine.

[0587] Step 4: Mix the drink

[0588] AI machine selects materials

[0589] AI Machine: Pick up a Chardonnay (France).

[0590] AI machine: Selects specific ingredients to enhance the sourness.

[0591] AI machines mix drinks

[0592] AI machine: Mixes selected ingredients in precise proportions.

[0593] AI machine: Adjusts the balance of acidity, bitterness, richness, and astringency.

[0594] AI machine pours drinks into containers

[0595] AI machine: Mixes and pours drinks into glasses (or other containers).

[0596] Step 5: Serve the drink

[0597] The AI ​​machine sends a completion notification to the device

[0598] AI machine: Notifies the device when the drink is ready.

[0599] The device notifies the user

[0600] Terminal: Display the message "Thank you for waiting. Your French Chardonnay is ready!"

[0601] The user receives a drink

[0602] User: Receives the finished drink and enjoys it.

[0603] These steps enable the system to quickly and accurately provide the optimal beverage tailored to the user's preferences and mood of the day. For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2", the system can provide a French Chardonnay with the optimal blend. In this way, users can enjoy beverages that bring new experiences and satisfaction to their daily lives.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] The device displays a question to the user.

[0607] The device displays the following question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0608] Step 2:

[0609] The user enters information

[0610] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[0611] Step 3:

[0612] The device sends the data to the server

[0613] The terminal sends the ratio data entered by the user (acidity "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server.

[0614] Step 4:

[0615] The server receives the data

[0616] The server receives the user input data sent from the terminal.

[0617] Step 5:

[0618] The server searches the recipe database

[0619] The server searches the recipe database based on the user's input data, prioritizing French drinks in particular, and lists candidates that match the ratio specified by the user.

[0620] Step 6:

[0621] The server selects the best recipe

[0622] The server analyzes the recipes listed and selects the one that most closely matches the user's specified ratio (acidity: 7, bitterness: 3, richness: 5, astringency: 2). In this example, a French Chardonnay (highly acidic) is selected.

[0623] Step 7:

[0624] The server generates the formulation instructions.

[0625] The server generates specific mixing instructions based on the selected recipe, such as "Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency."

[0626] Step 8:

[0627] The server sends compounding instructions to the AI ​​machine.

[0628] The server generates mixing instructions and sends them to the AI ​​machine.

[0629] Step 9:

[0630] AI machine selects materials

[0631] The AI ​​machine receives recipe instructions from the server and selects the specified ingredients (Chardonnay (France)) and also selects specific ingredients to enhance the acidity.

[0632] Step 10:

[0633] AI machines mix drinks

[0634] The AI ​​machine selects ingredients and mixes them in precise proportions based on instructions, adjusting the balance of sourness, bitterness, richness, and astringency.

[0635] Step 11:

[0636] AI machine pours drinks into containers

[0637] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0638] Step 12:

[0639] The AI ​​machine sends a completion notification to the device

[0640] The AI ​​machine will notify the device that the drink is ready.

[0641] Step 13:

[0642] The device notifies the user

[0643] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0644] Step 14:

[0645] The user receives a drink

[0646] The user follows the notification on the terminal and receives the finished drink.

[0647] Example 1

[0648] 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."

[0649] Conventional beverage delivery systems have had difficulty responding precisely to users' preferences and moods. Furthermore, there were no systems that allowed users to input specific flavor ratios or desired origins, making it difficult to deliver beverages that met individual needs. This posed a risk of lowering user satisfaction. Furthermore, the accuracy and efficiency of beverage preparation were also issues.

[0650] 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.

[0651] In this invention, the server includes means for a user to input their beverage preferences and mood, means for a terminal to receive and display the user's input information, means for the terminal to transmit the information received to the server, means for the server to receive the user's input information and search a database based on it to select an optimal beverage recipe, means for the server to generate mixing instructions based on the selected beverage recipe and transmit them to the AI ​​machine, and means for the AI ​​machine to mix a beverage based on the mixing instructions and serve it to the user. This makes it possible to quickly and accurately provide an optimal beverage that matches the user's detailed preferences and mood.

[0652] "User" means an individual or organization that receives beverages using the system.

[0653] A "terminal" is an electronic device used by a user to enter information and view results.

[0654] A "server" is a computer system that receives user input, searches a database, selects recipes, and generates mixing instructions.

[0655] The "database" is a digital information management system that stores information about beverage recipes and various ingredients.

[0656] A "recipe" is information that indicates the combination of ingredients and how to mix them to make a specific drink.

[0657] "Mixing instructions" are specific instructions for creating a particular beverage that are generated by the server and sent to the AI ​​machine.

[0658] An "AI machine" is a device that automatically mixes beverages based on mixing instructions from a server and provides them to users.

[0659] "Preferences and moods" refers to input information regarding the ratio of sourness, bitterness, richness, and astringency that the user desires in a beverage, as well as the desired place of origin.

[0660] A "beverage" is a drinkable liquid that is dispensed for presentation to a user.

[0661] This invention is a system that inputs a user's beverage preferences and moods and provides the most suitable beverage based on that information. A specific implementation method for this system will be described below.

[0662] Hardware and Software Configuration

[0663] The system includes the following main components:

[0664] User input means: A device (e.g., tablet, smartphone) through which the user inputs the ratio of sourness, bitterness, richness, and astringency, as well as the desired origin.

[0665] Terminal: A device that receives user input and sends it to a server.

[0666] Server: A computer system that receives user input, searches and selects beverage recipes, and sends mixing instructions to the AI ​​machine. This includes a database management system (e.g., MySQL), a recipe search algorithm, and mixing instruction generation software (e.g., FreeMarker).

[0667] AI machine: An automatic mixing machine (e.g., a robotic arm, a mixer with a dispenser) that mixes drinks based on mixing instructions from a server and provides them to the user.

[0668] How it works

[0669] 1. Entering user data

[0670] The user uses a terminal to input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10. They can also select the desired region of origin.

[0671] Example: A user inputs sourness "7", bitterness "3", richness "5", and astringency "2", and sets the desired origin as "France".

[0672] 2. Sending and Receiving Data

[0673] The terminal sends the input information to the server, which receives the user's input data and searches the database based on that information.

[0674] 3. Searching for and selecting recipes

[0675] The server uses a specific algorithm (e.g., SQL query or machine learning algorithm) to list drink recipes from a database that match the user's preferences. If a desired region of origin is specified, drinks from that region are searched for first.

[0676] 4. Generate mixing instructions

[0677] The server selects the optimal recipe and generates mixing instructions based on it. These mixing instructions are written out in detail using a template engine (e.g., FreeMarker).

[0678] Example: Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency.

[0679] 5. Sending mixing instructions and mixing drinks

[0680] The server generates the mixing instructions and sends them to the AI ​​machine, which selects the bottle and ingredients, mixes the drink in the correct proportions according to the instructions, and finally pours it into a container.

[0681] 6. Drink Serving and Notification

[0682] The AI ​​machine serves the blended drink to the user. It notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry for the wait. Your French Chardonnay is ready!"

[0683] Specific examples

[0684] If a user requests a "French" beverage with a ratio of "acidity 7: bitterness 3: richness 5: astringency 2," the system will provide a "French Chardonnay" with the optimal blend.

[0685] Example prompt:

[0686] User: "I'd like a wine from France with acidity 7, bitterness 3, body 5, and astringency 2."

[0687] Terminal: "Your requested beverage is being prepared. Please wait a moment."

[0688] Server: "Selecting the French Chardonnay that best suits your needs..."

[0689] AI machine: "The selected Chardonnay is blended in the appropriate proportions and poured into a container..."

[0690] Terminal: "Sorry to keep you waiting. Our French Chardonnay is ready!"

[0691] This allows users to quickly and accurately choose the perfect beverage to suit their specific preferences and moods, resulting in high levels of satisfaction.

[0692] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0693] Step 1: Enter user data

[0694] The user operates the device

[0695] Input: Ratio of sourness, bitterness, richness, astringency, desired origin

[0696] How it works: The user enters the ratio of sourness, bitterness, richness, and astringency on the device screen on a scale of 1 to 10 and selects the desired origin.

[0697] Output: Input data (e.g., sourness "7", bitterness "3", richness "5", astringency "2", desired origin "France")

[0698] Step 2: Sending and Receiving Data

[0699] The device sends the data to the server

[0700] Input: Data entered by the user

[0701] How it works: The device organizes the data entered by the user and sends it to a server over the internet using the HTTP or HTTPS protocol.

[0702] Output: Data sent to the server

[0703] Step 3: Data processing and recipe selection

[0704] The server receives the data and starts processing it.

[0705] Input: User data sent from the terminal

[0706] How it works: The server receives user data and generates a database query, using SQL to search the recipe database for recipes that best match the user's preferences.

[0707] Output: A list of candidate drink recipes

[0708] The server narrows down the recipes

[0709] Input: A list of candidate drink recipes

[0710] How it works: Uses machine learning algorithms (e.g. KNN or cosine similarity) to select the best recipe from a list of candidates.

[0711] Output: Optimal drink recipe (e.g., French Chardonnay)

[0712] Step 4: Generate and send formulation instructions

[0713] The server generates the formulation instructions.

[0714] Input: Optimal Drink Recipe

[0715] How it works: Using a template engine (e.g., FreeMarker), specific mixing instructions are generated. The mixing instructions include the types and amounts of ingredients needed, as well as mixing procedures.

[0716] Output: Generated mixing instructions

[0717] The server sends the compounding instructions to the AI ​​machine.

[0718] Input: Generated mixing instructions

[0719] How it works: The server uses a RESTful API to send JSON-formatted formula instruction data to the AI ​​machine.

[0720] Output: Mixing instructions sent to the AI ​​machine

[0721] Step 5: Mix the drink

[0722] AI machine selects materials

[0723] Input: Mixing instructions sent from the server

[0724] How it works: The AI ​​machine retrieves the required ingredients (e.g., Chardonnay (France)) from storage based on the recipe instructions.

[0725] Output: Selected material

[0726] AI machines mix drinks

[0727] Input: Selected material

[0728] How it works: The AI ​​machine mixes ingredients in precise proportions, balancing sourness, bitterness, richness, and astringency. Reference sensors measure the amount of ingredients.

[0729] Output: Dispensed beverage

[0730] AI machine pours drinks into containers

[0731] Input: Dispensed beverage

[0732] How it works: The AI ​​machine uses a dispenser to dispense a mixed drink into a glass or other container.

[0733] Output: A drink ready to be served to the user

[0734] Step 6: Offer drinks and notify

[0735] The AI ​​machine sends a completion notification to the device

[0736] Input: Drink ready state

[0737] How it works: The AI ​​machine uses a message queuing method (e.g. RabbitMQ) to notify the device that the drink is ready.

[0738] Output: Completion notification sent to the terminal

[0739] The device notifies the user

[0740] Input: Completion notification from AI machine

[0741] What it does: The device displays a message to the user on the screen saying, "Sorry for the wait. Your French Chardonnay is ready!"

[0742] Output: The notification displayed to the user

[0743] The user receives a drink

[0744] Input: Completion notification from the terminal

[0745] How it works: The user checks the notification on their device, goes to the dispenser station, and receives a drink. They are authenticated using an RFID tag or QR code.

[0746] Output: Drinks received

[0747] (Application example 1)

[0748] 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."

[0749] Conventional beverage supply systems have difficulty in quickly providing the optimal beverage according to the user's preferences and mood, and are therefore unable to provide a service that satisfies the user. In addition, there is also the problem that the beverage is manually mixed based on the user's input information in a physical store, which takes time and results in long waiting times for the user.

[0750] 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.

[0751] In this invention, the server includes a means for a user to input their beverage preferences and mood, a means for the server to receive the user's input information and search a database based on the information to select an optimal beverage recipe, a means for the server to generate blending instructions based on the beverage recipe selected by the server and transmit the instructions to a remote blending device, and a means for the blending device to blend the beverage based on the blending instructions and receive the beverage at a location specified by the user. This makes it possible to quickly and accurately provide a beverage that suits the user's preferences.

[0752] A "user" is a user of the system who inputs their beverage preferences and mood and receives beverages.

[0753] "Preferences and moods" refer to the user's taste preferences for beverages and their psychological state at the time.

[0754] The "server" is a central computer system that searches a database based on information received from the user, selects the optimal drink recipe, and issues instructions.

[0755] The "database" is a repository of information that stores and manages recipe data for various beverages and can be searched as needed.

[0756] A "drink recipe" is a set of instructions outlining the ingredients, proportions, and steps needed to prepare a particular drink.

[0757] "Mixing instructions" are instructions that show a specific mixing method based on a selected beverage recipe.

[0758] A "mixing machine" is a machine that automatically mixes beverages based on mixing instructions received from a server.

[0759] An "affiliated store" is a commercial food and beverage establishment that provides beverages in cooperation with the server.

[0760] "Notification" is a communication means to inform the user that the dispensed beverage is ready.

[0761] The present invention relates to a system that provides optimal beverages based on a user's input of their beverage preferences and moods. The system is implemented using a smartphone application, a server, and a dispensing device.

[0762] Hardware and software used

[0763] Hardware:

[0764] Smartphone

[0765] server

[0766] Mixing device (AI machine)

[0767] software:

[0768] Frontend: React Native

[0769] Backend: Node.js, Express.js

[0770] Database: MySQL

[0771] Communication: Socket.IO

[0772] overview

[0773] First, the user uses a smartphone application to input their beverage preferences and feelings. Specifically, they input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and select the desired origin of the beverage. The application also uses GPS functionality to display affiliated stores near the user's current location.

[0774] The terminal sends the input data to the server, which receives it and searches a recipe database to create a list of beverage candidates that match the user's preferences.The server then selects the optimal recipe from the list and generates mixing instructions.

[0775] The generated mixing instructions are sent to the mixing device at the partner store. The mixing device automatically mixes the specified beverage based on the instructions. When mixing is complete, the server sends a notification to the terminal, prompting the user to pick up the beverage.

[0776] Specific examples

[0777] For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2" on their smartphone, the server will select the optimal French beverage recipe based on this data and send instructions to the blending device. In this case, a recipe for French Chardonnay (strong acidity) is selected.

[0778] The blending device receives instructions from the server and blends French Chardonnay. Once blending is complete, the server notifies the user via a smartphone application with a message saying, "Sorry to keep you waiting! Your French Chardonnay is ready!" The user can then pick up the beverage at the designated store based on this notification.

[0779] Prompt Sentence Examples

[0780] The user has submitted the following preference data:

[0781] Acidity: 7

[0782] Bitterness: 3

[0783] Body: 5

[0784] Astringency: 2

[0785] Preferred origin: France

[0786] Find the perfect drink recipe.

[0787] This system allows users to quickly and accurately receive beverages tailored to their preferences, providing new experiences and satisfaction.

[0788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0789] Step 1:

[0790] The user opens the smartphone application. The user inputs the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and selects the desired origin. The application collects this input data and obtains the user's location information. The user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin and current location information) is generated.

[0791] Step 2:

[0792] The device sends the user's input data to the server. The device converts the input data into JSON format and sends it to the server using an HTTP POST request. The input data here is the user's preference information and location information, and the output is data sent to the server.

[0793] Step 3:

[0794] The server receives user input data. The server analyzes the received data and searches the database to list candidate drink recipes that match the user's preferences. The server extracts matching items from the recipe database. The input data here is the analyzed user data, and the output data is a list of matching drink recipes.

