system

Abstract

We provide the system. [Solution] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients, A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes, An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients, A three-dimensional food printer means that cooks and serves ingredients based on generated instructions, A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] For people with reduced chewing ability or swallowing disorders, it is important to make meals enjoyable. However, it takes time and effort to adjust the texture of ingredients according to individual users, and there is a current situation where it is difficult to provide while maintaining visual beauty, aroma, and nutritional balance. To solve such problems, there is a demand for a system that adjusts the texture of ingredients based on user preferences and constraints and provides a delicious and visually satisfying meal.

Means for Solving the Problems

[0005] This invention provides the following means: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of selected ingredients, and a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; and a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time. This enables the efficient provision of meals with textures tailored to the user, thereby enhancing the enjoyment and satisfaction of the meal.

[0006] A "conversational artificial intelligence system" is a system that has the function of listening to the user's preferences and constraints through dialogue and selecting appropriate ingredients and recipes based on that information.

[0007] "Data analysis means" refers to a method of analyzing collected information such as user preferences and constraints, and processing it to select the most suitable ingredients and recipes for the user.

[0008] The "instruction generation means" is a means for generating specific processing instructions for texture adjustment based on the ingredients and recipes selected by the data analysis means.

[0009] A "three-dimensional food printer" is a device that has the function of printing food ingredients based on generated processing instructions, and then cooking and serving them.

[0010] A "feedback collection method" is a means of collecting feedback from users after a meal regarding taste, appearance, aroma, etc.

[0011] A "menu generation means" is a means of generating a customized menu based on analyzed user information.

[0012] The "processing method determination means" is a means for determining the specific processing method necessary for adjusting the texture of the selected ingredients. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0035] System programming and processing

[0036] 1. User information collection

[0037] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include what the user wants to eat today, their preferred ingredients and allergy information, and the degree of their chewing and swallowing difficulties.

[0038] The user responds to this question and enters their preferences and constraints.

[0039] 2. Data Analysis and Menu Proposal

[0040] The server analyzes the collected user information and selects ingredients and recipes that match the user's preferences. Furthermore, it determines whether or not the texture of each ingredient needs to be adjusted.

[0041] The server generates a customized menu based on this information and sends the suggestions to the user's terminal.

[0042] The user reviews the proposed menu and either approves it or requests changes.

[0043] 3. Texture Customization

[0044] The server determines the specific processing methods (such as mixing, steaming, or gelling) to adjust the texture of the ingredients included in the menu.

[0045] The server generates detailed instructions based on the processing method and sends them to the three-dimensional food printer.

[0046] 4. Cooking and serving using a 3D food printer.

[0047] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking.

[0048] Users receive custom-textured food delivered from a 3D food printer.

[0049] 5. Gathering feedback and making improvements

[0050] The server asks users for feedback after their meal. This feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0051] The user enters feedback, and that information is sent to the server.

[0052] The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments.

[0053] Specific example

[0054] For example, if a user indicates a preference for soft foods due to reduced chewing ability, the system will select fish as the main ingredient and consider how to prepare it to be tender. The server sends detailed instructions to a 3D food printer, which will then perform mixing and gelling to tenderize the fish. Once the dish is complete, it is served to the user. Based on the user's feedback, the system then further improves its suggestions for the next time.

[0055] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or swallowing difficulties.

[0056] The following describes the processing flow.

[0057] Step 1:

[0058] User authentication and login

[0059] The server displays an authentication screen on the terminal for the user to log in.

[0060] The user enters their authentication information (user ID, password, etc.).

[0061] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0062] Step 2:

[0063] Interviewing users about their preferences and constraints

[0064] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0065] What I want to eat today

[0066] Favorite foods and foods you dislike

[0067] Allergy Information

[0068] Degree of chewing and swallowing difficulties

[0069] The user answers these questions and enters the relevant information.

[0070] Step 3:

[0071] Data analysis and menu generation

[0072] The server analyzes the collected user responses using data analysis tools.

[0073] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it can generate menus such as sea bream soufflé and vegetable puree.

[0074] The server sends the generated menu to the terminal along with a message suggesting it.

[0075] Step 4:

[0076] Menu confirmation and approval

[0077] The user reviews the suggested menu.

[0078] The user can either accept the proposed menu or request a change.

[0079] The server will suggest new menus as needed, based on the user's selection.

[0080] Step 5:

[0081] Texture adjustment decision

[0082] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0083] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[0084] Step 6:

[0085] Cooking using a 3D food printer

[0086] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0087] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0088] Step 7:

[0089] Food service

[0090] The terminal, a 3D food printer, dispenses the finished dish.

[0091] The user receives a custom-textured hood provided by the printer.

[0092] Step 8:

[0093] Gathering feedback

[0094] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0095] The user enters their response into the feedback form and submits it.

[0096] Step 9:

[0097] Feedback analysis and improvement

[0098] The server analyzes the feedback received from the user and records it in a database.

[0099] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[0100] (Example 1)

[0101] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Traditional systems struggled to accommodate diverse user preferences and constraints, providing quickly and appropriately customized meals. Furthermore, the lack of means to adjust the texture of ingredients based on user chewing and swallowing abilities made it difficult to deliver a high-quality dining experience. Additionally, the absence of a system for effectively collecting post-meal feedback and incorporating it into future recommendations hindered improvements in service quality.

[0103] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0104] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a means for receiving and analyzing prompt sentences generated using the interactive artificial intelligence means. This enables the provision of meals optimized to the individual needs of the user and continuous improvement of service quality.

[0105] "Interactive artificial intelligence means" refers to technology that collects information through dialogue with the user and generates appropriate questions and answers based on that information.

[0106] "Data analysis means" refers to technologies that analyze collected user information and select the optimal ingredients and recipes based on the user's needs and constraints.

[0107] "Instruction generation means" refers to a technology for determining specific processing methods to adjust the texture of selected ingredients and generating those instructions.

[0108] A "three-dimensional food printer" is a technology for creating and serving food by arranging and processing ingredients in three dimensions based on generated instructions.

[0109] A "feedback collection method" is a technology that collects opinions and feedback from users after a meal, analyzes that data, and uses it to improve future suggestions and adjustment methods.

[0110] A "prompt message" refers to the format of questions and instructions generated by an interactive artificial intelligence based on input text or audio information.

[0111] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0112] The server uses conversational artificial intelligence (AI) to send questions to the user's terminal. These questions include what the user wants to eat, preferred ingredients, allergy information, and the degree of chewing and swallowing difficulties. This allows the server to collect information about the user's preferences and constraints. For example, the user might respond, "Today I want to eat a soft fish dish." Conversational AI such as IBM Watson® or Google® Dialogflow are used for this information collection.

[0113] Next, the server analyzes the collected user information using data analysis tools. Python libraries such as Pandas and Numpy are used for this analysis. Based on the collected information, ingredients and recipes tailored to the user's preferences are selected. For example, if the user prefers soft ingredients, a soft fish dish will be selected.

[0114] Next, the server uses an instruction generation mechanism to determine specific processing methods (such as mixing, steaming, and gelling) to adjust the texture of the selected ingredients. A recipe generation algorithm is used in this process. The generated instructions are then sent to a three-dimensional food printer.

[0115] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking. For example, an XYZ Food Printer is used. The printer performs mixing and gelling to create a fish dish with a soft texture. The user receives the cooked dish from this printer.

[0116] Finally, the server uses a feedback collection mechanism to gather post-meal feedback from users. This feedback includes the taste, appearance, aroma, and overall satisfaction of the meal. Users input this feedback via a terminal, and it is sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Python Numpy library is used for this analysis.

[0117] Examples of prompt statements as concrete examples:

[0118] User preferences and constraints: Likes soft foods, wants to eat fish, no allergies.

[0119] Suggested menu: Soft gelled fish dish

[0120] Processing method: Mixing, gelation

[0121] Feedback: It tastes good and looks beautiful, but it would be even better if the aroma were a little stronger.

[0122] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or dysphagia. Furthermore, by utilizing user feedback, the quality of the service can be continuously improved.

[0123] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0124] Step 1:

[0125] User information collection

[0126] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include "What do you want to eat today?", "What are your favorite foods and allergy information?", and "What is the degree of your chewing ability and swallowing difficulties?". The user answers these questions using their terminal and inputs their preferences and constraints. The collected information includes specific data such as "I like soft foods, I want to eat fish, I have no allergies".

[0127] Input: User preferences and constraints (text format)

[0128] Output: Database of user preferences and constraints

[0129] Step 2:

[0130] Data analysis and menu proposals

[0131] The server analyzes the collected user information using data analysis tools. Using Python libraries such as Pandas and Numpy, it analyzes the user's input data and selects appropriate ingredients and recipes. For example, if the user prefers soft ingredients, a soft fish dish will be selected. The server then generates a customized menu and sends the suggestion to the user's terminal. The user reviews the suggested menu and requests approval or modification.

[0132] Input: Database of user preferences and constraints

[0133] Data processing: Analyze user input data.

[0134] Output: Suggested menu (text format)

[0135] Step 3:

[0136] Texture customization

[0137] The server determines whether texture adjustment is necessary for the selected ingredients. A recipe generation algorithm is used to determine specific processing methods (mixing, steaming, gelling, etc.). For example, if a soft fish dish is selected, mixing and gelling will be used. The server generates detailed processing instructions and sends them to the 3D food printer.

[0138] Input: Suggested menu

[0139] Data processing: Determination of processing method using a recipe generation algorithm.

[0140] Output: Processing instructions (text format)

[0141] Step 4:

[0142] Cooking and serving using a 3D food printer.

[0143] A 3D food printer selects ingredients according to instructions received from a server, and prints them while performing processing such as mixing and gelling. For example, an XYZ Food Printer is used to create a soft, gelled fish dish. The user receives the cooked dish from the printer.

[0144] Input: Processing Instructions

[0145] Output: Cooked food

[0146] Step 5:

[0147] Feedback gathering and improvement

[0148] The server requests feedback from users after their meal. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction of the meal. Users enter their feedback, which is then sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Numpy library in Python is used to analyze the feedback data.

[0149] Input: User feedback (text format)

[0150] Data processing: Analysis of feedback data

[0151] Output: Improved suggestions and adjustment methods

[0152] Through these steps, the system can provide meals optimized to the individual needs of users and continuously improve the quality of service.

[0153] (Application Example 1)

[0154] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0155] In recent years, the aging population and increasing health consciousness have led to a growing demand for meals tailored to the individual preferences and health conditions of users. Furthermore, with the proliferation of food delivery services, there is a growing need for more customized menus. However, current systems generally only offer standardized menus, making it difficult to meet individual user needs. Therefore, a system is needed that provides meals based on ingredients with textures adjusted according to each user's preferences and constraints, while maintaining visual appeal, aroma, and nutritional balance, and further collecting feedback to improve future offerings.

[0156] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0157] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing the collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a suggestion means for creating a custom menu and providing it to the food delivery service. This makes it possible to provide an optimal meal based on the individual user's preferences and constraints, and to further improve suggestions for the next time by utilizing the feedback.

[0158] "Gathering user preferences and constraints" means using conversational artificial intelligence to collect information about users' food preferences, allergies, health status, and other related information.

[0159] "Adjusting the texture of ingredients" means optimally changing the firmness and mouthfeel of ingredients according to the individual user's requests.

[0160] "Interactive artificial intelligence means" refers to a system that collects information and answers questions through dialogue with the user using natural language.

[0161] "Analyzing collected user information" means performing data analysis based on collected user preferences, constraints, and health information to select appropriate ingredients and recipes.

[0162] A "data analysis method" is a system that analyzes collected data to select the most suitable ingredients and recipes.

[0163] "Generating processing instructions for texture adjustment of selected ingredients" means creating detailed instructions on how to process the ingredients based on the results of data analysis.

[0164] "Instruction generation means" refers to a system means that automatically generates instructions regarding the processing method of food ingredients.

[0165] A "three-dimensional food printer" is a device that prints food ingredients in three dimensions based on digital instructions and then cooks them.

[0166] A "feedback collection method" is a system for collecting information such as user feedback and suggestions for improvement after a meal.

[0167] "A means of proposing the creation of a custom menu and providing it to a food delivery service" refers to a system that creates a customized menu according to the user's request and proposes providing that menu to a food delivery service.

[0168] The system for implementing the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides customized meals that are visually appealing and maintain aroma and nutritional balance. The system includes interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and proposal means for creating custom menus and providing them to food delivery services.

[0169] The server uses conversational artificial intelligence to collect information from the user regarding their food preferences, allergies, chewing ability, and other related information. This collected information is analyzed by a data analysis system to select ingredients and recipes that match the user's requirements. It also determines whether the texture of the ingredients needs to be adjusted. Based on the selected ingredients and recipes, an instruction generation system automatically generates specific processing instructions, such as mixing, steaming, and gelling.

[0170] The 3D food printer prints ingredients based on generated instructions and cooks them appropriately. The cooked custom-textured food is validated by a feedback collection mechanism before being served to the user. After the meal, the user provides feedback on taste, appearance, aroma, etc., and this information is sent to the server to improve future suggestions and texture adjustments.

