System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving state information of a day input by a user; artificial intelligence means for generating an optimal mixed beverage recipe based on the state information; means for providing the generated recipe to the user; means for receiving feedback information from the user; and means for learning the feedback information to update the artificial intelligence means.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] These are the "problems that the invention attempts to solve" and "means for solving the problems" in the patent specification.

[0005] ---

[0006] Conventional mixed juice preparation methods have limited opportunities for users to create original juices that suit their mood and physical condition on that day, and have difficulty determining which fruits and vegetables to combine in what ratios. Furthermore, because they rely on fixed menus, they have the problem of not being able to fully meet the preferences and needs of individual users. The present invention aims to solve these problems. [Means for solving the problem]

[0007] The present invention solves the above-mentioned problems with a system including a means for receiving status information input by a user for that day, an artificial intelligence means for generating an optimal mixed drink recipe based on the status information, a means for providing the generated recipe to the user, a means for receiving feedback information from the user, and a means for learning the feedback information and updating the artificial intelligence means. This allows the user to easily create an optimal mixed juice that suits their mood and physical condition for that day, and also caters to the preferences and needs of each individual user.

[0008] "User" refers to an individual who uses the system to receive a mixed juice recipe that suits their preferences and condition.

[0009] "Status information" refers to information about the mood and physical condition of the day that is input by the user.

[0010] "Means for receiving" refers to a device or software for obtaining information from a user and processing that information appropriately.

[0011] "Artificial intelligence means" refers to algorithms or software that generate optimal mixed drink recipes based on user status information.

[0012] "Generating means" refers to a device or software that has a process for producing a result based on multiple inputs.

[0013] "Recipe" refers to information that includes specific ingredients and their amounts, as well as instructions for making a particular mixed drink.

[0014] "Means for providing" refers to the device or software used to deliver the generated results or information to the user.

[0015] "Feedback information" refers to information such as impressions, evaluations, and requests for improvement entered by users after use.

[0016] "Learning means" refers to the process of using feedback information to improve the algorithms of the artificial intelligence means to increase the accuracy of future results.

[0017] "Update Measures" refers to devices or software that modify or improve existing settings or algorithms based on newly obtained information. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides a system that allows users to obtain the optimal mixed juice recipe based on their mood and physical condition on that day. This system receives information about the user's condition, generates a recipe using artificial intelligence, and provides the recipe to the user. Furthermore, by receiving feedback from the user and continually improving the artificial intelligence, the system can always provide the optimal recipe.

[0040] Program processing explanation

[0041] 1. User status input

[0042] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[0043] 2. Transmission of information

[0044] The terminal generates a request for transmitting the state information input by the user to the server, and transmits it to the server.

[0045] 3. Recipe Generation

[0046] The server uses artificial intelligence to generate the optimal mixed juice recipe based on the received user status information. First, the server compares it with its past database to select the appropriate combination of fruits and vegetables. Then, the server calculates the quantities of each combination and creates a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0047] 4. Providing recipes

[0048] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[0049] 5. Gathering Feedback

[0050] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may give feedback such as "I'd like it to be a little sweeter."

[0051] 6. Learning Feedback

[0052] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[0053] Specific examples

[0054] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0055] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[0056] The processing flow will be explained below.

[0057] Step 1: User enters state

[0058] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[0059] Step 2: User Enters Preferences

[0060] The user inputs their preferred flavor (e.g., "I like fruit-based") and any additional information, and presses the "Send" button to send the input to the terminal.

[0061] Step 3: The device sends the information

[0062] The terminal formats the user's input information and generates request data to be sent to the server.

[0063] The terminal transmits the generated request data to the server.

[0064] Step 4: The server receives the data

[0065] The server analyzes the request data received from the terminal and extracts the user's status information and preference information.

[0066] Step 5: The server generates the recipe

[0067] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[0068] Specifically, the server compares the data with a past database, selects the appropriate combination of fruits and vegetables, and calculates the portion sizes of those combinations.

[0069] Build the generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0070] Step 6: The server sends the recipe

[0071] The server transmits the generated recipe to the terminal as response data.

[0072] Step 7: The device receives the recipe

[0073] The terminal displays the recipe information received from the server on a user interface.

[0074] Step 8: User reviews the recipe

[0075] The user checks the recipe displayed on the device screen.

[0076] Step 9: User creates juice

[0077] The user actually creates a mixed juice according to the provided recipe.

[0078] Step 10: User Enters Feedback

[0079] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[0080] Step 11: Device sends feedback

[0081] The terminal formats the user's feedback information and generates request data to be sent to the server.

[0082] The terminal transmits the generated feedback request data to the server.

[0083] Step 12: Server receives feedback

[0084] The server processes the feedback information received from the terminals and stores it in a database.

[0085] Step 13: Server learns feedback

[0086] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[0087] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[0088] Step 14: Server updates the algorithm

[0089] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[0090] ---

[0091] By following these steps, users can create the perfect mixed juice that suits their mood and physical condition that day. The system continually learns based on feedback and always suggests the best recipe.

[0092] Example 1

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

[0094] In modern society, people's lifestyles and health conditions are diverse, and their daily diets demand optimal nutritional supplementation tailored to their mood and physical condition on that day. However, selecting the appropriate beverage and creating a recipe based on that day's mood and physical condition is a time-consuming task, and providing personalized recipes tailored to individual needs is not easy. Furthermore, collecting appropriate feedback on the results and reflecting it in future recipe generation requires specialized knowledge and skills. The present invention aims to solve these problems.

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

[0096] In this invention, the server includes: means for a user to input that day's condition information via a dedicated terminal or smartphone application; means for transmitting the input condition information to the server; an artificial intelligence model that generates an optimal mixed drink recipe based on the condition information; means for analyzing the user's condition information and selecting an appropriate ingredient combination using the artificial intelligence model; means for calculating the amounts of the selected ingredients and generating a detailed recipe; means for providing the generated recipe to the user; means for inputting feedback information from the user to the dedicated terminal and transmitting it to the server; and means for learning the feedback information and updating the artificial intelligence model. This allows the user to easily obtain an optimal mixed drink recipe based on their mood and physical condition for that day, and further makes it possible to always provide personalized recipes that reflect user feedback.

[0097] "User" refers to any individual or organization that uses this system.

[0098] "Dedicated terminal" refers to a hardware device designed specifically for this system.

[0099] "Smartphone application" refers to a dedicated software program that runs on a smartphone.

[0100] "Status information" refers to input information such as the user's mood and physical condition on that day, and their preferred flavor tendencies.

[0101] "Server" refers to a central processing unit that receives and processes information from users.

[0102] "Artificial intelligence model" refers to software that has an algorithm for generating optimal mixed drink recipes based on user status information.

[0103] "Ingredient combination" refers to the types and proportions of individual ingredients used in a mixed drink.

[0104] "Quantity" refers to the specific quantity of the selected material.

[0105] A "detailed recipe" refers to information that includes all ingredients needed to make a mixed drink, their specific amounts, and instructions for making the drink.

[0106] "Feedback information" refers to impressions and desired improvements provided by a user after actually creating a mixed drink.

[0107] "User interface means" refers to an interactive screen or form through which a user inputs status information.

[0108] "Database" refers to a collection of information used to store and analyze user status and feedback information.

[0109] This invention provides a system that allows users to obtain optimal mixed drink recipes based on their mood and physical condition on that day. This system consists of a dedicated terminal or smartphone application for inputting and sending status information, a server for receiving and processing that information, and an artificial intelligence model for generating optimal recipes.

[0110] System configuration

[0111] Dedicated device / smartphone application

[0112] Users use a dedicated device or smartphone application to input their mood, physical condition, and preferred tastes for the day. The input interface is intuitive and easy to use, offering options such as text input and selection from a selection list. This information is sent to the server in a recommended format (e.g., JSON format).

[0113] server

[0114] The server receives the user's input information and processes it to analyze it. Specifically, it follows the following procedure:

[0115] 1. Information Analysis: Analyzes the status information entered by the user and classifies it into the appropriate category.

[0116] 2. Recipe generation: Using an artificial intelligence model (e.g., a model trained with TensorFlow or PyTorch), the optimal combination of ingredients for a mixed drink is selected, and the appropriate ingredients are selected by comparing them with a database of past recipes.

[0117] 3. Calculate quantities: Calculate the specific quantities of the selected ingredients and generate a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0118] The generated recipe is sent to a dedicated terminal or smartphone application and provided to the user.

[0119] Gathering feedback and learning

[0120] Users actually make juice and enter their impressions and suggestions for improvement on a feedback input screen. For example, they can say, "I'd like it a little sweeter," and send that feedback to the server via a dedicated device or smartphone application.

[0121] The server stores the received feedback information in a database and uses it to train the AI ​​model. Based on the learning results, the accuracy of recipe generation will be improved from the next time onwards.

[0122] Specific examples

[0123] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server suggests a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0124] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes, thus ensuring that the server can continue to provide optimal recipes.

[0125] Prompt Sentence Examples

[0126] "The user has entered the state information, 'I'm sleep-deprived today and want to relax.' Please generate a recipe for a mixed juice that will have a relaxing effect based on past data and feedback information."

[0127] The above is a specific embodiment for carrying out the present invention. Throughout the system, not only can mixed drink recipes be provided that are optimized for the individual user's situation, but feedback can also be used to continually improve the system.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The user enters status information

[0131] explanation:

[0132] Users input their mood, physical condition, and preferred flavors for the day via a dedicated device or smartphone application.

[0133] input:

[0134] User state information (e.g., "I'm sleep-deprived today and want to relax").

[0135] Specific behavior:

[0136] The user launches the app and enters status information in text format according to the interface. Once the information is complete, the user presses the "Submit" button.

[0137] Step 2:

[0138] Sending information to the server

[0139] explanation:

[0140] The device sends the state information entered by the user to the server, which converts this information into an appropriate format (e.g., JSON format).

[0141] input:

[0142] User status information.

[0143] Specific behavior:

[0144] The device converts the input information into JSON format, generates an HTTP POST request, and sends it to the server's API endpoint.

[0145] Step 3:

[0146] Parsing state information

[0147] explanation:

[0148] The server analyzes the received state information, specifically tokenizing the user's input and classifying it into the appropriate category.

[0149] input:

[0150] Status information sent from the device in JSON format.

[0151] Data processing:

[0152] Text analysis algorithms are used to tokenize and categorize the data.

[0153] output:

[0154] Parsed category information.

[0155] Specific behavior:

[0156] The server invokes a natural language processing algorithm to analyze the user's input and categorize it into categories such as "I want to relax" or "I'm not getting enough sleep."

[0157] Step 4:

[0158] Generate a recipe

[0159] explanation:

[0160] The server uses the analyzed category information to select the optimal ingredient combination for a mixed drink using an artificial intelligence model.

[0161] input:

[0162] Parsed category information.

[0163] Data processing:

[0164] Use artificial intelligence models (e.g., TensorFlow or PyTorch) to generate appropriate material combinations.

[0165] output:

[0166] The combination of materials produced.

[0167] Specific behavior:

[0168] The server inputs category information into an artificial intelligence model and selects the most suitable ingredients (e.g., banana, cherry, honey, water).

[0169] Step 5:

[0170] Calculate portions

[0171] explanation:

[0172] Calculate the specific amounts of selected ingredients and generate a detailed recipe.

[0173] input:

[0174] The combination of materials produced.

[0175] Data processing:

[0176] It uses an algorithm to calculate the optimal amount of ingredients.

[0177] output:

[0178] Detailed recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0179] Specific behavior:

[0180] The server calculates the appropriate amounts for the selected ingredients and generates a detailed recipe.

[0181] Step 6:

[0182] Provide a recipe

[0183] explanation:

[0184] The generated recipe information is transmitted from the server to the terminal, which then displays it to the user.

[0185] input:

[0186] Detailed recipe.

[0187] output:

[0188] The recipe displayed in the user interface.

[0189] Specific behavior:

[0190] The server converts the generated recipe information into JSON format and sends it to the terminal as an HTTP response. The terminal receives this response, analyzes it, and displays it on the user interface.

[0191] Step 7:

[0192] Collect feedback

[0193] explanation:

[0194] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[0195] input:

[0196] User feedback information (e.g., "I'd like it a little sweeter").

[0197] Specific behavior:

[0198] Users taste the finished juice and enter and submit feedback via a dedicated terminal or smartphone application.

[0199] Step 8:

[0200] Send feedback to the server

[0201] explanation:

[0202] The device sends the feedback information entered by the user to the server, which also converts this information into an appropriate format (e.g., JSON format).

[0203] input:

[0204] User feedback information.

[0205] Specific behavior:

[0206] The device converts the feedback information into JSON format and generates an HTTP POST request to send it to the server's API endpoint.

[0207] Step 9:

[0208] Learning Feedback

[0209] explanation:

[0210] The server stores the received feedback information in a database and uses it to train an artificial intelligence model.

[0211] input:

[0212] Feedback information sent from the device in JSON format.

[0213] Data processing:

[0214] The feedback information is input as training data into the artificial intelligence model, and the model is retrained.

[0215] output:

[0216] Updated artificial intelligence model.

[0217] Specific behavior:

[0218] The server stores the feedback information in a database and inputs it into the AI ​​model for retraining, improving the accuracy of recipe generation in future.

[0219] (Application example 1)

[0220] 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."

[0221] Conventional beverage delivery systems have difficulty providing users with optimal mixed drink recipes tailored to their mood and physical condition on that day. Furthermore, they lacked a mechanism for effectively utilizing user feedback and incorporating it into the next recipe generation, making it difficult to provide personalized service. Furthermore, there was a lack of a way for users to easily order and receive mixed drinks that matched their preferences at physical stores.

[0222] 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.

[0223] In this invention, the server includes means for receiving status information for that day input by the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information, means for providing the generated recipe to the user, means for receiving feedback information from the user, means for learning the feedback information and updating the artificial intelligence means, means for displaying the recipe on a dedicated terminal in the store so that the user can check the recipe, and a dedicated terminal for creating a mixed drink based on the recipe, thereby enabling the user to easily order and receive the optimal mixed drink according to their mood and physical condition on that day.

[0224] "User" refers to a person who uses the service to request a mixed drink recipe that suits their mood or physical condition on that day.

[0225] "Status information" refers to information such as the user's mood and physical condition on that day, and favorite flavors.

[0226] "Artificial intelligence means" refers to an algorithm or program that generates an optimal mixed drink recipe based on status information received from a user.

[0227] "Feedback information" refers to information including user's impressions and suggestions for improvement regarding the mixed drink created based on the provided recipe.

[0228] "Dedicated terminal" refers to a device such as a tablet or smartphone that users use in the store to input and check recipes.

[0229] "Mixed drinks" refers to drinks such as juices and smoothies that are made by combining multiple beverage ingredients.

[0230] "Recipe" means information describing the ingredients and quantities of a mixed drink and how to make it.

[0231] "Learning" refers to the process by which the artificial intelligence means acquires new knowledge based on feedback information from the user and improves the accuracy of recipe generation from the next time onwards.

[0232] The present invention is a system that provides optimal mixed drink recipes based on the user's mood and physical condition. In this system, the user inputs information about their condition for the day via a dedicated terminal or smartphone application, and an artificial intelligence means generates the optimal recipe based on that information. The following describes how this system is specifically implemented.

[0233] 1. Enter and send user status information

[0234] Using a dedicated device or smartphone application, users input status information such as their mood and physical condition for the day, and their preferred flavors. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." The device then sends the input status information to the server in JSON format.

[0235] 2. Creating a recipe

[0236] The server receives the state information sent by the user and compares it with a historical database. This process uses deep learning frameworks such as TensorFlow and PyTorch. Based on the results of the comparison, the AI ​​model generates an optimal mixed drink recipe. The recipe includes specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0237] 3. Providing recipes

[0238] The generated recipe information is sent from the server to a dedicated terminal, where users can check the provided recipes on the dedicated terminal or smartphone screen.

[0239] 4. Gathering Feedback

[0240] The user actually creates a mixed drink and enters their impressions and suggestions for improvement into a feedback input screen via a dedicated terminal or smartphone application (e.g., "I'd like a little more honey"). This information is again sent to the server in JSON format.

[0241] 5. Learning Feedback

[0242] The server stores the feedback information received from users in a database and uses it to train the algorithms of the artificial intelligence means. The hardware required is a server equipped with a GPU suitable for training deep learning models. As a result of the learning, the accuracy of the next recipe generation will improve based on the new feedback information.

[0243] (Example)

[0244] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through the application, the dedicated device will send this information to the server. Based on past data and feedback, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically calls for one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user creates this juice and provides feedback that "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future uses. This allows the server to always provide the optimal recipe.

[0245] (Example of a prompt)

[0246] "Please suggest a recipe for a mixed drink that would be suitable if I'm stressed out and want to relax today. Favorite flavor: Fruity."

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] Users input information about their mood, physical condition, favorite flavors, and other information about their condition on that day through a dedicated device or smartphone application. This information is temporarily stored in the device in JSON format.

[0250] Input: "I'm tired today," "I want to relax," "I like fruit-based drinks."

[0251] Output: Status information in JSON format

[0252] Step 2:

[0253] The device sends the status information entered by the user to the server, which receives the data using an HTTP POST request and stores it in an internal database.

[0254] Input: State information in JSON format

[0255] Output: Status information sent to the server

[0256] Step 3:

[0257] The server analyzes the received status information and generates the optimal mixed drink recipe using artificial intelligence means, specifically by comparing it with a historical database and selecting the appropriate ingredients and their quantities. The main tools used in this process are TensorFlow or PyTorch.

[0258] Input: Parsed state information

[0259] Output: Generated recipe ("1 banana, 10 cherries, 1 tablespoon honey, 200ml water")

[0260] Step 4:

[0261] The server sends the generated recipe in JSON format to the terminal, and the terminal displays the recipe in the user interface. The user can check the displayed recipe.

[0262] Input: Generated recipe

[0263] Output: The recipe displayed in the user interface

[0264] Step 5:

[0265] The user creates a mixed drink based on the displayed recipe, and then inputs their impressions and suggestions for improvement via a dedicated terminal or application. This feedback information is also temporarily stored in the terminal in JSON format.