[0795] Step 4:

[0796] The server selects the optimal drink recipe. From the list of drink recipes, it analyzes and prioritizes the drinks that have the closest ratio of sourness, bitterness, richness, and astringency. It also takes into account the origin of the drink desired by the user to determine the optimal recipe. The input data is a list of recipes, and the output data is the optimal recipe.

[0797] Step 5:

[0798] The server generates mixing instructions based on the optimal beverage recipe and sends them to the mixing device at the corresponding partner store. The server then converts the mixing instructions into specific steps, formats them in a format that the mixing device can understand, and sends them. The input data here is the optimal recipe, and the output data is the mixing instructions to the store.

[0799] Step 6:

[0800] The blending machine blends the beverage based on blending instructions received from the server. The blending machine takes the specified ingredients and measures and mixes them in the correct amounts. The input data is the blending instructions and the output data is the blended beverage.

[0801] Step 7:

[0802] When the blending device has finished preparing the blended beverage, it sends a completion notification to the server. The server receives this notification and forwards it to the terminal. The input data here is the blending completion notification, and the output data is the notification to the terminal.

[0803] Step 8:

[0804] The terminal notifies the user that the blending is complete. The terminal then displays a message on the smartphone saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" and prompts the user to pick it up at the store. The input data here is the notification from the server, and the output data is the notification to the user.

[0805] This series of steps allows the user to quickly and accurately receive a beverage tailored to their preferences.

[0806] 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.

[0807] This invention combines a system that allows users to input their beverage preferences and moods and provides the optimal beverage based on those preferences with an emotion engine that recognizes the user's emotions. Users can specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. They can also select the desired region of origin. Furthermore, the emotion engine analyzes the user's emotions from their facial expressions and voice, and sends that data to a server, enabling the provision of a more personalized beverage.

[0808] Program processing flow

[0809] Entering User Data

[0810] The device displays a question to the user.

[0811] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency in the range of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0812] Emotion engine analyzes user emotions

[0813] The emotion engine analyzes emotions from the user's facial expressions and voice in real time and obtains emotional data.

[0814] The user enters information

[0815] The user inputs into the terminal sourness "7", bitterness "3", richness "5", and astringency "2", and also inputs "France" as the desired origin.

[0816] The emotion engine sends the emotion data to the server.

[0817] The emotion engine sends the analysis results to the server, for example, telling the server that the user is feeling "happy" or "relaxed."

[0818] The device sends the data to the server

[0819] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[0820] Data processing and recipe selection

[0821] The server receives the data

[0822] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[0823] The server searches the recipe database

[0824] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[0825] The server selects the best recipe

[0826] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. For example, if the user is feeling relaxed, the server selects a recipe that contains many ingredients that are expected to have a relaxing effect.

[0827] Generate and send formulation instructions

[0828] The server generates the formulation instructions.

[0829] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[0830] The server sends compounding instructions to the AI ​​machine.

[0831] The server generates mixing instructions and sends them to the AI ​​machine.

[0832] Drink mixing

[0833] AI machine selects materials

[0834] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) as well as specific ingredients to enhance the relaxing effect.

[0835] AI machines mix drinks

[0836] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[0837] AI machine pours drinks into containers

[0838] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0839] Drinks provided

[0840] The AI ​​machine sends a completion notification to the device

[0841] The AI ​​machine will notify the device that the drink is ready.

[0842] The device notifies the user

[0843] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0844] The user receives a drink

[0845] The user follows the notification on the terminal and receives the finished drink.

[0846] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even their emotions that day. For example, if a user requests a French beverage with a ratio of acidity (7), bitterness (3), richness (5), and astringency (2), and their emotion is "relaxation," the system can provide a French Chardonnay with ingredients that emphasize its relaxing effect. In this way, a personalized experience can be provided based on the user's individual needs.

[0847] The processing flow will be explained below.

[0848] Step 1:

[0849] The device displays a question to the user.

[0850] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[0851] Step 2:

[0852] Emotion engine analyzes user emotions

[0853] The emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data such as "happiness," "relaxation," and "excitement."

[0854] Step 3:

[0855] The user enters information

[0856] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[0857] Step 4:

[0858] The emotion engine sends the emotion data to the server.

[0859] The emotion engine analyzes the emotion data and sends it to the server. For example, it can tell the user that their emotion is "relaxed."

[0860] Step 5:

[0861] The device sends the data to the server

[0862] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[0863] Step 6:

[0864] The server receives the data

[0865] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[0866] Step 7:

[0867] The server searches the recipe database

[0868] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[0869] Step 8:

[0870] The server selects the best recipe

[0871] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. In this case, the server selects a recipe made with French Chardonnay, which contains ingredients expected to have a relaxing effect.

[0872] Step 9:

[0873] The server generates the formulation instructions.

[0874] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[0875] Step 10:

[0876] The server sends compounding instructions to the AI ​​machine.

[0877] The server generates mixing instructions and sends them to the AI ​​machine.

[0878] Step 11:

[0879] AI machine selects materials

[0880] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) and specific ingredients to enhance the relaxing effect.

[0881] Step 12:

[0882] AI machines mix drinks

[0883] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[0884] Step 13:

[0885] AI machine pours drinks into containers

[0886] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[0887] Step 14:

[0888] The AI ​​machine sends a completion notification to the device

[0889] The AI ​​machine will notify the device that the drink is ready.

[0890] Step 15:

[0891] The device notifies the user

[0892] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[0893] Step 16:

[0894] The user receives a drink

[0895] The user follows the notification on the terminal and receives the finished drink.

[0896] Example 2

[0897] 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."

[0898] Conventional beverage delivery systems have difficulty responding to a user's individual preferences or mood of the day. Furthermore, they are unable to provide the optimal beverage while taking into account the user's emotions, and thus do not achieve sufficient personalization. As a result, users may receive a dissatisfied beverage.

[0899] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input their beverage preferences and mood, a means for the server to receive the user's input information and emotion data extracted from the user's facial expression and voice, and to search a database based on the received information and select an optimal beverage recipe, a means for generating blending instructions based on the beverage recipe selected by the server and sending the instructions to the machine, and a means for notifying the user. This makes it possible to provide an optimal beverage based on the user's individual preferences, mood of the day, and emotions in real time.

[0900] "User" refers to an individual who inputs their beverage preferences and mood and uses the system to receive a beverage.

[0901] "Beverage" refers to a consumable liquid that is dispensed based on a user's preferences, mood, or even emotion.

[0902] "Preferences" refer to specific characteristics such as the ratio of sourness, bitterness, richness, and astringency that a user desires in a beverage, as well as a desired place of origin.

[0903] "Mood" refers to the temporary emotions and psychological state of a user when selecting a beverage.

[0904] The term "means" refers to various methods and devices required to configure the system.

[0905] "Server" refers to a computer device that receives user input information and emotion data, selects an optimal drink recipe based on the received information, and generates and transmits blending instructions to the machine.

[0906] "Facial expression" refers to data on the emotional state obtained from the user's facial movements and expressions.

[0907] "Voice" refers to emotional state data obtained from the user's voice.

[0908] "Emotion data" refers to information about emotions extracted from the user's facial expressions and voice.

[0909] A "database" refers to an information system that stores information such as beverage recipes and manages them in a searchable format.

[0910] "Mixing instructions" refers to instruction data for instructing the machine on specific mixing contents based on the beverage recipe selected by the server.

[0911] "Machine" refers to a device that receives mixing instructions from the server, actually mixes the beverage, and provides it to the user.

[0912] "Region" refers to information indicating the origin of the beverage desired by the user.

[0913] "Notification" refers to a communication means for informing a user about a dispensed beverage.

[0914] This invention is a system that provides the optimal beverage based on the user's input of their beverage preferences, mood, and even emotions. This system also utilizes an emotion engine that recognizes emotions from the user's facial expressions and voice, thereby providing a more personalized beverage.

[0915] A terminal is used as a way for users to input their preferences and feelings about beverages. This terminal is equipped with a user interface and displays an input screen for inputting the ratios of sourness, bitterness, richness, and astringency. Users can also select their desired origin.

[0916] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, and processes this data in real time to analyze their emotions. Specifically, it uses image recognition and voice analysis technologies to obtain the user's emotional data. For example, it uses facial recognition technology to detect smiling and sad expressions, and voice analysis technology to evaluate the tone and speed of the voice.

[0917] When a user inputs the necessary information into the device, this data is sent to the server. The server receives the user's input data and emotion data sent from the device and emotion engine. It then searches the database based on this data and runs an algorithm to select the optimal drink recipe. This search process prioritizes drinks from specific regions and lists candidates that match the ratios and emotions specified by the user.

[0918] After the server selects the optimal recipe, it generates mixing instructions based on this recipe. For example, specific mixing instructions such as "Chardonnay (France), strong acidity, relaxing effect" are created. These mixing instructions are then sent to the AI ​​machine.

[0919] The AI ​​machine selects the specified ingredients based on the mixing instructions received from the server. During this process, it automatically selects ingredients from its inventory and also selects additional ingredients to complement specific ingredients. The AI ​​machine then mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. Ingredients intended for relaxation effects can also be added.

[0920] Once the drink is mixed, the AI ​​machine pours it into a glass (or other container), with the pouring program controlling the amount to be dispensed.

[0921] Finally, the AI ​​machine notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The user confirms the notification and collects the finished drink from the designated pick-up location.

[0922] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even real-time emotions. For example, if a user enters the following prompt, the system will automatically begin the process of blending a beverage:

[0923] Example prompt sentence:

[0924] "My mood is relaxation. I like French drinks and I like the balance of acidity 7, bitterness 3, body 5 and astringency 2."

[0925] Based on this prompt, the system will provide the best French Chardonnay that matches the user's tastes and feelings.

[0926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0927] Step 1:

[0928] The device displays the input screen.

[0929] The terminal displays the question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency in a range from 1 to 10." It also displays the option, "Please select a preferred origin, if any." The input format consists of a numeric input field and a drop-down list for selecting origin. For example, the input data can be sourness "7," bitterness "3," richness "5," astringency "2," and the desired origin "France."

[0930] Step 2:

[0931] Emotion engine analyzes emotions

[0932] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, then processes this data in real time to analyze their emotions. The emotion engine performs facial recognition and voice analysis to distinguish between smiling and sad expressions, as well as the tone and speed of the voice. This data processing uses the captured images and voice as input data and outputs emotions such as "happy" or "relaxed."

[0933] Step 3:

[0934] The user enters information

[0935] The user inputs the values ​​for sourness (7), bitterness (3), richness (5), and astringency (2) on the terminal screen, and selects "France" as the desired origin. After completing the input, the user presses the "Send" button, which sends the data to the server. The input data includes the user's preferred ratio and origin information.

[0936] Step 4:

[0937] The emotion engine sends the emotion data to the server.

[0938] The emotion engine sends the emotion data obtained as a result of its analysis, such as "happy" or "relaxed," to the server. Specifically, the emotion data is sent to the server in JSON format. The emotion engine's analysis results are the input data, and the emotion information received by the server is the output data.

[0939] Step 5:

[0940] The device sends user data to the server

[0941] The terminal sends the ratio data entered by the user (sourness "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server. This data is also sent in JSON format. The information entered by the user is obtained as input data, and the preference information received by the server is obtained as output data.

[0942] Step 6:

[0943] The server receives the data

[0944] The server receives the user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin) and emotion data sent from the device and emotion engine. Various information from the device and emotion engine is obtained as input data, and data for searching in the internal database is obtained as output data.

[0945] Step 7:

[0946] The server searches the recipe database

[0947] Based on the data received by the server, a database search engine is used to query the recipe database. Candidates that match the user's specified ratios, desired origin, and emotional information are extracted. SQL queries and other data calculations are executed. The search query is used as input data, and the extracted recipe candidates are used as output data.

[0948] Step 8:

[0949] The server selects the best recipe

[0950] The server uses an algorithm to select the optimal recipe from the search results. For example, if a user is looking for a relaxing effect, it will identify recipes that contain that ingredient. The selection process is carried out through algorithmic processing. The extracted recipe candidates are obtained as input data, and the optimal recipe is obtained as output data.

[0951] Step 9:

[0952] The server generates the formulation instructions.

[0953] The server creates blending instructions based on the selected recipe. For example, it generates blending instructions such as "Chardonnay (France), strong acidity, relaxing effect." Blending instruction data is created as data processing. The selected recipe is obtained as input data, and the generated blending instructions are obtained as output data.

[0954] Step 10:

[0955] The server sends compounding instructions to the AI ​​machine.

[0956] The server sends the generated compounding instructions to the AI ​​machine via the network. This is usually done through an API. The generated compounding instructions are the input data, and the instruction information received by the AI ​​machine is the output data.

[0957] Step 11:

[0958] AI machine selects materials

[0959] Based on the compounding instructions received from the server, the AI ​​machine automatically selects the specified materials from the warehouse. Based on the material selection logic, the necessary materials are identified and retrieved. The instruction information is obtained as input data, and the selected materials are output data.

[0960] Step 12:

[0961] AI machines mix drinks

[0962] The AI ​​machine mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. The ingredients are mixed and the blend is carried out according to the program. The selected ingredients are the input data, and the blended beverage is the output data.

[0963] Step 13:

[0964] AI machine pours drinks into containers

[0965] Once the AI ​​machine has mixed the drink, it automatically pours it into a glass (or other container). The pouring method is also controlled by the program, providing the appropriate amount. The input data is the mixed drink, and the output data is the drink poured into the container.

[0966] Step 14:

[0967] The AI ​​machine sends a completion notification to the device

[0968] The AI ​​machine notifies the server that the drink is ready and transfers that information to the device. The input data is information about the completion of mixing, and the output data is information about the notification sent to the device.

[0969] Step 15:

[0970] The device notifies the user

[0971] The terminal displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The notification message is displayed on the screen. The input data is the completion notification, and the output data is the message to be displayed to the user.

[0972] Step 16:

[0973] The user receives a drink

[0974] The user confirms the notification from the device and collects the completed drink from the designated pick-up location. The process is completed when the user picks up the drink at the pick-up location. The notification to the user is obtained as input data, and the drink provided to the user is obtained as output data.

[0975] (Application example 2)

[0976] 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."

[0977] Conventional user beverage selection systems provide beverages based on the user's preferences and mood, but do not consider the user's emotional state. As a result, the beverage provided may not necessarily be optimal for the user's current emotion or mood. To improve customer experience, especially in cafes and beverage stores, there is a need to analyze the user's emotional state in real time and provide personalized beverages based on that analysis.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0979] In this invention, the server includes: means for a user to input their preferences and mood regarding beverages; means for an emotion engine to analyze emotions from the user's facial expressions and voice and acquire emotion data; means for the server to receive the user's input information and emotion data and search a database based on the information and emotion data to select an optimal beverage recipe; means for the server to generate blending instructions based on the beverage recipe selected and send them to the blending device; means for the blending device to blend a beverage based on the blending instructions and provide it to the user; and means for the terminal used by the user to input information to send the emotion engine's analysis results to the server. This makes it possible to provide an optimal beverage according to the user's current emotional state and preferences.

[0980] "A means for users to input their preferences and mood regarding beverages" refers to an interface that allows users to input the characteristics of beverages (sourness, bitterness, richness, astringency) and moods (relaxed, happy, etc.) using specific numerical values ​​or text.

[0981] "Means for the emotion engine to analyze emotions from the user's facial expressions and voice and obtain emotional data" refers to a system that uses sensors and cameras to obtain the user's real-time facial expressions and voice, and analyzes that data to identify the user's emotional state.