[0171] To implement this system, the following hardware and software will be used: a server, an interactive artificial intelligence engine (e.g., OpenAI® API), data analysis tools, a 3D food printer (e.g., XYZprinting's food printer), and an application for collecting user feedback.

[0172] As a concrete example, if a user enters "I want to eat a soft fish dish today" into the application and indicates "shellfish allergy" as allergy information, the server will suggest a soft white fish fillet. It will also create a customized menu that includes steamed vegetables and low-sugar jelly. Once this menu is approved, the instructions are sent to a 3D food printer, and the cooked customized menu is served.

[0173] Examples of prompts for generative AI models:

[0174] User preferences: "I like soft foods and seafood."

[0175] Allergies: "Shellfish allergy"

[0176] Chewing Ability: "Decreased"

[0177] Create a customized menu that adheres to these preferences and restrictions.

[0178] As a result, we can provide optimal meals based on each user's individual preferences and constraints, and further improve meal suggestions for future visits by utilizing their feedback.

[0179] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0180] Step 1:

[0181] The server sends questions to the user through an interactive artificial intelligence system. Specifically, the server displays questions on the user's terminal such as, "What kind of food would you like to eat today?", "Do you have any preferred ingredients or allergy information?", and "What is the degree of your chewing ability or swallowing difficulties?". The input data consists of the user's preferences and constraints, and data analysis is performed based on this data.

[0182] Step 2:

[0183] Users operate a terminal to answer questions and input their preferences and constraints. For example, they might submit information such as "likes soft foods and seafood," "shellfish allergy," or "has difficulty chewing." The input data is sent to a server and becomes the basis for the next analysis step.

[0184] Step 3:

[0185] The data analysis tool analyzes the collected user information. The server selects ingredients and recipes suitable for the user based on the input preferences and constraints. This process uses a generative AI model to generate prompts and select ingredients and recipes. For example, if the input is "User Preferences: Soft dishes, likes seafood, Allergies: Shellfish allergy, Chewing Ability: Reduced, Create a customized menu that adheres to these preferences and restrictions," the analysis results will generate a custom menu such as "Soft white fish fillet," "Steamed vegetables," and "Low-sugar jelly."

[0186] Step 4:

[0187] The server generates processing instructions based on the selected ingredients and recipe. The instruction generation mechanism determines specific processing methods to adjust the texture of the ingredients. For example, it generates detailed processing instructions such as "mix and gel to soften the white fish" or "steam the vegetables." These instructions become the input data for the 3D food printer.

[0188] Step 5:

[0189] The 3D food printer system prints and cooks ingredients based on generated instructions. The 3D food printer terminal receives instructions from the server and processes the ingredients according to those instructions. The ingredients are mixed, gelled, printed, and cooked to produce a custom-textured food. The output data is the cooked custom menu.

[0190] Step 6:

[0191] Users receive custom-textured food delivered from a 3D food printer. After eating, users operate a terminal to send feedback. The feedback collection mechanism displays questions to the user such as, "How was the taste, appearance, aroma, and overall satisfaction of the meal?" and collects feedback. The input data is user feedback information, which serves as the basis for the next improvement steps.

[0192] Step 7:

[0193] The server analyzes the collected feedback and updates the database. Analyzing the feedback information allows for further improvements to future menu suggestions and texture adjustments. Ultimately, the data analysis is based on the feedback, resulting in improved quality for future custom menus. The output data consists of new database entries resulting from the analysis.

[0194] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0195] This invention is a system that improves the dining experience by listening to the user's preferences and constraints, adjusting the texture of ingredients based on them, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine.

[0196] System programming and processing

[0197] 1. User authentication and login

[0198] The server displays an authentication screen on the terminal for the user to log in.

[0199] The user enters their authentication information (user ID, password, etc.).

[0200] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0201] 2. Gathering information on user preferences and constraints.

[0202] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0203] What I want to eat today

[0204] Favorite foods and foods you dislike

[0205] Allergy Information

[0206] Degree of chewing and swallowing difficulties

[0207] The user answers these questions and enters the relevant information.

[0208] 3. Recognition of user emotions

[0209] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions.

[0210] The server collects emotional data to understand the user's mood on any given day.

[0211] 4. Data analysis and menu generation

[0212] The server analyzes the collected user responses and sentiment data using data analysis tools.

[0213] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[0214] The server sends the generated menu to the terminal along with a message suggesting it.

[0215] 5. Menu confirmation and approval

[0216] The user reviews the suggested menu.

[0217] The user can either accept the proposed menu or request a change.

[0218] The server will suggest new menus as needed, based on the user's selection.

[0219] 6. Decision on texture adjustments

[0220] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0221] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[0222] 7. Cooking using a 3D food printer

[0223] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0224] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0225] 8. Serving the food

[0226] The terminal, a 3D food printer, dispenses the finished dish.

[0227] The user receives a custom-textured hood provided by the printer.

[0228] 9. Gathering Feedback

[0229] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0230] The user enters their response into the feedback form and submits it.

[0231] 10. Analysis and Improvement of Feedback

[0232] The server analyzes the feedback received from the user and records it in a database.

[0233] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[0234] Specific example

[0235] For example, if a user indicates that they are feeling a little down today, the system will take that emotional data into consideration and suggest ingredients and menu items that will help calm them down. The server uses an emotion engine to analyze the user's mood and, for example, suggest a warm vegetable soup and a mild-flavored dessert. Based on this information, the server can issue specific cooking instructions to a 3D food printer, providing a meal that takes the user's emotions into consideration.

[0236] Through the above process, the system of the present invention can provide people with reduced chewing ability or swallowing difficulties with delicious, visually satisfying meals that take their emotions into consideration.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] User authentication and login

[0240] The server displays an authentication screen on the terminal for the user to log in.

[0241] The user enters their authentication information (user ID, password, etc.).

[0242] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0243] Step 2:

[0244] Interviewing users about their preferences and constraints

[0245] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0246] What I want to eat today

[0247] Favorite foods and foods you dislike

[0248] Allergy Information

[0249] Degree of chewing and swallowing difficulties

[0250] The user answers these questions and enters the relevant information.

[0251] Step 3:

[0252] Recognition of user emotions

[0253] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. For example, it analyzes the user's current mood by detecting changes in voice tone, word choice, and facial expressions.

[0254] The server collects emotional data to understand the user's mood on any given day.

[0255] Step 4:

[0256] Data analysis and menu generation

[0257] The server analyzes the collected user responses and emotional data using data analysis tools. For example, based on emotional data indicating that a user is "a little depressed," it selects menu items that have a relaxing effect.

[0258] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[0259] The server sends the generated menu to the terminal along with a message suggesting it.

[0260] Step 5:

[0261] Menu confirmation and approval

[0262] The user reviews the suggested menu. For example, they might see "Sea bream soufflé with vegetable puree" as a menu suggested by the server on their device.

[0263] The user can either accept the suggested menu or request a change. For example, the user might request "soup instead of vegetable puree."

[0264] The server will suggest new menu items as needed, based on the user's selection. For example, it might suggest soup instead of vegetable puree.

[0265] Step 6:

[0266] Texture adjustment decision

[0267] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0268] The server translates these processing methods into specific instructions and sends them to the three-dimensional food printer. For example, it generates instructions to "use a gelling agent and mix" for a sea bream soufflé.

[0269] Step 7:

[0270] Cooking using a 3D food printer

[0271] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0272] The terminal performs a specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture. For example, it can make a sea bream soufflé soft.

[0273] Step 8:

[0274] Food service

[0275] The three-dimensional food printer, which is the terminal, provides a finished dish.

[0276] The user receives the custom texture food provided by the printer. For example, receives a served sea bream soufflé and vegetable soup.

[0277] Step 9:

[0278] Collection of feedback

[0279] The server sends a feedback form to the user's terminal after the meal. The feedback includes the taste, appearance, aroma, satisfaction level, etc. of the meal.

[0280] The user inputs and sends an answer to the feedback form. For example, inputs "The taste was good, but I want a stronger aroma".

[0281] Step 10:

[0282] Analysis and improvement of feedback

[0283] The server analyzes the feedback received from the user and records it in the database. For example, records the feedback regarding the strength of the aroma.

[0284] Based on the analysis results of the feedback, the server updates the algorithm for improving the next proposal and adjustment method. For example, reflects the cooking method for enhancing the aroma in the next selection.

[0285] (Example 2)

[0286] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0287] Conventional meal suggestion systems offer menus that take into account user preferences and limitations, but they fail to provide appropriate meal suggestions based on the user's emotional state. Therefore, providing meals that are sensitive to the emotional needs of users, particularly those with reduced chewing ability or swallowing difficulties, presents a challenge. Furthermore, system improvements utilizing feedback have been insufficient.

[0288] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0289] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and emotional data and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and an emotion engine for recognizing emotions from the user's voice and facial expressions. This enables appropriate menu suggestions according to the user's emotional state and texture adjustments that take those emotions into consideration.

[0290] "Interactive artificial intelligence means" refers to a means of providing an interface that uses artificial intelligence to gather user preferences and constraints.

[0291] "Data analysis means" refers to methods for analyzing collected user information and sentiment data to select appropriate ingredients and recipes.

[0292] The "instruction generation means" is a means for generating specific processing instructions for adjusting the texture of food ingredients based on the analysis results.

[0293] A "three-dimensional food printer means" is a means for printing food ingredients based on generated instructions, and then cooking and serving them.

[0294] "Feedback collection methods" refer to methods for collecting feedback from users after a meal and using that feedback to improve future suggestions and adjustments.

[0295] An "emotion engine" is a means of recognizing emotions from a user's voice and facial expressions and providing that information to data analysis tools.

[0296] This invention is a system that improves the dining experience by thoroughly interviewing users about their preferences and constraints, adjusting the texture of ingredients based on that information, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine. The operation of each means and the hardware and software used will be described in detail below.

[0297] User authentication and login

[0298] The server displays an authentication screen on the terminal for the user to log in. This generates an HTML form containing input fields for user ID and password. Once the user submits the information, the server queries the database (e.g., MySQL®) to verify that the user exists. If authentication is successful, the server reads the user profile and proceeds to the next process.

[0299] Interviewing users about their preferences and constraints

[0300] The server activates an interactive artificial intelligence (e.g., IBM Watson) to generate questions about what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of chewing and swallowing difficulties. The generated series of questions are sent to the user's terminal, and the user answers them.

[0301] Recognition of user emotions

[0302] The server utilizes an emotion engine (e.g., Microsoft (R) Azure (R) Cognitive Services) to collect the user's voice and facial expression data and recognize emotions. The collected emotion data is saved for analysis.

[0303] Data Analysis and Menu Generation

[0304] The server analyzes the collected user responses and emotion data using data analysis means (data analysis algorithms implemented in Python). Based on the analysis results, appropriate ingredients and recipes (e.g., sea bream soufflé and vegetable purée) are selected. The generated menu is proposed to the user terminal.

[0305] Menu Confirmation and Approval

[0306] The user checks the proposed menu. Input is made in response to requests for approval or modification. The server receives this and either proposes a new menu or proceeds to the next process.

[0307] Determination of Texture Adjustment

[0308] The server determines specific processing methods (e.g., mixing, steaming, gelling) based on the approved menu. These processing procedures are sent as specific instructions to the three-dimensional food printer means.

[0309] Cooking by a Three-Dimensional Food Printer

[0310] The three-dimensional food printer, which is the terminal, prints the ingredients and cooks according to the instructions received from the server. It executes the specified process (e.g., mixing and gelling of fish) to complete a dish with the optimal texture.

[0311] Provision of the Dish

[0312] The terminal, a 3D food printer, delivers the finished dish to the user. The user receives the custom-textured food provided by the printer.

[0313] Gathering feedback

[0314] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal, where the user answers questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[0315] Feedback analysis and improvement

[0316] The server analyzes the feedback received from users and records it in a database. Furthermore, it updates its algorithms based on the feedback to improve future suggestions and adjustment methods.

[0317] Specific example

[0318] For example, if a user indicates that they are feeling a little down today, the server uses an emotion engine to analyze this information. Based on the analysis results, it suggests a warm vegetable soup and a mild-flavored dessert. The server then uses this information to send specific cooking instructions (e.g., steaming and smoothing vegetables) to a 3D food printer. As a result, it can provide users with a meal that takes their emotions into consideration.

[0319] Example of a prompt

[0320] "A user is feeling a bit down today. Please suggest a menu that will help them calm down. What ingredients and cooking methods would be appropriate to include in that menu?"

[0321] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0322] Step 1:

[0323] User authentication and login

[0324] The server displays an authentication screen on the terminal for the user to log in. The screen includes fields for entering the user ID and password.

[0325] The user enters their user ID and password in the input fields and submits the form.

[0326] The server compares the received user ID and password with a database (e.g., MySQL) and retrieves the user profile if authentication is successful. If authentication is successful, the user profile is output.

[0327] Input: User ID, Password

[0328] Output: User Profile

[0329] Step 2:

[0330] Interviewing users about their preferences and constraints

[0331] The server activates an interactive artificial intelligence tool (e.g., IBM Watson) to generate the following questions: what you want to eat today, your favorite and disliked ingredients, allergy information, and the degree of your chewing and swallowing difficulties.