[0266] Input: User feedback

[0267] Output: Feedback information in JSON format

[0268] Step 6:

[0269] The terminal transmits the feedback information collected from the user to the server, which stores the received feedback information in an internal database.

[0270] Input: Feedback information in JSON format

[0271] Output: Feedback information sent to the server

[0272] Step 7:

[0273] The server analyzes the feedback information and uses it to train the AI ​​algorithm. Specifically, it integrates past training data with newly received feedback information to improve the accuracy of the algorithm. This process is also carried out using TensorFlow or PyTorch.

[0274] Input: Feedback information stored in an internal database

[0275] Output: An improved AI model

[0276] Through the specific operations described above, users can easily obtain the optimal mixed drink recipe based on their mood and physical condition on that day, making it possible to improve service in physical stores via dedicated terminals.

[0277] 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.

[0278] The present invention provides a system that generates and provides personalized mixed juice recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates a recipe using artificial intelligence. The generated recipe is provided to the user, and by receiving feedback from the user and continually improving the artificial intelligence, it is possible to always provide the optimal recipe.

[0279] Program processing explanation

[0280] 1. User status input

[0281] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[0282] 2. Obtaining user emotional information

[0283] The emotion engine means acquires the user's facial expressions and tone of voice using a camera and a microphone, thereby recognizing the user's emotion information (for example, joy, sadness, stress, etc.).

[0284] 3. Transmission of Information

[0285] The terminal generates request data for transmitting the user's state information and emotion information to the server, and transmits it to the server.

[0286] 4. Recipe Generation

[0287] The server uses artificial intelligence to generate an optimal mixed juice recipe based on the received user status and emotional information. First, the server compares the recipe with a past database to select an appropriate combination of fruits and vegetables and calculates the amounts of each combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water) consists of specific ingredients and their amounts.

[0288] 5. Providing recipes

[0289] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[0290] 6. Gathering Feedback

[0291] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they can provide feedback such as "I wish it was a little sweeter."

[0292] 7. Learning Feedback

[0293] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[0294] Specific examples

[0295] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0296] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[0297] The processing flow will be explained below.

[0298] Step 1: User enters state

[0299] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[0300] Step 2: User Enters Preferences

[0301] The user inputs their preferred flavor (e.g., "I like fruit-based") and other additional information, and presses the "Send" button to send the input to the terminal.

[0302] Step 3: The emotion engine retrieves emotions

[0303] Using the device's camera and microphone, the emotion engine acquires emotional information from the user's facial expressions and tone of voice. For example, if the user is smiling, it will be recognized as "joy," and if they are speaking in a straightforward voice, it will be recognized as "stress."

[0304] Step 4: The device sends the information

[0305] The terminal formats the user's status information, preference information, and emotion information and generates request data to be sent to the server.

[0306] The terminal transmits the generated request data to the server.

[0307] Step 5: The server receives the data

[0308] The server analyzes the request data received from the terminal and extracts the user's status information, preference information, and emotion information.

[0309] Step 6: The server generates the recipe

[0310] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe. For example, the server can compare the recipe with a database of past data to select the appropriate combination of fruits and vegetables.

[0311] The server takes into account emotional information and selects materials that have a relaxing effect for a user who is "feeling stressed," for example.

[0312] The server calculates the detailed measurements of the recipe and generates a specific recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0313] Step 7: Server Sends Recipe

[0314] The server transmits the generated recipe to the terminal as response data.

[0315] Step 8: Device receives recipe

[0316] The terminal displays the recipe information received from the server on a user interface.

[0317] Step 9: User reviews the recipe

[0318] The user checks the recipe displayed on the device screen.

[0319] Step 10: User creates juice

[0320] The user actually creates a mixed juice according to the provided recipe.

[0321] Step 11: User Enters Feedback

[0322] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[0323] Step 12: Device sends feedback

[0324] The terminal formats the user's feedback information and generates request data to be sent to the server.

[0325] The terminal transmits the generated feedback request data to the server.

[0326] Step 13: Server receives feedback

[0327] The server processes the feedback information received from the terminals and stores it in a database.

[0328] Step 14: Server learns feedback

[0329] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[0330] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[0331] Step 15: Server updates the algorithm

[0332] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[0333] ---

[0334] By following these steps, users can create the perfect mixed juice that suits their mood, physical condition, and emotions that day. The system continually learns based on feedback and always suggests the best recipe.

[0335] Example 2

[0336] 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."

[0337] In modern society, people's health and mental satisfaction are important, but conventional mixed drink recipe generation systems do not take into account the user's mood, physical condition, or emotions on that day. As a result, it is difficult to provide optimal recipes that meet individual needs, resulting in low user satisfaction.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving status information of the day input by the user, emotion engine means for acquiring emotion information of the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning the feedback information and updating the artificial intelligence means. This makes it possible to provide personalized recipes that take into account the user's mood, physical condition, and emotion of the day.

[0339] A "user" refers to a person who uses the system to request a mixed drink recipe that suits their mood, physical condition, and emotions of the day.

[0340] "Status information" refers to data input by the user including the user's mood and physical condition for that day, as well as preferences for flavors.

[0341] "Emotion information" refers to data relating to emotions analyzed from the user's facial expressions and tone of voice obtained by the emotion engine means.

[0342] The term "emotion engine means" refers to means for acquiring user emotion information, including devices and software for analyzing the user's facial expressions and tone of voice.

[0343] "Artificial intelligence means" refers to algorithms and computer programs for generating optimal mixed drink recipes based on state information and emotional information.

[0344] "Feedback information" refers to data entered by a user about their impressions of the mixed drink they have actually created and any improvements they would like to see made.

[0345] "User interface means" refers to an interface for a user to input status information and feedback information.

[0346] "Means" refers to the general term for the various devices and software that make up the system.

[0347] "Database" refers to a storage device or system for storing and managing user state information, emotion information, and feedback information.

[0348] "Server" refers to a computer system that processes information sent by users and generates and serves recipes.

[0349] The present invention is a system that generates and provides personalized mixed drink recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates recipes using artificial intelligence. The generated recipes are provided to the user, and furthermore, feedback from the user is received, and the artificial intelligence is continually improved to always provide optimal recipes.

[0350] First, the user inputs their mood and physical condition for the day, as well as their preferred flavor preferences, via a dedicated terminal or smartphone application. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." Next, the emotion engine means uses a camera and microphone to capture the user's facial expressions and tone of voice, thereby recognizing the user's emotional information (e.g., joy, sadness, stress, etc.).

[0351] This information is sent from the device to a server, which uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information. The server first compares the information with a past database to select an appropriate combination of fruits and vegetables and calculates the serving sizes for that combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0352] The generated recipe information is sent from the server to the terminal, and the terminal displays the received recipe information on the user interface so that the user can check it. The user then actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may provide feedback such as "I would like it a little sweeter." This feedback information is sent to the server, which stores it in a database and uses the algorithm of the artificial intelligence means to learn, improving the accuracy of recipe generation from the next time onwards.

[0353] Specific examples

[0354] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server will suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user makes this juice and provides feedback such as "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future recipes.

[0355] Prompt Sentence Examples

[0356] "How are you feeling today? Do you want to relax? Are you stressed? What's your favorite fruit?"

[0357] In this way, this system can provide optimal mixed drink recipes to individual users based on their state and emotional information. The aim is for the server and device to cooperate and continuously propose improved recipes based on user feedback.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Program processing flow

[0360] Step 1: Enter user status

[0361] Users launch a dedicated device or smartphone application and input their mood and physical condition for the day, as well as their preferred flavor preferences.

[0362] Specific actions

[0363] 1. The user opens the application.

[0364] 2. The user interface displays questions such as "How are you feeling today?" and "Do you want to relax?"

[0365] 3. The user enters information using input fields and options.

[0366] 4. The information entered by the user is stored on the device as a data packet in JSON format or similar.

[0367] Input and Output

[0368] Input: Information about the user's mood or physical condition (e.g., "I'm tired," "I want to relax," "I like fruit-based foods").

[0369] Output: Data packets converted into JSON format etc.

[0370] Step 2: Acquiring emotional information

[0371] The camera and microphone, which are emotion engine means, are activated and emotion information is obtained by capturing the user's facial expressions and tone of voice.

[0372] Specific actions

[0373] 1. The device camera captures the user's face.

[0374] 2. The device's microphone records the user's voice.

[0375] 3. The emotion engine analyzes emotional information using facial expression analysis algorithms and voice analysis algorithms.

[0376] Input and Output

[0377] Input: Data on the user's facial expressions and tone of voice.

[0378] Output: Parsed emotion information (e.g., joy, sadness, stress).

[0379] Step 3: Submit your information

[0380] The terminal transmits the state information input by the user and the emotion information acquired by the emotion engine to the server.

[0381] Specific actions

[0382] 1. The device generates a data packet that combines the input state information and the acquired emotion information.

[0383] 2. Generate a send request to the server and send the data to the server.

[0384] Input and Output

[0385] Input: A data packet containing state and emotion information.

[0386] Output: The data sent to the server.

[0387] Step 4: Recipe Generation

[0388] The server uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information.

[0389] Specific actions

[0390] 1. The server analyzes the received data and retrieves relevant past information from the database.

[0391] 2. A generative AI model uses relevant data to calculate optimal fruit and vegetable combinations.

[0392] 3. Based on emotional information, adjust the selected ingredients and quantities to generate a personalized recipe.

[0393] Input and Output

[0394] Input: User state information, emotion information, past database information.

[0395] Output: The generated recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0396] Step 5: Provide the recipe

[0397] The generated recipe information is transmitted from the server to the terminal and displayed on the user interface.

[0398] Specific actions

[0399] 1. The server sends the generated recipe to the device.

[0400] 2. The terminal displays the received recipe information on the user interface and notifies the user.

[0401] Input and Output

[0402] Input: Generated recipe information.

[0403] Output: The recipe displayed in the user interface.

[0404] Step 6: Gather feedback

[0405] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[0406] Specific actions

[0407] 1. The user creates and samples the juice.

[0408] 2. Open the application's feedback screen and enter your specific thoughts and suggestions for improvement (e.g., "I'd like it to be a little sweeter").

[0409] Input and Output

[0410] Input: User feedback information.

[0411] Output: Feedback data converted into JSON format etc.

[0412] Step 7: Learning feedback

[0413] The server stores the feedback information received from users in a database and uses it to train the algorithm of the generative AI model.

[0414] Specific actions

[0415] 1. The server stores the received feedback information in a database.

[0416] 2. The generative AI model learns from the feedback information and adjusts and improves future recipe generation algorithms.

[0417] Input and Output

[0418] Input: Feedback information from the user.

[0419] Output: Tweaked and improved generative AI model.

[0420] This concludes the specific processing flow of this system. The system provides personalized mixed drink recipes by combining the user's state information and emotional information. The system is designed so that the server and the device can cooperate to provide constantly improving recipes.

[0421] (Application example 2)

[0422] 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."

[0423] Conventional mixed juice recipe generation systems are mostly based on basic information such as the user's mood and physical condition that day, and are unable to take the user's emotions into account. As a result, they are unable to provide optimal recipes based on the user's emotional state, leaving room for improvement in the user experience. Furthermore, the system's ability to receive feedback from users and improve the system's accuracy is limited. To solve these problems, the development of a system that incorporates emotional information was required.

[0424] 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.

[0425] In this invention, the server includes means for receiving status information for that day input by the user, emotion engine means for acquiring emotion information, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning from the feedback information and updating the artificial intelligence means. This makes it possible to generate and provide personalized recipes that take into account the user's emotion information as well as their mood and physical condition. Furthermore, by incorporating user feedback into the learning process, the accuracy of the system can be improved.

[0426] The "means for receiving user-entered daily status information" is a part of the system that obtains information about the user's mood, physical condition, and preferences for the day through an interface that allows the user to input such information.

[0427] The "emotion engine means" is a part of the system that uses sensors such as a camera and a microphone to analyze the user's facial expressions and tone of voice and recognize the user's emotional information.

[0428] The "artificial intelligence means" is a computer system having an algorithm and learning function for generating an optimal mixed drink recipe based on the user's state information and emotional information.

[0429] The "means for providing a recipe to a user" is an interface for displaying or communicating the recipe of the created mixed drink to the user.

[0430] The "means for receiving feedback information from users" is a part of the system that allows users to input their thoughts and suggestions for improvement on the provided recipes.

[0431] The "means for learning feedback information and updating the artificial intelligence means" is a computer system that improves the artificial intelligence algorithm based on feedback received from the user, thereby improving the accuracy of recipe generation from the next time onwards.

[0432] "User interface means" is a general term referring to an input device and a screen display device that allow the user to input the mood, physical condition, and preferences of the day.

[0433] The "means for generating a plurality of candidate recipes and allowing the user to select" is a part of the system that presents the generated plurality of mixed drink recipes to the user and allows the user to select the one they like best.

[0434] This invention is a system for generating and providing personalized mixed juice recipes based on the user's mood, physical condition, and emotional information for that day. This system receives user input information through a user interface such as a dedicated terminal or smartphone application, recognizes the emotional information using an emotional engine, and generates and provides the optimal recipe to the user using artificial intelligence. Furthermore, by collecting feedback from users and continually improving the algorithm of the artificial intelligence, it is possible to consistently provide optimal recipes.

[0435] Hardware and software used

[0436] 1. Dedicated device or smartphone:

[0437] It serves as an interface for the user to input status information for the day.

[0438] Possible applications include iOS and Android smartphone applications.

[0439] 2. Camera and Microphone:

[0440] Used as a sensor to obtain emotional information.

[0441] For example, the camera and microphone built into a smartphone can be used.

[0442] 3. Emotion engine means:

[0443] Software for facial expression recognition and voice analysis.

[0444] Possible examples include OpenCV (a library for camera image processing) and Python-based libraries.

[0445] 4. Artificial Intelligence Means:

[0446] An algorithm for generating recipes based on the user's state and emotional information.

[0447] Possible machine learning models include those using Scikit-learn and TensorFlow.

[0448] 5. Server:

[0449] A central system that receives information, processes it, generates recipes, and learns from feedback.

[0450] You can use cloud platforms (AWS, Google Cloud Platform, etc.).

[0451] Program processing explanation

[0452] 1. Input acceptance:

[0453] Users input their mood, physical condition, and preferred taste preferences for the day through a smartphone application.

[0454] For example, enter information such as "I want to relax today" or "I like fruit-based flavors."

[0455] 2. Acquiring emotional information:

[0456] The emotion engine means uses the smartphone's camera and microphone to acquire the user's facial expressions and tone of voice, and recognizes emotion information (for example, stress, joy, etc.).

[0457] 3. Transmission of Information:

[0458] The user's state information and emotion information are transmitted from the terminal to the server.

[0459] 4. Recipe generation:

[0460] Based on the information received, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[0461] For example, it compares it with a past database to select a combination of fruits and vegetables and calculate their portions.

[0462] 5. Recipe provided by:

[0463] The generated recipe information is sent from the server to the user interface and displayed for the user to review.

[0464] 6. Feedback Collection:

[0465] Users create juice and provide feedback on the results and areas for improvement.

[0466] For example, provide feedback such as "I'd like it a little sweeter."

[0467] 7. Feedback Learning:

[0468] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means.

[0469] Specific examples

[0470] For example, if a user inputs status information such as "I want to relax today," and the emotion engine means obtains emotion information such as "I'm feeling stressed," the device will send this information to the server. Based on past data and feedback information, the server can suggest a "banana and cherry relaxation juice." This recipe specifically consists of ingredients and quantities such as one banana, ten cherries, one tablespoon of honey, and 200 ml of water.

[0471] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. Through this feedback, the AI ​​can always provide the best recipes.

[0472] Prompt Sentence Examples

[0473] Here is an example of a prompt that the user might enter:

[0474] "Please tell us how you feel today:

[0475] Mood: Relaxing

[0476] Physical condition: Lack of sleep

[0477] Preferences: Fruit-based

[0478] Emotional information (e.g., feeling stressed) was obtained.

[0479] Use the information above to generate the perfect mixed juice recipe.

[0480] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0481] Step 1:

[0482] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they might input information such as "I want to relax today" or "I like fruit-based drinks." This information is entered into the device as text data.

[0483] Step 2:

[0484] The device receives the input text data and uses a camera and microphone to capture the user's facial expressions and tone of voice. Using facial expression recognition software (e.g., OpenCV) and voice analysis software, emotional information is analyzed in real time. This emotional information is expressed as categories such as "stress" or "joy."

[0485] Step 3:

[0486] The device sends the user's status information and emotional information to the server using an HTTP request. The input data (mood, physical condition, preferred tastes) and emotional information are sent to the server as JSON format data.

[0487] Step 4:

[0488] The server uses a generative AI model to generate an optimal mixed juice recipe based on the received state and emotion information. First, it compares the results with a database of past data to select an appropriate combination of fruits and vegetables. Next, it uses a generative AI model (e.g., Scikit-learn or TensorFlow) to calculate the quantities of each combination. The output recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0489] Step 5:

[0490] The server sends the generated recipe information to the terminal, which receives the recipe information and displays it on the user interface, allowing the user to check the proposed mixed juice recipe.

[0491] Step 6:

[0492] The user makes juice based on the proposed recipe and enters the results and suggestions for improvement on the feedback input screen. For example, they can provide feedback such as "I'd like it a little sweeter." This feedback information is entered into the terminal as text data.

[0493] Step 7:

[0494] The device sends the user's feedback information to the server as JSON format data.

[0495] Step 8:

[0496] The server stores the received feedback information in a database and uses it to improve the algorithm of the generative AI model. Specifically, the feedback information is used as additional training data to retrain the model, thereby improving the accuracy of recipe generation from the next time onwards.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] [Second embodiment]

[0501] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0502] 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.

[0503] 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).

[0504] 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.

[0505] 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.

[0506] 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).

[0507] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] In the smart glasses 214, 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.

[0512] 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."

[0513] The present invention provides a system that allows users to obtain the optimal mixed juice recipe based on their mood and physical condition on that day. This system receives information about the user's condition, generates a recipe using artificial intelligence, and provides the recipe to the user. Furthermore, by receiving feedback from the user and continually improving the artificial intelligence, the system can always provide the optimal recipe.