[0982] "Means for the server to receive the user's input information and emotional data, and search the database based on that to select the optimal drink recipe" refers to the process in which the server receives the preference and mood information entered by the user, as well as the emotional data obtained by the emotion engine, and searches the drink recipe database based on that to select the optimal drink recipe.

[0983] The "means for generating mixing instructions based on a beverage recipe selected by the server and transmitting them to the mixing device" refers to a mechanism for generating specific mixing instructions based on a selected beverage recipe and transmitting these instructions to the mixing device.

[0984] The "means for the blending device to blend a beverage based on blending instructions and provide it to a user" refers to a device and process that mixes specified ingredients based on blending instructions sent from a server to create a beverage and provides it to a user.

[0985] "Means for the terminal to transmit the emotion engine's analysis results to the server when the user inputs information" refers to the process in which the user inputs input data into the terminal and the terminal transmits emotion data, which is the emotion engine's analysis result, to the server.

[0986] MODE FOR CARRYING OUT THE INVENTION

[0987] This invention is a system that combines a system that allows a user to input their beverage preferences and moods and provides the optimal beverage based on the input, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[0988] Hardware and Software

[0989] This system uses the following main hardware and software:

[0990] User device: Smartphone, tablet, etc. (with camera and microphone)

[0991] Sentiment engine: Software for sentiment analysis (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text)

[0992] Server: AWS Lambda or similar cloud service

[0993] Database: A database that stores drink recipes (e.g., Amazon RDS)

[0994] Mixing device: Robot barista (e.g., SoftBank Robotics' Pepper, CafeX)

[0995] System Operation

[0996] The user terminal collects the user's input information and emotion data and sends it to the server. The server searches the database, selects the optimal drink recipe, and sends mixing instructions to the mixing device. The mixing device mixes the drink based on the mixing instructions and serves it to the user.

[0997] User data input and sentiment analysis

[0998] The user inputs the ratio of sourness, bitterness, richness, and astringency of the beverage, as well as the desired origin, through a smartphone app. Using the app's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, if a user inputs "sourness 7, bitterness 3, richness 5, astringency 2" and the desired origin is "France," the emotion engine may recognize the emotion "relaxed."

[0999] Data processing and recipe selection

[1000] The device sends input data and emotional data from the user to the server. The server receives this data and searches the database to select the optimal drink recipe. Since the user is in a relaxed state, a recipe containing ingredients with a relaxing effect is selected.

[1001] Mixing instructions and beverage preparation

[1002] The server generates mixing instructions based on the selected drink recipe and sends them to the mixing device. The mixing device receives instructions from the server, selects the specified ingredients, and mixes them in the correct ratios to prepare the drink. For example, a drink can be prepared based on specific instructions such as "Chardonnay (France), strong acidity, and a relaxing effect."

[1003] Drink offering and notification

[1004] When the blending is complete, the blending device sends the information to the terminal, which then notifies the user, "Your French Chardonnay is ready!" The user follows the notification and receives the finished drink.

[1005] Prompt Sentence Examples

[1006] Here are some example prompts for using a generative AI model:

[1007] Build an algorithm to select the recipe that provides the optimal drink based on the user's preferred ratio of sourness, bitterness, richness, and astringency, as well as emotional data. Explain this using the example where the user's current emotional state is "Relaxed," the preferred ratios are sourness "7," bitterness "3," richness "5," astringency "2," and the origin is "France."

[1008] The present invention makes it possible to provide the user with an optimal beverage according to their current emotional state and preferences.

[1009] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1010] Step 1:

[1011] User Input

[1012] The user device displays questions about the beverage to the user. The user uses the smartphone interface to input "acidity 7, bitterness 3, richness 5, astringency 2" and selects "France" as the desired origin. Once the user has completed the input, the device temporarily stores the data.

[1013] Input: User's desired ratio of sourness, bitterness, richness, and astringency of the beverage, as well as desired origin information

[1014] Output: User input data stored on the device

[1015] Step 2:

[1016] sentiment analysis

[1017] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time. For example, Microsoft Azure Face API detects the emotion "relaxed" from the user's facial expressions, and Google Cloud Speech-to-Text analyzes emotions from the voice. The emotion engine then sends the acquired emotional data to the device.

[1018] Input: User's facial and voice data

[1019] Output: Emotion data analyzed by the emotion engine (e.g., "Relaxed")

[1020] Step 3:

[1021] Data transmission

[1022] The device transmits the user's input data and emotion data to a server via an internet connection.

[1023] Input: User input data (sourness 7, bitterness 3, richness 5, astringency 2, desired origin: France) and emotional data (relaxed)

[1024] Output: The user's dataset that is sent to the server

[1025] Step 4:

[1026] Database search

[1027] The server searches a database of beverage recipes based on the received user and emotion data. For example, it uses Amazon RDS to find a list of French beverage recipes from the database that best fit the user's preferences and emotions.

[1028] Input: Your dataset

[1029] Output: A shortlist of optimal drink recipes

[1030] Step 5:

[1031] Recipe Selection

[1032] The server selects the drink recipe that best suits the user's taste and emotions from the list of drink recipes. For example, it selects a recipe such as "Chardonnay (France), strong acidity, relaxing effect."

[1033] Input: List of drink recipe candidates

[1034] Output: Selected optimal drink recipe

[1035] Step 6:

[1036] Mixing instructions generation

[1037] The server generates specific mixing instructions based on the selected drink recipe, including the ingredients and their proportions. For example, the server might generate instructions such as "Mix Chardonnay (France) with a high acidity and add a relaxing ingredient."

[1038] Input: Selected drink recipe

[1039] Output: Mixing instructions

[1040] Step 7:

[1041] Sending mixing instructions

[1042] The server sends the generated mixing instructions to the mixing device (robot barista), and this communication is also carried out via the Internet.

[1043] Input: Mixing instructions

[1044] Output: Mixing instructions sent to the mixer

[1045] Step 8:

[1046] Beverage blending

[1047] The blending device receives the blending instructions and blends the beverage in proportion to the specified ingredients, for example, using Chardonnay (France) to create a more acidic drink and adding a relaxing ingredient.

[1048] Input: Mixing instructions

[1049] Output: Finished beverage

[1050] Step 9:

[1051] Completion notification

[1052] The dispensing device notifies the terminal that the beverage is ready, and the terminal displays the message "Your French Chardonnay is ready!" to the user.

[1053] Input: Completion notification from compounding device

[1054] Output: A notification message to the user

[1055] Step 10:

[1056] Receiving beverages

[1057] The user follows the notification on the terminal and receives the finished drink.

[1058] Input: Notification message

[1059] Output: The finished beverage received by the user

[1060] This allows users to easily enjoy personalized beverages that match their mood and preferences that day.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] [Third embodiment]

[1065] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1066] 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.

[1067] 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).

[1068] 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.

[1069] 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.

[1070] 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).

[1071] 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. 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.

[1072] 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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."

[1077] This invention is a system that provides the optimal beverage based on a user's input of their beverage preferences and mood. The system allows users to specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. Furthermore, users can select the desired region of origin. Based on this information, the system selects the optimal beverage recipe, blends it, and provides it to the user.

[1078] The specific processing flow of this system will be explained in natural language.

[1079] Step 1: Enter your user data

[1080] The device displays a question to the user.

[1081] Terminal: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10."

[1082] Terminal: "Please select your preferred origin."

[1083] The user enters information

[1084] User: Enter sourness "7", bitterness "3", richness "5", and astringency "2".

[1085] User: Enters "France" as the desired origin.

[1086] The device sends the data to the server

[1087] Terminal: Sends the entered ratio data and desired origin information to the server.

[1088] Step 2: Data processing and recipe selection

[1089] The server receives the data

[1090] Server: Receives the user's input data (acidity "7", bitterness "3", richness "5", astringency "2", origin "France").

[1091] The server searches the recipe database

[1092] Server: Searches the recipe database and lists possible drinks that match the user's preferences.

[1093] Server: Prefer French wines.

[1094] The server further narrows down the candidates

[1095] Server: Analyze the recipes that most closely match the above ratios to determine the best recipe.

[1096] Server: Choose a French Chardonnay (high acidity).

[1097] Step 3: Generate and send formulation instructions

[1098] The server generates the formulation instructions.

[1099] Server: Generates mixing instructions based on the selected recipe.

[1100] Server: Create a request for "Chardonnay (France), high acidity, low bitterness, medium body, low astringency."

[1101] The server sends compounding instructions to the AI ​​machine

[1102] Server: Sends mixing instructions to the AI ​​machine.

[1103] Step 4: Mix the drink

[1104] AI machine selects materials

[1105] AI Machine: Pick up a Chardonnay (France).

[1106] AI machine: Selects specific ingredients to enhance the sourness.

[1107] AI machines mix drinks

[1108] AI machine: Mixes selected ingredients in precise proportions.

[1109] AI machine: Adjusts the balance of acidity, bitterness, richness, and astringency.

[1110] AI machine pours drinks into containers

[1111] AI machine: Mixes and pours drinks into glasses (or other containers).

[1112] Step 5: Serve the drink

[1113] The AI ​​machine sends a completion notification to the device

[1114] AI machine: Notifies the device when the drink is ready.

[1115] The device notifies the user

[1116] Terminal: Display the message "Thank you for waiting. Your French Chardonnay is ready!"

[1117] The user receives a drink

[1118] User: Receives the finished drink and enjoys it.

[1119] These steps enable the system to quickly and accurately provide the optimal beverage tailored to the user's preferences and mood of the day. For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2", the system can provide a French Chardonnay with the optimal blend. In this way, users can enjoy beverages that bring new experiences and satisfaction to their daily lives.

[1120] The processing flow will be explained below.

[1121] Step 1:

[1122] The device displays a question to the user.

[1123] The device displays the following question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1124] Step 2:

[1125] The user enters information

[1126] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[1127] Step 3:

[1128] The device sends the data to the server

[1129] The terminal sends the ratio data entered by the user (acidity "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server.

[1130] Step 4:

[1131] The server receives the data

[1132] The server receives the user input data sent from the terminal.

[1133] Step 5:

[1134] The server searches the recipe database

[1135] The server searches the recipe database based on the user's input data, prioritizing French drinks in particular, and lists candidates that match the ratio specified by the user.

[1136] Step 6:

[1137] The server selects the best recipe

[1138] The server analyzes the recipes listed and selects the one that most closely matches the user's specified ratio (acidity: 7, bitterness: 3, richness: 5, astringency: 2). In this example, a French Chardonnay (highly acidic) is selected.

[1139] Step 7:

[1140] The server generates the formulation instructions.

[1141] The server generates specific mixing instructions based on the selected recipe, such as "Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency."

[1142] Step 8:

[1143] The server sends compounding instructions to the AI ​​machine.

[1144] The server generates mixing instructions and sends them to the AI ​​machine.

[1145] Step 9:

[1146] AI machine selects materials

[1147] The AI ​​machine receives recipe instructions from the server and selects the specified ingredients (Chardonnay (France)) and also selects specific ingredients to enhance the acidity.

[1148] Step 10:

[1149] AI machines mix drinks

[1150] The AI ​​machine selects ingredients and mixes them in precise proportions based on instructions, adjusting the balance of sourness, bitterness, richness, and astringency.

[1151] Step 11:

[1152] AI machine pours drinks into containers

[1153] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1154] Step 12:

[1155] The AI ​​machine sends a completion notification to the device

[1156] The AI ​​machine will notify the device that the drink is ready.

[1157] Step 13:

[1158] The device notifies the user

[1159] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1160] Step 14:

[1161] The user receives a drink

[1162] The user follows the notification on the terminal and receives the finished drink.

[1163] Example 1

[1164] 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."

[1165] Conventional beverage delivery systems have had difficulty responding precisely to users' preferences and moods. Furthermore, there were no systems that allowed users to input specific flavor ratios or desired origins, making it difficult to deliver beverages that met individual needs. This posed a risk of lowering user satisfaction. Furthermore, the accuracy and efficiency of beverage preparation were also issues.

[1166] 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.

[1167] In this invention, the server includes means for a user to input their beverage preferences and mood, means for a terminal to receive and display the user's input information, means for the terminal to transmit the information received to the server, means for the server to receive the user's input information and search a database based on it to select an optimal beverage recipe, means for the server to generate mixing instructions based on the selected beverage recipe and transmit them to the AI ​​machine, and means for the AI ​​machine to mix a beverage based on the mixing instructions and serve it to the user. This makes it possible to quickly and accurately provide an optimal beverage that matches the user's detailed preferences and mood.

[1168] "User" means an individual or organization that receives beverages using the system.

[1169] A "terminal" is an electronic device used by a user to enter information and view results.

[1170] A "server" is a computer system that receives user input, searches a database, selects recipes, and generates mixing instructions.

[1171] The "database" is a digital information management system that stores information about beverage recipes and various ingredients.

[1172] A "recipe" is information that indicates the combination of ingredients and how to mix them to make a specific drink.

[1173] "Mixing instructions" are specific instructions for creating a particular beverage that are generated by the server and sent to the AI ​​machine.

[1174] An "AI machine" is a device that automatically mixes beverages based on mixing instructions from a server and provides them to users.

[1175] "Preferences and moods" refers to input information regarding the ratio of sourness, bitterness, richness, and astringency that the user desires in a beverage, as well as the desired place of origin.

[1176] A "beverage" is a drinkable liquid that is dispensed for presentation to a user.

[1177] This invention is a system that inputs a user's beverage preferences and moods and provides the most suitable beverage based on that information. A specific implementation method for this system will be described below.

[1178] Hardware and Software Configuration

[1179] The system includes the following main components:

[1180] User input means: A device (e.g., tablet, smartphone) through which the user inputs the ratio of sourness, bitterness, richness, and astringency, as well as the desired origin.

[1181] Terminal: A device that receives user input and sends it to a server.

[1182] Server: A computer system that receives user input, searches and selects beverage recipes, and sends mixing instructions to the AI ​​machine. This includes a database management system (e.g., MySQL), a recipe search algorithm, and mixing instruction generation software (e.g., FreeMarker).

[1183] AI machine: An automatic mixing machine (e.g., a robotic arm, a mixer with a dispenser) that mixes drinks based on mixing instructions from a server and provides them to the user.

[1184] How it works

[1185] 1. Entering user data

[1186] The user uses a terminal to input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10. They can also select the desired region of origin.

[1187] Example: A user inputs sourness "7", bitterness "3", richness "5", and astringency "2", and sets the desired origin as "France".

[1188] 2. Sending and Receiving Data

[1189] The terminal sends the input information to the server, which receives the user's input data and searches the database based on that information.

[1190] 3. Searching for and selecting recipes

[1191] The server uses a specific algorithm (e.g., SQL query or machine learning algorithm) to list drink recipes from a database that match the user's preferences. If a desired region of origin is specified, drinks from that region are searched for first.

[1192] 4. Generate mixing instructions

[1193] The server selects the optimal recipe and generates mixing instructions based on it. These mixing instructions are written out in detail using a template engine (e.g., FreeMarker).

[1194] Example: Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency.

[1195] 5. Sending mixing instructions and mixing drinks

[1196] The server generates the mixing instructions and sends them to the AI ​​machine, which selects the bottle and ingredients, mixes the drink in the correct proportions according to the instructions, and finally pours it into a container.

[1197] 6. Drink Serving and Notification

[1198] The AI ​​machine serves the blended drink to the user. It notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry for the wait. Your French Chardonnay is ready!"