[0332] The server sends the generated question to the user's terminal.

[0333] The user answers the questions displayed on the device and enters the required information.

[0334] The server receives and stores the user's response.

[0335] Input: Questions about user preferences and constraints

[0336] Output: User response data

[0337] Step 3:

[0338] Recognition of user emotions

[0339] The server uses an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions.

[0340] The server analyzes emotional data and stores it to understand the user's mood on that day.

[0341] Input: User voice data, facial expression data

[0342] Output: Recognized emotion data

[0343] Step 4:

[0344] Data analysis and menu generation

[0345] The server analyzes the collected user response data and sentiment data using data analysis tools (data analysis algorithms implemented in Python).

[0346] Based on the analysis results, the server selects appropriate ingredients and recipes (e.g., sea bream soufflé and vegetable puree) that match the user's preferences and constraints.

[0347] The server sends the generated menu to the user's terminal along with a suggestion message.

[0348] Input: User response data, sentiment data

[0349] Output: Analysis results, suggested menu

[0350] Step 5:

[0351] Menu confirmation and approval

[0352] The user checks the suggested menu on their device.

[0353] The user either approves the menu or requests a change.

[0354] The server may also suggest new menu options based on the user's selection.

[0355] Input: User approval or modification request for the suggested menu.

[0356] Output: Approved menu, or menu to be re-proposed.

[0357] Step 6:

[0358] Texture adjustment decision

[0359] The server determines the specific processing method (mixing, steaming, gelling) based on the approved menu.

[0360] The server converts these processing methods into instructions and transmits them to the three-dimensional food printer.

[0361] Input: Approved menu

[0362] Output: Specific processing instructions

[0363] Step 7:

[0364] Cooking using a 3D food printer

[0365] The terminal, a 3D food printer, prints selected ingredients and prepares them based on instructions received from the server.

[0366] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0367] Input: Specific processing instructions

[0368] Output: Cooked custom textured food

[0369] Step 8:

[0370] Food service

[0371] The terminal, a 3D food printer, dispenses the finished dish.

[0372] The user receives a custom-textured hood provided by the printer.

[0373] Input: Cooked custom textured food

[0374] Output: Served dishes

[0375] Step 9:

[0376] Gathering feedback

[0377] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal. The feedback includes questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[0378] The user enters their response into the feedback form and submits it.

[0379] The server receives and stores the feedback.

[0380] Input: Questions from the feedback form, user responses

[0381] Output: Feedback data

[0382] Step 10:

[0383] Feedback analysis and improvement

[0384] The server analyzes the received feedback data and records it in the database.

[0385] The server updates its algorithms based on feedback to improve future suggestions and adjustment methods.

[0386] Input: Feedback data

[0387] Output: Updated algorithm and adjustments reflected in the next proposal.

[0388] (Application Example 2)

[0389] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0390] Traditional meal delivery systems offer customization based on user preferences and constraints, but they do not consider the user's emotional state when suggesting menus or preparing meals. Furthermore, they lack features that allow users to select ingredients and review menus through interactive experiences in virtual restaurants. Therefore, the quality of the user experience is limited, and providing meals that are sensitive to the user's emotions presents a significant challenge.

[0391] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0392] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of selected ingredients; a three-dimensional food printer means for cooking and serving ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; an emotion recognition means for identifying the user's emotions and suggesting menus according to their emotional state; and an interactive interface means for accessing a virtual store and interactively selecting ingredients and checking menus. This makes it possible to provide more personalized meals that take into account the user's emotional state.

[0393] "Gathering information about user preferences and constraints" involves collecting information about the foods users are looking for, allergy information, ingredient preferences, and physical limitations.

[0394] "Adjusting the texture of ingredients" means changing the physical texture and shape of ingredients according to the user's preferences and constraints.

[0395] "Interactive artificial intelligence means" refers to technologies that interact with users using natural language to collect information and provide guidance.

[0396] This refers to "collected user information," which includes data such as preferences, constraints, and emotional states obtained from users.

[0397] "Data analysis methods" refer to technologies that analyze patterns and trends based on collected user information to select appropriate ingredients and recipes.

[0398] "Instruction generation means" refers to a technology that generates specific cooking instructions based on analyzed data.

[0399] A "three-dimensional food printer" is a device that, based on generated instructions, stacks ingredients in a three-dimensional manner to create the final dish.

[0400] "Feedback collection methods" refer to technologies used to collect user feedback and evaluations to inform future adjustments and improvements.

[0401] "Emotion recognition means" refers to technology that analyzes the user's voice and facial expressions to identify their current emotional state.

[0402] An "interactive interface means" is a technology that allows users to interactively select ingredients and check menus in a virtual store.

[0403] This invention is a system that provides individually customized meals using a three-dimensional food printer, taking into account the user's preferences, constraints, and emotional state. Specifically, it is implemented by three entities: a server, a terminal, and a user.

[0404] First, the server uses conversational artificial intelligence to gather information about the user's preferences and constraints. The server collects information such as what the user wants to eat today, their favorite and disliked ingredients, allergy information, and the degree of chewing and swallowing difficulties. This information is stored as text data.

[0405] Next, the server uses an emotion engine to recognize emotions from the user's voice and facial expressions. This emotion data is analyzed and used to understand the user's current state of mind. The data obtained through emotion recognition is crucial when selecting menu items.

[0406] Based on collected user information and sentiment data, the server uses data analysis tools to select the optimal ingredients and recipes. Based on the analysis results, the server generates a specific menu and proposes it to the user. This proposal is presented to the user in text format.

[0407] After the user reviews and approves the proposed menu, the server generates specific processing instructions for adjusting the texture of the ingredients. These instructions include methods such as mixing, steaming, and gelling. The generated instructions are then sent to a three-dimensional food printer.

[0408] The terminal, a 3D food printer, cooks and serves ingredients based on received instructions. The 3D food printer executes the specified process to complete a dish with the optimal texture. This dish is then served to the user.

[0409] After the meal, the server uses feedback collection tools to gather feedback from the user. This includes taste, appearance, aroma, and overall satisfaction. The collected feedback is stored in a database and used to improve future suggestions and adjustments.

[0410] Furthermore, the server provides an interactive interface to enable users to interactively select ingredients and check menus within the virtual store. Users can access and operate the virtual store using smartphones, smart glasses, head-mounted displays, etc. Within the virtual store, users can receive real-time feedback on the ingredients and menus they select.

[0411] Specific example

[0412] For example, if a user is feeling "a little tired today," the server, through emotion recognition, will understand that emotion and suggest foods and menu items that will soothe the user. For instance, it could suggest a warm vegetable soup and a mild-flavored dessert. This information is then sent to a 3D food printer for preparation.

[0413] Example of a prompt

[0414] "What do you want to eat today?"

[0415] "Do you have any favorite or least favorite foods?"

[0416] "Please provide allergy information."

[0417] "Please tell us about your chewing ability and the degree of your swallowing difficulties."

[0418] This invention makes it possible to provide more personalized meals that take into account a variety of information, including the user's emotional state.

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The server displays an authentication screen on the user's smartphone or smart glasses for login. The user enters their authentication information (user ID and password) and sends it to the server. The server compares this information with the database, and if authentication is successful, retrieves the user profile and proceeds to the next step. The input is the user ID and password, and the output is the user profile.

[0422] Step 2:

[0423] The server uses conversational artificial intelligence to send questions to the user. These questions cover topics such as what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of their chewing and swallowing difficulties. The user answers these questions and sends the information to the server. The input is the user's answers to the questions, and the output is the collected user information.

[0424] Step 3:

[0425] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. It analyzes the user's voice and image data to identify their emotional state. The input is the user's voice and image data, and the output is data of the recognized emotional state.

[0426] Step 4:

[0427] The server analyzes collected user information and recognized sentiment data using data analysis tools. Through this analysis, it selects appropriate ingredients and recipes based on the user's preferences and constraints. The input is user information and sentiment data, and the output is data on the selected menu.

[0428] Step 5:

[0429] The server proposes a menu to the user based on the analysis results. The user reviews the proposed menu and requests approval or modification. The input is the data of the analyzed menu, and the output is the user's approval or modification request.

[0430] Step 6:

[0431] The server determines specific processing methods to adjust the texture of the ingredients based on the menu approved by the user. It generates specific processing instructions and sends them to a 3D food printer. The input is the data of the approved menu, and the output is the specific processing instructions.

[0432] Step 7:

[0433] The terminal, a 3D food printer, prints and cooks ingredients based on processing instructions received from the server. The input is the processing instructions from the server, and the output is the final cooked dish.

[0434] Step 8:

[0435] A 3D food printer delivers a finished dish to the user. The user receives the dish from the printer and enjoys the meal. The input is the cooked food, and the output is the user's dining experience.

[0436] Step 9:

[0437] After the meal, the server collects feedback from the user using questionnaires and conversational AI. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction with the meal. The input is the user's feedback, and the output is the collected feedback data.

[0438] Step 10:

[0439] The server analyzes the feedback collected using the feedback collection mechanism and updates the algorithm to improve future proposals and adjustment methods. The input is feedback data, and the output is the improved proposal and adjustment algorithm.

[0440] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0441] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0442] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0443] [Second Embodiment]

[0444] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0445] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0446] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0447] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0448] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0449] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0450] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0451] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0452] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0453] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0454] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0455] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0456] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0457] System programming and processing

[0458] 1. User information collection

[0459] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include what the user wants to eat today, their preferred ingredients and allergy information, and the degree of their chewing and swallowing difficulties.

[0460] The user responds to this question and enters their preferences and constraints.

[0461] 2. Data Analysis and Menu Proposal

[0462] The server analyzes the collected user information and selects ingredients and recipes that match the user's preferences. Furthermore, it determines whether or not the texture of each ingredient needs to be adjusted.

[0463] The server generates a customized menu based on this information and sends the suggestions to the user's terminal.

[0464] The user reviews the proposed menu and either approves it or requests changes.

[0465] 3. Texture Customization

[0466] The server determines the specific processing methods (such as mixing, steaming, or gelling) to adjust the texture of the ingredients included in the menu.

[0467] The server generates detailed instructions based on the processing method and sends them to the three-dimensional food printer.

[0468] 4. Cooking and serving using a 3D food printer.

[0469] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking.

[0470] Users receive custom-textured food delivered from a 3D food printer.

[0471] 5. Gathering feedback and making improvements

[0472] The server asks users for feedback after their meal. This feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0473] The user enters feedback, and that information is sent to the server.

[0474] The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments.

[0475] Specific example

[0476] For example, if a user indicates a preference for soft foods due to reduced chewing ability, the system will select fish as the main ingredient and consider how to prepare it to be tender. The server sends detailed instructions to a 3D food printer, which will then perform mixing and gelling to tenderize the fish. Once the dish is complete, it is served to the user. Based on the user's feedback, the system then further improves its suggestions for the next time.

[0477] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or swallowing difficulties.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] User authentication and login

[0481] The server displays an authentication screen on the terminal for the user to log in.

[0482] The user enters their authentication information (user ID, password, etc.).

[0483] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0484] Step 2:

[0485] Interviewing users about their preferences and constraints

[0486] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0487] What I want to eat today

[0488] Favorite foods and foods you dislike

[0489] Allergy Information

[0490] Degree of chewing and swallowing difficulties

[0491] The user answers these questions and enters the relevant information.

[0492] Step 3:

[0493] Data analysis and menu generation

[0494] The server analyzes the collected user responses using data analysis tools.

[0495] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it can generate menus such as sea bream soufflé and vegetable puree.

[0496] The server sends the generated menu to the terminal along with a message suggesting it.

[0497] Step 4:

[0498] Menu confirmation and approval

[0499] The user reviews the suggested menu.

[0500] The user can either accept the proposed menu or request a change.

[0501] The server will suggest new menus as needed, based on the user's selection.

[0502] Step 5:

[0503] Texture adjustment decision

[0504] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0505] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[0506] Step 6:

[0507] Cooking using a 3D food printer

[0508] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0509] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0510] Step 7:

[0511] Food service

[0512] The terminal, a 3D food printer, dispenses the finished dish.

[0513] The user receives a custom-textured hood provided by the printer.

[0514] Step 8:

[0515] Gathering feedback

[0516] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0517] The user enters their response into the feedback form and submits it.

[0518] Step 9:

[0519] Feedback analysis and improvement

[0520] The server analyzes the feedback received from the user and records it in a database.

[0521] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[0522] (Example 1)

[0523] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0524] Traditional systems struggled to accommodate diverse user preferences and constraints, providing quickly and appropriately customized meals. Furthermore, the lack of means to adjust the texture of ingredients based on user chewing and swallowing abilities made it difficult to deliver a high-quality dining experience. Additionally, the absence of a system for effectively collecting post-meal feedback and incorporating it into future recommendations hindered improvements in service quality.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0526] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a means for receiving and analyzing prompt sentences generated using the interactive artificial intelligence means. This enables the provision of meals optimized to the individual needs of the user and continuous improvement of service quality.

[0527] "Interactive artificial intelligence means" refers to technology that collects information through dialogue with the user and generates appropriate questions and answers based on that information.