[0514] Program processing explanation

[0515] 1. User status input

[0516] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[0517] 2. Transmission of information

[0518] The terminal generates a request for transmitting the state information input by the user to the server, and transmits it to the server.

[0519] 3. Recipe Generation

[0520] The server uses artificial intelligence to generate the optimal mixed juice recipe based on the received user status information. First, the server compares it with its past database to select the appropriate combination of fruits and vegetables. Then, the server calculates the quantities of each combination and creates a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0521] 4. Providing recipes

[0522] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[0523] 5. Gathering Feedback

[0524] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may give feedback such as "I'd like it to be a little sweeter."

[0525] 6. Learning Feedback

[0526] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[0527] Specific examples

[0528] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0529] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[0530] The processing flow will be explained below.

[0531] Step 1: User enters state

[0532] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[0533] Step 2: User Enters Preferences

[0534] The user inputs their preferred flavor (e.g., "I like fruit-based") and any additional information, and presses the "Send" button to send the input to the terminal.

[0535] Step 3: The device sends the information

[0536] The terminal formats the user's input information and generates request data to be sent to the server.

[0537] The terminal transmits the generated request data to the server.

[0538] Step 4: The server receives the data

[0539] The server analyzes the request data received from the terminal and extracts the user's status information and preference information.

[0540] Step 5: The server generates the recipe

[0541] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[0542] Specifically, the server compares the data with a past database, selects the appropriate combination of fruits and vegetables, and calculates the portion sizes of those combinations.

[0543] Build the generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0544] Step 6: The server sends the recipe

[0545] The server transmits the generated recipe to the terminal as response data.

[0546] Step 7: The device receives the recipe

[0547] The terminal displays the recipe information received from the server on a user interface.

[0548] Step 8: User reviews the recipe

[0549] The user checks the recipe displayed on the device screen.

[0550] Step 9: User creates juice

[0551] The user actually creates a mixed juice according to the provided recipe.

[0552] Step 10: User Enters Feedback

[0553] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[0554] Step 11: Device sends feedback

[0555] The terminal formats the user's feedback information and generates request data to be sent to the server.

[0556] The terminal transmits the generated feedback request data to the server.

[0557] Step 12: Server receives feedback

[0558] The server processes the feedback information received from the terminals and stores it in a database.

[0559] Step 13: Server learns feedback

[0560] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[0561] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[0562] Step 14: Server updates the algorithm

[0563] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[0564] ---

[0565] By following these steps, users can create the perfect mixed juice that suits their mood and physical condition that day. The system continually learns based on feedback and always suggests the best recipe.

[0566] Example 1

[0567] 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."

[0568] In modern society, people's lifestyles and health conditions are diverse, and their daily diets demand optimal nutritional supplementation tailored to their mood and physical condition on that day. However, selecting the appropriate beverage and creating a recipe based on that day's mood and physical condition is a time-consuming task, and providing personalized recipes tailored to individual needs is not easy. Furthermore, collecting appropriate feedback on the results and reflecting it in future recipe generation requires specialized knowledge and skills. The present invention aims to solve these problems.

[0569] 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.

[0570] In this invention, the server includes: means for a user to input that day's condition information via a dedicated terminal or smartphone application; means for transmitting the input condition information to the server; an artificial intelligence model that generates an optimal mixed drink recipe based on the condition information; means for analyzing the user's condition information and selecting an appropriate ingredient combination using the artificial intelligence model; means for calculating the amounts of the selected ingredients and generating a detailed recipe; means for providing the generated recipe to the user; means for inputting feedback information from the user to the dedicated terminal and transmitting it to the server; and means for learning the feedback information and updating the artificial intelligence model. This allows the user to easily obtain an optimal mixed drink recipe based on their mood and physical condition for that day, and further makes it possible to always provide personalized recipes that reflect user feedback.

[0571] "User" refers to any individual or organization that uses this system.

[0572] "Dedicated terminal" refers to a hardware device designed specifically for this system.

[0573] "Smartphone application" refers to a dedicated software program that runs on a smartphone.

[0574] "Status information" refers to input information such as the user's mood and physical condition on that day, and their preferred flavor tendencies.

[0575] "Server" refers to a central processing unit that receives and processes information from users.

[0576] "Artificial intelligence model" refers to software that has an algorithm for generating optimal mixed drink recipes based on user status information.

[0577] "Ingredient combination" refers to the types and proportions of individual ingredients used in a mixed drink.

[0578] "Quantity" refers to the specific quantity of the selected material.

[0579] A "detailed recipe" refers to information that includes all ingredients needed to make a mixed drink, their specific amounts, and instructions for making the drink.

[0580] "Feedback information" refers to impressions and desired improvements provided by a user after actually creating a mixed drink.

[0581] "User interface means" refers to an interactive screen or form through which a user inputs status information.

[0582] "Database" refers to a collection of information used to store and analyze user status and feedback information.

[0583] This invention provides a system that allows users to obtain optimal mixed drink recipes based on their mood and physical condition on that day. This system consists of a dedicated terminal or smartphone application for inputting and sending status information, a server for receiving and processing that information, and an artificial intelligence model for generating optimal recipes.

[0584] System configuration

[0585] Dedicated device / smartphone application

[0586] Users use a dedicated device or smartphone application to input their mood, physical condition, and preferred tastes for the day. The input interface is intuitive and easy to use, offering options such as text input and selection from a selection list. This information is sent to the server in a recommended format (e.g., JSON format).

[0587] server

[0588] The server receives the user's input information and processes it to analyze it. Specifically, it follows the following procedure:

[0589] 1. Information Analysis: Analyzes the status information entered by the user and classifies it into the appropriate category.

[0590] 2. Recipe generation: Using an artificial intelligence model (e.g., a model trained with TensorFlow or PyTorch), the optimal combination of ingredients for a mixed drink is selected, and the appropriate ingredients are selected by comparing them with a database of past recipes.

[0591] 3. Calculate quantities: Calculate the specific quantities of the selected ingredients and generate a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0592] The generated recipe is sent to a dedicated terminal or smartphone application and provided to the user.

[0593] Gathering feedback and learning

[0594] Users actually make juice and enter their impressions and suggestions for improvement on a feedback input screen. For example, they can say, "I'd like it a little sweeter," and send that feedback to the server via a dedicated device or smartphone application.

[0595] The server stores the received feedback information in a database and uses it to train the AI ​​model. Based on the learning results, the accuracy of recipe generation will be improved from the next time onwards.

[0596] Specific examples

[0597] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server suggests a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0598] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes, thus ensuring that the server can continue to provide optimal recipes.

[0599] Prompt Sentence Examples

[0600] "The user has entered the state information, 'I'm sleep-deprived today and want to relax.' Please generate a recipe for a mixed juice that will have a relaxing effect based on past data and feedback information."

[0601] The above is a specific embodiment for carrying out the present invention. Throughout the system, not only can mixed drink recipes be provided that are optimized for the individual user's situation, but feedback can also be used to continually improve the system.

[0602] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0603] Step 1:

[0604] The user enters status information

[0605] explanation:

[0606] Users input their mood, physical condition, and preferred flavors for the day via a dedicated device or smartphone application.

[0607] input:

[0608] User state information (e.g., "I'm sleep-deprived today and want to relax").

[0609] Specific behavior:

[0610] The user launches the app and enters status information in text format according to the interface. Once the information is complete, the user presses the "Submit" button.

[0611] Step 2:

[0612] Sending information to the server

[0613] explanation:

[0614] The device sends the state information entered by the user to the server, which converts this information into an appropriate format (e.g., JSON format).

[0615] input:

[0616] User status information.

[0617] Specific behavior:

[0618] The device converts the input information into JSON format, generates an HTTP POST request, and sends it to the server's API endpoint.

[0619] Step 3:

[0620] Parsing state information

[0621] explanation:

[0622] The server analyzes the received state information, specifically tokenizing the user's input and classifying it into the appropriate category.

[0623] input:

[0624] Status information sent from the device in JSON format.

[0625] Data processing:

[0626] Text analysis algorithms are used to tokenize and categorize the data.

[0627] output:

[0628] Parsed category information.

[0629] Specific behavior:

[0630] The server invokes a natural language processing algorithm to analyze the user's input and categorize it into categories such as "I want to relax" or "I'm not getting enough sleep."

[0631] Step 4:

[0632] Generate a recipe

[0633] explanation:

[0634] The server uses the analyzed category information to select the optimal ingredient combination for a mixed drink using an artificial intelligence model.

[0635] input:

[0636] Parsed category information.

[0637] Data processing:

[0638] Use artificial intelligence models (e.g., TensorFlow or PyTorch) to generate appropriate material combinations.

[0639] output:

[0640] The combination of materials produced.

[0641] Specific behavior:

[0642] The server inputs category information into an artificial intelligence model and selects the most suitable ingredients (e.g., banana, cherry, honey, water).

[0643] Step 5:

[0644] Calculate portions

[0645] explanation:

[0646] Calculate the specific amounts of selected ingredients and generate a detailed recipe.

[0647] input:

[0648] The combination of materials produced.

[0649] Data processing:

[0650] It uses an algorithm to calculate the optimal amount of ingredients.

[0651] output:

[0652] Detailed recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0653] Specific behavior:

[0654] The server calculates the appropriate amounts for the selected ingredients and generates a detailed recipe.

[0655] Step 6:

[0656] Provide a recipe

[0657] explanation:

[0658] The generated recipe information is transmitted from the server to the terminal, which then displays it to the user.

[0659] input:

[0660] Detailed recipe.

[0661] output:

[0662] The recipe displayed in the user interface.

[0663] Specific behavior:

[0664] The server converts the generated recipe information into JSON format and sends it to the terminal as an HTTP response. The terminal receives this response, analyzes it, and displays it on the user interface.

[0665] Step 7:

[0666] Collect feedback

[0667] explanation:

[0668] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[0669] input:

[0670] User feedback information (e.g., "I'd like it a little sweeter").

[0671] Specific behavior:

[0672] Users taste the finished juice and enter and submit feedback via a dedicated terminal or smartphone application.

[0673] Step 8:

[0674] Send feedback to the server

[0675] explanation:

[0676] The device sends the feedback information entered by the user to the server, which also converts this information into an appropriate format (e.g., JSON format).

[0677] input:

[0678] User feedback information.

[0679] Specific behavior:

[0680] The device converts the feedback information into JSON format and generates an HTTP POST request to send it to the server's API endpoint.

[0681] Step 9:

[0682] Learning Feedback

[0683] explanation:

[0684] The server stores the received feedback information in a database and uses it to train an artificial intelligence model.

[0685] input:

[0686] Feedback information sent from the device in JSON format.

[0687] Data processing:

[0688] The feedback information is input as training data into the artificial intelligence model, and the model is retrained.

[0689] output:

[0690] Updated artificial intelligence model.

[0691] Specific behavior:

[0692] The server stores the feedback information in a database and inputs it into the AI ​​model for retraining, improving the accuracy of recipe generation in future.

[0693] (Application example 1)

[0694] 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."

[0695] Conventional beverage delivery systems have difficulty providing users with optimal mixed drink recipes tailored to their mood and physical condition on that day. Furthermore, they lacked a mechanism for effectively utilizing user feedback and incorporating it into the next recipe generation, making it difficult to provide personalized service. Furthermore, there was a lack of a way for users to easily order and receive mixed drinks that matched their preferences at physical stores.

[0696] 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.

[0697] In this invention, the server includes means for receiving status information for that day input by the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information, means for providing the generated recipe to the user, means for receiving feedback information from the user, means for learning the feedback information and updating the artificial intelligence means, means for displaying the recipe on a dedicated terminal in the store so that the user can check the recipe, and a dedicated terminal for creating a mixed drink based on the recipe, thereby enabling the user to easily order and receive the optimal mixed drink according to their mood and physical condition on that day.

[0698] "User" refers to a person who uses the service to request a mixed drink recipe that suits their mood or physical condition on that day.

[0699] "Status information" refers to information such as the user's mood and physical condition on that day, and favorite flavors.

[0700] "Artificial intelligence means" refers to an algorithm or program that generates an optimal mixed drink recipe based on status information received from a user.

[0701] "Feedback information" refers to information including user's impressions and suggestions for improvement regarding the mixed drink created based on the provided recipe.

[0702] "Dedicated terminal" refers to a device such as a tablet or smartphone that users use in the store to input and check recipes.

[0703] "Mixed drinks" refers to drinks such as juices and smoothies that are made by combining multiple beverage ingredients.

[0704] "Recipe" means information describing the ingredients and quantities of a mixed drink and how to make it.

[0705] "Learning" refers to the process by which the artificial intelligence means acquires new knowledge based on feedback information from the user and improves the accuracy of recipe generation from the next time onwards.

[0706] The present invention is a system that provides optimal mixed drink recipes based on the user's mood and physical condition. In this system, the user inputs information about their condition for the day via a dedicated terminal or smartphone application, and an artificial intelligence means generates the optimal recipe based on that information. The following describes how this system is specifically implemented.

[0707] 1. Enter and send user status information

[0708] Using a dedicated device or smartphone application, users input status information such as their mood and physical condition for the day, and their preferred flavors. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." The device then sends the input status information to the server in JSON format.

[0709] 2. Creating a recipe

[0710] The server receives the state information sent by the user and compares it with a historical database. This process uses deep learning frameworks such as TensorFlow and PyTorch. Based on the results of the comparison, the AI ​​model generates an optimal mixed drink recipe. The recipe includes specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0711] 3. Providing recipes

[0712] The generated recipe information is sent from the server to a dedicated terminal, where users can check the provided recipes on the dedicated terminal or smartphone screen.

[0713] 4. Gathering Feedback

[0714] The user actually creates a mixed drink and enters their impressions and suggestions for improvement into a feedback input screen via a dedicated terminal or smartphone application (e.g., "I'd like a little more honey"). This information is again sent to the server in JSON format.

[0715] 5. Learning Feedback

[0716] The server stores the feedback information received from users in a database and uses it to train the algorithms of the artificial intelligence means. The hardware required is a server equipped with a GPU suitable for training deep learning models. As a result of the learning, the accuracy of the next recipe generation will improve based on the new feedback information.

[0717] (Example)

[0718] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through the application, the dedicated device will send this information to the server. Based on past data and feedback, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically calls for one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user creates this juice and provides feedback that "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future uses. This allows the server to always provide the optimal recipe.

[0719] (Example of a prompt)

[0720] "Please suggest a recipe for a mixed drink that would be suitable if I'm stressed out and want to relax today. Favorite flavor: Fruity."

[0721] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0722] Step 1:

[0723] Users input information about their mood, physical condition, favorite flavors, and other information about their condition on that day through a dedicated device or smartphone application. This information is temporarily stored in the device in JSON format.

[0724] Input: "I'm tired today," "I want to relax," "I like fruit-based drinks."

[0725] Output: Status information in JSON format

[0726] Step 2:

[0727] The device sends the status information entered by the user to the server, which receives the data using an HTTP POST request and stores it in an internal database.

[0728] Input: State information in JSON format

[0729] Output: Status information sent to the server

[0730] Step 3:

[0731] The server analyzes the received status information and generates the optimal mixed drink recipe using artificial intelligence means, specifically by comparing it with a historical database and selecting the appropriate ingredients and their quantities. The main tools used in this process are TensorFlow or PyTorch.

[0732] Input: Parsed state information

[0733] Output: Generated recipe ("1 banana, 10 cherries, 1 tablespoon honey, 200ml water")

[0734] Step 4:

[0735] The server sends the generated recipe in JSON format to the terminal, and the terminal displays the recipe in the user interface. The user can check the displayed recipe.

[0736] Input: Generated recipe

[0737] Output: The recipe displayed in the user interface

[0738] Step 5:

[0739] The user creates a mixed drink based on the displayed recipe, and then inputs their impressions and suggestions for improvement via a dedicated terminal or application. This feedback information is also temporarily stored in the terminal in JSON format.

[0740] Input: User feedback

[0741] Output: Feedback information in JSON format

[0742] Step 6:

[0743] The terminal transmits the feedback information collected from the user to the server, which stores the received feedback information in an internal database.

[0744] Input: Feedback information in JSON format

[0745] Output: Feedback information sent to the server

[0746] Step 7:

[0747] The server analyzes the feedback information and uses it to train the AI ​​algorithm. Specifically, it integrates past training data with newly received feedback information to improve the accuracy of the algorithm. This process is also carried out using TensorFlow or PyTorch.

[0748] Input: Feedback information stored in an internal database

[0749] Output: An improved AI model

[0750] Through the specific operations described above, users can easily obtain the optimal mixed drink recipe based on their mood and physical condition on that day, making it possible to improve service in physical stores via dedicated terminals.

[0751] 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.

[0752] The present invention provides a system that generates and provides personalized mixed juice recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates a recipe using artificial intelligence. The generated recipe is provided to the user, and by receiving feedback from the user and continually improving the artificial intelligence, it is possible to always provide the optimal recipe.

[0753] Program processing explanation

[0754] 1. User status input

[0755] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[0756] 2. Obtaining user emotional information

[0757] The emotion engine means acquires the user's facial expressions and tone of voice using a camera and a microphone, thereby recognizing the user's emotion information (for example, joy, sadness, stress, etc.).

[0758] 3. Transmission of Information

[0759] The terminal generates request data for transmitting the user's state information and emotion information to the server, and transmits it to the server.

[0760] 4. Recipe Generation

[0761] The server uses artificial intelligence to generate an optimal mixed juice recipe based on the received user status and emotional information. First, the server compares the recipe with a past database to select an appropriate combination of fruits and vegetables and calculates the amounts of each combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water) consists of specific ingredients and their amounts.

[0762] 5. Providing recipes

[0763] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[0764] 6. Gathering Feedback

[0765] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they can provide feedback such as "I wish it was a little sweeter."

[0766] 7. Learning Feedback

[0767] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[0768] Specific examples

[0769] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[0770] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[0771] The processing flow will be explained below.

[0772] Step 1: User enters state

[0773] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[0774] Step 2: User Enters Preferences

[0775] The user inputs their preferred flavor (e.g., "I like fruit-based") and other additional information, and presses the "Send" button to send the input to the terminal.