[1199] Specific examples

[1200] If a user requests a "French" beverage with a ratio of "acidity 7: bitterness 3: richness 5: astringency 2," the system will provide a "French Chardonnay" with the optimal blend.

[1201] Example prompt:

[1202] User: "I'd like a wine from France with acidity 7, bitterness 3, body 5, and astringency 2."

[1203] Terminal: "Your requested beverage is being prepared. Please wait a moment."

[1204] Server: "Selecting the French Chardonnay that best suits your needs..."

[1205] AI machine: "The selected Chardonnay is blended in the appropriate proportions and poured into a container..."

[1206] Terminal: "Sorry to keep you waiting. Our French Chardonnay is ready!"

[1207] This allows users to quickly and accurately choose the perfect beverage to suit their specific preferences and moods, resulting in high levels of satisfaction.

[1208] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1209] Step 1: Enter user data

[1210] The user operates the device

[1211] Input: Ratio of sourness, bitterness, richness, astringency, desired origin

[1212] How it works: The user enters the ratio of sourness, bitterness, richness, and astringency on the device screen on a scale of 1 to 10 and selects the desired origin.

[1213] Output: Input data (e.g., sourness "7", bitterness "3", richness "5", astringency "2", desired origin "France")

[1214] Step 2: Sending and Receiving Data

[1215] The device sends the data to the server

[1216] Input: Data entered by the user

[1217] How it works: The device organizes the data entered by the user and sends it to a server over the internet using the HTTP or HTTPS protocol.

[1218] Output: Data sent to the server

[1219] Step 3: Data processing and recipe selection

[1220] The server receives the data and starts processing it.

[1221] Input: User data sent from the terminal

[1222] How it works: The server receives user data and generates a database query, using SQL to search the recipe database for recipes that best match the user's preferences.

[1223] Output: A list of candidate drink recipes

[1224] The server narrows down the recipes

[1225] Input: A list of candidate drink recipes

[1226] How it works: Uses machine learning algorithms (e.g. KNN or cosine similarity) to select the best recipe from a list of candidates.

[1227] Output: Optimal drink recipe (e.g., French Chardonnay)

[1228] Step 4: Generate and send formulation instructions

[1229] The server generates the formulation instructions.

[1230] Input: Optimal Drink Recipe

[1231] How it works: Using a template engine (e.g., FreeMarker), specific mixing instructions are generated. The mixing instructions include the types and amounts of ingredients needed, as well as mixing procedures.

[1232] Output: Generated mixing instructions

[1233] The server sends the compounding instructions to the AI ​​machine.

[1234] Input: Generated mixing instructions

[1235] How it works: The server uses a RESTful API to send JSON-formatted formula instruction data to the AI ​​machine.

[1236] Output: Mixing instructions sent to the AI ​​machine

[1237] Step 5: Mix the drink

[1238] AI machine selects materials

[1239] Input: Mixing instructions sent from the server

[1240] How it works: The AI ​​machine retrieves the required ingredients (e.g., Chardonnay (France)) from storage based on the recipe instructions.

[1241] Output: Selected material

[1242] AI machines mix drinks

[1243] Input: Selected material

[1244] How it works: The AI ​​machine mixes ingredients in precise proportions, balancing sourness, bitterness, richness, and astringency. Reference sensors measure the amount of ingredients.

[1245] Output: Dispensed beverage

[1246] AI machine pours drinks into containers

[1247] Input: Dispensed beverage

[1248] How it works: The AI ​​machine uses a dispenser to dispense a mixed drink into a glass or other container.

[1249] Output: A drink ready to be served to the user

[1250] Step 6: Offer drinks and notify

[1251] The AI ​​machine sends a completion notification to the device

[1252] Input: Drink ready state

[1253] How it works: The AI ​​machine uses a message queuing method (e.g. RabbitMQ) to notify the device that the drink is ready.

[1254] Output: Completion notification sent to the terminal

[1255] The device notifies the user

[1256] Input: Completion notification from AI machine

[1257] What it does: The device displays a message to the user on the screen saying, "Sorry for the wait. Your French Chardonnay is ready!"

[1258] Output: The notification displayed to the user

[1259] The user receives a drink

[1260] Input: Completion notification from the terminal

[1261] How it works: The user checks the notification on their device, goes to the dispenser station, and receives a drink. They are authenticated using an RFID tag or QR code.

[1262] Output: Drinks received

[1263] (Application example 1)

[1264] 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."

[1265] Conventional beverage supply systems have difficulty in quickly providing the optimal beverage according to the user's preferences and mood, and are therefore unable to provide a service that satisfies the user. In addition, there is also the problem that the beverage is manually mixed based on the user's input information in a physical store, which takes time and results in long waiting times for the user.

[1266] 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.

[1267] In this invention, the server includes a means for a user to input their beverage preferences and mood, a means for the server to receive the user's input information and search a database based on the information to select an optimal beverage recipe, a means for the server to generate blending instructions based on the beverage recipe selected by the server and transmit the instructions to a remote blending device, and a means for the blending device to blend the beverage based on the blending instructions and receive the beverage at a location specified by the user. This makes it possible to quickly and accurately provide a beverage that suits the user's preferences.

[1268] A "user" is a user of the system who inputs their beverage preferences and mood and receives beverages.

[1269] "Preferences and moods" refer to the user's taste preferences for beverages and their psychological state at the time.

[1270] The "server" is a central computer system that searches a database based on information received from the user, selects the optimal drink recipe, and issues instructions.

[1271] The "database" is a repository of information that stores and manages recipe data for various beverages and can be searched as needed.

[1272] A "drink recipe" is a set of instructions outlining the ingredients, proportions, and steps needed to prepare a particular drink.

[1273] "Mixing instructions" are instructions that show a specific mixing method based on a selected beverage recipe.

[1274] A "mixing machine" is a machine that automatically mixes beverages based on mixing instructions received from a server.

[1275] An "affiliated store" is a commercial food and beverage establishment that provides beverages in cooperation with the server.

[1276] "Notification" is a communication means to inform the user that the dispensed beverage is ready.

[1277] The present invention relates to a system that provides optimal beverages based on a user's input of their beverage preferences and moods. The system is implemented using a smartphone application, a server, and a dispensing device.

[1278] Hardware and software used

[1279] Hardware:

[1280] Smartphone

[1281] server

[1282] Mixing device (AI machine)

[1283] software:

[1284] Frontend: React Native

[1285] Backend: Node.js, Express.js

[1286] Database: MySQL

[1287] Communication: Socket.IO

[1288] overview

[1289] First, the user uses a smartphone application to input their beverage preferences and feelings. Specifically, they input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and select the desired origin of the beverage. The application also uses GPS functionality to display affiliated stores near the user's current location.

[1290] The terminal sends the input data to the server, which receives it and searches a recipe database to create a list of beverage candidates that match the user's preferences.The server then selects the optimal recipe from the list and generates mixing instructions.

[1291] The generated mixing instructions are sent to the mixing device at the partner store. The mixing device automatically mixes the specified beverage based on the instructions. When mixing is complete, the server sends a notification to the terminal, prompting the user to pick up the beverage.

[1292] Specific examples

[1293] For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2" on their smartphone, the server will select the optimal French beverage recipe based on this data and send instructions to the blending device. In this case, a recipe for French Chardonnay (strong acidity) is selected.

[1294] The blending device receives instructions from the server and blends French Chardonnay. Once blending is complete, the server notifies the user via a smartphone application with a message saying, "Sorry to keep you waiting! Your French Chardonnay is ready!" The user can then pick up the beverage at the designated store based on this notification.

[1295] Prompt Sentence Examples

[1296] The user has submitted the following preference data:

[1297] Acidity: 7

[1298] Bitterness: 3

[1299] Body: 5

[1300] Astringency: 2

[1301] Preferred origin: France

[1302] Find the perfect drink recipe.

[1303] This system allows users to quickly and accurately receive beverages tailored to their preferences, providing new experiences and satisfaction.

[1304] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1305] Step 1:

[1306] The user opens the smartphone application. The user inputs the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and selects the desired origin. The application collects this input data and obtains the user's location information. The user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin and current location information) is generated.

[1307] Step 2:

[1308] The device sends the user's input data to the server. The device converts the input data into JSON format and sends it to the server using an HTTP POST request. The input data here is the user's preference information and location information, and the output is data sent to the server.

[1309] Step 3:

[1310] The server receives user input data. The server analyzes the received data and searches the database to list candidate drink recipes that match the user's preferences. The server extracts matching items from the recipe database. The input data here is the analyzed user data, and the output data is a list of matching drink recipes.

[1311] Step 4:

[1312] The server selects the optimal drink recipe. From the list of drink recipes, it analyzes and prioritizes the drinks that have the closest ratio of sourness, bitterness, richness, and astringency. It also takes into account the origin of the drink desired by the user to determine the optimal recipe. The input data is a list of recipes, and the output data is the optimal recipe.

[1313] Step 5:

[1314] The server generates mixing instructions based on the optimal beverage recipe and sends them to the mixing device at the corresponding partner store. The server then converts the mixing instructions into specific steps, formats them in a format that the mixing device can understand, and sends them. The input data here is the optimal recipe, and the output data is the mixing instructions to the store.

[1315] Step 6:

[1316] The blending machine blends the beverage based on blending instructions received from the server. The blending machine takes the specified ingredients and measures and mixes them in the correct amounts. The input data is the blending instructions and the output data is the blended beverage.

[1317] Step 7:

[1318] When the blending device has finished preparing the blended beverage, it sends a completion notification to the server. The server receives this notification and forwards it to the terminal. The input data here is the blending completion notification, and the output data is the notification to the terminal.

[1319] Step 8:

[1320] The terminal notifies the user that the blending is complete. The terminal then displays a message on the smartphone saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" and prompts the user to pick it up at the store. The input data here is the notification from the server, and the output data is the notification to the user.

[1321] This series of steps allows the user to quickly and accurately receive a beverage tailored to their preferences.

[1322] 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.

[1323] This invention combines a system that allows users to input their beverage preferences and moods and provides the optimal beverage based on those preferences with an emotion engine that recognizes the user's emotions. Users can specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. They can also select the desired region of origin. Furthermore, the emotion engine analyzes the user's emotions from their facial expressions and voice, and sends that data to a server, enabling the provision of a more personalized beverage.

[1324] Program processing flow

[1325] Entering User Data

[1326] The device displays a question to the user.

[1327] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency in the range of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1328] Emotion engine analyzes user emotions

[1329] The emotion engine analyzes emotions from the user's facial expressions and voice in real time and obtains emotional data.

[1330] The user enters information

[1331] The user inputs into the terminal sourness "7", bitterness "3", richness "5", and astringency "2", and also inputs "France" as the desired origin.

[1332] The emotion engine sends the emotion data to the server.

[1333] The emotion engine sends the analysis results to the server, for example, telling the server that the user is feeling "happy" or "relaxed."

[1334] The device sends the data to the server

[1335] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[1336] Data processing and recipe selection

[1337] The server receives the data

[1338] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[1339] The server searches the recipe database

[1340] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[1341] The server selects the best recipe

[1342] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. For example, if the user is feeling relaxed, the server selects a recipe that contains many ingredients that are expected to have a relaxing effect.

[1343] Generate and send formulation instructions

[1344] The server generates the formulation instructions.

[1345] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[1346] The server sends compounding instructions to the AI ​​machine.

[1347] The server generates mixing instructions and sends them to the AI ​​machine.

[1348] Drink mixing

[1349] AI machine selects materials

[1350] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) as well as specific ingredients to enhance the relaxing effect.

[1351] AI machines mix drinks

[1352] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[1353] AI machine pours drinks into containers

[1354] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1355] Drinks provided

[1356] The AI ​​machine sends a completion notification to the device

[1357] The AI ​​machine will notify the device that the drink is ready.

[1358] The device notifies the user

[1359] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1360] The user receives a drink

[1361] The user follows the notification on the terminal and receives the finished drink.

[1362] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even their emotions that day. For example, if a user requests a French beverage with a ratio of acidity (7), bitterness (3), richness (5), and astringency (2), and their emotion is "relaxation," the system can provide a French Chardonnay with ingredients that emphasize its relaxing effect. In this way, a personalized experience can be provided based on the user's individual needs.

[1363] The processing flow will be explained below.

[1364] Step 1:

[1365] The device displays a question to the user.

[1366] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1367] Step 2:

[1368] Emotion engine analyzes user emotions

[1369] The emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data such as "happiness," "relaxation," and "excitement."

[1370] Step 3:

[1371] The user enters information

[1372] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[1373] Step 4:

[1374] The emotion engine sends the emotion data to the server.

[1375] The emotion engine analyzes the emotion data and sends it to the server. For example, it can tell the user that their emotion is "relaxed."

[1376] Step 5:

[1377] The device sends the data to the server

[1378] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[1379] Step 6:

[1380] The server receives the data

[1381] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[1382] Step 7:

[1383] The server searches the recipe database

[1384] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[1385] Step 8:

[1386] The server selects the best recipe

[1387] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. In this case, the server selects a recipe made with French Chardonnay, which contains ingredients expected to have a relaxing effect.

[1388] Step 9:

[1389] The server generates the formulation instructions.

[1390] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[1391] Step 10:

[1392] The server sends compounding instructions to the AI ​​machine.

[1393] The server generates mixing instructions and sends them to the AI ​​machine.

[1394] Step 11:

[1395] AI machine selects materials

[1396] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) and specific ingredients to enhance the relaxing effect.

[1397] Step 12:

[1398] AI machines mix drinks

[1399] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[1400] Step 13:

[1401] AI machine pours drinks into containers

[1402] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1403] Step 14:

[1404] The AI ​​machine sends a completion notification to the device

[1405] The AI ​​machine will notify the device that the drink is ready.

[1406] Step 15:

[1407] The device notifies the user

[1408] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1409] Step 16:

[1410] The user receives a drink

[1411] The user follows the notification on the terminal and receives the finished drink.

[1412] Example 2

[1413] 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."

[1414] Conventional beverage delivery systems have difficulty responding to a user's individual preferences or mood of the day. Furthermore, they are unable to provide the optimal beverage while taking into account the user's emotions, and thus do not achieve sufficient personalization. As a result, users may receive a dissatisfied beverage.

[1415] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input their beverage preferences and mood, a means for the server to receive the user's input information and emotion data extracted from the user's facial expression and voice, and to search a database based on the received information and select an optimal beverage recipe, a means for generating blending instructions based on the beverage recipe selected by the server and sending the instructions to the machine, and a means for notifying the user. This makes it possible to provide an optimal beverage based on the user's individual preferences, mood of the day, and emotions in real time.

[1416] "User" refers to an individual who inputs their beverage preferences and mood and uses the system to receive a beverage.

[1417] "Beverage" refers to a consumable liquid that is dispensed based on a user's preferences, mood, or even emotion.

[1418] "Preferences" refer to specific characteristics such as the ratio of sourness, bitterness, richness, and astringency that a user desires in a beverage, as well as a desired place of origin.

[1419] "Mood" refers to the temporary emotions and psychological state of a user when selecting a beverage.

[1420] The term "means" refers to various methods and devices required to configure the system.

[1421] "Server" refers to a computer device that receives user input information and emotion data, selects an optimal drink recipe based on the received information, and generates and transmits blending instructions to the machine.

[1422] "Facial expression" refers to data on the emotional state obtained from the user's facial movements and expressions.

[1423] "Voice" refers to emotional state data obtained from the user's voice.