[0528] "Data analysis means" refers to technologies that analyze collected user information and select the optimal ingredients and recipes based on the user's needs and constraints.

[0529] "Instruction generation means" refers to a technology for determining specific processing methods to adjust the texture of selected ingredients and generating those instructions.

[0530] A "three-dimensional food printer" is a technology for creating and serving food by arranging and processing ingredients in three dimensions based on generated instructions.

[0531] A "feedback collection method" is a technology that collects opinions and feedback from users after a meal, analyzes that data, and uses it to improve future suggestions and adjustment methods.

[0532] A "prompt message" refers to the format of questions and instructions generated by an interactive artificial intelligence based on input text or audio information.

[0533] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0534] The server uses conversational artificial intelligence (AI) to send questions to the user's terminal. These questions include what the user wants to eat, preferred ingredients, allergy information, and the degree of chewing and swallowing difficulties. This allows the server to collect information about the user's preferences and constraints. For example, the user might respond, "Today I want to eat a soft fish dish." Conversational AI such as IBM Watson or Google Dialogflow are used for this information collection.

[0535] Next, the server analyzes the collected user information using data analysis tools. Python libraries such as Pandas and Numpy are used for this analysis. Based on the collected information, ingredients and recipes tailored to the user's preferences are selected. For example, if the user prefers soft ingredients, a soft fish dish will be selected.

[0536] Next, the server uses an instruction generation mechanism to determine specific processing methods (such as mixing, steaming, and gelling) to adjust the texture of the selected ingredients. A recipe generation algorithm is used in this process. The generated instructions are then sent to a three-dimensional food printer.

[0537] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking. For example, an XYZ Food Printer is used. The printer performs mixing and gelling to create a fish dish with a soft texture. The user receives the cooked dish from this printer.

[0538] Finally, the server uses a feedback collection mechanism to gather post-meal feedback from users. This feedback includes the taste, appearance, aroma, and overall satisfaction of the meal. Users input this feedback via a terminal, and it is sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Python Numpy library is used for this analysis.

[0539] Examples of prompt statements as concrete examples:

[0540] User preferences and constraints: Likes soft foods, wants to eat fish, no allergies.

[0541] Suggested menu: Soft gelled fish dish

[0542] Processing method: Mixing, gelation

[0543] Feedback: It tastes good and looks beautiful, but it would be even better if the aroma were a little stronger.

[0544] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or dysphagia. Furthermore, by utilizing user feedback, the quality of the service can be continuously improved.

[0545] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0546] Step 1:

[0547] User information collection

[0548] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include "What do you want to eat today?", "What are your favorite foods and allergy information?", and "What is the degree of your chewing ability and swallowing difficulties?". The user answers these questions using their terminal and inputs their preferences and constraints. The collected information includes specific data such as "I like soft foods, I want to eat fish, I have no allergies".

[0549] Input: User preferences and constraints (text format)

[0550] Output: Database of user preferences and constraints

[0551] Step 2:

[0552] Data analysis and menu proposals

[0553] The server analyzes the collected user information using data analysis tools. Using Python libraries such as Pandas and Numpy, it analyzes the user's input data and selects appropriate ingredients and recipes. For example, if the user prefers soft ingredients, a soft fish dish will be selected. The server then generates a customized menu and sends the suggestion to the user's terminal. The user reviews the suggested menu and requests approval or modification.

[0554] Input: Database of user preferences and constraints

[0555] Data processing: Analyze user input data.

[0556] Output: Suggested menu (text format)

[0557] Step 3:

[0558] Texture customization

[0559] The server determines whether texture adjustment is necessary for the selected ingredients. A recipe generation algorithm is used to determine specific processing methods (mixing, steaming, gelling, etc.). For example, if a soft fish dish is selected, mixing and gelling will be used. The server generates detailed processing instructions and sends them to the 3D food printer.

[0560] Input: Suggested menu

[0561] Data processing: Determination of processing method using a recipe generation algorithm.

[0562] Output: Processing instructions (text format)

[0563] Step 4:

[0564] Cooking and serving using a 3D food printer.

[0565] A 3D food printer selects ingredients according to instructions received from a server, and prints them while performing processing such as mixing and gelling. For example, an XYZ Food Printer is used to create a soft, gelled fish dish. The user receives the cooked dish from the printer.

[0566] Input: Processing Instructions

[0567] Output: Cooked food

[0568] Step 5:

[0569] Feedback gathering and improvement

[0570] The server requests feedback from users after their meal. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction of the meal. Users enter their feedback, which is then sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Numpy library in Python is used to analyze the feedback data.

[0571] Input: User feedback (text format)

[0572] Data processing: Analysis of feedback data

[0573] Output: Improved suggestions and adjustment methods

[0574] Through these steps, the system can provide meals optimized to the individual needs of users and continuously improve the quality of service.

[0575] (Application Example 1)

[0576] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0577] In recent years, the aging population and increasing health consciousness have led to a growing demand for meals tailored to the individual preferences and health conditions of users. Furthermore, with the proliferation of food delivery services, there is a growing need for more customized menus. However, current systems generally only offer standardized menus, making it difficult to meet individual user needs. Therefore, a system is needed that provides meals based on ingredients with textures adjusted according to each user's preferences and constraints, while maintaining visual appeal, aroma, and nutritional balance, and further collecting feedback to improve future offerings.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0579] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing the collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a suggestion means for creating a custom menu and providing it to the food delivery service. This makes it possible to provide an optimal meal based on the individual user's preferences and constraints, and to further improve suggestions for the next time by utilizing the feedback.

[0580] "Gathering user preferences and constraints" means using conversational artificial intelligence to collect information about users' food preferences, allergies, health status, and other related information.

[0581] "Adjusting the texture of ingredients" means optimally changing the firmness and mouthfeel of ingredients according to the individual user's requests.

[0582] "Interactive artificial intelligence means" refers to a system that collects information and answers questions through dialogue with the user using natural language.

[0583] "Analyzing collected user information" means performing data analysis based on collected user preferences, constraints, and health information to select appropriate ingredients and recipes.

[0584] A "data analysis method" is a system that analyzes collected data to select the most suitable ingredients and recipes.

[0585] "Generating processing instructions for texture adjustment of selected ingredients" means creating detailed instructions on how to process the ingredients based on the results of data analysis.

[0586] "Instruction generation means" refers to a system means that automatically generates instructions regarding the processing method of food ingredients.

[0587] A "three-dimensional food printer" is a device that prints food ingredients in three dimensions based on digital instructions and then cooks them.

[0588] A "feedback collection method" is a system for collecting information such as user feedback and suggestions for improvement after a meal.

[0589] "A means of proposing the creation of a custom menu and providing it to a food delivery service" refers to a system that creates a customized menu according to the user's request and proposes providing that menu to a food delivery service.

[0590] The system for implementing the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides customized meals that are visually appealing and maintain aroma and nutritional balance. The system includes interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and proposal means for creating custom menus and providing them to food delivery services.

[0591] The server uses conversational artificial intelligence to collect information from the user regarding their food preferences, allergies, chewing ability, and other related information. This collected information is analyzed by a data analysis system to select ingredients and recipes that match the user's requirements. It also determines whether the texture of the ingredients needs to be adjusted. Based on the selected ingredients and recipes, an instruction generation system automatically generates specific processing instructions, such as mixing, steaming, and gelling.

[0592] The 3D food printer prints ingredients based on generated instructions and cooks them appropriately. The cooked custom-textured food is validated by a feedback collection mechanism before being served to the user. After the meal, the user provides feedback on taste, appearance, aroma, etc., and this information is sent to the server to improve future suggestions and texture adjustments.

[0593] To implement this system, the following hardware and software will be used: a server, an interactive artificial intelligence engine (e.g., OpenAI API), data analysis tools, a 3D food printer (e.g., XYZprinting's food printer), and an application for collecting user feedback.

[0594] As a concrete example, if a user enters "I want to eat a soft fish dish today" into the application and indicates "shellfish allergy" as allergy information, the server will suggest a soft white fish fillet. It will also create a customized menu that includes steamed vegetables and low-sugar jelly. Once this menu is approved, the instructions are sent to a 3D food printer, and the cooked customized menu is served.

[0595] Examples of prompts for generative AI models:

[0596] User preferences: "I like soft foods and seafood."

[0597] Allergies: "Shellfish allergy"

[0598] Chewing Ability: "Decreased"

[0599] Create a customized menu that adheres to these preferences and restrictions.

[0600] As a result, we can provide optimal meals based on each user's individual preferences and constraints, and further improve meal suggestions for future visits by utilizing their feedback.

[0601] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0602] Step 1:

[0603] The server sends questions to the user through an interactive artificial intelligence system. Specifically, the server displays questions on the user's terminal such as, "What kind of food would you like to eat today?", "Do you have any preferred ingredients or allergy information?", and "What is the degree of your chewing ability or swallowing difficulties?". The input data consists of the user's preferences and constraints, and data analysis is performed based on this data.

[0604] Step 2:

[0605] Users operate a terminal to answer questions and input their preferences and constraints. For example, they might submit information such as "likes soft foods and seafood," "shellfish allergy," or "has difficulty chewing." The input data is sent to a server and becomes the basis for the next analysis step.

[0606] Step 3:

[0607] The data analysis tool analyzes the collected user information. The server selects ingredients and recipes suitable for the user based on the input preferences and constraints. This process uses a generative AI model to generate prompts and select ingredients and recipes. For example, if the input is "User Preferences: Soft dishes, likes seafood, Allergies: Shellfish allergy, Chewing Ability: Reduced, Create a customized menu that adheres to these preferences and restrictions," the analysis results will generate a custom menu such as "Soft white fish fillet," "Steamed vegetables," and "Low-sugar jelly."

[0608] Step 4:

[0609] The server generates processing instructions based on the selected ingredients and recipe. The instruction generation mechanism determines specific processing methods to adjust the texture of the ingredients. For example, it generates detailed processing instructions such as "mix and gel to soften the white fish" or "steam the vegetables." These instructions become the input data for the 3D food printer.

[0610] Step 5:

[0611] The 3D food printer system prints and cooks ingredients based on generated instructions. The 3D food printer terminal receives instructions from the server and processes the ingredients according to those instructions. The ingredients are mixed, gelled, printed, and cooked to produce a custom-textured food. The output data is the cooked custom menu.

[0612] Step 6:

[0613] Users receive custom-textured food delivered from a 3D food printer. After eating, users operate a terminal to send feedback. The feedback collection mechanism displays questions to the user such as, "How was the taste, appearance, aroma, and overall satisfaction of the meal?" and collects feedback. The input data is user feedback information, which serves as the basis for the next improvement steps.

[0614] Step 7:

[0615] The server analyzes the collected feedback and updates the database. Analyzing the feedback information allows for further improvements to future menu suggestions and texture adjustments. Ultimately, the data analysis is based on the feedback, resulting in improved quality for future custom menus. The output data consists of new database entries resulting from the analysis.

[0616] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0617] This invention is a system that improves the dining experience by listening to the user's preferences and constraints, adjusting the texture of ingredients based on them, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine.

[0618] System programming and processing

[0619] 1. User authentication and login

[0620] The server displays an authentication screen on the terminal for the user to log in.

[0621] The user enters their authentication information (user ID, password, etc.).

[0622] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0623] 2. Gathering information on user preferences and constraints.

[0624] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0625] What I want to eat today

[0626] Favorite foods and foods you dislike

[0627] Allergy Information

[0628] Degree of chewing and swallowing difficulties

[0629] The user answers these questions and enters the relevant information.

[0630] 3. Recognition of user emotions

[0631] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions.

[0632] The server collects emotional data to understand the user's mood on any given day.

[0633] 4. Data analysis and menu generation

[0634] The server analyzes the collected user responses and sentiment data using data analysis tools.

[0635] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[0636] The server sends the generated menu to the terminal along with a message suggesting it.

[0637] 5. Menu confirmation and approval

[0638] The user reviews the suggested menu.

[0639] The user can either accept the proposed menu or request a change.

[0640] The server will suggest new menus as needed, based on the user's selection.

[0641] 6. Decision on texture adjustments

[0642] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0643] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[0644] 7. Cooking using a 3D food printer

[0645] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0646] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0647] 8. Serving the food

[0648] The terminal, a 3D food printer, dispenses the finished dish.

[0649] The user receives a custom-textured hood provided by the printer.

[0650] 9. Gathering Feedback

[0651] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0652] The user enters their response into the feedback form and submits it.

[0653] 10. Analysis and Improvement of Feedback

[0654] The server analyzes the feedback received from the user and records it in a database.

[0655] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[0656] Specific example

[0657] For example, if a user indicates that they are feeling a little down today, the system will take that emotional data into consideration and suggest ingredients and menu items that will help calm them down. The server uses an emotion engine to analyze the user's mood and, for example, suggest a warm vegetable soup and a mild-flavored dessert. Based on this information, the server can issue specific cooking instructions to a 3D food printer, providing a meal that takes the user's emotions into consideration.

[0658] Through the above process, the system of the present invention can provide people with reduced chewing ability or swallowing difficulties with delicious, visually satisfying meals that take their emotions into consideration.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] User authentication and login

[0662] The server displays an authentication screen on the terminal for the user to log in.