[0776] Step 3: The emotion engine retrieves emotions

[0777] Using the device's camera and microphone, the emotion engine acquires emotional information from the user's facial expressions and tone of voice. For example, if the user is smiling, it will be recognized as "joy," and if they are speaking in a straightforward voice, it will be recognized as "stress."

[0778] Step 4: The device sends the information

[0779] The terminal formats the user's status information, preference information, and emotion information and generates request data to be sent to the server.

[0780] The terminal transmits the generated request data to the server.

[0781] Step 5: The server receives the data

[0782] The server analyzes the request data received from the terminal and extracts the user's status information, preference information, and emotion information.

[0783] Step 6: The server generates the recipe

[0784] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe. For example, the server can compare the recipe with a database of past data to select the appropriate combination of fruits and vegetables.

[0785] The server takes into account emotional information and selects materials that have a relaxing effect for a user who is "feeling stressed," for example.

[0786] The server calculates the detailed measurements of the recipe and generates a specific recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0787] Step 7: Server Sends Recipe

[0788] The server transmits the generated recipe to the terminal as response data.

[0789] Step 8: Device receives recipe

[0790] The terminal displays the recipe information received from the server on a user interface.

[0791] Step 9: User reviews the recipe

[0792] The user checks the recipe displayed on the device screen.

[0793] Step 10: User creates juice

[0794] The user actually creates a mixed juice according to the provided recipe.

[0795] Step 11: User Enters Feedback

[0796] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[0797] Step 12: Device sends feedback

[0798] The terminal formats the user's feedback information and generates request data to be sent to the server.

[0799] The terminal transmits the generated feedback request data to the server.

[0800] Step 13: Server receives feedback

[0801] The server processes the feedback information received from the terminals and stores it in a database.

[0802] Step 14: Server learns feedback

[0803] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[0804] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[0805] Step 15: Server updates the algorithm

[0806] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[0807] ---

[0808] By following these steps, users can create the perfect mixed juice that suits their mood, physical condition, and emotions that day. The system continually learns based on feedback and always suggests the best recipe.

[0809] Example 2

[0810] 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."

[0811] In modern society, people's health and mental satisfaction are important, but conventional mixed drink recipe generation systems do not take into account the user's mood, physical condition, or emotions on that day. As a result, it is difficult to provide optimal recipes that meet individual needs, resulting in low user satisfaction.

[0812] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving status information of the day input by the user, emotion engine means for acquiring emotion information of the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning the feedback information and updating the artificial intelligence means. This makes it possible to provide personalized recipes that take into account the user's mood, physical condition, and emotion of the day.

[0813] A "user" refers to a person who uses the system to request a mixed drink recipe that suits their mood, physical condition, and emotions of the day.

[0814] "Status information" refers to data input by the user including the user's mood and physical condition for that day, as well as preferences for flavors.

[0815] "Emotion information" refers to data relating to emotions analyzed from the user's facial expressions and tone of voice obtained by the emotion engine means.

[0816] The term "emotion engine means" refers to means for acquiring user emotion information, including devices and software for analyzing the user's facial expressions and tone of voice.

[0817] "Artificial intelligence means" refers to algorithms and computer programs for generating optimal mixed drink recipes based on state information and emotional information.

[0818] "Feedback information" refers to data entered by a user about their impressions of the mixed drink they have actually created and any improvements they would like to see made.

[0819] "User interface means" refers to an interface for a user to input status information and feedback information.

[0820] "Means" refers to the general term for the various devices and software that make up the system.

[0821] "Database" refers to a storage device or system for storing and managing user state information, emotion information, and feedback information.

[0822] "Server" refers to a computer system that processes information sent by users and generates and serves recipes.

[0823] The present invention is a system that generates and provides personalized mixed drink recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates recipes using artificial intelligence. The generated recipes are provided to the user, and furthermore, feedback from the user is received, and the artificial intelligence is continually improved to always provide optimal recipes.

[0824] First, the user inputs their mood and physical condition for the day, as well as their preferred flavor preferences, via a dedicated terminal or smartphone application. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." Next, the emotion engine means uses a camera and microphone to capture the user's facial expressions and tone of voice, thereby recognizing the user's emotional information (e.g., joy, sadness, stress, etc.).

[0825] This information is sent from the device to a server, which uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information. The server first compares the information with a past database to select an appropriate combination of fruits and vegetables and calculates the serving sizes for that combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0826] The generated recipe information is sent from the server to the terminal, and the terminal displays the received recipe information on the user interface so that the user can check it. The user then actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may provide feedback such as "I would like it a little sweeter." This feedback information is sent to the server, which stores it in a database and uses the algorithm of the artificial intelligence means to learn, improving the accuracy of recipe generation from the next time onwards.

[0827] Specific examples

[0828] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server will suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user makes this juice and provides feedback such as "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future recipes.

[0829] Prompt Sentence Examples

[0830] "How are you feeling today? Do you want to relax? Are you stressed? What's your favorite fruit?"

[0831] In this way, this system can provide optimal mixed drink recipes to individual users based on their state and emotional information. The aim is for the server and device to cooperate and continuously propose improved recipes based on user feedback.

[0832] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0833] Program processing flow

[0834] Step 1: Enter user status

[0835] Users launch a dedicated device or smartphone application and input their mood and physical condition for the day, as well as their preferred flavor preferences.

[0836] Specific actions

[0837] 1. The user opens the application.

[0838] 2. The user interface displays questions such as "How are you feeling today?" and "Do you want to relax?"

[0839] 3. The user enters information using input fields and options.

[0840] 4. The information entered by the user is stored on the device as a data packet in JSON format or similar.

[0841] Input and Output

[0842] Input: Information about the user's mood or physical condition (e.g., "I'm tired," "I want to relax," "I like fruit-based foods").

[0843] Output: Data packets converted into JSON format etc.

[0844] Step 2: Acquiring emotional information

[0845] The camera and microphone, which are emotion engine means, are activated and emotion information is obtained by capturing the user's facial expressions and tone of voice.

[0846] Specific actions

[0847] 1. The device camera captures the user's face.

[0848] 2. The device's microphone records the user's voice.

[0849] 3. The emotion engine analyzes emotional information using facial expression analysis algorithms and voice analysis algorithms.

[0850] Input and Output

[0851] Input: Data on the user's facial expressions and tone of voice.

[0852] Output: Parsed emotion information (e.g., joy, sadness, stress).

[0853] Step 3: Submit your information

[0854] The terminal transmits the state information input by the user and the emotion information acquired by the emotion engine to the server.

[0855] Specific actions

[0856] 1. The device generates a data packet that combines the input state information and the acquired emotion information.

[0857] 2. Generate a send request to the server and send the data to the server.

[0858] Input and Output

[0859] Input: A data packet containing state and emotion information.

[0860] Output: The data sent to the server.

[0861] Step 4: Recipe Generation

[0862] The server uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information.

[0863] Specific actions

[0864] 1. The server analyzes the received data and retrieves relevant past information from the database.

[0865] 2. A generative AI model uses relevant data to calculate optimal fruit and vegetable combinations.

[0866] 3. Based on emotional information, adjust the selected ingredients and quantities to generate a personalized recipe.

[0867] Input and Output

[0868] Input: User state information, emotion information, past database information.

[0869] Output: The generated recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[0870] Step 5: Provide the recipe

[0871] The generated recipe information is transmitted from the server to the terminal and displayed on the user interface.

[0872] Specific actions

[0873] 1. The server sends the generated recipe to the device.

[0874] 2. The terminal displays the received recipe information on the user interface and notifies the user.

[0875] Input and Output

[0876] Input: Generated recipe information.

[0877] Output: The recipe displayed in the user interface.

[0878] Step 6: Gather feedback

[0879] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[0880] Specific actions

[0881] 1. The user creates and samples the juice.

[0882] 2. Open the application's feedback screen and enter your specific thoughts and suggestions for improvement (e.g., "I'd like it to be a little sweeter").

[0883] Input and Output

[0884] Input: User feedback information.

[0885] Output: Feedback data converted into JSON format etc.

[0886] Step 7: Learning feedback

[0887] The server stores the feedback information received from users in a database and uses it to train the algorithm of the generative AI model.

[0888] Specific actions

[0889] 1. The server stores the received feedback information in a database.

[0890] 2. The generative AI model learns from the feedback information and adjusts and improves future recipe generation algorithms.

[0891] Input and Output

[0892] Input: Feedback information from the user.

[0893] Output: Tweaked and improved generative AI model.

[0894] This concludes the specific processing flow of this system. The system provides personalized mixed drink recipes by combining the user's state information and emotional information. The system is designed so that the server and the device can cooperate to provide constantly improving recipes.

[0895] (Application example 2)

[0896] 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."

[0897] Conventional mixed juice recipe generation systems are mostly based on basic information such as the user's mood and physical condition that day, and are unable to take the user's emotions into account. As a result, they are unable to provide optimal recipes based on the user's emotional state, leaving room for improvement in the user experience. Furthermore, the system's ability to receive feedback from users and improve the system's accuracy is limited. To solve these problems, the development of a system that incorporates emotional information was required.

[0898] 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.

[0899] In this invention, the server includes means for receiving status information for that day input by the user, emotion engine means for acquiring emotion information, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning from the feedback information and updating the artificial intelligence means. This makes it possible to generate and provide personalized recipes that take into account the user's emotion information as well as their mood and physical condition. Furthermore, by incorporating user feedback into the learning process, the accuracy of the system can be improved.

[0900] The "means for receiving user-entered daily status information" is a part of the system that obtains information about the user's mood, physical condition, and preferences for the day through an interface that allows the user to input such information.

[0901] The "emotion engine means" is a part of the system that uses sensors such as a camera and a microphone to analyze the user's facial expressions and tone of voice and recognize the user's emotional information.

[0902] The "artificial intelligence means" is a computer system having an algorithm and learning function for generating an optimal mixed drink recipe based on the user's state information and emotional information.

[0903] The "means for providing a recipe to a user" is an interface for displaying or communicating the recipe of the created mixed drink to the user.

[0904] The "means for receiving feedback information from users" is a part of the system that allows users to input their thoughts and suggestions for improvement on the provided recipes.

[0905] The "means for learning feedback information and updating the artificial intelligence means" is a computer system that improves the artificial intelligence algorithm based on feedback received from the user, thereby improving the accuracy of recipe generation from the next time onwards.

[0906] "User interface means" is a general term referring to an input device and a screen display device that allow the user to input the mood, physical condition, and preferences of the day.

[0907] The "means for generating a plurality of candidate recipes and allowing the user to select" is a part of the system that presents the generated plurality of mixed drink recipes to the user and allows the user to select the one they like best.

[0908] This invention is a system for generating and providing personalized mixed juice recipes based on the user's mood, physical condition, and emotional information for that day. This system receives user input information through a user interface such as a dedicated terminal or smartphone application, recognizes the emotional information using an emotional engine, and generates and provides the optimal recipe to the user using artificial intelligence. Furthermore, by collecting feedback from users and continually improving the algorithm of the artificial intelligence, it is possible to consistently provide optimal recipes.

[0909] Hardware and software used

[0910] 1. Dedicated device or smartphone:

[0911] It serves as an interface for the user to input status information for the day.

[0912] Possible applications include iOS and Android smartphone applications.

[0913] 2. Camera and Microphone:

[0914] Used as a sensor to obtain emotional information.

[0915] For example, the camera and microphone built into a smartphone can be used.

[0916] 3. Emotion engine means:

[0917] Software for facial expression recognition and voice analysis.

[0918] Possible examples include OpenCV (a library for camera image processing) and Python-based libraries.

[0919] 4. Artificial Intelligence Means:

[0920] An algorithm for generating recipes based on the user's state and emotional information.

[0921] Possible machine learning models include those using Scikit-learn and TensorFlow.

[0922] 5. Server:

[0923] A central system that receives information, processes it, generates recipes, and learns from feedback.

[0924] You can use cloud platforms (AWS, Google Cloud Platform, etc.).

[0925] Program processing explanation

[0926] 1. Input acceptance:

[0927] Users input their mood, physical condition, and preferred taste preferences for the day through a smartphone application.

[0928] For example, enter information such as "I want to relax today" or "I like fruit-based flavors."

[0929] 2. Acquiring emotional information:

[0930] The emotion engine means uses the smartphone's camera and microphone to acquire the user's facial expressions and tone of voice, and recognizes emotion information (for example, stress, joy, etc.).

[0931] 3. Transmission of Information:

[0932] The user's state information and emotion information are transmitted from the terminal to the server.

[0933] 4. Recipe generation:

[0934] Based on the information received, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[0935] For example, it compares it with a past database to select a combination of fruits and vegetables and calculate their portions.

[0936] 5. Recipe provided by:

[0937] The generated recipe information is sent from the server to the user interface and displayed for the user to review.

[0938] 6. Feedback Collection:

[0939] Users create juice and provide feedback on the results and areas for improvement.

[0940] For example, provide feedback such as "I'd like it a little sweeter."

[0941] 7. Feedback Learning:

[0942] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means.

[0943] Specific examples

[0944] For example, if a user inputs status information such as "I want to relax today," and the emotion engine means obtains emotion information such as "I'm feeling stressed," the device will send this information to the server. Based on past data and feedback information, the server can suggest a "banana and cherry relaxation juice." This recipe specifically consists of ingredients and quantities such as one banana, ten cherries, one tablespoon of honey, and 200 ml of water.

[0945] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. Through this feedback, the AI ​​can always provide the best recipes.

[0946] Prompt Sentence Examples

[0947] Here is an example of a prompt that the user might enter:

[0948] "Please tell us how you feel today:

[0949] Mood: Relaxing

[0950] Physical condition: Lack of sleep

[0951] Preferences: Fruit-based

[0952] Emotional information (e.g., feeling stressed) was obtained.

[0953] Use the information above to generate the perfect mixed juice recipe.

[0954] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0955] Step 1:

[0956] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they might input information such as "I want to relax today" or "I like fruit-based drinks." This information is entered into the device as text data.

[0957] Step 2:

[0958] The device receives the input text data and uses a camera and microphone to capture the user's facial expressions and tone of voice. Using facial expression recognition software (e.g., OpenCV) and voice analysis software, emotional information is analyzed in real time. This emotional information is expressed as categories such as "stress" or "joy."

[0959] Step 3:

[0960] The device sends the user's status information and emotional information to the server using an HTTP request. The input data (mood, physical condition, preferred tastes) and emotional information are sent to the server as JSON format data.

[0961] Step 4:

[0962] The server uses a generative AI model to generate an optimal mixed juice recipe based on the received state and emotion information. First, it compares the results with a database of past data to select an appropriate combination of fruits and vegetables. Next, it uses a generative AI model (e.g., Scikit-learn or TensorFlow) to calculate the quantities of each combination. The output recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0963] Step 5:

[0964] The server sends the generated recipe information to the terminal, which receives the recipe information and displays it on the user interface, allowing the user to check the proposed mixed juice recipe.

[0965] Step 6:

[0966] The user makes juice based on the proposed recipe and enters the results and suggestions for improvement on the feedback input screen. For example, they can provide feedback such as "I'd like it a little sweeter." This feedback information is entered into the terminal as text data.

[0967] Step 7:

[0968] The device sends the user's feedback information to the server as JSON format data.

[0969] Step 8:

[0970] The server stores the received feedback information in a database and uses it to improve the algorithm of the generative AI model. Specifically, the feedback information is used as additional training data to retrain the model, thereby improving the accuracy of recipe generation from the next time onwards.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] [Third embodiment]

[0975] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0976] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0977] 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).

[0978] 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.

[0979] 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.

[0980] 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).

[0981] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] 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.

[0986] 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."

[0987] The present invention provides a system that allows users to obtain the optimal mixed juice recipe based on their mood and physical condition on that day. This system receives information about the user's condition, generates a recipe using artificial intelligence, and provides the recipe to the user. Furthermore, by receiving feedback from the user and continually improving the artificial intelligence, the system can always provide the optimal recipe.

[0988] Program processing explanation

[0989] 1. User status input

[0990] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[0991] 2. Transmission of information

[0992] The terminal generates a request for transmitting the state information input by the user to the server, and transmits it to the server.

[0993] 3. Recipe Generation

[0994] The server uses artificial intelligence to generate the optimal mixed juice recipe based on the received user status information. First, the server compares it with its past database to select the appropriate combination of fruits and vegetables. Then, the server calculates the quantities of each combination and creates a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[0995] 4. Providing recipes

[0996] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[0997] 5. Gathering Feedback

[0998] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may give feedback such as "I'd like it to be a little sweeter."

[0999] 6. Learning Feedback

[1000] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[1001] Specific examples

[1002] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1003] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[1004] The processing flow will be explained below.

[1005] Step 1: User enters state

[1006] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[1007] Step 2: User Enters Preferences

[1008] The user inputs their preferred flavor (e.g., "I like fruit-based") and any additional information, and presses the "Send" button to send the input to the terminal.

[1009] Step 3: The device sends the information

[1010] The terminal formats the user's input information and generates request data to be sent to the server.

[1011] The terminal transmits the generated request data to the server.

[1012] Step 4: The server receives the data

[1013] The server analyzes the request data received from the terminal and extracts the user's status information and preference information.

[1014] Step 5: The server generates the recipe

[1015] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[1016] Specifically, the server compares the data with a past database, selects the appropriate combination of fruits and vegetables, and calculates the portion sizes of those combinations.

[1017] Build the generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1018] Step 6: The server sends the recipe

[1019] The server transmits the generated recipe to the terminal as response data.

[1020] Step 7: The device receives the recipe

[1021] The terminal displays the recipe information received from the server on a user interface.

[1022] Step 8: User reviews the recipe

[1023] The user checks the recipe displayed on the device screen.

[1024] Step 9: User creates juice

[1025] The user actually creates a mixed juice according to the provided recipe.

[1026] Step 10: User Enters Feedback

[1027] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[1028] Step 11: Device sends feedback

[1029] The terminal formats the user's feedback information and generates request data to be sent to the server.

[1030] The terminal transmits the generated feedback request data to the server.

[1031] Step 12: Server receives feedback

[1032] The server processes the feedback information received from the terminals and stores it in a database.