[1424] "Emotion data" refers to information about emotions extracted from the user's facial expressions and voice.

[1425] A "database" refers to an information system that stores information such as beverage recipes and manages them in a searchable format.

[1426] "Mixing instructions" refers to instruction data for instructing the machine on specific mixing contents based on the beverage recipe selected by the server.

[1427] "Machine" refers to a device that receives mixing instructions from the server, actually mixes the beverage, and provides it to the user.

[1428] "Region" refers to information indicating the origin of the beverage desired by the user.

[1429] "Notification" refers to a communication means for informing a user about a dispensed beverage.

[1430] This invention is a system that provides the optimal beverage based on the user's input of their beverage preferences, mood, and even emotions. This system also utilizes an emotion engine that recognizes emotions from the user's facial expressions and voice, thereby providing a more personalized beverage.

[1431] A terminal is used as a way for users to input their preferences and feelings about beverages. This terminal is equipped with a user interface and displays an input screen for inputting the ratios of sourness, bitterness, richness, and astringency. Users can also select their desired origin.

[1432] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, and processes this data in real time to analyze their emotions. Specifically, it uses image recognition and voice analysis technologies to obtain the user's emotional data. For example, it uses facial recognition technology to detect smiling and sad expressions, and voice analysis technology to evaluate the tone and speed of the voice.

[1433] When a user inputs the necessary information into the device, this data is sent to the server. The server receives the user's input data and emotion data sent from the device and emotion engine. It then searches the database based on this data and runs an algorithm to select the optimal drink recipe. This search process prioritizes drinks from specific regions and lists candidates that match the ratios and emotions specified by the user.

[1434] After the server selects the optimal recipe, it generates mixing instructions based on this recipe. For example, specific mixing instructions such as "Chardonnay (France), strong acidity, relaxing effect" are created. These mixing instructions are then sent to the AI ​​machine.

[1435] The AI ​​machine selects the specified ingredients based on the mixing instructions received from the server. During this process, it automatically selects ingredients from its inventory and also selects additional ingredients to complement specific ingredients. The AI ​​machine then mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. Ingredients intended for relaxation effects can also be added.

[1436] Once the drink is mixed, the AI ​​machine pours it into a glass (or other container), with the pouring program controlling the amount to be dispensed.

[1437] Finally, the AI ​​machine notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The user confirms the notification and collects the finished drink from the designated pick-up location.

[1438] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even real-time emotions. For example, if a user enters the following prompt, the system will automatically begin the process of blending a beverage:

[1439] Example prompt sentence:

[1440] "My mood is relaxation. I like French drinks and I like the balance of acidity 7, bitterness 3, body 5 and astringency 2."

[1441] Based on this prompt, the system will provide the best French Chardonnay that matches the user's tastes and feelings.

[1442] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1443] Step 1:

[1444] The device displays the input screen.

[1445] The terminal displays the question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency in a range from 1 to 10." It also displays the option, "Please select a preferred origin, if any." The input format consists of a numeric input field and a drop-down list for selecting origin. For example, the input data can be sourness "7," bitterness "3," richness "5," astringency "2," and the desired origin "France."

[1446] Step 2:

[1447] Emotion engine analyzes emotions

[1448] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, then processes this data in real time to analyze their emotions. The emotion engine performs facial recognition and voice analysis to distinguish between smiling and sad expressions, as well as the tone and speed of the voice. This data processing uses the captured images and voice as input data and outputs emotions such as "happy" or "relaxed."

[1449] Step 3:

[1450] The user enters information

[1451] The user inputs the values ​​for sourness (7), bitterness (3), richness (5), and astringency (2) on the terminal screen, and selects "France" as the desired origin. After completing the input, the user presses the "Send" button, which sends the data to the server. The input data includes the user's preferred ratio and origin information.

[1452] Step 4:

[1453] The emotion engine sends the emotion data to the server.

[1454] The emotion engine sends the emotion data obtained as a result of its analysis, such as "happy" or "relaxed," to the server. Specifically, the emotion data is sent to the server in JSON format. The emotion engine's analysis results are the input data, and the emotion information received by the server is the output data.

[1455] Step 5:

[1456] The device sends user data to the server

[1457] The terminal sends the ratio data entered by the user (sourness "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server. This data is also sent in JSON format. The information entered by the user is obtained as input data, and the preference information received by the server is obtained as output data.

[1458] Step 6:

[1459] The server receives the data

[1460] The server receives the user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin) and emotion data sent from the device and emotion engine. Various information from the device and emotion engine is obtained as input data, and data for searching in the internal database is obtained as output data.

[1461] Step 7:

[1462] The server searches the recipe database

[1463] Based on the data received by the server, a database search engine is used to query the recipe database. Candidates that match the user's specified ratios, desired origin, and emotional information are extracted. SQL queries and other data calculations are executed. The search query is used as input data, and the extracted recipe candidates are used as output data.

[1464] Step 8:

[1465] The server selects the best recipe

[1466] The server uses an algorithm to select the optimal recipe from the search results. For example, if a user is looking for a relaxing effect, it will identify recipes that contain that ingredient. The selection process is carried out through algorithmic processing. The extracted recipe candidates are obtained as input data, and the optimal recipe is obtained as output data.

[1467] Step 9:

[1468] The server generates the formulation instructions.

[1469] The server creates blending instructions based on the selected recipe. For example, it generates blending instructions such as "Chardonnay (France), strong acidity, relaxing effect." Blending instruction data is created as data processing. The selected recipe is obtained as input data, and the generated blending instructions are obtained as output data.

[1470] Step 10:

[1471] The server sends compounding instructions to the AI ​​machine.

[1472] The server sends the generated compounding instructions to the AI ​​machine via the network. This is usually done through an API. The generated compounding instructions are the input data, and the instruction information received by the AI ​​machine is the output data.

[1473] Step 11:

[1474] AI machine selects materials

[1475] Based on the compounding instructions received from the server, the AI ​​machine automatically selects the specified materials from the warehouse. Based on the material selection logic, the necessary materials are identified and retrieved. The instruction information is obtained as input data, and the selected materials are output data.

[1476] Step 12:

[1477] AI machines mix drinks

[1478] The AI ​​machine mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. The ingredients are mixed and the blend is carried out according to the program. The selected ingredients are the input data, and the blended beverage is the output data.

[1479] Step 13:

[1480] AI machine pours drinks into containers

[1481] Once the AI ​​machine has mixed the drink, it automatically pours it into a glass (or other container). The pouring method is also controlled by the program, providing the appropriate amount. The input data is the mixed drink, and the output data is the drink poured into the container.

[1482] Step 14:

[1483] The AI ​​machine sends a completion notification to the device

[1484] The AI ​​machine notifies the server that the drink is ready and transfers that information to the device. The input data is information about the completion of mixing, and the output data is information about the notification sent to the device.

[1485] Step 15:

[1486] The device notifies the user

[1487] The terminal displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The notification message is displayed on the screen. The input data is the completion notification, and the output data is the message to be displayed to the user.

[1488] Step 16:

[1489] The user receives a drink

[1490] The user confirms the notification from the device and collects the completed drink from the designated pick-up location. The process is completed when the user picks up the drink at the pick-up location. The notification to the user is obtained as input data, and the drink provided to the user is obtained as output data.

[1491] (Application example 2)

[1492] 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."

[1493] Conventional user beverage selection systems provide beverages based on the user's preferences and mood, but do not consider the user's emotional state. As a result, the beverage provided may not necessarily be optimal for the user's current emotion or mood. To improve customer experience, especially in cafes and beverage stores, there is a need to analyze the user's emotional state in real time and provide personalized beverages based on that analysis.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1495] In this invention, the server includes: means for a user to input their preferences and mood regarding beverages; means for an emotion engine to analyze emotions from the user's facial expressions and voice and acquire emotion data; means for the server to receive the user's input information and emotion data and search a database based on the information and emotion data to select an optimal beverage recipe; means for the server to generate blending instructions based on the beverage recipe selected and send them to the blending device; means for the blending device to blend a beverage based on the blending instructions and provide it to the user; and means for the terminal used by the user to input information to send the emotion engine's analysis results to the server. This makes it possible to provide an optimal beverage according to the user's current emotional state and preferences.

[1496] "A means for users to input their preferences and mood regarding beverages" refers to an interface that allows users to input the characteristics of beverages (sourness, bitterness, richness, astringency) and moods (relaxed, happy, etc.) using specific numerical values ​​or text.

[1497] "Means for the emotion engine to analyze emotions from the user's facial expressions and voice and obtain emotional data" refers to a system that uses sensors and cameras to obtain the user's real-time facial expressions and voice, and analyzes that data to identify the user's emotional state.

[1498] "Means for the server to receive the user's input information and emotional data, and search the database based on that to select the optimal drink recipe" refers to the process in which the server receives the preference and mood information entered by the user, as well as the emotional data obtained by the emotion engine, and searches the drink recipe database based on that to select the optimal drink recipe.

[1499] The "means for generating mixing instructions based on a beverage recipe selected by the server and transmitting them to the mixing device" refers to a mechanism for generating specific mixing instructions based on a selected beverage recipe and transmitting these instructions to the mixing device.

[1500] The "means for the blending device to blend a beverage based on blending instructions and provide it to a user" refers to a device and process that mixes specified ingredients based on blending instructions sent from a server to create a beverage and provides it to a user.

[1501] "Means for the terminal to transmit the emotion engine's analysis results to the server when the user inputs information" refers to the process in which the user inputs input data into the terminal and the terminal transmits emotion data, which is the emotion engine's analysis result, to the server.

[1502] MODE FOR CARRYING OUT THE INVENTION

[1503] This invention is a system that combines a system that allows a user to input their beverage preferences and moods and provides the optimal beverage based on the input, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[1504] Hardware and Software

[1505] This system uses the following main hardware and software:

[1506] User device: Smartphone, tablet, etc. (with camera and microphone)

[1507] Sentiment engine: Software for sentiment analysis (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text)

[1508] Server: AWS Lambda or similar cloud service

[1509] Database: A database that stores drink recipes (e.g., Amazon RDS)

[1510] Mixing device: Robot barista (e.g., SoftBank Robotics' Pepper, CafeX)

[1511] System Operation

[1512] The user terminal collects the user's input information and emotion data and sends it to the server. The server searches the database, selects the optimal drink recipe, and sends mixing instructions to the mixing device. The mixing device mixes the drink based on the mixing instructions and serves it to the user.

[1513] User data input and sentiment analysis

[1514] The user inputs the ratio of sourness, bitterness, richness, and astringency of the beverage, as well as the desired origin, through a smartphone app. Using the app's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, if a user inputs "sourness 7, bitterness 3, richness 5, astringency 2" and the desired origin is "France," the emotion engine may recognize the emotion "relaxed."

[1515] Data processing and recipe selection

[1516] The device sends input data and emotional data from the user to the server. The server receives this data and searches the database to select the optimal drink recipe. Since the user is in a relaxed state, a recipe containing ingredients with a relaxing effect is selected.

[1517] Mixing instructions and beverage preparation

[1518] The server generates mixing instructions based on the selected drink recipe and sends them to the mixing device. The mixing device receives instructions from the server, selects the specified ingredients, and mixes them in the correct ratios to prepare the drink. For example, a drink can be prepared based on specific instructions such as "Chardonnay (France), strong acidity, and a relaxing effect."

[1519] Drink offering and notification

[1520] When the blending is complete, the blending device sends the information to the terminal, which then notifies the user, "Your French Chardonnay is ready!" The user follows the notification and receives the finished drink.

[1521] Prompt Sentence Examples

[1522] Here are some example prompts for using a generative AI model:

[1523] Build an algorithm to select the recipe that provides the optimal drink based on the user's preferred ratio of sourness, bitterness, richness, and astringency, as well as emotional data. Explain this using the example where the user's current emotional state is "Relaxed," the preferred ratios are sourness "7," bitterness "3," richness "5," astringency "2," and the origin is "France."

[1524] The present invention makes it possible to provide the user with an optimal beverage according to their current emotional state and preferences.

[1525] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1526] Step 1:

[1527] User Input

[1528] The user device displays questions about the beverage to the user. The user uses the smartphone interface to input "acidity 7, bitterness 3, richness 5, astringency 2" and selects "France" as the desired origin. Once the user has completed the input, the device temporarily stores the data.

[1529] Input: User's desired ratio of sourness, bitterness, richness, and astringency of the beverage, as well as desired origin information

[1530] Output: User input data stored on the device

[1531] Step 2:

[1532] sentiment analysis

[1533] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time. For example, Microsoft Azure Face API detects the emotion "relaxed" from the user's facial expressions, and Google Cloud Speech-to-Text analyzes emotions from the voice. The emotion engine then sends the acquired emotional data to the device.

[1534] Input: User's facial and voice data

[1535] Output: Emotion data analyzed by the emotion engine (e.g., "Relaxed")

[1536] Step 3:

[1537] Data transmission

[1538] The device transmits the user's input data and emotion data to a server via an internet connection.

[1539] Input: User input data (sourness 7, bitterness 3, richness 5, astringency 2, desired origin: France) and emotional data (relaxed)

[1540] Output: The user's dataset that is sent to the server

[1541] Step 4:

[1542] Database search

[1543] The server searches a database of beverage recipes based on the received user and emotion data. For example, it uses Amazon RDS to find a list of French beverage recipes from the database that best fit the user's preferences and emotions.

[1544] Input: Your dataset

[1545] Output: A shortlist of optimal drink recipes

[1546] Step 5:

[1547] Recipe Selection

[1548] The server selects the drink recipe that best suits the user's taste and emotions from the list of drink recipes. For example, it selects a recipe such as "Chardonnay (France), strong acidity, relaxing effect."

[1549] Input: List of drink recipe candidates

[1550] Output: Selected optimal drink recipe

[1551] Step 6:

[1552] Mixing instructions generation

[1553] The server generates specific mixing instructions based on the selected drink recipe, including the ingredients and their proportions. For example, the server might generate instructions such as "Mix Chardonnay (France) with a high acidity and add a relaxing ingredient."

[1554] Input: Selected drink recipe

[1555] Output: Mixing instructions

[1556] Step 7:

[1557] Sending mixing instructions

[1558] The server sends the generated mixing instructions to the mixing device (robot barista), and this communication is also carried out via the Internet.

[1559] Input: Mixing instructions

[1560] Output: Mixing instructions sent to the mixer

[1561] Step 8:

[1562] Beverage blending

[1563] The blending device receives the blending instructions and blends the beverage in proportion to the specified ingredients, for example, using Chardonnay (France) to create a more acidic drink and adding a relaxing ingredient.

[1564] Input: Mixing instructions

[1565] Output: Finished beverage

[1566] Step 9:

[1567] Completion notification

[1568] The dispensing device notifies the terminal that the beverage is ready, and the terminal displays the message "Your French Chardonnay is ready!" to the user.

[1569] Input: Completion notification from compounding device

[1570] Output: A notification message to the user

[1571] Step 10:

[1572] Receiving beverages

[1573] The user follows the notification on the terminal and receives the finished drink.

[1574] Input: Notification message

[1575] Output: The finished beverage received by the user

[1576] This allows users to easily enjoy personalized beverages that match their mood and preferences that day.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] [Fourth embodiment]

[1581] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1582] 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.

[1583] 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).

[1584] 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.

[1585] 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.

[1586] 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).

[1587] 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. 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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."