[0663] The user enters their authentication information (user ID, password, etc.).

[0664] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0665] Step 2:

[0666] Interviewing users about their preferences and constraints

[0667] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0668] What I want to eat today

[0669] Favorite foods and foods you dislike

[0670] Allergy Information

[0671] Degree of chewing and swallowing difficulties

[0672] The user answers these questions and enters the relevant information.

[0673] Step 3:

[0674] Recognition of user emotions

[0675] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. For example, it analyzes the user's current mood by detecting changes in voice tone, word choice, and facial expressions.

[0676] The server collects emotional data to understand the user's mood on any given day.

[0677] Step 4:

[0678] Data analysis and menu generation

[0679] The server analyzes the collected user responses and emotional data using data analysis tools. For example, based on emotional data indicating that a user is "a little depressed," it selects menu items that have a relaxing effect.

[0680] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[0681] The server sends the generated menu to the terminal along with a message suggesting it.

[0682] Step 5:

[0683] Menu confirmation and approval

[0684] The user reviews the suggested menu. For example, they might see "Sea bream soufflé with vegetable puree" as a menu suggested by the server on their device.

[0685] The user can either accept the suggested menu or request a change. For example, the user might request "soup instead of vegetable puree."

[0686] The server will suggest new menu items as needed, based on the user's selection. For example, it might suggest soup instead of vegetable puree.

[0687] Step 6:

[0688] Texture adjustment decision

[0689] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0690] The server translates these processing methods into specific instructions and sends them to the three-dimensional food printer. For example, it generates instructions to "use a gelling agent and mix" for a sea bream soufflé.

[0691] Step 7:

[0692] Cooking using a 3D food printer

[0693] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0694] The terminal performs a specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture. For example, it can make a sea bream soufflé soft.

[0695] Step 8:

[0696] Food service

[0697] The terminal, a 3D food printer, dispenses the finished dish.

[0698] The user receives custom-textured food provided by the printer. For example, they might receive a serving of sea bream soufflé and vegetable soup.

[0699] Step 9:

[0700] Gathering feedback

[0701] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0702] The user enters their response into a feedback form and submits it. For example, they might enter, "The taste was good, but I would like the aroma to be a little stronger."

[0703] Step 10:

[0704] Feedback analysis and improvement

[0705] The server analyzes the feedback received from users and records it in a database. For example, it records feedback regarding the intensity of a scent.

[0706] The server updates its algorithms based on the analysis of feedback to improve future suggestions and adjustment methods. For example, next time, it might incorporate cooking methods that enhance the aroma into its selection.

[0707] (Example 2)

[0708] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0709] Conventional meal suggestion systems offer menus that take into account user preferences and limitations, but they fail to provide appropriate meal suggestions based on the user's emotional state. Therefore, providing meals that are sensitive to the emotional needs of users, particularly those with reduced chewing ability or swallowing difficulties, presents a challenge. Furthermore, system improvements utilizing feedback have been insufficient.

[0710] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0711] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and emotional data and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and an emotion engine for recognizing emotions from the user's voice and facial expressions. This enables appropriate menu suggestions according to the user's emotional state and texture adjustments that take those emotions into consideration.

[0712] "Interactive artificial intelligence means" refers to a means of providing an interface that uses artificial intelligence to gather user preferences and constraints.

[0713] "Data analysis means" refers to methods for analyzing collected user information and sentiment data to select appropriate ingredients and recipes.

[0714] The "instruction generation means" is a means for generating specific processing instructions for adjusting the texture of food ingredients based on the analysis results.

[0715] A "three-dimensional food printer means" is a means for printing food ingredients based on generated instructions, and then cooking and serving them.

[0716] "Feedback collection methods" refer to methods for collecting feedback from users after a meal and using that feedback to improve future suggestions and adjustments.

[0717] An "emotion engine" is a means of recognizing emotions from a user's voice and facial expressions and providing that information to data analysis tools.

[0718] This invention is a system that improves the dining experience by thoroughly interviewing users about their preferences and constraints, adjusting the texture of ingredients based on that information, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine. The operation of each means and the hardware and software used will be described in detail below.

[0719] User authentication and login

[0720] The server displays an authentication screen on the terminal for the user to log in. This generates an HTML form containing input fields for user ID and password. Once the user submits the information, the server queries the database (e.g., MySQL) to verify that the user exists. If authentication is successful, the server reads the user profile and proceeds to the next process.

[0721] Interviewing users about their preferences and constraints

[0722] The server activates an interactive artificial intelligence (e.g., IBM Watson) to generate questions about what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of chewing and swallowing difficulties. The generated series of questions are sent to the user's terminal, and the user answers them.

[0723] Recognition of user emotions

[0724] The server utilizes an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions. The collected emotion data is stored for analysis.

[0725] Data analysis and menu generation

[0726] The server analyzes the collected user responses and sentiment data using data analysis tools (data analysis algorithms implemented in Python). Based on the analysis results, it selects appropriate ingredients and recipes (for example, sea bream soufflé and vegetable puree). The generated menu is then suggested to the user's terminal.

[0727] Menu confirmation and approval

[0728] The user reviews the suggested menu. They enter their information as requested for approval or modification. The server then either suggests a new menu or proceeds to the next process.

[0729] Texture adjustment decision

[0730] The server determines the specific processing method (e.g., mixing, steaming, gelling) based on the approved menu. These processing steps are then transmitted as specific instructions to the 3D food printer.

[0731] Cooking using a 3D food printer

[0732] The terminal, a 3D food printer, prints and cooks ingredients according to instructions received from the server. It executes the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0733] Food service

[0734] The terminal, a 3D food printer, delivers the finished dish to the user. The user receives the custom-textured food provided by the printer.

[0735] Gathering feedback

[0736] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal, where the user answers questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[0737] Feedback analysis and improvement

[0738] The server analyzes the feedback received from users and records it in a database. Furthermore, it updates its algorithms based on the feedback to improve future suggestions and adjustment methods.

[0739] Specific example

[0740] For example, if a user indicates that they are feeling a little down today, the server uses an emotion engine to analyze this information. Based on the analysis results, it suggests a warm vegetable soup and a mild-flavored dessert. The server then uses this information to send specific cooking instructions (e.g., steaming and smoothing vegetables) to a 3D food printer. As a result, it can provide users with a meal that takes their emotions into consideration.

[0741] Example of a prompt

[0742] "A user is feeling a bit down today. Please suggest a menu that will help them calm down. What ingredients and cooking methods would be appropriate to include in that menu?"

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] User authentication and login

[0746] The server displays an authentication screen on the terminal for the user to log in. The screen includes fields for entering the user ID and password.

[0747] The user enters their user ID and password in the input fields and submits the form.

[0748] The server compares the received user ID and password with a database (e.g., MySQL) and retrieves the user profile if authentication is successful. If authentication is successful, the user profile is output.

[0749] Input: User ID, Password

[0750] Output: User Profile

[0751] Step 2:

[0752] Interviewing users about their preferences and constraints

[0753] The server activates an interactive artificial intelligence tool (e.g., IBM Watson) to generate the following questions: what you want to eat today, your favorite and disliked ingredients, allergy information, and the degree of your chewing and swallowing difficulties.

[0754] The server sends the generated question to the user's terminal.

[0755] The user answers the questions displayed on the device and enters the required information.

[0756] The server receives and stores the user's response.

[0757] Input: Questions about user preferences and constraints

[0758] Output: User response data

[0759] Step 3:

[0760] Recognition of user emotions

[0761] The server uses an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions.

[0762] The server analyzes emotional data and stores it to understand the user's mood on that day.

[0763] Input: User voice data, facial expression data

[0764] Output: Recognized emotion data

[0765] Step 4:

[0766] Data analysis and menu generation

[0767] The server analyzes the collected user response data and sentiment data using data analysis tools (data analysis algorithms implemented in Python).

[0768] Based on the analysis results, the server selects appropriate ingredients and recipes (e.g., sea bream soufflé and vegetable puree) that match the user's preferences and constraints.

[0769] The server sends the generated menu to the user's terminal along with a suggestion message.

[0770] Input: User response data, sentiment data

[0771] Output: Analysis results, suggested menu

[0772] Step 5:

[0773] Menu confirmation and approval

[0774] The user checks the suggested menu on their device.

[0775] The user either approves the menu or requests a change.

[0776] The server may also suggest new menu options based on the user's selection.

[0777] Input: User approval or modification request for the suggested menu.

[0778] Output: Approved menu, or menu to be re-proposed.

[0779] Step 6:

[0780] Texture adjustment decision

[0781] The server determines the specific processing method (mixing, steaming, gelling) based on the approved menu.

[0782] The server converts these processing methods into instructions and transmits them to the three-dimensional food printer.

[0783] Input: Approved menu

[0784] Output: Specific processing instructions

[0785] Step 7:

[0786] Cooking using a 3D food printer

[0787] The terminal, a 3D food printer, prints selected ingredients and prepares them based on instructions received from the server.

[0788] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0789] Input: Specific processing instructions

[0790] Output: Cooked custom textured food

[0791] Step 8:

[0792] Food service

[0793] The terminal, a 3D food printer, dispenses the finished dish.

[0794] The user receives a custom-textured hood provided by the printer.

[0795] Input: Cooked custom textured food

[0796] Output: Served dishes

[0797] Step 9:

[0798] Gathering feedback

[0799] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal. The feedback includes questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[0800] The user enters their response into the feedback form and submits it.

[0801] The server receives and stores the feedback.

[0802] Input: Questions from the feedback form, user responses

[0803] Output: Feedback data

[0804] Step 10:

[0805] Feedback analysis and improvement

[0806] The server analyzes the received feedback data and records it in the database.

[0807] The server updates its algorithms based on feedback to improve future suggestions and adjustment methods.

[0808] Input: Feedback data

[0809] Output: Updated algorithm and adjustments reflected in the next proposal.

[0810] (Application Example 2)

[0811] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0812] Traditional meal delivery systems offer customization based on user preferences and constraints, but they do not consider the user's emotional state when suggesting menus or preparing meals. Furthermore, they lack features that allow users to select ingredients and review menus through interactive experiences in virtual restaurants. Therefore, the quality of the user experience is limited, and providing meals that are sensitive to the user's emotions presents a significant challenge.

[0813] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0814] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of selected ingredients; a three-dimensional food printer means for cooking and serving ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; an emotion recognition means for identifying the user's emotions and suggesting menus according to their emotional state; and an interactive interface means for accessing a virtual store and interactively selecting ingredients and checking menus. This makes it possible to provide more personalized meals that take into account the user's emotional state.

[0815] "Gathering information about user preferences and constraints" involves collecting information about the foods users are looking for, allergy information, ingredient preferences, and physical limitations.

[0816] "Adjusting the texture of ingredients" means changing the physical texture and shape of ingredients according to the user's preferences and constraints.

[0817] "Interactive artificial intelligence means" refers to technologies that interact with users using natural language to collect information and provide guidance.

[0818] This refers to "collected user information," which includes data such as preferences, constraints, and emotional states obtained from users.

[0819] "Data analysis methods" refer to technologies that analyze patterns and trends based on collected user information to select appropriate ingredients and recipes.

[0820] "Instruction generation means" refers to a technology that generates specific cooking instructions based on analyzed data.

[0821] A "three-dimensional food printer" is a device that, based on generated instructions, stacks ingredients in a three-dimensional manner to create the final dish.

[0822] "Feedback collection methods" refer to technologies used to collect user feedback and evaluations to inform future adjustments and improvements.

[0823] "Emotion recognition means" refers to technology that analyzes the user's voice and facial expressions to identify their current emotional state.

[0824] An "interactive interface means" is a technology that allows users to interactively select ingredients and check menus in a virtual store.

[0825] This invention is a system that provides individually customized meals using a three-dimensional food printer, taking into account the user's preferences, constraints, and emotional state. Specifically, it is implemented by three entities: a server, a terminal, and a user.

[0826] First, the server uses conversational artificial intelligence to gather information about the user's preferences and constraints. The server collects information such as what the user wants to eat today, their favorite and disliked ingredients, allergy information, and the degree of chewing and swallowing difficulties. This information is stored as text data.

[0827] Next, the server uses an emotion engine to recognize emotions from the user's voice and facial expressions. This emotion data is analyzed and used to understand the user's current state of mind. The data obtained through emotion recognition is crucial when selecting menu items.

[0828] Based on collected user information and sentiment data, the server uses data analysis tools to select the optimal ingredients and recipes. Based on the analysis results, the server generates a specific menu and proposes it to the user. This proposal is presented to the user in text format.

[0829] After the user reviews and approves the proposed menu, the server generates specific processing instructions for adjusting the texture of the ingredients. These instructions include methods such as mixing, steaming, and gelling. The generated instructions are then sent to a three-dimensional food printer.

[0830] The terminal, a 3D food printer, cooks and serves ingredients based on received instructions. The 3D food printer executes the specified process to complete a dish with the optimal texture. This dish is then served to the user.

[0831] After the meal, the server uses feedback collection tools to gather feedback from the user. This includes taste, appearance, aroma, and overall satisfaction. The collected feedback is stored in a database and used to improve future suggestions and adjustments.