[1033] Step 13: Server learns feedback

[1034] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[1035] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[1036] Step 14: Server updates the algorithm

[1037] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[1038] ---

[1039] By following these steps, users can create the perfect mixed juice that suits their mood and physical condition that day. The system continually learns based on feedback and always suggests the best recipe.

[1040] Example 1

[1041] 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."

[1042] In modern society, people's lifestyles and health conditions are diverse, and their daily diets demand optimal nutritional supplementation tailored to their mood and physical condition on that day. However, selecting the appropriate beverage and creating a recipe based on that day's mood and physical condition is a time-consuming task, and providing personalized recipes tailored to individual needs is not easy. Furthermore, collecting appropriate feedback on the results and reflecting it in future recipe generation requires specialized knowledge and skills. The present invention aims to solve these problems.

[1043] 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.

[1044] In this invention, the server includes: means for a user to input that day's condition information via a dedicated terminal or smartphone application; means for transmitting the input condition information to the server; an artificial intelligence model that generates an optimal mixed drink recipe based on the condition information; means for analyzing the user's condition information and selecting an appropriate ingredient combination using the artificial intelligence model; means for calculating the amounts of the selected ingredients and generating a detailed recipe; means for providing the generated recipe to the user; means for inputting feedback information from the user to the dedicated terminal and transmitting it to the server; and means for learning the feedback information and updating the artificial intelligence model. This allows the user to easily obtain an optimal mixed drink recipe based on their mood and physical condition for that day, and further makes it possible to always provide personalized recipes that reflect user feedback.

[1045] "User" refers to any individual or organization that uses this system.

[1046] "Dedicated terminal" refers to a hardware device designed specifically for this system.

[1047] "Smartphone application" refers to a dedicated software program that runs on a smartphone.

[1048] "Status information" refers to input information such as the user's mood and physical condition on that day, and their preferred flavor tendencies.

[1049] "Server" refers to a central processing unit that receives and processes information from users.

[1050] "Artificial intelligence model" refers to software that has an algorithm for generating optimal mixed drink recipes based on user status information.

[1051] "Ingredient combination" refers to the types and proportions of individual ingredients used in a mixed drink.

[1052] "Quantity" refers to the specific quantity of the selected material.

[1053] A "detailed recipe" refers to information that includes all ingredients needed to make a mixed drink, their specific amounts, and instructions for making the drink.

[1054] "Feedback information" refers to impressions and desired improvements provided by a user after actually creating a mixed drink.

[1055] "User interface means" refers to an interactive screen or form through which a user inputs status information.

[1056] "Database" refers to a collection of information used to store and analyze user status and feedback information.

[1057] This invention provides a system that allows users to obtain optimal mixed drink recipes based on their mood and physical condition on that day. This system consists of a dedicated terminal or smartphone application for inputting and sending status information, a server for receiving and processing that information, and an artificial intelligence model for generating optimal recipes.

[1058] System configuration

[1059] Dedicated device / smartphone application

[1060] Users use a dedicated device or smartphone application to input their mood, physical condition, and preferred tastes for the day. The input interface is intuitive and easy to use, offering options such as text input and selection from a selection list. This information is sent to the server in a recommended format (e.g., JSON format).

[1061] server

[1062] The server receives the user's input information and processes it to analyze it. Specifically, it follows the following procedure:

[1063] 1. Information Analysis: Analyzes the status information entered by the user and classifies it into the appropriate category.

[1064] 2. Recipe generation: Using an artificial intelligence model (e.g., a model trained with TensorFlow or PyTorch), the optimal combination of ingredients for a mixed drink is selected, and the appropriate ingredients are selected by comparing them with a database of past recipes.

[1065] 3. Calculate quantities: Calculate the specific quantities of the selected ingredients and generate a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1066] The generated recipe is sent to a dedicated terminal or smartphone application and provided to the user.

[1067] Gathering feedback and learning

[1068] Users actually make juice and enter their impressions and suggestions for improvement on a feedback input screen. For example, they can say, "I'd like it a little sweeter," and send that feedback to the server via a dedicated device or smartphone application.

[1069] The server stores the received feedback information in a database and uses it to train the AI ​​model. Based on the learning results, the accuracy of recipe generation will be improved from the next time onwards.

[1070] Specific examples

[1071] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server suggests a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1072] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes, thus ensuring that the server can continue to provide optimal recipes.

[1073] Prompt Sentence Examples

[1074] "The user has entered the state information, 'I'm sleep-deprived today and want to relax.' Please generate a recipe for a mixed juice that will have a relaxing effect based on past data and feedback information."

[1075] The above is a specific embodiment for carrying out the present invention. Throughout the system, not only can mixed drink recipes be provided that are optimized for the individual user's situation, but feedback can also be used to continually improve the system.

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1077] Step 1:

[1078] The user enters status information

[1079] explanation:

[1080] Users input their mood, physical condition, and preferred flavors for the day via a dedicated device or smartphone application.

[1081] input:

[1082] User state information (e.g., "I'm sleep-deprived today and want to relax").

[1083] Specific behavior:

[1084] The user launches the app and enters status information in text format according to the interface. Once the information is complete, the user presses the "Submit" button.

[1085] Step 2:

[1086] Sending information to the server

[1087] explanation:

[1088] The device sends the state information entered by the user to the server, which converts this information into an appropriate format (e.g., JSON format).

[1089] input:

[1090] User status information.

[1091] Specific behavior:

[1092] The device converts the input information into JSON format, generates an HTTP POST request, and sends it to the server's API endpoint.

[1093] Step 3:

[1094] Parsing state information

[1095] explanation:

[1096] The server analyzes the received state information, specifically tokenizing the user's input and classifying it into the appropriate category.

[1097] input:

[1098] Status information sent from the device in JSON format.

[1099] Data processing:

[1100] Text analysis algorithms are used to tokenize and categorize the data.

[1101] output:

[1102] Parsed category information.

[1103] Specific behavior:

[1104] The server invokes a natural language processing algorithm to analyze the user's input and categorize it into categories such as "I want to relax" or "I'm not getting enough sleep."

[1105] Step 4:

[1106] Generate a recipe

[1107] explanation:

[1108] The server uses the analyzed category information to select the optimal ingredient combination for a mixed drink using an artificial intelligence model.

[1109] input:

[1110] Parsed category information.

[1111] Data processing:

[1112] Use artificial intelligence models (e.g., TensorFlow or PyTorch) to generate appropriate material combinations.

[1113] output:

[1114] The combination of materials produced.

[1115] Specific behavior:

[1116] The server inputs category information into an artificial intelligence model and selects the most suitable ingredients (e.g., banana, cherry, honey, water).

[1117] Step 5:

[1118] Calculate portions

[1119] explanation:

[1120] Calculate the specific amounts of selected ingredients and generate a detailed recipe.

[1121] input:

[1122] The combination of materials produced.

[1123] Data processing:

[1124] It uses an algorithm to calculate the optimal amount of ingredients.

[1125] output:

[1126] Detailed recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1127] Specific behavior:

[1128] The server calculates the appropriate amounts for the selected ingredients and generates a detailed recipe.

[1129] Step 6:

[1130] Provide a recipe

[1131] explanation:

[1132] The generated recipe information is transmitted from the server to the terminal, which then displays it to the user.

[1133] input:

[1134] Detailed recipe.

[1135] output:

[1136] The recipe displayed in the user interface.

[1137] Specific behavior:

[1138] The server converts the generated recipe information into JSON format and sends it to the terminal as an HTTP response. The terminal receives this response, analyzes it, and displays it on the user interface.

[1139] Step 7:

[1140] Collect feedback

[1141] explanation:

[1142] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[1143] input:

[1144] User feedback information (e.g., "I'd like it a little sweeter").

[1145] Specific behavior:

[1146] Users taste the finished juice and enter and submit feedback via a dedicated terminal or smartphone application.

[1147] Step 8:

[1148] Send feedback to the server

[1149] explanation:

[1150] The device sends the feedback information entered by the user to the server, which also converts this information into an appropriate format (e.g., JSON format).

[1151] input:

[1152] User feedback information.

[1153] Specific behavior:

[1154] The device converts the feedback information into JSON format and generates an HTTP POST request to send it to the server's API endpoint.

[1155] Step 9:

[1156] Learning Feedback

[1157] explanation:

[1158] The server stores the received feedback information in a database and uses it to train an artificial intelligence model.

[1159] input:

[1160] Feedback information sent from the device in JSON format.

[1161] Data processing:

[1162] The feedback information is input as training data into the artificial intelligence model, and the model is retrained.

[1163] output:

[1164] Updated artificial intelligence model.

[1165] Specific behavior:

[1166] The server stores the feedback information in a database and inputs it into the AI ​​model for retraining, improving the accuracy of recipe generation in future.

[1167] (Application example 1)

[1168] 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."

[1169] Conventional beverage delivery systems have difficulty providing users with optimal mixed drink recipes tailored to their mood and physical condition on that day. Furthermore, they lacked a mechanism for effectively utilizing user feedback and incorporating it into the next recipe generation, making it difficult to provide personalized service. Furthermore, there was a lack of a way for users to easily order and receive mixed drinks that matched their preferences at physical stores.

[1170] 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.

[1171] In this invention, the server includes means for receiving status information for that day input by the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information, means for providing the generated recipe to the user, means for receiving feedback information from the user, means for learning the feedback information and updating the artificial intelligence means, means for displaying the recipe on a dedicated terminal in the store so that the user can check the recipe, and a dedicated terminal for creating a mixed drink based on the recipe, thereby enabling the user to easily order and receive the optimal mixed drink according to their mood and physical condition on that day.

[1172] "User" refers to a person who uses the service to request a mixed drink recipe that suits their mood or physical condition on that day.

[1173] "Status information" refers to information such as the user's mood and physical condition on that day, and favorite flavors.

[1174] "Artificial intelligence means" refers to an algorithm or program that generates an optimal mixed drink recipe based on status information received from a user.

[1175] "Feedback information" refers to information including user's impressions and suggestions for improvement regarding the mixed drink created based on the provided recipe.

[1176] "Dedicated terminal" refers to a device such as a tablet or smartphone that users use in the store to input and check recipes.

[1177] "Mixed drinks" refers to drinks such as juices and smoothies that are made by combining multiple beverage ingredients.

[1178] "Recipe" means information describing the ingredients and quantities of a mixed drink and how to make it.

[1179] "Learning" refers to the process by which the artificial intelligence means acquires new knowledge based on feedback information from the user and improves the accuracy of recipe generation from the next time onwards.

[1180] The present invention is a system that provides optimal mixed drink recipes based on the user's mood and physical condition. In this system, the user inputs information about their condition for the day via a dedicated terminal or smartphone application, and an artificial intelligence means generates the optimal recipe based on that information. The following describes how this system is specifically implemented.

[1181] 1. Enter and send user status information

[1182] Using a dedicated device or smartphone application, users input status information such as their mood and physical condition for the day, and their preferred flavors. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." The device then sends the input status information to the server in JSON format.

[1183] 2. Creating a recipe

[1184] The server receives the state information sent by the user and compares it with a historical database. This process uses deep learning frameworks such as TensorFlow and PyTorch. Based on the results of the comparison, the AI ​​model generates an optimal mixed drink recipe. The recipe includes specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1185] 3. Providing recipes

[1186] The generated recipe information is sent from the server to a dedicated terminal, where users can check the provided recipes on the dedicated terminal or smartphone screen.

[1187] 4. Gathering Feedback

[1188] The user actually creates a mixed drink and enters their impressions and suggestions for improvement into a feedback input screen via a dedicated terminal or smartphone application (e.g., "I'd like a little more honey"). This information is again sent to the server in JSON format.

[1189] 5. Learning Feedback

[1190] The server stores the feedback information received from users in a database and uses it to train the algorithms of the artificial intelligence means. The hardware required is a server equipped with a GPU suitable for training deep learning models. As a result of the learning, the accuracy of the next recipe generation will improve based on the new feedback information.

[1191] (Example)

[1192] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through the application, the dedicated device will send this information to the server. Based on past data and feedback, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically calls for one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user creates this juice and provides feedback that "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future uses. This allows the server to always provide the optimal recipe.

[1193] (Example of a prompt)

[1194] "Please suggest a recipe for a mixed drink that would be suitable if I'm stressed out and want to relax today. Favorite flavor: Fruity."

[1195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1196] Step 1:

[1197] Users input information about their mood, physical condition, favorite flavors, and other information about their condition on that day through a dedicated device or smartphone application. This information is temporarily stored in the device in JSON format.

[1198] Input: "I'm tired today," "I want to relax," "I like fruit-based drinks."

[1199] Output: Status information in JSON format

[1200] Step 2:

[1201] The device sends the status information entered by the user to the server, which receives the data using an HTTP POST request and stores it in an internal database.

[1202] Input: State information in JSON format

[1203] Output: Status information sent to the server

[1204] Step 3:

[1205] The server analyzes the received status information and generates the optimal mixed drink recipe using artificial intelligence means, specifically by comparing it with a historical database and selecting the appropriate ingredients and their quantities. The main tools used in this process are TensorFlow or PyTorch.

[1206] Input: Parsed state information

[1207] Output: Generated recipe ("1 banana, 10 cherries, 1 tablespoon honey, 200ml water")

[1208] Step 4:

[1209] The server sends the generated recipe in JSON format to the terminal, and the terminal displays the recipe in the user interface. The user can check the displayed recipe.

[1210] Input: Generated recipe

[1211] Output: The recipe displayed in the user interface

[1212] Step 5:

[1213] The user creates a mixed drink based on the displayed recipe, and then inputs their impressions and suggestions for improvement via a dedicated terminal or application. This feedback information is also temporarily stored in the terminal in JSON format.

[1214] Input: User feedback

[1215] Output: Feedback information in JSON format

[1216] Step 6:

[1217] The terminal transmits the feedback information collected from the user to the server, which stores the received feedback information in an internal database.

[1218] Input: Feedback information in JSON format

[1219] Output: Feedback information sent to the server

[1220] Step 7:

[1221] The server analyzes the feedback information and uses it to train the AI ​​algorithm. Specifically, it integrates past training data with newly received feedback information to improve the accuracy of the algorithm. This process is also carried out using TensorFlow or PyTorch.

[1222] Input: Feedback information stored in an internal database

[1223] Output: An improved AI model

[1224] Through the specific operations described above, users can easily obtain the optimal mixed drink recipe based on their mood and physical condition on that day, making it possible to improve service in physical stores via dedicated terminals.

[1225] 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.

[1226] The present invention provides a system that generates and provides personalized mixed juice recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates a recipe using artificial intelligence. The generated recipe is provided to the user, and by receiving feedback from the user and continually improving the artificial intelligence, it is possible to always provide the optimal recipe.

[1227] Program processing explanation

[1228] 1. User status input

[1229] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[1230] 2. Obtaining user emotional information

[1231] The emotion engine means acquires the user's facial expressions and tone of voice using a camera and a microphone, thereby recognizing the user's emotion information (for example, joy, sadness, stress, etc.).

[1232] 3. Transmission of Information

[1233] The terminal generates request data for transmitting the user's state information and emotion information to the server, and transmits it to the server.

[1234] 4. Recipe Generation

[1235] The server uses artificial intelligence to generate an optimal mixed juice recipe based on the received user status and emotional information. First, the server compares the recipe with a past database to select an appropriate combination of fruits and vegetables and calculates the amounts of each combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water) consists of specific ingredients and their amounts.

[1236] 5. Providing recipes

[1237] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[1238] 6. Gathering Feedback

[1239] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they can provide feedback such as "I wish it was a little sweeter."

[1240] 7. Learning Feedback

[1241] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[1242] Specific examples

[1243] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1244] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[1245] The processing flow will be explained below.

[1246] Step 1: User enters state

[1247] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[1248] Step 2: User Enters Preferences

[1249] The user inputs their preferred flavor (e.g., "I like fruit-based") and other additional information, and presses the "Send" button to send the input to the terminal.

[1250] Step 3: The emotion engine retrieves emotions

[1251] Using the device's camera and microphone, the emotion engine acquires emotional information from the user's facial expressions and tone of voice. For example, if the user is smiling, it will be recognized as "joy," and if they are speaking in a straightforward voice, it will be recognized as "stress."

[1252] Step 4: The device sends the information

[1253] The terminal formats the user's status information, preference information, and emotion information and generates request data to be sent to the server.

[1254] The terminal transmits the generated request data to the server.

[1255] Step 5: The server receives the data

[1256] The server analyzes the request data received from the terminal and extracts the user's status information, preference information, and emotion information.

[1257] Step 6: The server generates the recipe

[1258] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe. For example, the server can compare the recipe with a database of past data to select the appropriate combination of fruits and vegetables.

[1259] The server takes into account emotional information and selects materials that have a relaxing effect for a user who is "feeling stressed," for example.

[1260] The server calculates the detailed measurements of the recipe and generates a specific recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1261] Step 7: Server Sends Recipe

[1262] The server transmits the generated recipe to the terminal as response data.

[1263] Step 8: Device receives recipe

[1264] The terminal displays the recipe information received from the server on a user interface.

[1265] Step 9: User reviews the recipe

[1266] The user checks the recipe displayed on the device screen.

[1267] Step 10: User creates juice

[1268] The user actually creates a mixed juice according to the provided recipe.

[1269] Step 11: User Enters Feedback

[1270] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[1271] Step 12: Device sends feedback

[1272] The terminal formats the user's feedback information and generates request data to be sent to the server.

[1273] The terminal transmits the generated feedback request data to the server.

[1274] Step 13: Server receives feedback

[1275] The server processes the feedback information received from the terminals and stores it in a database.

[1276] Step 14: Server learns feedback

[1277] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[1278] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[1279] Step 15: Server updates the algorithm

[1280] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[1281] ---

[1282] By following these steps, users can create the perfect mixed juice that suits their mood, physical condition, and emotions that day. The system continually learns based on feedback and always suggests the best recipe.

[1283] Example 2

[1284] 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."

[1285] In modern society, people's health and mental satisfaction are important, but conventional mixed drink recipe generation systems do not take into account the user's mood, physical condition, or emotions on that day. As a result, it is difficult to provide optimal recipes that meet individual needs, resulting in low user satisfaction.