[1594] This invention is a system that provides the optimal beverage based on a user's input of their beverage preferences and mood. The system allows users to specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. Furthermore, users can select the desired region of origin. Based on this information, the system selects the optimal beverage recipe, blends it, and provides it to the user.

[1595] The specific processing flow of this system will be explained in natural language.

[1596] Step 1: Enter your user data

[1597] The device displays a question to the user.

[1598] Terminal: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10."

[1599] Terminal: "Please select your preferred origin."

[1600] The user enters information

[1601] User: Enter sourness "7", bitterness "3", richness "5", and astringency "2".

[1602] User: Enters "France" as the desired origin.

[1603] The device sends the data to the server

[1604] Terminal: Sends the entered ratio data and desired origin information to the server.

[1605] Step 2: Data processing and recipe selection

[1606] The server receives the data

[1607] Server: Receives the user's input data (acidity "7", bitterness "3", richness "5", astringency "2", origin "France").

[1608] The server searches the recipe database

[1609] Server: Searches the recipe database and lists possible drinks that match the user's preferences.

[1610] Server: Prefer French wines.

[1611] The server further narrows down the candidates

[1612] Server: Analyze the recipes that most closely match the above ratios to determine the best recipe.

[1613] Server: Choose a French Chardonnay (high acidity).

[1614] Step 3: Generate and send formulation instructions

[1615] The server generates the formulation instructions.

[1616] Server: Generates mixing instructions based on the selected recipe.

[1617] Server: Create a request for "Chardonnay (France), high acidity, low bitterness, medium body, low astringency."

[1618] The server sends compounding instructions to the AI ​​machine

[1619] Server: Sends mixing instructions to the AI ​​machine.

[1620] Step 4: Mix the drink

[1621] AI machine selects materials

[1622] AI Machine: Pick up a Chardonnay (France).

[1623] AI machine: Selects specific ingredients to enhance the sourness.

[1624] AI machines mix drinks

[1625] AI machine: Mixes selected ingredients in precise proportions.

[1626] AI machine: Adjusts the balance of acidity, bitterness, richness, and astringency.

[1627] AI machine pours drinks into containers

[1628] AI machine: Mixes and pours drinks into glasses (or other containers).

[1629] Step 5: Serve the drink

[1630] The AI ​​machine sends a completion notification to the device

[1631] AI machine: Notifies the device when the drink is ready.

[1632] The device notifies the user

[1633] Terminal: Display the message "Thank you for waiting. Your French Chardonnay is ready!"

[1634] The user receives a drink

[1635] User: Receives the finished drink and enjoys it.

[1636] These steps enable the system to quickly and accurately provide the optimal beverage tailored to the user's preferences and mood of the day. For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2", the system can provide a French Chardonnay with the optimal blend. In this way, users can enjoy beverages that bring new experiences and satisfaction to their daily lives.

[1637] The processing flow will be explained below.

[1638] Step 1:

[1639] The device displays a question to the user.

[1640] The device displays the following question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1641] Step 2:

[1642] The user enters information

[1643] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[1644] Step 3:

[1645] The device sends the data to the server

[1646] The terminal sends the ratio data entered by the user (acidity "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server.

[1647] Step 4:

[1648] The server receives the data

[1649] The server receives the user input data sent from the terminal.

[1650] Step 5:

[1651] The server searches the recipe database

[1652] The server searches the recipe database based on the user's input data, prioritizing French drinks in particular, and lists candidates that match the ratio specified by the user.

[1653] Step 6:

[1654] The server selects the best recipe

[1655] The server analyzes the recipes listed and selects the one that most closely matches the user's specified ratio (acidity: 7, bitterness: 3, richness: 5, astringency: 2). In this example, a French Chardonnay (highly acidic) is selected.

[1656] Step 7:

[1657] The server generates the formulation instructions.

[1658] The server generates specific mixing instructions based on the selected recipe, such as "Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency."

[1659] Step 8:

[1660] The server sends compounding instructions to the AI ​​machine.

[1661] The server generates mixing instructions and sends them to the AI ​​machine.

[1662] Step 9:

[1663] AI machine selects materials

[1664] The AI ​​machine receives recipe instructions from the server and selects the specified ingredients (Chardonnay (France)) and also selects specific ingredients to enhance the acidity.

[1665] Step 10:

[1666] AI machines mix drinks

[1667] The AI ​​machine selects ingredients and mixes them in precise proportions based on instructions, adjusting the balance of sourness, bitterness, richness, and astringency.

[1668] Step 11:

[1669] AI machine pours drinks into containers

[1670] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1671] Step 12:

[1672] The AI ​​machine sends a completion notification to the device

[1673] The AI ​​machine will notify the device that the drink is ready.

[1674] Step 13:

[1675] The device notifies the user

[1676] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1677] Step 14:

[1678] The user receives a drink

[1679] The user follows the notification on the terminal and receives the finished drink.

[1680] Example 1

[1681] 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."

[1682] Conventional beverage delivery systems have had difficulty responding precisely to users' preferences and moods. Furthermore, there were no systems that allowed users to input specific flavor ratios or desired origins, making it difficult to deliver beverages that met individual needs. This posed a risk of lowering user satisfaction. Furthermore, the accuracy and efficiency of beverage preparation were also issues.

[1683] 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.

[1684] In this invention, the server includes means for a user to input their beverage preferences and mood, means for a terminal to receive and display the user's input information, means for the terminal to transmit the information received to the server, means for the server to receive the user's input information and search a database based on it to select an optimal beverage recipe, means for the server to generate mixing instructions based on the selected beverage recipe and transmit them to the AI ​​machine, and means for the AI ​​machine to mix a beverage based on the mixing instructions and serve it to the user. This makes it possible to quickly and accurately provide an optimal beverage that matches the user's detailed preferences and mood.

[1685] "User" means an individual or organization that receives beverages using the system.

[1686] A "terminal" is an electronic device used by a user to enter information and view results.

[1687] A "server" is a computer system that receives user input, searches a database, selects recipes, and generates mixing instructions.

[1688] The "database" is a digital information management system that stores information about beverage recipes and various ingredients.

[1689] A "recipe" is information that indicates the combination of ingredients and how to mix them to make a specific drink.

[1690] "Mixing instructions" are specific instructions for creating a particular beverage that are generated by the server and sent to the AI ​​machine.

[1691] An "AI machine" is a device that automatically mixes beverages based on mixing instructions from a server and provides them to users.

[1692] "Preferences and moods" refers to input information regarding the ratio of sourness, bitterness, richness, and astringency that the user desires in a beverage, as well as the desired place of origin.

[1693] A "beverage" is a drinkable liquid that is dispensed for presentation to a user.

[1694] This invention is a system that inputs a user's beverage preferences and moods and provides the most suitable beverage based on that information. A specific implementation method for this system will be described below.

[1695] Hardware and Software Configuration

[1696] The system includes the following main components:

[1697] User input means: A device (e.g., tablet, smartphone) through which the user inputs the ratio of sourness, bitterness, richness, and astringency, as well as the desired origin.

[1698] Terminal: A device that receives user input and sends it to a server.

[1699] Server: A computer system that receives user input, searches and selects beverage recipes, and sends mixing instructions to the AI ​​machine. This includes a database management system (e.g., MySQL), a recipe search algorithm, and mixing instruction generation software (e.g., FreeMarker).

[1700] AI machine: An automatic mixing machine (e.g., a robotic arm, a mixer with a dispenser) that mixes drinks based on mixing instructions from a server and provides them to the user.

[1701] How it works

[1702] 1. Entering user data

[1703] The user uses a terminal to input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10. They can also select the desired region of origin.

[1704] Example: A user inputs sourness "7", bitterness "3", richness "5", and astringency "2", and sets the desired origin as "France".

[1705] 2. Sending and Receiving Data

[1706] The terminal sends the input information to the server, which receives the user's input data and searches the database based on that information.

[1707] 3. Searching for and selecting recipes

[1708] The server uses a specific algorithm (e.g., SQL query or machine learning algorithm) to list drink recipes from a database that match the user's preferences. If a desired region of origin is specified, drinks from that region are searched for first.

[1709] 4. Generate mixing instructions

[1710] The server selects the optimal recipe and generates mixing instructions based on it. These mixing instructions are written out in detail using a template engine (e.g., FreeMarker).

[1711] Example: Chardonnay (France), strong acidity, mild bitterness, medium body, mild astringency.

[1712] 5. Sending mixing instructions and mixing drinks

[1713] The server generates the mixing instructions and sends them to the AI ​​machine, which selects the bottle and ingredients, mixes the drink in the correct proportions according to the instructions, and finally pours it into a container.

[1714] 6. Drink Serving and Notification

[1715] The AI ​​machine serves the blended drink to the user. It notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry for the wait. Your French Chardonnay is ready!"

[1716] Specific examples

[1717] If a user requests a "French" beverage with a ratio of "acidity 7: bitterness 3: richness 5: astringency 2," the system will provide a "French Chardonnay" with the optimal blend.

[1718] Example prompt:

[1719] User: "I'd like a wine from France with acidity 7, bitterness 3, body 5, and astringency 2."

[1720] Terminal: "Your requested beverage is being prepared. Please wait a moment."

[1721] Server: "Selecting the French Chardonnay that best suits your needs..."

[1722] AI machine: "The selected Chardonnay is blended in the appropriate proportions and poured into a container..."

[1723] Terminal: "Sorry to keep you waiting. Our French Chardonnay is ready!"

[1724] This allows users to quickly and accurately choose the perfect beverage to suit their specific preferences and moods, resulting in high levels of satisfaction.

[1725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1726] Step 1: Enter user data

[1727] The user operates the device

[1728] Input: Ratio of sourness, bitterness, richness, astringency, desired origin

[1729] How it works: The user enters the ratio of sourness, bitterness, richness, and astringency on the device screen on a scale of 1 to 10 and selects the desired origin.

[1730] Output: Input data (e.g., sourness "7", bitterness "3", richness "5", astringency "2", desired origin "France")

[1731] Step 2: Sending and Receiving Data

[1732] The device sends the data to the server

[1733] Input: Data entered by the user

[1734] How it works: The device organizes the data entered by the user and sends it to a server over the internet using the HTTP or HTTPS protocol.

[1735] Output: Data sent to the server

[1736] Step 3: Data processing and recipe selection

[1737] The server receives the data and starts processing it.

[1738] Input: User data sent from the terminal

[1739] How it works: The server receives user data and generates a database query, using SQL to search the recipe database for recipes that best match the user's preferences.

[1740] Output: A list of candidate drink recipes

[1741] The server narrows down the recipes

[1742] Input: A list of candidate drink recipes

[1743] How it works: Uses machine learning algorithms (e.g. KNN or cosine similarity) to select the best recipe from a list of candidates.

[1744] Output: Optimal drink recipe (e.g., French Chardonnay)

[1745] Step 4: Generate and send formulation instructions

[1746] The server generates the formulation instructions.

[1747] Input: Optimal Drink Recipe

[1748] How it works: Using a template engine (e.g., FreeMarker), specific mixing instructions are generated. The mixing instructions include the types and amounts of ingredients needed, as well as mixing procedures.

[1749] Output: Generated mixing instructions

[1750] The server sends the compounding instructions to the AI ​​machine.

[1751] Input: Generated mixing instructions

[1752] How it works: The server uses a RESTful API to send JSON-formatted formula instruction data to the AI ​​machine.

[1753] Output: Mixing instructions sent to the AI ​​machine

[1754] Step 5: Mix the drink

[1755] AI machine selects materials

[1756] Input: Mixing instructions sent from the server

[1757] How it works: The AI ​​machine retrieves the required ingredients (e.g., Chardonnay (France)) from storage based on the recipe instructions.

[1758] Output: Selected material

[1759] AI machines mix drinks

[1760] Input: Selected material

[1761] How it works: The AI ​​machine mixes ingredients in precise proportions, balancing sourness, bitterness, richness, and astringency. Reference sensors measure the amount of ingredients.

[1762] Output: Dispensed beverage

[1763] AI machine pours drinks into containers

[1764] Input: Dispensed beverage

[1765] How it works: The AI ​​machine uses a dispenser to dispense a mixed drink into a glass or other container.

[1766] Output: A drink ready to be served to the user

[1767] Step 6: Offer drinks and notify

[1768] The AI ​​machine sends a completion notification to the device

[1769] Input: Drink ready state

[1770] How it works: The AI ​​machine uses a message queuing method (e.g. RabbitMQ) to notify the device that the drink is ready.

[1771] Output: Completion notification sent to the terminal

[1772] The device notifies the user

[1773] Input: Completion notification from AI machine

[1774] What it does: The device displays a message to the user on the screen saying, "Sorry for the wait. Your French Chardonnay is ready!"

[1775] Output: The notification displayed to the user

[1776] The user receives a drink

[1777] Input: Completion notification from the terminal

[1778] How it works: The user checks the notification on their device, goes to the dispenser station, and receives a drink. They are authenticated using an RFID tag or QR code.

[1779] Output: Drinks received

[1780] (Application example 1)

[1781] 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."

[1782] Conventional beverage supply systems have difficulty in quickly providing the optimal beverage according to the user's preferences and mood, and are therefore unable to provide a service that satisfies the user. In addition, there is also the problem that the beverage is manually mixed based on the user's input information in a physical store, which takes time and results in long waiting times for the user.

[1783] 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.

[1784] In this invention, the server includes a means for a user to input their beverage preferences and mood, a means for the server to receive the user's input information and search a database based on the information to select an optimal beverage recipe, a means for the server to generate blending instructions based on the beverage recipe selected by the server and transmit the instructions to a remote blending device, and a means for the blending device to blend the beverage based on the blending instructions and receive the beverage at a location specified by the user. This makes it possible to quickly and accurately provide a beverage that suits the user's preferences.

[1785] A "user" is a user of the system who inputs their beverage preferences and mood and receives beverages.

[1786] "Preferences and moods" refer to the user's taste preferences for beverages and their psychological state at the time.

[1787] The "server" is a central computer system that searches a database based on information received from the user, selects the optimal drink recipe, and issues instructions.

[1788] The "database" is a repository of information that stores and manages recipe data for various beverages and can be searched as needed.

[1789] A "drink recipe" is a set of instructions outlining the ingredients, proportions, and steps needed to prepare a particular drink.

[1790] "Mixing instructions" are instructions that show a specific mixing method based on a selected beverage recipe.

[1791] A "mixing machine" is a machine that automatically mixes beverages based on mixing instructions received from a server.

[1792] An "affiliated store" is a commercial food and beverage establishment that provides beverages in cooperation with the server.

[1793] "Notification" is a communication means to inform the user that the dispensed beverage is ready.

[1794] The present invention relates to a system that provides optimal beverages based on a user's input of their beverage preferences and moods. The system is implemented using a smartphone application, a server, and a dispensing device.

[1795] Hardware and software used

[1796] Hardware:

[1797] Smartphone

[1798] server

[1799] Mixing device (AI machine)

[1800] software:

[1801] Frontend: React Native

[1802] Backend: Node.js, Express.js

[1803] Database: MySQL

[1804] Communication: Socket.IO

[1805] overview

[1806] First, the user uses a smartphone application to input their beverage preferences and feelings. Specifically, they input the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and select the desired origin of the beverage. The application also uses GPS functionality to display affiliated stores near the user's current location.

[1807] The terminal sends the input data to the server, which receives it and searches a recipe database to create a list of beverage candidates that match the user's preferences.The server then selects the optimal recipe from the list and generates mixing instructions.