[0832] Furthermore, the server provides an interactive interface to enable users to interactively select ingredients and check menus within the virtual store. Users can access and operate the virtual store using smartphones, smart glasses, head-mounted displays, etc. Within the virtual store, users can receive real-time feedback on the ingredients and menus they select.

[0833] Specific example

[0834] For example, if a user is feeling "a little tired today," the server, through emotion recognition, will understand that emotion and suggest foods and menu items that will soothe the user. For instance, it could suggest a warm vegetable soup and a mild-flavored dessert. This information is then sent to a 3D food printer for preparation.

[0835] Example of a prompt

[0836] "What do you want to eat today?"

[0837] "Do you have any favorite or least favorite foods?"

[0838] "Please provide allergy information."

[0839] "Please tell us about your chewing ability and the degree of your swallowing difficulties."

[0840] This invention makes it possible to provide more personalized meals that take into account a variety of information, including the user's emotional state.

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The server displays an authentication screen on the user's smartphone or smart glasses for login. The user enters their authentication information (user ID and password) and sends it to the server. The server compares this information with the database, and if authentication is successful, retrieves the user profile and proceeds to the next step. The input is the user ID and password, and the output is the user profile.

[0844] Step 2:

[0845] The server uses conversational artificial intelligence to send questions to the user. These questions cover topics such as what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of their chewing and swallowing difficulties. The user answers these questions and sends the information to the server. The input is the user's answers to the questions, and the output is the collected user information.

[0846] Step 3:

[0847] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. It analyzes the user's voice and image data to identify their emotional state. The input is the user's voice and image data, and the output is data of the recognized emotional state.

[0848] Step 4:

[0849] The server analyzes collected user information and recognized sentiment data using data analysis tools. Through this analysis, it selects appropriate ingredients and recipes based on the user's preferences and constraints. The input is user information and sentiment data, and the output is data on the selected menu.

[0850] Step 5:

[0851] The server proposes a menu to the user based on the analysis results. The user reviews the proposed menu and requests approval or modification. The input is the data of the analyzed menu, and the output is the user's approval or modification request.

[0852] Step 6:

[0853] The server determines specific processing methods to adjust the texture of the ingredients based on the menu approved by the user. It generates specific processing instructions and sends them to a 3D food printer. The input is the data of the approved menu, and the output is the specific processing instructions.

[0854] Step 7:

[0855] The terminal, a 3D food printer, prints and cooks ingredients based on processing instructions received from the server. The input is the processing instructions from the server, and the output is the final cooked dish.

[0856] Step 8:

[0857] A 3D food printer delivers a finished dish to the user. The user receives the dish from the printer and enjoys the meal. The input is the cooked food, and the output is the user's dining experience.

[0858] Step 9:

[0859] After the meal, the server collects feedback from the user using questionnaires and conversational AI. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction with the meal. The input is the user's feedback, and the output is the collected feedback data.

[0860] Step 10:

[0861] The server analyzes the feedback collected using the feedback collection mechanism and updates the algorithm to improve future proposals and adjustment methods. The input is feedback data, and the output is the improved proposal and adjustment algorithm.

[0862] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0863] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

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

[0865] [Third Embodiment]

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

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

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

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

[0870] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

[0872] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0873] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0874] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0876] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0877] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0878] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0879] System programming and processing

[0880] 1. User information collection

[0881] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include what the user wants to eat today, their preferred ingredients and allergy information, and the degree of their chewing and swallowing difficulties.

[0882] The user responds to this question and enters their preferences and constraints.

[0883] 2. Data Analysis and Menu Proposal

[0884] The server analyzes the collected user information and selects ingredients and recipes that match the user's preferences. Furthermore, it determines whether or not the texture of each ingredient needs to be adjusted.

[0885] The server generates a customized menu based on this information and sends the suggestions to the user's terminal.

[0886] The user reviews the proposed menu and either approves it or requests changes.

[0887] 3. Texture Customization

[0888] The server determines the specific processing methods (such as mixing, steaming, or gelling) to adjust the texture of the ingredients included in the menu.

[0889] The server generates detailed instructions based on the processing method and sends them to the three-dimensional food printer.

[0890] 4. Cooking and serving using a 3D food printer.

[0891] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking.

[0892] Users receive custom-textured food delivered from a 3D food printer.

[0893] 5. Gathering feedback and making improvements

[0894] The server asks users for feedback after their meal. This feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0895] The user enters feedback, and that information is sent to the server.

[0896] The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments.

[0897] Specific example

[0898] For example, if a user indicates a preference for soft foods due to reduced chewing ability, the system will select fish as the main ingredient and consider how to prepare it to be tender. The server sends detailed instructions to a 3D food printer, which will then perform mixing and gelling to tenderize the fish. Once the dish is complete, it is served to the user. Based on the user's feedback, the system then further improves its suggestions for the next time.

[0899] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or swallowing difficulties.

[0900] The following describes the processing flow.

[0901] Step 1:

[0902] User authentication and login

[0903] The server displays an authentication screen on the terminal for the user to log in.

[0904] The user enters their authentication information (user ID, password, etc.).

[0905] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[0906] Step 2:

[0907] Interviewing users about their preferences and constraints

[0908] The server uses interactive artificial intelligence to send the following questions to the terminal.

[0909] What I want to eat today

[0910] Favorite foods and foods you dislike

[0911] Allergy Information

[0912] Degree of chewing and swallowing difficulties

[0913] The user answers these questions and enters the relevant information.

[0914] Step 3:

[0915] Data analysis and menu generation

[0916] The server analyzes the collected user responses using data analysis tools.

[0917] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it can generate menus such as sea bream soufflé and vegetable puree.

[0918] The server sends the generated menu to the terminal along with a message suggesting it.

[0919] Step 4:

[0920] Menu confirmation and approval

[0921] The user reviews the suggested menu.

[0922] The user can either accept the proposed menu or request a change.

[0923] The server will suggest new menus as needed, based on the user's selection.

[0924] Step 5:

[0925] Texture adjustment decision

[0926] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[0927] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[0928] Step 6:

[0929] Cooking using a 3D food printer

[0930] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[0931] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[0932] Step 7:

[0933] Food service

[0934] The terminal, a 3D food printer, dispenses the finished dish.

[0935] The user receives a custom-textured hood provided by the printer.

[0936] Step 8:

[0937] Gathering feedback

[0938] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[0939] The user enters their response into the feedback form and submits it.

[0940] Step 9:

[0941] Feedback analysis and improvement

[0942] The server analyzes the feedback received from the user and records it in a database.

[0943] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[0944] (Example 1)

[0945] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0946] Traditional systems struggled to accommodate diverse user preferences and constraints, providing quickly and appropriately customized meals. Furthermore, the lack of means to adjust the texture of ingredients based on user chewing and swallowing abilities made it difficult to deliver a high-quality dining experience. Additionally, the absence of a system for effectively collecting post-meal feedback and incorporating it into future recommendations hindered improvements in service quality.

[0947] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0948] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a means for receiving and analyzing prompt sentences generated using the interactive artificial intelligence means. This enables the provision of meals optimized to the individual needs of the user and continuous improvement of service quality.

[0949] "Interactive artificial intelligence means" refers to technology that collects information through dialogue with the user and generates appropriate questions and answers based on that information.

[0950] "Data analysis means" refers to technologies that analyze collected user information and select the optimal ingredients and recipes based on the user's needs and constraints.

[0951] "Instruction generation means" refers to a technology for determining specific processing methods to adjust the texture of selected ingredients and generating those instructions.

[0952] A "three-dimensional food printer" is a technology for creating and serving food by arranging and processing ingredients in three dimensions based on generated instructions.

[0953] A "feedback collection method" is a technology that collects opinions and feedback from users after a meal, analyzes that data, and uses it to improve future suggestions and adjustment methods.

[0954] A "prompt message" refers to the format of questions and instructions generated by an interactive artificial intelligence based on input text or audio information.

[0955] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[0956] The server uses conversational artificial intelligence (AI) to send questions to the user's terminal. These questions include what the user wants to eat, preferred ingredients, allergy information, and the degree of chewing and swallowing difficulties. This allows the server to collect information about the user's preferences and constraints. For example, the user might respond, "Today I want to eat a soft fish dish." Conversational AI such as IBM Watson or Google Dialogflow are used for this information collection.

[0957] Next, the server analyzes the collected user information using data analysis tools. Python libraries such as Pandas and Numpy are used for this analysis. Based on the collected information, ingredients and recipes tailored to the user's preferences are selected. For example, if the user prefers soft ingredients, a soft fish dish will be selected.

[0958] Next, the server uses an instruction generation mechanism to determine specific processing methods (such as mixing, steaming, and gelling) to adjust the texture of the selected ingredients. A recipe generation algorithm is used in this process. The generated instructions are then sent to a three-dimensional food printer.

[0959] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking. For example, an XYZ Food Printer is used. The printer performs mixing and gelling to create a fish dish with a soft texture. The user receives the cooked dish from this printer.

[0960] Finally, the server uses a feedback collection mechanism to gather post-meal feedback from users. This feedback includes the taste, appearance, aroma, and overall satisfaction of the meal. Users input this feedback via a terminal, and it is sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Python Numpy library is used for this analysis.

[0961] Examples of prompt statements as concrete examples:

[0962] User preferences and constraints: Likes soft foods, wants to eat fish, no allergies.

[0963] Suggested menu: Soft gelled fish dish

[0964] Processing method: Mixing, gelation

[0965] Feedback: It tastes good and looks beautiful, but it would be even better if the aroma were a little stronger.

[0966] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or dysphagia. Furthermore, by utilizing user feedback, the quality of the service can be continuously improved.

[0967] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0968] Step 1:

[0969] User information collection

[0970] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include "What do you want to eat today?", "What are your favorite foods and allergy information?", and "What is the degree of your chewing ability and swallowing difficulties?". The user answers these questions using their terminal and inputs their preferences and constraints. The collected information includes specific data such as "I like soft foods, I want to eat fish, I have no allergies".

[0971] Input: User preferences and constraints (text format)

[0972] Output: Database of user preferences and constraints

[0973] Step 2:

[0974] Data analysis and menu proposals

[0975] The server analyzes the collected user information using data analysis tools. Using Python libraries such as Pandas and Numpy, it analyzes the user's input data and selects appropriate ingredients and recipes. For example, if the user prefers soft ingredients, a soft fish dish will be selected. The server then generates a customized menu and sends the suggestion to the user's terminal. The user reviews the suggested menu and requests approval or modification.

[0976] Input: Database of user preferences and constraints

[0977] Data processing: Analyze user input data.

[0978] Output: Suggested menu (text format)

[0979] Step 3:

[0980] Texture customization

[0981] The server determines whether texture adjustment is necessary for the selected ingredients. A recipe generation algorithm is used to determine specific processing methods (mixing, steaming, gelling, etc.). For example, if a soft fish dish is selected, mixing and gelling will be used. The server generates detailed processing instructions and sends them to the 3D food printer.

[0982] Input: Suggested menu

[0983] Data processing: Determination of processing method using a recipe generation algorithm.

[0984] Output: Processing instructions (text format)

[0985] Step 4:

[0986] Cooking and serving using a 3D food printer.

[0987] A 3D food printer selects ingredients according to instructions received from a server, and prints them while performing processing such as mixing and gelling. For example, an XYZ Food Printer is used to create a soft, gelled fish dish. The user receives the cooked dish from the printer.

[0988] Input: Processing Instructions

[0989] Output: Cooked food

[0990] Step 5:

[0991] Feedback gathering and improvement

[0992] The server requests feedback from users after their meal. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction of the meal. Users enter their feedback, which is then sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Numpy library in Python is used to analyze the feedback data.

[0993] Input: User feedback (text format)

[0994] Data processing: Analysis of feedback data

[0995] Output: Improved suggestions and adjustment methods

[0996] Through these steps, the system can provide meals optimized to the individual needs of users and continuously improve the quality of service.

[0997] (Application Example 1)

[0998] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0999] In recent years, the aging population and increasing health consciousness have led to a growing demand for meals tailored to the individual preferences and health conditions of users. Furthermore, with the proliferation of food delivery services, there is a growing need for more customized menus. However, current systems generally only offer standardized menus, making it difficult to meet individual user needs. Therefore, a system is needed that provides meals based on ingredients with textures adjusted according to each user's preferences and constraints, while maintaining visual appeal, aroma, and nutritional balance, and further collecting feedback to improve future offerings.

[1000] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1001] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing the collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a suggestion means for creating a custom menu and providing it to the food delivery service. This makes it possible to provide an optimal meal based on the individual user's preferences and constraints, and to further improve suggestions for the next time by utilizing the feedback.

[1002] "Gathering user preferences and constraints" means using conversational artificial intelligence to collect information about users' food preferences, allergies, health status, and other related information.

[1003] "Adjusting the texture of ingredients" means optimally changing the firmness and mouthfeel of ingredients according to the individual user's requests.

[1004] "Interactive artificial intelligence means" refers to a system that collects information and answers questions through dialogue with the user using natural language.

[1005] "Analyzing collected user information" means performing data analysis based on collected user preferences, constraints, and health information to select appropriate ingredients and recipes.