[1286] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving status information of the day input by the user, emotion engine means for acquiring emotion information of the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning the feedback information and updating the artificial intelligence means. This makes it possible to provide personalized recipes that take into account the user's mood, physical condition, and emotion of the day.

[1287] A "user" refers to a person who uses the system to request a mixed drink recipe that suits their mood, physical condition, and emotions of the day.

[1288] "Status information" refers to data input by the user including the user's mood and physical condition for that day, as well as preferences for flavors.

[1289] "Emotion information" refers to data relating to emotions analyzed from the user's facial expressions and tone of voice obtained by the emotion engine means.

[1290] The term "emotion engine means" refers to means for acquiring user emotion information, including devices and software for analyzing the user's facial expressions and tone of voice.

[1291] "Artificial intelligence means" refers to algorithms and computer programs for generating optimal mixed drink recipes based on state information and emotional information.

[1292] "Feedback information" refers to data entered by a user about their impressions of the mixed drink they have actually created and any improvements they would like to see made.

[1293] "User interface means" refers to an interface for a user to input status information and feedback information.

[1294] "Means" refers to the general term for the various devices and software that make up the system.

[1295] "Database" refers to a storage device or system for storing and managing user state information, emotion information, and feedback information.

[1296] "Server" refers to a computer system that processes information sent by users and generates and serves recipes.

[1297] The present invention is a system that generates and provides personalized mixed drink recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates recipes using artificial intelligence. The generated recipes are provided to the user, and furthermore, feedback from the user is received, and the artificial intelligence is continually improved to always provide optimal recipes.

[1298] First, the user inputs their mood and physical condition for the day, as well as their preferred flavor preferences, via a dedicated terminal or smartphone application. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." Next, the emotion engine means uses a camera and microphone to capture the user's facial expressions and tone of voice, thereby recognizing the user's emotional information (e.g., joy, sadness, stress, etc.).

[1299] This information is sent from the device to a server, which uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information. The server first compares the information with a past database to select an appropriate combination of fruits and vegetables and calculates the serving sizes for that combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1300] The generated recipe information is sent from the server to the terminal, and the terminal displays the received recipe information on the user interface so that the user can check it. The user then actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may provide feedback such as "I would like it a little sweeter." This feedback information is sent to the server, which stores it in a database and uses the algorithm of the artificial intelligence means to learn, improving the accuracy of recipe generation from the next time onwards.

[1301] Specific examples

[1302] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server will suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user makes this juice and provides feedback such as "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future recipes.

[1303] Prompt Sentence Examples

[1304] "How are you feeling today? Do you want to relax? Are you stressed? What's your favorite fruit?"

[1305] In this way, this system can provide optimal mixed drink recipes to individual users based on their state and emotional information. The aim is for the server and device to cooperate and continuously propose improved recipes based on user feedback.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Program processing flow

[1308] Step 1: Enter user status

[1309] Users launch a dedicated device or smartphone application and input their mood and physical condition for the day, as well as their preferred flavor preferences.

[1310] Specific actions

[1311] 1. The user opens the application.

[1312] 2. The user interface displays questions such as "How are you feeling today?" and "Do you want to relax?"

[1313] 3. The user enters information using input fields and options.

[1314] 4. The information entered by the user is stored on the device as a data packet in JSON format or similar.

[1315] Input and Output

[1316] Input: Information about the user's mood or physical condition (e.g., "I'm tired," "I want to relax," "I like fruit-based foods").

[1317] Output: Data packets converted into JSON format etc.

[1318] Step 2: Acquiring emotional information

[1319] The camera and microphone, which are emotion engine means, are activated and emotion information is obtained by capturing the user's facial expressions and tone of voice.

[1320] Specific actions

[1321] 1. The device camera captures the user's face.

[1322] 2. The device's microphone records the user's voice.

[1323] 3. The emotion engine analyzes emotional information using facial expression analysis algorithms and voice analysis algorithms.

[1324] Input and Output

[1325] Input: Data on the user's facial expressions and tone of voice.

[1326] Output: Parsed emotion information (e.g., joy, sadness, stress).

[1327] Step 3: Submit your information

[1328] The terminal transmits the state information input by the user and the emotion information acquired by the emotion engine to the server.

[1329] Specific actions

[1330] 1. The device generates a data packet that combines the input state information and the acquired emotion information.

[1331] 2. Generate a send request to the server and send the data to the server.

[1332] Input and Output

[1333] Input: A data packet containing state and emotion information.

[1334] Output: The data sent to the server.

[1335] Step 4: Recipe Generation

[1336] The server uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information.

[1337] Specific actions

[1338] 1. The server analyzes the received data and retrieves relevant past information from the database.

[1339] 2. A generative AI model uses relevant data to calculate optimal fruit and vegetable combinations.

[1340] 3. Based on emotional information, adjust the selected ingredients and quantities to generate a personalized recipe.

[1341] Input and Output

[1342] Input: User state information, emotion information, past database information.

[1343] Output: The generated recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1344] Step 5: Provide the recipe

[1345] The generated recipe information is transmitted from the server to the terminal and displayed on the user interface.

[1346] Specific actions

[1347] 1. The server sends the generated recipe to the device.

[1348] 2. The terminal displays the received recipe information on the user interface and notifies the user.

[1349] Input and Output

[1350] Input: Generated recipe information.

[1351] Output: The recipe displayed in the user interface.

[1352] Step 6: Gather feedback

[1353] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[1354] Specific actions

[1355] 1. The user creates and samples the juice.

[1356] 2. Open the application's feedback screen and enter your specific thoughts and suggestions for improvement (e.g., "I'd like it to be a little sweeter").

[1357] Input and Output

[1358] Input: User feedback information.

[1359] Output: Feedback data converted into JSON format etc.

[1360] Step 7: Learning feedback

[1361] The server stores the feedback information received from users in a database and uses it to train the algorithm of the generative AI model.

[1362] Specific actions

[1363] 1. The server stores the received feedback information in a database.

[1364] 2. The generative AI model learns from the feedback information and adjusts and improves future recipe generation algorithms.

[1365] Input and Output

[1366] Input: Feedback information from the user.

[1367] Output: Tweaked and improved generative AI model.

[1368] This concludes the specific processing flow of this system. The system provides personalized mixed drink recipes by combining the user's state information and emotional information. The system is designed so that the server and the device can cooperate to provide constantly improving recipes.

[1369] (Application example 2)

[1370] 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."

[1371] Conventional mixed juice recipe generation systems are mostly based on basic information such as the user's mood and physical condition that day, and are unable to take the user's emotions into account. As a result, they are unable to provide optimal recipes based on the user's emotional state, leaving room for improvement in the user experience. Furthermore, the system's ability to receive feedback from users and improve the system's accuracy is limited. To solve these problems, the development of a system that incorporates emotional information was required.

[1372] 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.

[1373] In this invention, the server includes means for receiving status information for that day input by the user, emotion engine means for acquiring emotion information, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning from the feedback information and updating the artificial intelligence means. This makes it possible to generate and provide personalized recipes that take into account the user's emotion information as well as their mood and physical condition. Furthermore, by incorporating user feedback into the learning process, the accuracy of the system can be improved.

[1374] The "means for receiving user-entered daily status information" is a part of the system that obtains information about the user's mood, physical condition, and preferences for the day through an interface that allows the user to input such information.

[1375] The "emotion engine means" is a part of the system that uses sensors such as a camera and a microphone to analyze the user's facial expressions and tone of voice and recognize the user's emotional information.

[1376] The "artificial intelligence means" is a computer system having an algorithm and learning function for generating an optimal mixed drink recipe based on the user's state information and emotional information.

[1377] The "means for providing a recipe to a user" is an interface for displaying or communicating the recipe of the created mixed drink to the user.

[1378] The "means for receiving feedback information from users" is a part of the system that allows users to input their thoughts and suggestions for improvement on the provided recipes.

[1379] The "means for learning feedback information and updating the artificial intelligence means" is a computer system that improves the artificial intelligence algorithm based on feedback received from the user, thereby improving the accuracy of recipe generation from the next time onwards.

[1380] "User interface means" is a general term referring to an input device and a screen display device that allow the user to input the mood, physical condition, and preferences of the day.

[1381] The "means for generating a plurality of candidate recipes and allowing the user to select" is a part of the system that presents the generated plurality of mixed drink recipes to the user and allows the user to select the one they like best.

[1382] This invention is a system for generating and providing personalized mixed juice recipes based on the user's mood, physical condition, and emotional information for that day. This system receives user input information through a user interface such as a dedicated terminal or smartphone application, recognizes the emotional information using an emotional engine, and generates and provides the optimal recipe to the user using artificial intelligence. Furthermore, by collecting feedback from users and continually improving the algorithm of the artificial intelligence, it is possible to consistently provide optimal recipes.

[1383] Hardware and software used

[1384] 1. Dedicated device or smartphone:

[1385] It serves as an interface for the user to input status information for the day.

[1386] Possible applications include iOS and Android smartphone applications.

[1387] 2. Camera and Microphone:

[1388] Used as a sensor to obtain emotional information.

[1389] For example, the camera and microphone built into a smartphone can be used.

[1390] 3. Emotion engine means:

[1391] Software for facial expression recognition and voice analysis.

[1392] Possible examples include OpenCV (a library for camera image processing) and Python-based libraries.

[1393] 4. Artificial Intelligence Means:

[1394] An algorithm for generating recipes based on the user's state and emotional information.

[1395] Possible machine learning models include those using Scikit-learn and TensorFlow.

[1396] 5. Server:

[1397] A central system that receives information, processes it, generates recipes, and learns from feedback.

[1398] You can use cloud platforms (AWS, Google Cloud Platform, etc.).

[1399] Program processing explanation

[1400] 1. Input acceptance:

[1401] Users input their mood, physical condition, and preferred taste preferences for the day through a smartphone application.

[1402] For example, enter information such as "I want to relax today" or "I like fruit-based flavors."

[1403] 2. Acquiring emotional information:

[1404] The emotion engine means uses the smartphone's camera and microphone to acquire the user's facial expressions and tone of voice, and recognizes emotion information (for example, stress, joy, etc.).

[1405] 3. Transmission of Information:

[1406] The user's state information and emotion information are transmitted from the terminal to the server.

[1407] 4. Recipe generation:

[1408] Based on the information received, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[1409] For example, it compares it with a past database to select a combination of fruits and vegetables and calculate their portions.

[1410] 5. Recipe provided by:

[1411] The generated recipe information is sent from the server to the user interface and displayed for the user to review.

[1412] 6. Feedback Collection:

[1413] Users create juice and provide feedback on the results and areas for improvement.

[1414] For example, provide feedback such as "I'd like it a little sweeter."

[1415] 7. Feedback Learning:

[1416] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means.

[1417] Specific examples

[1418] For example, if a user inputs status information such as "I want to relax today," and the emotion engine means obtains emotion information such as "I'm feeling stressed," the device will send this information to the server. Based on past data and feedback information, the server can suggest a "banana and cherry relaxation juice." This recipe specifically consists of ingredients and quantities such as one banana, ten cherries, one tablespoon of honey, and 200 ml of water.

[1419] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. Through this feedback, the AI ​​can always provide the best recipes.

[1420] Prompt Sentence Examples

[1421] Here is an example of a prompt that the user might enter:

[1422] "Please tell us how you feel today:

[1423] Mood: Relaxing

[1424] Physical condition: Lack of sleep

[1425] Preferences: Fruit-based

[1426] Emotional information (e.g., feeling stressed) was obtained.

[1427] Use the information above to generate the perfect mixed juice recipe.

[1428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1429] Step 1:

[1430] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they might input information such as "I want to relax today" or "I like fruit-based drinks." This information is entered into the device as text data.

[1431] Step 2:

[1432] The device receives the input text data and uses a camera and microphone to capture the user's facial expressions and tone of voice. Using facial expression recognition software (e.g., OpenCV) and voice analysis software, emotional information is analyzed in real time. This emotional information is expressed as categories such as "stress" or "joy."

[1433] Step 3:

[1434] The device sends the user's status information and emotional information to the server using an HTTP request. The input data (mood, physical condition, preferred tastes) and emotional information are sent to the server as JSON format data.

[1435] Step 4:

[1436] The server uses a generative AI model to generate an optimal mixed juice recipe based on the received state and emotion information. First, it compares the results with a database of past data to select an appropriate combination of fruits and vegetables. Next, it uses a generative AI model (e.g., Scikit-learn or TensorFlow) to calculate the quantities of each combination. The output recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1437] Step 5:

[1438] The server sends the generated recipe information to the terminal, which receives the recipe information and displays it on the user interface, allowing the user to check the proposed mixed juice recipe.

[1439] Step 6:

[1440] The user makes juice based on the proposed recipe and enters the results and suggestions for improvement on the feedback input screen. For example, they can provide feedback such as "I'd like it a little sweeter." This feedback information is entered into the terminal as text data.

[1441] Step 7:

[1442] The device sends the user's feedback information to the server as JSON format data.

[1443] Step 8:

[1444] The server stores the received feedback information in a database and uses it to improve the algorithm of the generative AI model. Specifically, the feedback information is used as additional training data to retrain the model, thereby improving the accuracy of recipe generation from the next time onwards.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] [Fourth embodiment]

[1449] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1450] 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.

[1451] 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).

[1452] 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.

[1453] 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.

[1454] 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).

[1455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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.

[1460] 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.

[1461] 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."

[1462] The present invention provides a system that allows users to obtain the optimal mixed juice recipe based on their mood and physical condition on that day. This system receives information about the user's condition, generates a recipe using artificial intelligence, and provides the recipe to the user. Furthermore, by receiving feedback from the user and continually improving the artificial intelligence, the system can always provide the optimal recipe.

[1463] Program processing explanation

[1464] 1. User status input

[1465] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[1466] 2. Transmission of information

[1467] The terminal generates a request for transmitting the state information input by the user to the server, and transmits it to the server.

[1468] 3. Recipe Generation

[1469] The server uses artificial intelligence to generate the optimal mixed juice recipe based on the received user status information. First, the server compares it with its past database to select the appropriate combination of fruits and vegetables. Then, the server calculates the quantities of each combination and creates a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1470] 4. Providing recipes

[1471] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[1472] 5. Gathering Feedback

[1473] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may give feedback such as "I'd like it to be a little sweeter."

[1474] 6. Learning Feedback

[1475] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[1476] Specific examples

[1477] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1478] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[1479] The processing flow will be explained below.

[1480] Step 1: User enters state

[1481] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[1482] Step 2: User Enters Preferences

[1483] The user inputs their preferred flavor (e.g., "I like fruit-based") and any additional information, and presses the "Send" button to send the input to the terminal.

[1484] Step 3: The device sends the information

[1485] The terminal formats the user's input information and generates request data to be sent to the server.

[1486] The terminal transmits the generated request data to the server.

[1487] Step 4: The server receives the data

[1488] The server analyzes the request data received from the terminal and extracts the user's status information and preference information.

[1489] Step 5: The server generates the recipe

[1490] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[1491] Specifically, the server compares the data with a past database, selects the appropriate combination of fruits and vegetables, and calculates the portion sizes of those combinations.

[1492] Build the generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1493] Step 6: The server sends the recipe

[1494] The server transmits the generated recipe to the terminal as response data.

[1495] Step 7: The device receives the recipe

[1496] The terminal displays the recipe information received from the server on a user interface.

[1497] Step 8: User reviews the recipe

[1498] The user checks the recipe displayed on the device screen.

[1499] Step 9: User creates juice

[1500] The user actually creates a mixed juice according to the provided recipe.

[1501] Step 10: User Enters Feedback

[1502] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[1503] Step 11: Device sends feedback

[1504] The terminal formats the user's feedback information and generates request data to be sent to the server.

[1505] The terminal transmits the generated feedback request data to the server.

[1506] Step 12: Server receives feedback

[1507] The server processes the feedback information received from the terminals and stores it in a database.

[1508] Step 13: Server learns feedback

[1509] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[1510] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[1511] Step 14: Server updates the algorithm

[1512] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[1513] ---

[1514] By following these steps, users can create the perfect mixed juice that suits their mood and physical condition that day. The system continually learns based on feedback and always suggests the best recipe.

[1515] Example 1

[1516] 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."

[1517] In modern society, people's lifestyles and health conditions are diverse, and their daily diets demand optimal nutritional supplementation tailored to their mood and physical condition on that day. However, selecting the appropriate beverage and creating a recipe based on that day's mood and physical condition is a time-consuming task, and providing personalized recipes tailored to individual needs is not easy. Furthermore, collecting appropriate feedback on the results and reflecting it in future recipe generation requires specialized knowledge and skills. The present invention aims to solve these problems.

[1518] 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.

[1519] In this invention, the server includes: means for a user to input that day's condition information via a dedicated terminal or smartphone application; means for transmitting the input condition information to the server; an artificial intelligence model that generates an optimal mixed drink recipe based on the condition information; means for analyzing the user's condition information and selecting an appropriate ingredient combination using the artificial intelligence model; means for calculating the amounts of the selected ingredients and generating a detailed recipe; means for providing the generated recipe to the user; means for inputting feedback information from the user to the dedicated terminal and transmitting it to the server; and means for learning the feedback information and updating the artificial intelligence model. This allows the user to easily obtain an optimal mixed drink recipe based on their mood and physical condition for that day, and further makes it possible to always provide personalized recipes that reflect user feedback.

[1520] "User" refers to any individual or organization that uses this system.

[1521] "Dedicated terminal" refers to a hardware device designed specifically for this system.

[1522] "Smartphone application" refers to a dedicated software program that runs on a smartphone.

[1523] "Status information" refers to input information such as the user's mood and physical condition on that day, and their preferred flavor tendencies.

[1524] "Server" refers to a central processing unit that receives and processes information from users.

[1525] "Artificial intelligence model" refers to software that has an algorithm for generating optimal mixed drink recipes based on user status information.

[1526] "Ingredient combination" refers to the types and proportions of individual ingredients used in a mixed drink.

[1527] "Quantity" refers to the specific quantity of the selected material.

[1528] A "detailed recipe" refers to information that includes all ingredients needed to make a mixed drink, their specific amounts, and instructions for making the drink.