[1808] The generated mixing instructions are sent to the mixing device at the partner store. The mixing device automatically mixes the specified beverage based on the instructions. When mixing is complete, the server sends a notification to the terminal, prompting the user to pick up the beverage.

[1809] Specific examples

[1810] For example, if a user requests a French beverage with a ratio of acidity "7", bitterness "3", richness "5", and astringency "2" on their smartphone, the server will select the optimal French beverage recipe based on this data and send instructions to the blending device. In this case, a recipe for French Chardonnay (strong acidity) is selected.

[1811] The blending device receives instructions from the server and blends French Chardonnay. Once blending is complete, the server notifies the user via a smartphone application with a message saying, "Sorry to keep you waiting! Your French Chardonnay is ready!" The user can then pick up the beverage at the designated store based on this notification.

[1812] Prompt Sentence Examples

[1813] The user has submitted the following preference data:

[1814] Acidity: 7

[1815] Bitterness: 3

[1816] Body: 5

[1817] Astringency: 2

[1818] Preferred origin: France

[1819] Find the perfect drink recipe.

[1820] This system allows users to quickly and accurately receive beverages tailored to their preferences, providing new experiences and satisfaction.

[1821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1822] Step 1:

[1823] The user opens the smartphone application. The user inputs the ratios of sourness, bitterness, richness, and astringency on a scale of 1 to 10, and selects the desired origin. The application collects this input data and obtains the user's location information. The user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin and current location information) is generated.

[1824] Step 2:

[1825] The device sends the user's input data to the server. The device converts the input data into JSON format and sends it to the server using an HTTP POST request. The input data here is the user's preference information and location information, and the output is data sent to the server.

[1826] Step 3:

[1827] The server receives user input data. The server analyzes the received data and searches the database to list candidate drink recipes that match the user's preferences. The server extracts matching items from the recipe database. The input data here is the analyzed user data, and the output data is a list of matching drink recipes.

[1828] Step 4:

[1829] The server selects the optimal drink recipe. From the list of drink recipes, it analyzes and prioritizes the drinks that have the closest ratio of sourness, bitterness, richness, and astringency. It also takes into account the origin of the drink desired by the user to determine the optimal recipe. The input data is a list of recipes, and the output data is the optimal recipe.

[1830] Step 5:

[1831] The server generates mixing instructions based on the optimal beverage recipe and sends them to the mixing device at the corresponding partner store. The server then converts the mixing instructions into specific steps, formats them in a format that the mixing device can understand, and sends them. The input data here is the optimal recipe, and the output data is the mixing instructions to the store.

[1832] Step 6:

[1833] The blending machine blends the beverage based on blending instructions received from the server. The blending machine takes the specified ingredients and measures and mixes them in the correct amounts. The input data is the blending instructions and the output data is the blended beverage.

[1834] Step 7:

[1835] When the blending device has finished preparing the blended beverage, it sends a completion notification to the server. The server receives this notification and forwards it to the terminal. The input data here is the blending completion notification, and the output data is the notification to the terminal.

[1836] Step 8:

[1837] The terminal notifies the user that the blending is complete. The terminal then displays a message on the smartphone saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" and prompts the user to pick it up at the store. The input data here is the notification from the server, and the output data is the notification to the user.

[1838] This series of steps allows the user to quickly and accurately receive a beverage tailored to their preferences.

[1839] 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.

[1840] This invention combines a system that allows users to input their beverage preferences and moods and provides the optimal beverage based on those preferences with an emotion engine that recognizes the user's emotions. Users can specify their beverage preferences by inputting the ratios of sourness, bitterness, richness, and astringency using a panel or mobile device. They can also select the desired region of origin. Furthermore, the emotion engine analyzes the user's emotions from their facial expressions and voice, and sends that data to a server, enabling the provision of a more personalized beverage.

[1841] Program processing flow

[1842] Entering User Data

[1843] The device displays a question to the user.

[1844] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency in the range of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1845] Emotion engine analyzes user emotions

[1846] The emotion engine analyzes emotions from the user's facial expressions and voice in real time and obtains emotional data.

[1847] The user enters information

[1848] The user inputs into the terminal sourness "7", bitterness "3", richness "5", and astringency "2", and also inputs "France" as the desired origin.

[1849] The emotion engine sends the emotion data to the server.

[1850] The emotion engine sends the analysis results to the server, for example, telling the server that the user is feeling "happy" or "relaxed."

[1851] The device sends the data to the server

[1852] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[1853] Data processing and recipe selection

[1854] The server receives the data

[1855] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[1856] The server searches the recipe database

[1857] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[1858] The server selects the best recipe

[1859] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. For example, if the user is feeling relaxed, the server selects a recipe that contains many ingredients that are expected to have a relaxing effect.

[1860] Generate and send formulation instructions

[1861] The server generates the formulation instructions.

[1862] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[1863] The server sends compounding instructions to the AI ​​machine.

[1864] The server generates mixing instructions and sends them to the AI ​​machine.

[1865] Drink mixing

[1866] AI machine selects materials

[1867] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) as well as specific ingredients to enhance the relaxing effect.

[1868] AI machines mix drinks

[1869] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[1870] AI machine pours drinks into containers

[1871] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1872] Drinks provided

[1873] The AI ​​machine sends a completion notification to the device

[1874] The AI ​​machine will notify the device that the drink is ready.

[1875] The device notifies the user

[1876] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1877] The user receives a drink

[1878] The user follows the notification on the terminal and receives the finished drink.

[1879] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even their emotions that day. For example, if a user requests a French beverage with a ratio of acidity (7), bitterness (3), richness (5), and astringency (2), and their emotion is "relaxation," the system can provide a French Chardonnay with ingredients that emphasize its relaxing effect. In this way, a personalized experience can be provided based on the user's individual needs.

[1880] The processing flow will be explained below.

[1881] Step 1:

[1882] The device displays a question to the user.

[1883] The terminal displays the question to the user, "Please enter the ratio of sourness, bitterness, richness, and astringency on a scale of 1 to 10." It also notifies the user, "Please select a preferred origin, if any."

[1884] Step 2:

[1885] Emotion engine analyzes user emotions

[1886] The emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data such as "happiness," "relaxation," and "excitement."

[1887] Step 3:

[1888] The user enters information

[1889] The user inputs into the terminal the following values ​​for sourness: "7", bitterness: "3", richness: "5", and astringency: "2". The user also inputs the desired origin as "France".

[1890] Step 4:

[1891] The emotion engine sends the emotion data to the server.

[1892] The emotion engine analyzes the emotion data and sends it to the server. For example, it can tell the user that their emotion is "relaxed."

[1893] Step 5:

[1894] The device sends the data to the server

[1895] The terminal transmits the ratio data (sourness "7", bitterness "3", richness "5", astringency "2") input by the user and the desired origin information ("France") to the server.

[1896] Step 6:

[1897] The server receives the data

[1898] The server receives the user's input data and emotion data sent from the terminal and the emotion engine.

[1899] Step 7:

[1900] The server searches the recipe database

[1901] The server searches the recipe database based on the user's input data and emotion data, and lists candidates that match the ratio and emotion specified by the user, with a particular priority given to French drinks.

[1902] Step 8:

[1903] The server selects the best recipe

[1904] The server analyzes the listed candidates to find the recipe that best matches the user's specified ratio and emotional data, and selects the optimal recipe. In this case, the server selects a recipe made with French Chardonnay, which contains ingredients expected to have a relaxing effect.

[1905] Step 9:

[1906] The server generates the formulation instructions.

[1907] The server generates specific mixing instructions based on the selected recipe. For example, it creates mixing instructions for "Chardonnay (France), strong acidity, relaxing effect."

[1908] Step 10:

[1909] The server sends compounding instructions to the AI ​​machine.

[1910] The server generates mixing instructions and sends them to the AI ​​machine.

[1911] Step 11:

[1912] AI machine selects materials

[1913] The AI ​​machine receives blending instructions from the server and selects the specified ingredients (Chardonnay (France)) and specific ingredients to enhance the relaxing effect.

[1914] Step 12:

[1915] AI machines mix drinks

[1916] The AI ​​machine selects ingredients and mixes them in precise proportions based on your instructions, adjusting the balance of sourness, bitterness, richness, and astringency, and adding ingredients based on emotional data.

[1917] Step 13:

[1918] AI machine pours drinks into containers

[1919] The AI ​​machine mixes the drink and pours it into a glass (or other container).

[1920] Step 14:

[1921] The AI ​​machine sends a completion notification to the device

[1922] The AI ​​machine will notify the device that the drink is ready.

[1923] Step 15:

[1924] The device notifies the user

[1925] The terminal displays the message to the user: "Sorry for the wait. Your French Chardonnay is ready!"

[1926] Step 16:

[1927] The user receives a drink

[1928] The user follows the notification on the terminal and receives the finished drink.

[1929] Example 2

[1930] 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."

[1931] Conventional beverage delivery systems have difficulty responding to a user's individual preferences or mood of the day. Furthermore, they are unable to provide the optimal beverage while taking into account the user's emotions, and thus do not achieve sufficient personalization. As a result, users may receive a dissatisfied beverage.

[1932] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input their beverage preferences and mood, a means for the server to receive the user's input information and emotion data extracted from the user's facial expression and voice, and to search a database based on the received information and select an optimal beverage recipe, a means for generating blending instructions based on the beverage recipe selected by the server and sending the instructions to the machine, and a means for notifying the user. This makes it possible to provide an optimal beverage based on the user's individual preferences, mood of the day, and emotions in real time.

[1933] "User" refers to an individual who inputs their beverage preferences and mood and uses the system to receive a beverage.

[1934] "Beverage" refers to a consumable liquid that is dispensed based on a user's preferences, mood, or even emotion.

[1935] "Preferences" refer to specific characteristics such as the ratio of sourness, bitterness, richness, and astringency that a user desires in a beverage, as well as a desired place of origin.

[1936] "Mood" refers to the temporary emotions and psychological state of a user when selecting a beverage.

[1937] The term "means" refers to various methods and devices required to configure the system.

[1938] "Server" refers to a computer device that receives user input information and emotion data, selects an optimal drink recipe based on the received information, and generates and transmits blending instructions to the machine.

[1939] "Facial expression" refers to data on the emotional state obtained from the user's facial movements and expressions.

[1940] "Voice" refers to emotional state data obtained from the user's voice.

[1941] "Emotion data" refers to information about emotions extracted from the user's facial expressions and voice.

[1942] A "database" refers to an information system that stores information such as beverage recipes and manages them in a searchable format.

[1943] "Mixing instructions" refers to instruction data for instructing the machine on specific mixing contents based on the beverage recipe selected by the server.

[1944] "Machine" refers to a device that receives mixing instructions from the server, actually mixes the beverage, and provides it to the user.

[1945] "Region" refers to information indicating the origin of the beverage desired by the user.

[1946] "Notification" refers to a communication means for informing a user about a dispensed beverage.

[1947] This invention is a system that provides the optimal beverage based on the user's input of their beverage preferences, mood, and even emotions. This system also utilizes an emotion engine that recognizes emotions from the user's facial expressions and voice, thereby providing a more personalized beverage.

[1948] A terminal is used as a way for users to input their preferences and feelings about beverages. This terminal is equipped with a user interface and displays an input screen for inputting the ratios of sourness, bitterness, richness, and astringency. Users can also select their desired origin.

[1949] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, and processes this data in real time to analyze their emotions. Specifically, it uses image recognition and voice analysis technologies to obtain the user's emotional data. For example, it uses facial recognition technology to detect smiling and sad expressions, and voice analysis technology to evaluate the tone and speed of the voice.

[1950] When a user inputs the necessary information into the device, this data is sent to the server. The server receives the user's input data and emotion data sent from the device and emotion engine. It then searches the database based on this data and runs an algorithm to select the optimal drink recipe. This search process prioritizes drinks from specific regions and lists candidates that match the ratios and emotions specified by the user.

[1951] After the server selects the optimal recipe, it generates mixing instructions based on this recipe. For example, specific mixing instructions such as "Chardonnay (France), strong acidity, relaxing effect" are created. These mixing instructions are then sent to the AI ​​machine.

[1952] The AI ​​machine selects the specified ingredients based on the mixing instructions received from the server. During this process, it automatically selects ingredients from its inventory and also selects additional ingredients to complement specific ingredients. The AI ​​machine then mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. Ingredients intended for relaxation effects can also be added.

[1953] Once the drink is mixed, the AI ​​machine pours it into a glass (or other container), with the pouring program controlling the amount to be dispensed.

[1954] Finally, the AI ​​machine notifies the device that the drink is ready, and the device displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The user confirms the notification and collects the finished drink from the designated pick-up location.

[1955] This system allows users to easily enjoy the perfect beverage based on their mood, preferences, and even real-time emotions. For example, if a user enters the following prompt, the system will automatically begin the process of blending a beverage:

[1956] Example prompt sentence:

[1957] "My mood is relaxation. I like French drinks and I like the balance of acidity 7, bitterness 3, body 5 and astringency 2."

[1958] Based on this prompt, the system will provide the best French Chardonnay that matches the user's tastes and feelings.

[1959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1960] Step 1:

[1961] The device displays the input screen.

[1962] The terminal displays the question to the user: "Please enter the ratio of sourness, bitterness, richness, and astringency in a range from 1 to 10." It also displays the option, "Please select a preferred origin, if any." The input format consists of a numeric input field and a drop-down list for selecting origin. For example, the input data can be sourness "7," bitterness "3," richness "5," astringency "2," and the desired origin "France."

[1963] Step 2:

[1964] Emotion engine analyzes emotions

[1965] The emotion engine captures the user's facial expressions with a camera and their voice with a microphone, then processes this data in real time to analyze their emotions. The emotion engine performs facial recognition and voice analysis to distinguish between smiling and sad expressions, as well as the tone and speed of the voice. This data processing uses the captured images and voice as input data and outputs emotions such as "happy" or "relaxed."

[1966] Step 3:

[1967] The user enters information

[1968] The user inputs the values ​​for sourness (7), bitterness (3), richness (5), and astringency (2) on the terminal screen, and selects "France" as the desired origin. After completing the input, the user presses the "Send" button, which sends the data to the server. The input data includes the user's preferred ratio and origin information.

[1969] Step 4:

[1970] The emotion engine sends the emotion data to the server.

[1971] The emotion engine sends the emotion data obtained as a result of its analysis, such as "happy" or "relaxed," to the server. Specifically, the emotion data is sent to the server in JSON format. The emotion engine's analysis results are the input data, and the emotion information received by the server is the output data.

[1972] Step 5:

[1973] The device sends user data to the server

[1974] The terminal sends the ratio data entered by the user (sourness "7", bitterness "3", richness "5", astringency "2") and the desired origin information ("France") to the server. This data is also sent in JSON format. The information entered by the user is obtained as input data, and the preference information received by the server is obtained as output data.

[1975] Step 6:

[1976] The server receives the data

[1977] The server receives the user's input data (ratios of sourness, bitterness, richness, and astringency, as well as the desired origin) and emotion data sent from the device and emotion engine. Various information from the device and emotion engine is obtained as input data, and data for searching in the internal database is obtained as output data.

[1978] Step 7:

[1979] The server searches the recipe database

[1980] Based on the data received by the server, a database search engine is used to query the recipe database. Candidates that match the user's specified ratios, desired origin, and emotional information are extracted. SQL queries and other data calculations are executed. The search query is used as input data, and the extracted recipe candidates are used as output data.