[1006] A "data analysis method" is a system that analyzes collected data to select the most suitable ingredients and recipes.

[1007] "Generating processing instructions for texture adjustment of selected ingredients" means creating detailed instructions on how to process the ingredients based on the results of data analysis.

[1008] "Instruction generation means" refers to a system means that automatically generates instructions regarding the processing method of food ingredients.

[1009] A "three-dimensional food printer" is a device that prints food ingredients in three dimensions based on digital instructions and then cooks them.

[1010] A "feedback collection method" is a system for collecting information such as user feedback and suggestions for improvement after a meal.

[1011] "A means of proposing the creation of a custom menu and providing it to a food delivery service" refers to a system that creates a customized menu according to the user's request and proposes providing that menu to a food delivery service.

[1012] The system for implementing the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides customized meals that are visually appealing and maintain aroma and nutritional balance. The system includes interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and proposal means for creating custom menus and providing them to food delivery services.

[1013] The server uses conversational artificial intelligence to collect information from the user regarding their food preferences, allergies, chewing ability, and other related information. This collected information is analyzed by a data analysis system to select ingredients and recipes that match the user's requirements. It also determines whether the texture of the ingredients needs to be adjusted. Based on the selected ingredients and recipes, an instruction generation system automatically generates specific processing instructions, such as mixing, steaming, and gelling.

[1014] The 3D food printer prints ingredients based on generated instructions and cooks them appropriately. The cooked custom-textured food is validated by a feedback collection mechanism before being served to the user. After the meal, the user provides feedback on taste, appearance, aroma, etc., and this information is sent to the server to improve future suggestions and texture adjustments.

[1015] To implement this system, the following hardware and software will be used: a server, an interactive artificial intelligence engine (e.g., OpenAI API), data analysis tools, a 3D food printer (e.g., XYZprinting's food printer), and an application for collecting user feedback.

[1016] As a concrete example, if a user enters "I want to eat a soft fish dish today" into the application and indicates "shellfish allergy" as allergy information, the server will suggest a soft white fish fillet. It will also create a customized menu that includes steamed vegetables and low-sugar jelly. Once this menu is approved, the instructions are sent to a 3D food printer, and the cooked customized menu is served.

[1017] Examples of prompts for generative AI models:

[1018] User preferences: "I like soft foods and seafood."

[1019] Allergies: "Shellfish allergy"

[1020] Chewing Ability: "Decreased"

[1021] Create a customized menu that adheres to these preferences and restrictions.

[1022] As a result, we can provide optimal meals based on each user's individual preferences and constraints, and further improve meal suggestions for future visits by utilizing their feedback.

[1023] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1024] Step 1:

[1025] The server sends questions to the user through an interactive artificial intelligence system. Specifically, the server displays questions on the user's terminal such as, "What kind of food would you like to eat today?", "Do you have any preferred ingredients or allergy information?", and "What is the degree of your chewing ability or swallowing difficulties?". The input data consists of the user's preferences and constraints, and data analysis is performed based on this data.

[1026] Step 2:

[1027] Users operate a terminal to answer questions and input their preferences and constraints. For example, they might submit information such as "likes soft foods and seafood," "shellfish allergy," or "has difficulty chewing." The input data is sent to a server and becomes the basis for the next analysis step.

[1028] Step 3:

[1029] The data analysis tool analyzes the collected user information. The server selects ingredients and recipes suitable for the user based on the input preferences and constraints. This process uses a generative AI model to generate prompts and select ingredients and recipes. For example, if the input is "User Preferences: Soft dishes, likes seafood, Allergies: Shellfish allergy, Chewing Ability: Reduced, Create a customized menu that adheres to these preferences and restrictions," the analysis results will generate a custom menu such as "Soft white fish fillet," "Steamed vegetables," and "Low-sugar jelly."

[1030] Step 4:

[1031] The server generates processing instructions based on the selected ingredients and recipe. The instruction generation mechanism determines specific processing methods to adjust the texture of the ingredients. For example, it generates detailed processing instructions such as "mix and gel to soften the white fish" or "steam the vegetables." These instructions become the input data for the 3D food printer.

[1032] Step 5:

[1033] The 3D food printer system prints and cooks ingredients based on generated instructions. The 3D food printer terminal receives instructions from the server and processes the ingredients according to those instructions. The ingredients are mixed, gelled, printed, and cooked to produce a custom-textured food. The output data is the cooked custom menu.

[1034] Step 6:

[1035] Users receive custom-textured food delivered from a 3D food printer. After eating, users operate a terminal to send feedback. The feedback collection mechanism displays questions to the user such as, "How was the taste, appearance, aroma, and overall satisfaction of the meal?" and collects feedback. The input data is user feedback information, which serves as the basis for the next improvement steps.

[1036] Step 7:

[1037] The server analyzes the collected feedback and updates the database. Analyzing the feedback information allows for further improvements to future menu suggestions and texture adjustments. Ultimately, the data analysis is based on the feedback, resulting in improved quality for future custom menus. The output data consists of new database entries resulting from the analysis.

[1038] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1039] This invention is a system that improves the dining experience by listening to the user's preferences and constraints, adjusting the texture of ingredients based on them, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine.

[1040] System programming and processing

[1041] 1. User authentication and login

[1042] The server displays an authentication screen on the terminal for the user to log in.

[1043] The user enters their authentication information (user ID, password, etc.).

[1044] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[1045] 2. Gathering information on user preferences and constraints.

[1046] The server uses interactive artificial intelligence to send the following questions to the terminal.

[1047] What I want to eat today

[1048] Favorite foods and foods you dislike

[1049] Allergy Information

[1050] Degree of chewing and swallowing difficulties

[1051] The user answers these questions and enters the relevant information.

[1052] 3. Recognition of user emotions

[1053] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions.

[1054] The server collects emotional data to understand the user's mood on any given day.

[1055] 4. Data analysis and menu generation

[1056] The server analyzes the collected user responses and sentiment data using data analysis tools.

[1057] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[1058] The server sends the generated menu to the terminal along with a message suggesting it.

[1059] 5. Menu confirmation and approval

[1060] The user reviews the suggested menu.

[1061] The user can either accept the proposed menu or request a change.

[1062] The server will suggest new menus as needed, based on the user's selection.

[1063] 6. Decision on texture adjustments

[1064] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[1065] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[1066] 7. Cooking using a 3D food printer

[1067] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[1068] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1069] 8. Serving the food

[1070] The terminal, a 3D food printer, dispenses the finished dish.

[1071] The user receives a custom-textured hood provided by the printer.

[1072] 9. Gathering Feedback

[1073] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1074] The user enters their response into the feedback form and submits it.

[1075] 10. Analysis and Improvement of Feedback

[1076] The server analyzes the feedback received from the user and records it in a database.

[1077] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[1078] Specific example

[1079] For example, if a user indicates that they are feeling a little down today, the system will take that emotional data into consideration and suggest ingredients and menu items that will help calm them down. The server uses an emotion engine to analyze the user's mood and, for example, suggest a warm vegetable soup and a mild-flavored dessert. Based on this information, the server can issue specific cooking instructions to a 3D food printer, providing a meal that takes the user's emotions into consideration.

[1080] Through the above process, the system of the present invention can provide people with reduced chewing ability or swallowing difficulties with delicious, visually satisfying meals that take their emotions into consideration.

[1081] The following describes the processing flow.

[1082] Step 1:

[1083] User authentication and login

[1084] The server displays an authentication screen on the terminal for the user to log in.

[1085] The user enters their authentication information (user ID, password, etc.).

[1086] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[1087] Step 2:

[1088] Interviewing users about their preferences and constraints

[1089] The server uses interactive artificial intelligence to send the following questions to the terminal.

[1090] What I want to eat today

[1091] Favorite foods and foods you dislike

[1092] Allergy Information

[1093] Degree of chewing and swallowing difficulties

[1094] The user answers these questions and enters the relevant information.

[1095] Step 3:

[1096] Recognition of user emotions

[1097] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. For example, it analyzes the user's current mood by detecting changes in voice tone, word choice, and facial expressions.

[1098] The server collects emotional data to understand the user's mood on any given day.

[1099] Step 4:

[1100] Data analysis and menu generation

[1101] The server analyzes the collected user responses and emotional data using data analysis tools. For example, based on emotional data indicating that a user is "a little depressed," it selects menu items that have a relaxing effect.

[1102] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[1103] The server sends the generated menu to the terminal along with a message suggesting it.

[1104] Step 5:

[1105] Menu confirmation and approval

[1106] The user reviews the suggested menu. For example, they might see "Sea bream soufflé with vegetable puree" as a menu suggested by the server on their device.

[1107] The user can either accept the suggested menu or request a change. For example, the user might request "soup instead of vegetable puree."

[1108] The server will suggest new menu items as needed, based on the user's selection. For example, it might suggest soup instead of vegetable puree.

[1109] Step 6:

[1110] Texture adjustment decision

[1111] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[1112] The server translates these processing methods into specific instructions and sends them to the three-dimensional food printer. For example, it generates instructions to "use a gelling agent and mix" for a sea bream soufflé.

[1113] Step 7:

[1114] Cooking using a 3D food printer

[1115] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[1116] The terminal performs a specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture. For example, it can make a sea bream soufflé soft.

[1117] Step 8:

[1118] Food service

[1119] The terminal, a 3D food printer, dispenses the finished dish.

[1120] The user receives custom-textured food provided by the printer. For example, they might receive a serving of sea bream soufflé and vegetable soup.

[1121] Step 9:

[1122] Gathering feedback

[1123] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1124] The user enters their response into a feedback form and submits it. For example, they might enter, "The taste was good, but I would like the aroma to be a little stronger."

[1125] Step 10:

[1126] Feedback analysis and improvement

[1127] The server analyzes the feedback received from users and records it in a database. For example, it records feedback regarding the intensity of a scent.

[1128] The server updates its algorithms based on the analysis of feedback to improve future suggestions and adjustment methods. For example, next time, it might incorporate cooking methods that enhance the aroma into its selection.

[1129] (Example 2)

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

[1131] Conventional meal suggestion systems offer menus that take into account user preferences and limitations, but they fail to provide appropriate meal suggestions based on the user's emotional state. Therefore, providing meals that are sensitive to the emotional needs of users, particularly those with reduced chewing ability or swallowing difficulties, presents a challenge. Furthermore, system improvements utilizing feedback have been insufficient.

[1132] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1133] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and emotional data and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and an emotion engine for recognizing emotions from the user's voice and facial expressions. This enables appropriate menu suggestions according to the user's emotional state and texture adjustments that take those emotions into consideration.

[1134] "Interactive artificial intelligence means" refers to a means of providing an interface that uses artificial intelligence to gather user preferences and constraints.

[1135] "Data analysis means" refers to methods for analyzing collected user information and sentiment data to select appropriate ingredients and recipes.

[1136] The "instruction generation means" is a means for generating specific processing instructions for adjusting the texture of food ingredients based on the analysis results.

[1137] A "three-dimensional food printer means" is a means for printing food ingredients based on generated instructions, and then cooking and serving them.

[1138] "Feedback collection methods" refer to methods for collecting feedback from users after a meal and using that feedback to improve future suggestions and adjustments.

[1139] An "emotion engine" is a means of recognizing emotions from a user's voice and facial expressions and providing that information to data analysis tools.

[1140] This invention is a system that improves the dining experience by thoroughly interviewing users about their preferences and constraints, adjusting the texture of ingredients based on that information, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine. The operation of each means and the hardware and software used will be described in detail below.

[1141] User authentication and login

[1142] The server displays an authentication screen on the terminal for the user to log in. This generates an HTML form containing input fields for user ID and password. Once the user submits the information, the server queries the database (e.g., MySQL) to verify that the user exists. If authentication is successful, the server reads the user profile and proceeds to the next process.

[1143] Interviewing users about their preferences and constraints

[1144] The server activates an interactive artificial intelligence (e.g., IBM Watson) to generate questions about what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of chewing and swallowing difficulties. The generated series of questions are sent to the user's terminal, and the user answers them.

[1145] Recognition of user emotions

[1146] The server utilizes an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions. The collected emotion data is stored for analysis.

[1147] Data analysis and menu generation

[1148] The server analyzes the collected user responses and sentiment data using data analysis tools (data analysis algorithms implemented in Python). Based on the analysis results, it selects appropriate ingredients and recipes (for example, sea bream soufflé and vegetable puree). The generated menu is then suggested to the user's terminal.

[1149] Menu confirmation and approval

[1150] The user reviews the suggested menu. They enter their information as requested for approval or modification. The server then either suggests a new menu or proceeds to the next process.

[1151] Texture adjustment decision

[1152] The server determines the specific processing method (e.g., mixing, steaming, gelling) based on the approved menu. These processing steps are then transmitted as specific instructions to the 3D food printer.

[1153] Cooking using a 3D food printer

[1154] The terminal, a 3D food printer, prints and cooks ingredients according to instructions received from the server. It executes the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1155] Food service

[1156] The terminal, a 3D food printer, delivers the finished dish to the user. The user receives the custom-textured food provided by the printer.