[1529] "Feedback information" refers to impressions and desired improvements provided by a user after actually creating a mixed drink.

[1530] "User interface means" refers to an interactive screen or form through which a user inputs status information.

[1531] "Database" refers to a collection of information used to store and analyze user status and feedback information.

[1532] This invention provides a system that allows users to obtain optimal mixed drink recipes based on their mood and physical condition on that day. This system consists of a dedicated terminal or smartphone application for inputting and sending status information, a server for receiving and processing that information, and an artificial intelligence model for generating optimal recipes.

[1533] System configuration

[1534] Dedicated device / smartphone application

[1535] Users use a dedicated device or smartphone application to input their mood, physical condition, and preferred tastes for the day. The input interface is intuitive and easy to use, offering options such as text input and selection from a selection list. This information is sent to the server in a recommended format (e.g., JSON format).

[1536] server

[1537] The server receives the user's input information and processes it to analyze it. Specifically, it follows the following procedure:

[1538] 1. Information Analysis: Analyzes the status information entered by the user and classifies it into the appropriate category.

[1539] 2. Recipe generation: Using an artificial intelligence model (e.g., a model trained with TensorFlow or PyTorch), the optimal combination of ingredients for a mixed drink is selected, and the appropriate ingredients are selected by comparing them with a database of past recipes.

[1540] 3. Calculate quantities: Calculate the specific quantities of the selected ingredients and generate a detailed recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1541] The generated recipe is sent to a dedicated terminal or smartphone application and provided to the user.

[1542] Gathering feedback and learning

[1543] Users actually make juice and enter their impressions and suggestions for improvement on a feedback input screen. For example, they can say, "I'd like it a little sweeter," and send that feedback to the server via a dedicated device or smartphone application.

[1544] The server stores the received feedback information in a database and uses it to train the AI ​​model. Based on the learning results, the accuracy of recipe generation will be improved from the next time onwards.

[1545] Specific examples

[1546] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, the device sends this information to the server. Based on past data and feedback information, the server suggests a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1547] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes, thus ensuring that the server can continue to provide optimal recipes.

[1548] Prompt Sentence Examples

[1549] "The user has entered the state information, 'I'm sleep-deprived today and want to relax.' Please generate a recipe for a mixed juice that will have a relaxing effect based on past data and feedback information."

[1550] The above is a specific embodiment for carrying out the present invention. Throughout the system, not only can mixed drink recipes be provided that are optimized for the individual user's situation, but feedback can also be used to continually improve the system.

[1551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1552] Step 1:

[1553] The user enters status information

[1554] explanation:

[1555] Users input their mood, physical condition, and preferred flavors for the day via a dedicated device or smartphone application.

[1556] input:

[1557] User state information (e.g., "I'm sleep-deprived today and want to relax").

[1558] Specific behavior:

[1559] The user launches the app and enters status information in text format according to the interface. Once the information is complete, the user presses the "Submit" button.

[1560] Step 2:

[1561] Sending information to the server

[1562] explanation:

[1563] The device sends the state information entered by the user to the server, which converts this information into an appropriate format (e.g., JSON format).

[1564] input:

[1565] User status information.

[1566] Specific behavior:

[1567] The device converts the input information into JSON format, generates an HTTP POST request, and sends it to the server's API endpoint.

[1568] Step 3:

[1569] Parsing state information

[1570] explanation:

[1571] The server analyzes the received state information, specifically tokenizing the user's input and classifying it into the appropriate category.

[1572] input:

[1573] Status information sent from the device in JSON format.

[1574] Data processing:

[1575] Text analysis algorithms are used to tokenize and categorize the data.

[1576] output:

[1577] Parsed category information.

[1578] Specific behavior:

[1579] The server invokes a natural language processing algorithm to analyze the user's input and categorize it into categories such as "I want to relax" or "I'm not getting enough sleep."

[1580] Step 4:

[1581] Generate a recipe

[1582] explanation:

[1583] The server uses the analyzed category information to select the optimal ingredient combination for a mixed drink using an artificial intelligence model.

[1584] input:

[1585] Parsed category information.

[1586] Data processing:

[1587] Use artificial intelligence models (e.g., TensorFlow or PyTorch) to generate appropriate material combinations.

[1588] output:

[1589] The combination of materials produced.

[1590] Specific behavior:

[1591] The server inputs category information into an artificial intelligence model and selects the most suitable ingredients (e.g., banana, cherry, honey, water).

[1592] Step 5:

[1593] Calculate portions

[1594] explanation:

[1595] Calculate the specific amounts of selected ingredients and generate a detailed recipe.

[1596] input:

[1597] The combination of materials produced.

[1598] Data processing:

[1599] It uses an algorithm to calculate the optimal amount of ingredients.

[1600] output:

[1601] Detailed recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1602] Specific behavior:

[1603] The server calculates the appropriate amounts for the selected ingredients and generates a detailed recipe.

[1604] Step 6:

[1605] Provide a recipe

[1606] explanation:

[1607] The generated recipe information is transmitted from the server to the terminal, which then displays it to the user.

[1608] input:

[1609] Detailed recipe.

[1610] output:

[1611] The recipe displayed in the user interface.

[1612] Specific behavior:

[1613] The server converts the generated recipe information into JSON format and sends it to the terminal as an HTTP response. The terminal receives this response, analyzes it, and displays it on the user interface.

[1614] Step 7:

[1615] Collect feedback

[1616] explanation:

[1617] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[1618] input:

[1619] User feedback information (e.g., "I'd like it a little sweeter").

[1620] Specific behavior:

[1621] Users taste the finished juice and enter and submit feedback via a dedicated terminal or smartphone application.

[1622] Step 8:

[1623] Send feedback to the server

[1624] explanation:

[1625] The device sends the feedback information entered by the user to the server, which also converts this information into an appropriate format (e.g., JSON format).

[1626] input:

[1627] User feedback information.

[1628] Specific behavior:

[1629] The device converts the feedback information into JSON format and generates an HTTP POST request to send it to the server's API endpoint.

[1630] Step 9:

[1631] Learning Feedback

[1632] explanation:

[1633] The server stores the received feedback information in a database and uses it to train an artificial intelligence model.

[1634] input:

[1635] Feedback information sent from the device in JSON format.

[1636] Data processing:

[1637] The feedback information is input as training data into the artificial intelligence model, and the model is retrained.

[1638] output:

[1639] Updated artificial intelligence model.

[1640] Specific behavior:

[1641] The server stores the feedback information in a database and inputs it into the AI ​​model for retraining, improving the accuracy of recipe generation in future.

[1642] (Application example 1)

[1643] 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."

[1644] Conventional beverage delivery systems have difficulty providing users with optimal mixed drink recipes tailored to their mood and physical condition on that day. Furthermore, they lacked a mechanism for effectively utilizing user feedback and incorporating it into the next recipe generation, making it difficult to provide personalized service. Furthermore, there was a lack of a way for users to easily order and receive mixed drinks that matched their preferences at physical stores.

[1645] 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.

[1646] In this invention, the server includes means for receiving status information for that day input by the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information, means for providing the generated recipe to the user, means for receiving feedback information from the user, means for learning the feedback information and updating the artificial intelligence means, means for displaying the recipe on a dedicated terminal in the store so that the user can check the recipe, and a dedicated terminal for creating a mixed drink based on the recipe, thereby enabling the user to easily order and receive the optimal mixed drink according to their mood and physical condition on that day.

[1647] "User" refers to a person who uses the service to request a mixed drink recipe that suits their mood or physical condition on that day.

[1648] "Status information" refers to information such as the user's mood and physical condition on that day, and favorite flavors.

[1649] "Artificial intelligence means" refers to an algorithm or program that generates an optimal mixed drink recipe based on status information received from a user.

[1650] "Feedback information" refers to information including user's impressions and suggestions for improvement regarding the mixed drink created based on the provided recipe.

[1651] "Dedicated terminal" refers to a device such as a tablet or smartphone that users use in the store to input and check recipes.

[1652] "Mixed drinks" refers to drinks such as juices and smoothies that are made by combining multiple beverage ingredients.

[1653] "Recipe" means information describing the ingredients and quantities of a mixed drink and how to make it.

[1654] "Learning" refers to the process by which the artificial intelligence means acquires new knowledge based on feedback information from the user and improves the accuracy of recipe generation from the next time onwards.

[1655] The present invention is a system that provides optimal mixed drink recipes based on the user's mood and physical condition. In this system, the user inputs information about their condition for the day via a dedicated terminal or smartphone application, and an artificial intelligence means generates the optimal recipe based on that information. The following describes how this system is specifically implemented.

[1656] 1. Enter and send user status information

[1657] Using a dedicated device or smartphone application, users input status information such as their mood and physical condition for the day, and their preferred flavors. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." The device then sends the input status information to the server in JSON format.

[1658] 2. Creating a recipe

[1659] The server receives the state information sent by the user and compares it with a historical database. This process uses deep learning frameworks such as TensorFlow and PyTorch. Based on the results of the comparison, the AI ​​model generates an optimal mixed drink recipe. The recipe includes specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1660] 3. Providing recipes

[1661] The generated recipe information is sent from the server to a dedicated terminal, where users can check the provided recipes on the dedicated terminal or smartphone screen.

[1662] 4. Gathering Feedback

[1663] The user actually creates a mixed drink and enters their impressions and suggestions for improvement into a feedback input screen via a dedicated terminal or smartphone application (e.g., "I'd like a little more honey"). This information is again sent to the server in JSON format.

[1664] 5. Learning Feedback

[1665] The server stores the feedback information received from users in a database and uses it to train the algorithms of the artificial intelligence means. The hardware required is a server equipped with a GPU suitable for training deep learning models. As a result of the learning, the accuracy of the next recipe generation will improve based on the new feedback information.

[1666] (Example)

[1667] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through the application, the dedicated device will send this information to the server. Based on past data and feedback, the server can suggest a "Banana & Cherry Relaxation Juice" that has a relaxing effect. This recipe specifically calls for one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user creates this juice and provides feedback that "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future uses. This allows the server to always provide the optimal recipe.

[1668] (Example of a prompt)

[1669] "Please suggest a recipe for a mixed drink that would be suitable if I'm stressed out and want to relax today. Favorite flavor: Fruity."

[1670] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1671] Step 1:

[1672] Users input information about their mood, physical condition, favorite flavors, and other information about their condition on that day through a dedicated device or smartphone application. This information is temporarily stored in the device in JSON format.

[1673] Input: "I'm tired today," "I want to relax," "I like fruit-based drinks."

[1674] Output: Status information in JSON format

[1675] Step 2:

[1676] The device sends the status information entered by the user to the server, which receives the data using an HTTP POST request and stores it in an internal database.

[1677] Input: State information in JSON format

[1678] Output: Status information sent to the server

[1679] Step 3:

[1680] The server analyzes the received status information and generates the optimal mixed drink recipe using artificial intelligence means, specifically by comparing it with a historical database and selecting the appropriate ingredients and their quantities. The main tools used in this process are TensorFlow or PyTorch.

[1681] Input: Parsed state information

[1682] Output: Generated recipe ("1 banana, 10 cherries, 1 tablespoon honey, 200ml water")

[1683] Step 4:

[1684] The server sends the generated recipe in JSON format to the terminal, and the terminal displays the recipe in the user interface. The user can check the displayed recipe.

[1685] Input: Generated recipe

[1686] Output: The recipe displayed in the user interface

[1687] Step 5:

[1688] The user creates a mixed drink based on the displayed recipe, and then inputs their impressions and suggestions for improvement via a dedicated terminal or application. This feedback information is also temporarily stored in the terminal in JSON format.

[1689] Input: User feedback

[1690] Output: Feedback information in JSON format

[1691] Step 6:

[1692] The terminal transmits the feedback information collected from the user to the server, which stores the received feedback information in an internal database.

[1693] Input: Feedback information in JSON format

[1694] Output: Feedback information sent to the server

[1695] Step 7:

[1696] The server analyzes the feedback information and uses it to train the AI ​​algorithm. Specifically, it integrates past training data with newly received feedback information to improve the accuracy of the algorithm. This process is also carried out using TensorFlow or PyTorch.

[1697] Input: Feedback information stored in an internal database

[1698] Output: An improved AI model

[1699] Through the specific operations described above, users can easily obtain the optimal mixed drink recipe based on their mood and physical condition on that day, making it possible to improve service in physical stores via dedicated terminals.

[1700] 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.

[1701] The present invention provides a system that generates and provides personalized mixed juice recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates a recipe using artificial intelligence. The generated recipe is provided to the user, and by receiving feedback from the user and continually improving the artificial intelligence, it is possible to always provide the optimal recipe.

[1702] Program processing explanation

[1703] 1. User status input

[1704] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they can input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks."

[1705] 2. Obtaining user emotional information

[1706] The emotion engine means acquires the user's facial expressions and tone of voice using a camera and a microphone, thereby recognizing the user's emotion information (for example, joy, sadness, stress, etc.).

[1707] 3. Transmission of Information

[1708] The terminal generates request data for transmitting the user's state information and emotion information to the server, and transmits it to the server.

[1709] 4. Recipe Generation

[1710] The server uses artificial intelligence to generate an optimal mixed juice recipe based on the received user status and emotional information. First, the server compares the recipe with a past database to select an appropriate combination of fruits and vegetables and calculates the amounts of each combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water) consists of specific ingredients and their amounts.

[1711] 5. Providing recipes

[1712] The generated recipe information is sent from the server to the terminal, which then displays the received recipe information on the user interface so that the user can check it.

[1713] 6. Gathering Feedback

[1714] The user actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they can provide feedback such as "I wish it was a little sweeter."

[1715] 7. Learning Feedback

[1716] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means, which then improves the accuracy of the next recipe generation based on the new feedback.

[1717] Specific examples

[1718] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server can suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: 1 banana, 10 cherries, 1 tablespoon of honey, and 200ml of water.

[1719] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. In this way, through feedback, the AI ​​can always provide the optimal recipe.

[1720] The processing flow will be explained below.

[1721] Step 1: User enters state

[1722] The user launches the smartphone application and opens a screen where they can input their mood and physical condition for the day (for example, "I'm tired" or "I want to relax").

[1723] Step 2: User Enters Preferences

[1724] The user inputs their preferred flavor (e.g., "I like fruit-based") and other additional information, and presses the "Send" button to send the input to the terminal.

[1725] Step 3: The emotion engine retrieves emotions

[1726] Using the device's camera and microphone, the emotion engine acquires emotional information from the user's facial expressions and tone of voice. For example, if the user is smiling, it will be recognized as "joy," and if they are speaking in a straightforward voice, it will be recognized as "stress."

[1727] Step 4: The device sends the information

[1728] The terminal formats the user's status information, preference information, and emotion information and generates request data to be sent to the server.

[1729] The terminal transmits the generated request data to the server.

[1730] Step 5: The server receives the data

[1731] The server analyzes the request data received from the terminal and extracts the user's status information, preference information, and emotion information.

[1732] Step 6: The server generates the recipe

[1733] Based on the extracted information, the server uses artificial intelligence to generate the optimal mixed juice recipe. For example, the server can compare the recipe with a database of past data to select the appropriate combination of fruits and vegetables.

[1734] The server takes into account emotional information and selects materials that have a relaxing effect for a user who is "feeling stressed," for example.

[1735] The server calculates the detailed measurements of the recipe and generates a specific recipe (e.g., 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1736] Step 7: Server Sends Recipe

[1737] The server transmits the generated recipe to the terminal as response data.

[1738] Step 8: Device receives recipe

[1739] The terminal displays the recipe information received from the server on a user interface.

[1740] Step 9: User reviews the recipe

[1741] The user checks the recipe displayed on the device screen.

[1742] Step 10: User creates juice

[1743] The user actually creates a mixed juice according to the provided recipe.

[1744] Step 11: User Enters Feedback

[1745] The user tastes the juice and enters their impressions and requests for improvement (for example, "I would like it to be a little sweeter") into a feedback input screen.

[1746] Step 12: Device sends feedback

[1747] The terminal formats the user's feedback information and generates request data to be sent to the server.

[1748] The terminal transmits the generated feedback request data to the server.

[1749] Step 13: Server receives feedback

[1750] The server processes the feedback information received from the terminals and stores it in a database.

[1751] Step 14: Server learns feedback

[1752] The server trains the algorithm of the artificial intelligence means based on the feedback information received.

[1753] Analyze feedback information, identify necessary improvements, and incorporate them into the algorithm.

[1754] Step 15: Server updates the algorithm

[1755] The server uses the learning results to update the recipe generation algorithm from the next time onwards, improving accuracy.

[1756] ---

[1757] By following these steps, users can create the perfect mixed juice that suits their mood, physical condition, and emotions that day. The system continually learns based on feedback and always suggests the best recipe.

[1758] Example 2

[1759] 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."

[1760] In modern society, people's health and mental satisfaction are important, but conventional mixed drink recipe generation systems do not take into account the user's mood, physical condition, or emotions on that day. As a result, it is difficult to provide optimal recipes that meet individual needs, resulting in low user satisfaction.

[1761] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving status information of the day input by the user, emotion engine means for acquiring emotion information of the user, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning the feedback information and updating the artificial intelligence means. This makes it possible to provide personalized recipes that take into account the user's mood, physical condition, and emotion of the day.

[1762] A "user" refers to a person who uses the system to request a mixed drink recipe that suits their mood, physical condition, and emotions of the day.

[1763] "Status information" refers to data input by the user including the user's mood and physical condition for that day, as well as preferences for flavors.

[1764] "Emotion information" refers to data relating to emotions analyzed from the user's facial expressions and tone of voice obtained by the emotion engine means.

[1765] The term "emotion engine means" refers to means for acquiring user emotion information, including devices and software for analyzing the user's facial expressions and tone of voice.

[1766] "Artificial intelligence means" refers to algorithms and computer programs for generating optimal mixed drink recipes based on state information and emotional information.

[1767] "Feedback information" refers to data entered by a user about their impressions of the mixed drink they have actually created and any improvements they would like to see made.

[1768] "User interface means" refers to an interface for a user to input status information and feedback information.