[1981] Step 8:

[1982] The server selects the best recipe

[1983] The server uses an algorithm to select the optimal recipe from the search results. For example, if a user is looking for a relaxing effect, it will identify recipes that contain that ingredient. The selection process is carried out through algorithmic processing. The extracted recipe candidates are obtained as input data, and the optimal recipe is obtained as output data.

[1984] Step 9:

[1985] The server generates the formulation instructions.

[1986] The server creates blending instructions based on the selected recipe. For example, it generates blending instructions such as "Chardonnay (France), strong acidity, relaxing effect." Blending instruction data is created as data processing. The selected recipe is obtained as input data, and the generated blending instructions are obtained as output data.

[1987] Step 10:

[1988] The server sends compounding instructions to the AI ​​machine.

[1989] The server sends the generated compounding instructions to the AI ​​machine via the network. This is usually done through an API. The generated compounding instructions are the input data, and the instruction information received by the AI ​​machine is the output data.

[1990] Step 11:

[1991] AI machine selects materials

[1992] Based on the compounding instructions received from the server, the AI ​​machine automatically selects the specified materials from the warehouse. Based on the material selection logic, the necessary materials are identified and retrieved. The instruction information is obtained as input data, and the selected materials are output data.

[1993] Step 12:

[1994] AI machines mix drinks

[1995] The AI ​​machine mixes the selected ingredients in the correct ratio, adjusting the balance of sourness, bitterness, richness, and astringency. The ingredients are mixed and the blend is carried out according to the program. The selected ingredients are the input data, and the blended beverage is the output data.

[1996] Step 13:

[1997] AI machine pours drinks into containers

[1998] Once the AI ​​machine has mixed the drink, it automatically pours it into a glass (or other container). The pouring method is also controlled by the program, providing the appropriate amount. The input data is the mixed drink, and the output data is the drink poured into the container.

[1999] Step 14:

[2000] The AI ​​machine sends a completion notification to the device

[2001] The AI ​​machine notifies the server that the drink is ready and transfers that information to the device. The input data is information about the completion of mixing, and the output data is information about the notification sent to the device.

[2002] Step 15:

[2003] The device notifies the user

[2004] The terminal displays a message to the user saying, "Sorry to keep you waiting. Your French Chardonnay is ready!" The notification message is displayed on the screen. The input data is the completion notification, and the output data is the message to be displayed to the user.

[2005] Step 16:

[2006] The user receives a drink

[2007] The user confirms the notification from the device and collects the completed drink from the designated pick-up location. The process is completed when the user picks up the drink at the pick-up location. The notification to the user is obtained as input data, and the drink provided to the user is obtained as output data.

[2008] (Application example 2)

[2009] 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."

[2010] Conventional user beverage selection systems provide beverages based on the user's preferences and mood, but do not consider the user's emotional state. As a result, the beverage provided may not necessarily be optimal for the user's current emotion or mood. To improve customer experience, especially in cafes and beverage stores, there is a need to analyze the user's emotional state in real time and provide personalized beverages based on that analysis.

[2011] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2012] In this invention, the server includes: means for a user to input their preferences and mood regarding beverages; means for an emotion engine to analyze emotions from the user's facial expressions and voice and acquire emotion data; means for the server to receive the user's input information and emotion data and search a database based on the information and emotion data to select an optimal beverage recipe; means for the server to generate blending instructions based on the beverage recipe selected and send them to the blending device; means for the blending device to blend a beverage based on the blending instructions and provide it to the user; and means for the terminal used by the user to input information to send the emotion engine's analysis results to the server. This makes it possible to provide an optimal beverage according to the user's current emotional state and preferences.

[2013] "A means for users to input their preferences and mood regarding beverages" refers to an interface that allows users to input the characteristics of beverages (sourness, bitterness, richness, astringency) and moods (relaxed, happy, etc.) using specific numerical values ​​or text.

[2014] "Means for the emotion engine to analyze emotions from the user's facial expressions and voice and obtain emotional data" refers to a system that uses sensors and cameras to obtain the user's real-time facial expressions and voice, and analyzes that data to identify the user's emotional state.

[2015] "Means for the server to receive the user's input information and emotional data, and search the database based on that to select the optimal drink recipe" refers to the process in which the server receives the preference and mood information entered by the user, as well as the emotional data obtained by the emotion engine, and searches the drink recipe database based on that to select the optimal drink recipe.

[2016] The "means for generating mixing instructions based on a beverage recipe selected by the server and transmitting them to the mixing device" refers to a mechanism for generating specific mixing instructions based on a selected beverage recipe and transmitting these instructions to the mixing device.

[2017] The "means for the blending device to blend a beverage based on blending instructions and provide it to a user" refers to a device and process that mixes specified ingredients based on blending instructions sent from a server to create a beverage and provides it to a user.

[2018] "Means for the terminal to transmit the emotion engine's analysis results to the server when the user inputs information" refers to the process in which the user inputs input data into the terminal and the terminal transmits emotion data, which is the emotion engine's analysis result, to the server.

[2019] MODE FOR CARRYING OUT THE INVENTION

[2020] This invention is a system that combines a system that allows a user to input their beverage preferences and moods and provides the optimal beverage based on the input, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[2021] Hardware and Software

[2022] This system uses the following main hardware and software:

[2023] User device: Smartphone, tablet, etc. (with camera and microphone)

[2024] Sentiment engine: Software for sentiment analysis (e.g., Microsoft Azure Face API, Google Cloud Speech-to-Text)

[2025] Server: AWS Lambda or similar cloud service

[2026] Database: A database that stores drink recipes (e.g., Amazon RDS)

[2027] Mixing device: Robot barista (e.g., SoftBank Robotics' Pepper, CafeX)

[2028] System Operation

[2029] The user terminal collects the user's input information and emotion data and sends it to the server. The server searches the database, selects the optimal drink recipe, and sends mixing instructions to the mixing device. The mixing device mixes the drink based on the mixing instructions and serves it to the user.

[2030] User data input and sentiment analysis

[2031] The user inputs the ratio of sourness, bitterness, richness, and astringency of the beverage, as well as the desired origin, through a smartphone app. Using the app's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, if a user inputs "sourness 7, bitterness 3, richness 5, astringency 2" and the desired origin is "France," the emotion engine may recognize the emotion "relaxed."

[2032] Data processing and recipe selection

[2033] The device sends input data and emotional data from the user to the server. The server receives this data and searches the database to select the optimal drink recipe. Since the user is in a relaxed state, a recipe containing ingredients with a relaxing effect is selected.

[2034] Mixing instructions and beverage preparation

[2035] The server generates mixing instructions based on the selected drink recipe and sends them to the mixing device. The mixing device receives instructions from the server, selects the specified ingredients, and mixes them in the correct ratios to prepare the drink. For example, a drink can be prepared based on specific instructions such as "Chardonnay (France), strong acidity, and a relaxing effect."

[2036] Drink offering and notification

[2037] When the blending is complete, the blending device sends the information to the terminal, which then notifies the user, "Your French Chardonnay is ready!" The user follows the notification and receives the finished drink.

[2038] Prompt Sentence Examples

[2039] Here are some example prompts for using a generative AI model:

[2040] Build an algorithm to select the recipe that provides the optimal drink based on the user's preferred ratio of sourness, bitterness, richness, and astringency, as well as emotional data. Explain this using the example where the user's current emotional state is "Relaxed," the preferred ratios are sourness "7," bitterness "3," richness "5," astringency "2," and the origin is "France."

[2041] The present invention makes it possible to provide the user with an optimal beverage according to their current emotional state and preferences.

[2042] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2043] Step 1:

[2044] User Input

[2045] The user device displays questions about the beverage to the user. The user uses the smartphone interface to input "acidity 7, bitterness 3, richness 5, astringency 2" and selects "France" as the desired origin. Once the user has completed the input, the device temporarily stores the data.

[2046] Input: User's desired ratio of sourness, bitterness, richness, and astringency of the beverage, as well as desired origin information

[2047] Output: User input data stored on the device

[2048] Step 2:

[2049] sentiment analysis

[2050] Using the device's camera and microphone, the emotion engine analyzes the user's facial expressions and voice in real time. For example, Microsoft Azure Face API detects the emotion "relaxed" from the user's facial expressions, and Google Cloud Speech-to-Text analyzes emotions from the voice. The emotion engine then sends the acquired emotional data to the device.

[2051] Input: User's facial and voice data

[2052] Output: Emotion data analyzed by the emotion engine (e.g., "Relaxed")

[2053] Step 3:

[2054] Data transmission

[2055] The device transmits the user's input data and emotion data to a server via an internet connection.

[2056] Input: User input data (sourness 7, bitterness 3, richness 5, astringency 2, desired origin: France) and emotional data (relaxed)

[2057] Output: The user's dataset that is sent to the server

[2058] Step 4:

[2059] Database search

[2060] The server searches a database of beverage recipes based on the received user and emotion data. For example, it uses Amazon RDS to find a list of French beverage recipes from the database that best fit the user's preferences and emotions.

[2061] Input: Your dataset

[2062] Output: A shortlist of optimal drink recipes

[2063] Step 5:

[2064] Recipe Selection

[2065] The server selects the drink recipe that best suits the user's taste and emotions from the list of drink recipes. For example, it selects a recipe such as "Chardonnay (France), strong acidity, relaxing effect."

[2066] Input: List of drink recipe candidates

[2067] Output: Selected optimal drink recipe

[2068] Step 6:

[2069] Mixing instructions generation

[2070] The server generates specific mixing instructions based on the selected drink recipe, including the ingredients and their proportions. For example, the server might generate instructions such as "Mix Chardonnay (France) with a high acidity and add a relaxing ingredient."

[2071] Input: Selected drink recipe

[2072] Output: Mixing instructions

[2073] Step 7:

[2074] Sending mixing instructions

[2075] The server sends the generated mixing instructions to the mixing device (robot barista), and this communication is also carried out via the Internet.

[2076] Input: Mixing instructions

[2077] Output: Mixing instructions sent to the mixer

[2078] Step 8:

[2079] Beverage blending

[2080] The blending device receives the blending instructions and blends the beverage in proportion to the specified ingredients, for example, using Chardonnay (France) to create a more acidic drink and adding a relaxing ingredient.

[2081] Input: Mixing instructions

[2082] Output: Finished beverage

[2083] Step 9:

[2084] Completion notification

[2085] The dispensing device notifies the terminal that the beverage is ready, and the terminal displays the message "Your French Chardonnay is ready!" to the user.

[2086] Input: Completion notification from compounding device

[2087] Output: A notification message to the user

[2088] Step 10:

[2089] Receiving beverages

[2090] The user follows the notification on the terminal and receives the finished drink.

[2091] Input: Notification message

[2092] Output: The finished beverage received by the user

[2093] This allows users to easily enjoy personalized beverages that match their mood and preferences that day.

[2094] 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.

[2095] 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.

[2096] 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.

[2097] 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.

[2098] 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.

[2099] 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.

[2100] 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).

[2101] 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.

[2102] 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."

[2103] 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.

[2104] 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).

[2105] 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.

[2106] 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.

[2107] 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.

[2108] 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.

[2109] 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.

[2110] 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.

[2111] 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.

[2112] 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.

[2113] 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.

[2114] 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.

[2115] The following is further disclosed regarding the above embodiment.

[2116] (Claim 1)

[2117] a means for the user to input beverage preferences and moods;

[2118] A means for the server to receive the input information from the user, search the database based on the information, and select an optimal drink recipe;

[2119] A means for generating mixing instructions based on the beverage recipe selected by the server and transmitting the instructions to the AI ​​machine;

[2120] A means for the AI ​​machine to mix a beverage based on the mixing instructions and provide it to the user;

[2121] A system including:

[2122] (Claim 2)

[2123] 10. The system of claim 1, including a desired origin for the input information.

[2124] (Claim 3)

[2125] 10. The system of claim 1, further comprising means for notifying a user of information regarding the dispensed beverage.

[2126] "Example 1"

[2127] (Claim 1)

[2128] a means for the user to input beverage preferences and moods;

[2129] means for the terminal to receive and display user input information;

[2130] means for transmitting the information received by the terminal to a server;

[2131] A means for the server to receive the input information from the user, search the database based on the information, and select an optimal drink recipe;

[2132] A means for generating mixing instructions based on the beverage recipe selected by the server and transmitting the instructions to the AI ​​machine;

[2133] A means for the AI ​​machine to mix a beverage based on the mixing instructions and provide it to the user;

[2134] A system including:

[2135] (Claim 2)

[2136] 10. The system of claim 1, including a desired origin for the input information.

[2137] (Claim 3)

[2138] 10. The system of claim 1, further comprising means for notifying a user of information regarding the dispensed beverage.

[2139] "Application Example 1"

[2140] (Claim 1)

[2141] a means for the user to input beverage preferences and moods;

[2142] A means for the server to receive the input information from the user, search the database based on the information, and select an optimal drink recipe;

[2143] means for generating brewing instructions based on the beverage recipe selected by the server and transmitting the instructions to a remote brewing device;

[2144] means for the dispenser to dispense the beverage based on the dispense instruction and for the beverage to be received at a location designated by the user;

[2145] A system including:

[2146] (Claim 2)

[2147] 2. The system according to claim 1, further comprising means for displaying affiliated stores near the user's current location based on the input information.

[2148] (Claim 3)

[2149] 10. The system of claim 1, further comprising means for communicating information regarding the dispensed beverage.

[2150] "Example 2: Combining Emotion Engines"

[2151] (Claim 1)

[2152] a means for the user to input beverage preferences and moods;

[2153] A server receives input information from the user and emotion data extracted from the user's facial expressions and voice, and searches a database based on the received information to select an optimal drink recipe;

[2154] means for generating and transmitting blending instructions to the machine based on the beverage recipe selected by the server;

[2155] means for the machine to prepare and provide a beverage to a user based on the preparation instructions;

[2156] A system including:

[2157] (Claim 2)

[2158] 10. The system of claim 1, including a desired region for the input information.

[2159] (Claim 3)

[2160] 10. The system of claim 1, further comprising means for notifying a user of information regarding the dispensed beverage.

[2161] "Application example 2 when combining emotion engines"

[2162] (Claim 1)

[2163] a means for the user to input beverage preferences and moods;

[2164] A means for an emotion engine to analyze emotions from a user's facial expressions and voice and acquire emotion data;

[2165] A means for the server to receive the input information and emotion data of the user, and to search the database based on the received information and emotion data to select an optimal drink recipe;

[2166] means for generating and transmitting blending instructions to the blending device based on the beverage recipe selected by the server;

[2167] means for the brewing device to brew a beverage based on the brewing instructions and provide the beverage to a user;

[2168] A means for a terminal when a user inputs information to transmit the analysis results of the emotion engine to a server;

[2169] A system including:

[2170] (Claim 2)

[2171] 10. The system of claim 1, including a desired origin for the input information.

[2172] (Claim 3)

[2173] 10. The system of claim 1, further comprising means for notifying a user of information regarding the dispensed beverage. [Explanation of symbols]

[2174] 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. a means for the user to input beverage preferences and moods; A means for the server to receive input information from the user, search the database based on the information, and select an optimal drink recipe; A means for generating mixing instructions based on the beverage recipe selected by the server and transmitting the instructions to the AI ​​machine; A means for the AI ​​machine to mix a beverage based on the mixing instructions and provide it to the user; A system including:

2. 10. The system of claim 1, including a desired origin for the input information.

3. 10. The system of claim 1, further comprising means for notifying a user of information regarding the dispensed beverage.

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