[1769] "Means" refers to the general term for the various devices and software that make up the system.

[1770] "Database" refers to a storage device or system for storing and managing user state information, emotion information, and feedback information.

[1771] "Server" refers to a computer system that processes information sent by users and generates and serves recipes.

[1772] The present invention is a system that generates and provides personalized mixed drink recipes by incorporating emotional information in addition to the user's mood and physical condition on that day. This system receives user input, recognizes the emotional information using an emotional engine, and generates recipes using artificial intelligence. The generated recipes are provided to the user, and furthermore, feedback from the user is received, and the artificial intelligence is continually improved to always provide optimal recipes.

[1773] First, the user inputs their mood and physical condition for the day, as well as their preferred flavor preferences, via a dedicated terminal or smartphone application. For example, they input information such as "I'm tired today," "I want to relax," or "I like fruit-based drinks." Next, the emotion engine means uses a camera and microphone to capture the user's facial expressions and tone of voice, thereby recognizing the user's emotional information (e.g., joy, sadness, stress, etc.).

[1774] This information is sent from the device to a server, which uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information. The server first compares the information with a past database to select an appropriate combination of fruits and vegetables and calculates the serving sizes for that combination. It then modifies the recipe generation process based on the emotional information to generate a recipe that is even more tailored to the user. The generated recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1775] The generated recipe information is sent from the server to the terminal, and the terminal displays the received recipe information on the user interface so that the user can check it. The user then actually makes the juice and enters their impressions and requests for improvement on the feedback input screen. For example, they may provide feedback such as "I would like it a little sweeter." This feedback information is sent to the server, which stores it in a database and uses the algorithm of the artificial intelligence means to learn, improving the accuracy of recipe generation from the next time onwards.

[1776] Specific examples

[1777] For example, if a user inputs status information such as "I'm sleep-deprived today and want to relax" through an application, and at the same time the emotion engine means obtains emotion information such as "I'm feeling stressed" from the user's facial expression and tone of voice, the device will send this information to the server. Based on past data and feedback information, the server will suggest a "Banana & Cherry Relaxation Juice." This recipe specifically consists of the following ingredients and quantities: one banana, ten cherries, one tablespoon of honey, and 200ml of water. If the user makes this juice and provides feedback such as "I'd like a little more honey," the server will use this feedback to adjust the recipe generation algorithm for future recipes.

[1778] Prompt Sentence Examples

[1779] "How are you feeling today? Do you want to relax? Are you stressed? What's your favorite fruit?"

[1780] In this way, this system can provide optimal mixed drink recipes to individual users based on their state and emotional information. The aim is for the server and device to cooperate and continuously propose improved recipes based on user feedback.

[1781] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1782] Program processing flow

[1783] Step 1: Enter user status

[1784] Users launch a dedicated device or smartphone application and input their mood and physical condition for the day, as well as their preferred flavor preferences.

[1785] Specific actions

[1786] 1. The user opens the application.

[1787] 2. The user interface displays questions such as "How are you feeling today?" and "Do you want to relax?"

[1788] 3. The user enters information using input fields and options.

[1789] 4. The information entered by the user is stored on the device as a data packet in JSON format or similar.

[1790] Input and Output

[1791] Input: Information about the user's mood or physical condition (e.g., "I'm tired," "I want to relax," "I like fruit-based foods").

[1792] Output: Data packets converted into JSON format etc.

[1793] Step 2: Acquiring emotional information

[1794] The camera and microphone, which are emotion engine means, are activated and emotion information is obtained by capturing the user's facial expressions and tone of voice.

[1795] Specific actions

[1796] 1. The device camera captures the user's face.

[1797] 2. The device's microphone records the user's voice.

[1798] 3. The emotion engine analyzes emotional information using facial expression analysis algorithms and voice analysis algorithms.

[1799] Input and Output

[1800] Input: Data on the user's facial expressions and tone of voice.

[1801] Output: Parsed emotion information (e.g., joy, sadness, stress).

[1802] Step 3: Submit your information

[1803] The terminal transmits the state information input by the user and the emotion information acquired by the emotion engine to the server.

[1804] Specific actions

[1805] 1. The device generates a data packet that combines the input state information and the acquired emotion information.

[1806] 2. Generate a send request to the server and send the data to the server.

[1807] Input and Output

[1808] Input: A data packet containing state and emotion information.

[1809] Output: The data sent to the server.

[1810] Step 4: Recipe Generation

[1811] The server uses a generative AI model to generate an optimal mixed drink recipe based on the received user status and emotional information.

[1812] Specific actions

[1813] 1. The server analyzes the received data and retrieves relevant past information from the database.

[1814] 2. A generative AI model uses relevant data to calculate optimal fruit and vegetable combinations.

[1815] 3. Based on emotional information, adjust the selected ingredients and quantities to generate a personalized recipe.

[1816] Input and Output

[1817] Input: User state information, emotion information, past database information.

[1818] Output: The generated recipe (e.g. 1 banana, 10 cherries, 1 tablespoon honey, 200ml water).

[1819] Step 5: Provide the recipe

[1820] The generated recipe information is transmitted from the server to the terminal and displayed on the user interface.

[1821] Specific actions

[1822] 1. The server sends the generated recipe to the device.

[1823] 2. The terminal displays the received recipe information on the user interface and notifies the user.

[1824] Input and Output

[1825] Input: Generated recipe information.

[1826] Output: The recipe displayed in the user interface.

[1827] Step 6: Gather feedback

[1828] The user actually makes the juice and enters their impressions and suggestions for improvement on the feedback input screen.

[1829] Specific actions

[1830] 1. The user creates and samples the juice.

[1831] 2. Open the application's feedback screen and enter your specific thoughts and suggestions for improvement (e.g., "I'd like it to be a little sweeter").

[1832] Input and Output

[1833] Input: User feedback information.

[1834] Output: Feedback data converted into JSON format etc.

[1835] Step 7: Learning feedback

[1836] The server stores the feedback information received from users in a database and uses it to train the algorithm of the generative AI model.

[1837] Specific actions

[1838] 1. The server stores the received feedback information in a database.

[1839] 2. The generative AI model learns from the feedback information and adjusts and improves future recipe generation algorithms.

[1840] Input and Output

[1841] Input: Feedback information from the user.

[1842] Output: Tweaked and improved generative AI model.

[1843] This concludes the specific processing flow of this system. The system provides personalized mixed drink recipes by combining the user's state information and emotional information. The system is designed so that the server and the device can cooperate to provide constantly improving recipes.

[1844] (Application example 2)

[1845] 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."

[1846] Conventional mixed juice recipe generation systems are mostly based on basic information such as the user's mood and physical condition that day, and are unable to take the user's emotions into account. As a result, they are unable to provide optimal recipes based on the user's emotional state, leaving room for improvement in the user experience. Furthermore, the system's ability to receive feedback from users and improve the system's accuracy is limited. To solve these problems, the development of a system that incorporates emotional information was required.

[1847] 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.

[1848] In this invention, the server includes means for receiving status information for that day input by the user, emotion engine means for acquiring emotion information, artificial intelligence means for generating an optimal mixed drink recipe based on the status information and emotion information, means for providing the generated recipe to the user, means for receiving feedback information from the user, and means for learning from the feedback information and updating the artificial intelligence means. This makes it possible to generate and provide personalized recipes that take into account the user's emotion information as well as their mood and physical condition. Furthermore, by incorporating user feedback into the learning process, the accuracy of the system can be improved.

[1849] The "means for receiving user-entered daily status information" is a part of the system that obtains information about the user's mood, physical condition, and preferences for the day through an interface that allows the user to input such information.

[1850] The "emotion engine means" is a part of the system that uses sensors such as a camera and a microphone to analyze the user's facial expressions and tone of voice and recognize the user's emotional information.

[1851] The "artificial intelligence means" is a computer system having an algorithm and learning function for generating an optimal mixed drink recipe based on the user's state information and emotional information.

[1852] The "means for providing a recipe to a user" is an interface for displaying or communicating the recipe of the created mixed drink to the user.

[1853] The "means for receiving feedback information from users" is a part of the system that allows users to input their thoughts and suggestions for improvement on the provided recipes.

[1854] The "means for learning feedback information and updating the artificial intelligence means" is a computer system that improves the artificial intelligence algorithm based on feedback received from the user, thereby improving the accuracy of recipe generation from the next time onwards.

[1855] "User interface means" is a general term referring to an input device and a screen display device that allow the user to input the mood, physical condition, and preferences of the day.

[1856] The "means for generating a plurality of candidate recipes and allowing the user to select" is a part of the system that presents the generated plurality of mixed drink recipes to the user and allows the user to select the one they like best.

[1857] This invention is a system for generating and providing personalized mixed juice recipes based on the user's mood, physical condition, and emotional information for that day. This system receives user input information through a user interface such as a dedicated terminal or smartphone application, recognizes the emotional information using an emotional engine, and generates and provides the optimal recipe to the user using artificial intelligence. Furthermore, by collecting feedback from users and continually improving the algorithm of the artificial intelligence, it is possible to consistently provide optimal recipes.

[1858] Hardware and software used

[1859] 1. Dedicated device or smartphone:

[1860] It serves as an interface for the user to input status information for the day.

[1861] Possible applications include iOS and Android smartphone applications.

[1862] 2. Camera and Microphone:

[1863] Used as a sensor to obtain emotional information.

[1864] For example, the camera and microphone built into a smartphone can be used.

[1865] 3. Emotion engine means:

[1866] Software for facial expression recognition and voice analysis.

[1867] Possible examples include OpenCV (a library for camera image processing) and Python-based libraries.

[1868] 4. Artificial Intelligence Means:

[1869] An algorithm for generating recipes based on the user's state and emotional information.

[1870] Possible machine learning models include those using Scikit-learn and TensorFlow.

[1871] 5. Server:

[1872] A central system that receives information, processes it, generates recipes, and learns from feedback.

[1873] You can use cloud platforms (AWS, Google Cloud Platform, etc.).

[1874] Program processing explanation

[1875] 1. Input acceptance:

[1876] Users input their mood, physical condition, and preferred taste preferences for the day through a smartphone application.

[1877] For example, enter information such as "I want to relax today" or "I like fruit-based flavors."

[1878] 2. Acquiring emotional information:

[1879] The emotion engine means uses the smartphone's camera and microphone to acquire the user's facial expressions and tone of voice, and recognizes emotion information (for example, stress, joy, etc.).

[1880] 3. Transmission of Information:

[1881] The user's state information and emotion information are transmitted from the terminal to the server.

[1882] 4. Recipe generation:

[1883] Based on the information received, the server uses artificial intelligence to generate the optimal mixed juice recipe.

[1884] For example, it compares it with a past database to select a combination of fruits and vegetables and calculate their portions.

[1885] 5. Recipe provided by:

[1886] The generated recipe information is sent from the server to the user interface and displayed for the user to review.

[1887] 6. Feedback Collection:

[1888] Users create juice and provide feedback on the results and areas for improvement.

[1889] For example, provide feedback such as "I'd like it a little sweeter."

[1890] 7. Feedback Learning:

[1891] The server stores the feedback information received from the user in a database and uses it to train the algorithm of the artificial intelligence means.

[1892] Specific examples

[1893] For example, if a user inputs status information such as "I want to relax today," and the emotion engine means obtains emotion information such as "I'm feeling stressed," the device will send this information to the server. Based on past data and feedback information, the server can suggest a "banana and cherry relaxation juice." This recipe specifically consists of ingredients and quantities such as one banana, ten cherries, one tablespoon of honey, and 200 ml of water.

[1894] If a user makes this juice and provides feedback such as "I'd like a little more honey," the server uses this feedback to adjust the recipe generation algorithm for future recipes. Through this feedback, the AI ​​can always provide the best recipes.

[1895] Prompt Sentence Examples

[1896] Here is an example of a prompt that the user might enter:

[1897] "Please tell us how you feel today:

[1898] Mood: Relaxing

[1899] Physical condition: Lack of sleep

[1900] Preferences: Fruit-based

[1901] Emotional information (e.g., feeling stressed) was obtained.

[1902] Use the information above to generate the perfect mixed juice recipe.

[1903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1904] Step 1:

[1905] Users input their mood and physical condition for the day, as well as their preferred flavor preferences, through a dedicated device or smartphone application. For example, they might input information such as "I want to relax today" or "I like fruit-based drinks." This information is entered into the device as text data.

[1906] Step 2:

[1907] The device receives the input text data and uses a camera and microphone to capture the user's facial expressions and tone of voice. Using facial expression recognition software (e.g., OpenCV) and voice analysis software, emotional information is analyzed in real time. This emotional information is expressed as categories such as "stress" or "joy."

[1908] Step 3:

[1909] The device sends the user's status information and emotional information to the server using an HTTP request. The input data (mood, physical condition, preferred tastes) and emotional information are sent to the server as JSON format data.

[1910] Step 4:

[1911] The server uses a generative AI model to generate an optimal mixed juice recipe based on the received state and emotion information. First, it compares the results with a database of past data to select an appropriate combination of fruits and vegetables. Next, it uses a generative AI model (e.g., Scikit-learn or TensorFlow) to calculate the quantities of each combination. The output recipe consists of specific ingredients and their quantities (e.g., 1 banana, 10 cherries, 1 tablespoon of honey, 200ml of water).

[1912] Step 5:

[1913] The server sends the generated recipe information to the terminal, which receives the recipe information and displays it on the user interface, allowing the user to check the proposed mixed juice recipe.

[1914] Step 6:

[1915] The user makes juice based on the proposed recipe and enters the results and suggestions for improvement on the feedback input screen. For example, they can provide feedback such as "I'd like it a little sweeter." This feedback information is entered into the terminal as text data.

[1916] Step 7:

[1917] The device sends the user's feedback information to the server as JSON format data.

[1918] Step 8:

[1919] The server stores the received feedback information in a database and uses it to improve the algorithm of the generative AI model. Specifically, the feedback information is used as additional training data to retrain the model, thereby improving the accuracy of recipe generation from the next time onwards.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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).

[1927] 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.

[1928] 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."

[1929] 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.

[1930] 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).

[1931] 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.

[1932] 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.

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] The following is further disclosed regarding the above embodiment.

[1942] (Claim 1)

[1943] means for receiving user-entered status information for the day;

[1944] an artificial intelligence means for generating an optimal mixed drink recipe based on the state information;

[1945] means for providing the generated recipe to a user;

[1946] means for receiving feedback information from a user;

[1947] means for learning said feedback information to update said artificial intelligence means.

[1948] (Claim 2)

[1949] The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and allowing a user to select from the candidate recipes.

[1950] (Claim 3)

[1951] 2. The system according to claim 1, further comprising a user interface means for allowing the user to input his / her mood and physical condition for the day.

[1952] "Example 1"

[1953] (Claim 1)

[1954] A means for users to input daily status information via a dedicated terminal or smartphone application;

[1955] means for transmitting the input status information to a server;

[1956] an artificial intelligence model that generates an optimal mixed drink recipe based on the state information; and means for analyzing the user's state information using the artificial intelligence model and selecting an appropriate combination of ingredients.

[1957] A means for calculating the amounts of selected ingredients and generating a detailed recipe;

[1958] means for providing the generated recipe to a user;

[1959] a means for inputting feedback information from users into a dedicated terminal and transmitting the information to a server;

[1960] A system including means for learning said feedback information and updating said artificial intelligence model.

[1961] (Claim 2)

[1962] The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and allowing a user to select from the candidate recipes.

[1963] (Claim 3)

[1964] 2. The system according to claim 1, further comprising a user interface means for allowing the user to input his / her mood and physical condition for the day.

[1965] "Application Example 1"

[1966] (Claim 1)

[1967] means for receiving user-entered status information for the day;

[1968] an artificial intelligence means for generating an optimal mixed drink recipe based on the state information;

[1969] means for providing the generated recipe to a user;

[1970] means for receiving feedback information from a user;

[1971] means for learning said feedback information to update said artificial intelligence means;

[1972] A means for displaying recipes on a dedicated terminal in the store so that users can check the recipes;

[1973] A system including a dedicated terminal for preparing mixed beverages based on said recipes.

[1974] (Claim 2)

[1975] The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and allowing a user to select from the candidate recipes.

[1976] (Claim 3)

[1977] 2. The system according to claim 1, further comprising a user interface means for allowing the user to input his / her mood and physical condition for the day.

[1978] "Example 2: Combining Emotion Engines"

[1979] (Claim 1)

[1980] means for receiving user-entered status information for the day;

[1981] emotion engine means for acquiring emotion information of the user based on the state information;

[1982] an artificial intelligence means for generating an optimal mixed drink recipe based on the state information and emotion information;

[1983] means for providing the generated recipe to a user;

[1984] means for receiving feedback information from a user;

[1985] means for learning said feedback information to update said artificial intelligence means.

[1986] (Claim 2)

[1987] The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and emotion information and allowing the user to select from the candidate recipes.

[1988] (Claim 3)

[1989] 2. The system according to claim 1, further comprising a user interface means for allowing the user to input his / her mood and physical condition for the day.

[1990] "Application example 2 when combining emotion engines"

[1991] (Claim 1)

[1992] means for receiving user-entered status information for the day;

[1993] emotion engine means for acquiring emotion information;

[1994] an artificial intelligence means for generating an optimal mixed drink recipe based on the state information and emotion information;

[1995] means for providing the generated recipe to a user;

[1996] means for receiving feedback information from a user;

[1997] means for learning said feedback information to update said artificial intelligence means.

[1998] (Claim 2)

[1999] The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and emotion information and allowing the user to select from the candidate recipes.

[2000] (Claim 3)

[2001] 10. The system of claim 1, further comprising a user interface means for allowing the user to input their mood, physical condition, and preferences for the day. [Explanation of symbols]

[2002] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user-entered status information for the day; an artificial intelligence means for generating an optimal mixed drink recipe based on the state information; means for providing the generated recipe to a user; means for receiving feedback information from a user; means for learning said feedback information to update said artificial intelligence means.

2. The system of claim 1 , further comprising means for generating a plurality of candidate recipes based on the state information and allowing a user to select from the candidate recipes.

3. 2. The system according to claim 1, further comprising a user interface means for allowing the user to input his / her mood and physical condition for the day.

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