[1157] Gathering feedback

[1158] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal, where the user answers questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[1159] Feedback analysis and improvement

[1160] The server analyzes the feedback received from users and records it in a database. Furthermore, it updates its algorithms based on the feedback to improve future suggestions and adjustment methods.

[1161] Specific example

[1162] For example, if a user indicates that they are feeling a little down today, the server uses an emotion engine to analyze this information. Based on the analysis results, it suggests a warm vegetable soup and a mild-flavored dessert. The server then uses this information to send specific cooking instructions (e.g., steaming and smoothing vegetables) to a 3D food printer. As a result, it can provide users with a meal that takes their emotions into consideration.

[1163] Example of a prompt

[1164] "A user is feeling a bit down today. Please suggest a menu that will help them calm down. What ingredients and cooking methods would be appropriate to include in that menu?"

[1165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1166] Step 1:

[1167] User authentication and login

[1168] The server displays an authentication screen on the terminal for the user to log in. The screen includes fields for entering the user ID and password.

[1169] The user enters their user ID and password in the input fields and submits the form.

[1170] The server compares the received user ID and password with a database (e.g., MySQL) and retrieves the user profile if authentication is successful. If authentication is successful, the user profile is output.

[1171] Input: User ID, Password

[1172] Output: User Profile

[1173] Step 2:

[1174] Interviewing users about their preferences and constraints

[1175] The server activates an interactive artificial intelligence tool (e.g., IBM Watson) to generate the following questions: what you want to eat today, your favorite and disliked ingredients, allergy information, and the degree of your chewing and swallowing difficulties.

[1176] The server sends the generated question to the user's terminal.

[1177] The user answers the questions displayed on the device and enters the required information.

[1178] The server receives and stores the user's response.

[1179] Input: Questions about user preferences and constraints

[1180] Output: User response data

[1181] Step 3:

[1182] Recognition of user emotions

[1183] The server uses an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions.

[1184] The server analyzes emotional data and stores it to understand the user's mood on that day.

[1185] Input: User voice data, facial expression data

[1186] Output: Recognized emotion data

[1187] Step 4:

[1188] Data analysis and menu generation

[1189] The server analyzes the collected user response data and sentiment data using data analysis tools (data analysis algorithms implemented in Python).

[1190] Based on the analysis results, the server selects appropriate ingredients and recipes (e.g., sea bream soufflé and vegetable puree) that match the user's preferences and constraints.

[1191] The server sends the generated menu to the user's terminal along with a suggestion message.

[1192] Input: User response data, sentiment data

[1193] Output: Analysis results, suggested menu

[1194] Step 5:

[1195] Menu confirmation and approval

[1196] The user checks the suggested menu on their device.

[1197] The user either approves the menu or requests a change.

[1198] The server may also suggest new menu options based on the user's selection.

[1199] Input: User approval or modification request for the suggested menu.

[1200] Output: Approved menu, or menu to be re-proposed.

[1201] Step 6:

[1202] Texture adjustment decision

[1203] The server determines the specific processing method (mixing, steaming, gelling) based on the approved menu.

[1204] The server converts these processing methods into instructions and transmits them to the three-dimensional food printer.

[1205] Input: Approved menu

[1206] Output: Specific processing instructions

[1207] Step 7:

[1208] Cooking using a 3D food printer

[1209] The terminal, a 3D food printer, prints selected ingredients and prepares them based on instructions received from the server.

[1210] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1211] Input: Specific processing instructions

[1212] Output: Cooked custom textured food

[1213] Step 8:

[1214] Food service

[1215] The terminal, a 3D food printer, dispenses the finished dish.

[1216] The user receives a custom-textured hood provided by the printer.

[1217] Input: Cooked custom textured food

[1218] Output: Served dishes

[1219] Step 9:

[1220] Gathering feedback

[1221] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal. The feedback includes questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[1222] The user enters their response into the feedback form and submits it.

[1223] The server receives and stores the feedback.

[1224] Input: Questions from the feedback form, user responses

[1225] Output: Feedback data

[1226] Step 10:

[1227] Feedback analysis and improvement

[1228] The server analyzes the received feedback data and records it in the database.

[1229] The server updates its algorithms based on feedback to improve future suggestions and adjustment methods.

[1230] Input: Feedback data

[1231] Output: Updated algorithm and adjustments reflected in the next proposal.

[1232] (Application Example 2)

[1233] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1234] Traditional meal delivery systems offer customization based on user preferences and constraints, but they do not consider the user's emotional state when suggesting menus or preparing meals. Furthermore, they lack features that allow users to select ingredients and review menus through interactive experiences in virtual restaurants. Therefore, the quality of the user experience is limited, and providing meals that are sensitive to the user's emotions presents a significant challenge.

[1235] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1236] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of selected ingredients; a three-dimensional food printer means for cooking and serving ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; an emotion recognition means for identifying the user's emotions and suggesting menus according to their emotional state; and an interactive interface means for accessing a virtual store and interactively selecting ingredients and checking menus. This makes it possible to provide more personalized meals that take into account the user's emotional state.

[1237] "Gathering information about user preferences and constraints" involves collecting information about the foods users are looking for, allergy information, ingredient preferences, and physical limitations.

[1238] "Adjusting the texture of ingredients" means changing the physical texture and shape of ingredients according to the user's preferences and constraints.

[1239] "Interactive artificial intelligence means" refers to technologies that interact with users using natural language to collect information and provide guidance.

[1240] This refers to "collected user information," which includes data such as preferences, constraints, and emotional states obtained from users.

[1241] "Data analysis methods" refer to technologies that analyze patterns and trends based on collected user information to select appropriate ingredients and recipes.

[1242] "Instruction generation means" refers to a technology that generates specific cooking instructions based on analyzed data.

[1243] A "three-dimensional food printer" is a device that, based on generated instructions, stacks ingredients in a three-dimensional manner to create the final dish.

[1244] "Feedback collection methods" refer to technologies used to collect user feedback and evaluations to inform future adjustments and improvements.

[1245] "Emotion recognition means" refers to technology that analyzes the user's voice and facial expressions to identify their current emotional state.

[1246] An "interactive interface means" is a technology that allows users to interactively select ingredients and check menus in a virtual store.

[1247] This invention is a system that provides individually customized meals using a three-dimensional food printer, taking into account the user's preferences, constraints, and emotional state. Specifically, it is implemented by three entities: a server, a terminal, and a user.

[1248] First, the server uses conversational artificial intelligence to gather information about the user's preferences and constraints. The server collects information such as what the user wants to eat today, their favorite and disliked ingredients, allergy information, and the degree of chewing and swallowing difficulties. This information is stored as text data.

[1249] Next, the server uses an emotion engine to recognize emotions from the user's voice and facial expressions. This emotion data is analyzed and used to understand the user's current state of mind. The data obtained through emotion recognition is crucial when selecting menu items.

[1250] Based on collected user information and sentiment data, the server uses data analysis tools to select the optimal ingredients and recipes. Based on the analysis results, the server generates a specific menu and proposes it to the user. This proposal is presented to the user in text format.

[1251] After the user reviews and approves the proposed menu, the server generates specific processing instructions for adjusting the texture of the ingredients. These instructions include methods such as mixing, steaming, and gelling. The generated instructions are then sent to a three-dimensional food printer.

[1252] The terminal, a 3D food printer, cooks and serves ingredients based on received instructions. The 3D food printer executes the specified process to complete a dish with the optimal texture. This dish is then served to the user.

[1253] After the meal, the server uses feedback collection tools to gather feedback from the user. This includes taste, appearance, aroma, and overall satisfaction. The collected feedback is stored in a database and used to improve future suggestions and adjustments.

[1254] Furthermore, the server provides an interactive interface to enable users to interactively select ingredients and check menus within the virtual store. Users can access and operate the virtual store using smartphones, smart glasses, head-mounted displays, etc. Within the virtual store, users can receive real-time feedback on the ingredients and menus they select.

[1255] Specific example

[1256] For example, if a user is feeling "a little tired today," the server, through emotion recognition, will understand that emotion and suggest foods and menu items that will soothe the user. For instance, it could suggest a warm vegetable soup and a mild-flavored dessert. This information is then sent to a 3D food printer for preparation.

[1257] Example of a prompt

[1258] "What do you want to eat today?"

[1259] "Do you have any favorite or least favorite foods?"

[1260] "Please provide allergy information."

[1261] "Please tell us about your chewing ability and the degree of your swallowing difficulties."

[1262] This invention makes it possible to provide more personalized meals that take into account a variety of information, including the user's emotional state.

[1263] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1264] Step 1:

[1265] The server displays an authentication screen on the user's smartphone or smart glasses for login. The user enters their authentication information (user ID and password) and sends it to the server. The server compares this information with the database, and if authentication is successful, retrieves the user profile and proceeds to the next step. The input is the user ID and password, and the output is the user profile.

[1266] Step 2:

[1267] The server uses conversational artificial intelligence to send questions to the user. These questions cover topics such as what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of their chewing and swallowing difficulties. The user answers these questions and sends the information to the server. The input is the user's answers to the questions, and the output is the collected user information.

[1268] Step 3:

[1269] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. It analyzes the user's voice and image data to identify their emotional state. The input is the user's voice and image data, and the output is data of the recognized emotional state.

[1270] Step 4:

[1271] The server analyzes collected user information and recognized sentiment data using data analysis tools. Through this analysis, it selects appropriate ingredients and recipes based on the user's preferences and constraints. The input is user information and sentiment data, and the output is data on the selected menu.

[1272] Step 5:

[1273] The server proposes a menu to the user based on the analysis results. The user reviews the proposed menu and requests approval or modification. The input is the data of the analyzed menu, and the output is the user's approval or modification request.

[1274] Step 6:

[1275] The server determines specific processing methods to adjust the texture of the ingredients based on the menu approved by the user. It generates specific processing instructions and sends them to a 3D food printer. The input is the data of the approved menu, and the output is the specific processing instructions.

[1276] Step 7:

[1277] The terminal, a 3D food printer, prints and cooks ingredients based on processing instructions received from the server. The input is the processing instructions from the server, and the output is the final cooked dish.

[1278] Step 8:

[1279] A 3D food printer delivers a finished dish to the user. The user receives the dish from the printer and enjoys the meal. The input is the cooked food, and the output is the user's dining experience.

[1280] Step 9:

[1281] After the meal, the server collects feedback from the user using questionnaires and conversational AI. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction with the meal. The input is the user's feedback, and the output is the collected feedback data.

[1282] Step 10:

[1283] The server analyzes the feedback collected using the feedback collection mechanism and updates the algorithm to improve future proposals and adjustment methods. The input is feedback data, and the output is the improved proposal and adjustment algorithm.

[1284] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1285] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1286] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1287] [Fourth Embodiment]

[1288] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1289] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1290] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1291] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1292] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1293] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1294] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1295] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1296] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1297] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1298] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1299] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1300] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1301] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[1302] System programming and processing

[1303] 1. User information collection

[1304] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include what the user wants to eat today, their preferred ingredients and allergy information, and the degree of their chewing and swallowing difficulties.

[1305] The user responds to this question and enters their preferences and constraints.

[1306] 2. Data Analysis and Menu Proposal

[1307] The server analyzes the collected user information and selects ingredients and recipes that match the user's preferences. Furthermore, it determines whether or not the texture of each ingredient needs to be adjusted.

[1308] The server generates a customized menu based on this information and sends the suggestions to the user's terminal.

[1309] The user reviews the proposed menu and either approves it or requests changes.

[1310] 3. Texture Customization

[1311] The server determines the specific processing methods (such as mixing, steaming, or gelling) to adjust the texture of the ingredients included in the menu.

[1312] The server generates detailed instructions based on the processing method and sends them to the three-dimensional food printer.

[1313] 4. Cooking and serving using a 3D food printer.

[1314] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking.

[1315] Users receive custom-textured food delivered from a 3D food printer.

[1316] 5. Gathering feedback and making improvements

[1317] The server asks users for feedback after their meal. This feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1318] The user enters feedback, and that information is sent to the server.

[1319] The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments.

[1320] Specific example

[1321] For example, if a user indicates a preference for soft foods due to reduced chewing ability, the system will select fish as the main ingredient and consider how to prepare it to be tender. The server sends detailed instructions to a 3D food printer, which will then perform mixing and gelling to tenderize the fish. Once the dish is complete, it is served to the user. Based on the user's feedback, the system then further improves its suggestions for the next time.

[1322] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or swallowing difficulties.

[1323] The following describes the processing flow.

[1324] Step 1:

[1325] User authentication and login

[1326] The server displays an authentication screen on the terminal for the user to log in.

[1327] The user enters their authentication information (user ID, password, etc.).

[1328] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[1329] Step 2:

[1330] Interviewing users about their preferences and constraints

[1331] The server uses interactive artificial intelligence to send the following questions to the terminal.

[1332] What I want to eat today

[1333] Favorite foods and foods you dislike

[1334] Allergy Information

[1335] Degree of chewing and swallowing difficulties

[1336] The user answers these questions and enters the relevant information.

[1337] Step 3:

[1338] Data analysis and menu generation

[1339] The server analyzes the collected user responses using data analysis tools.

[1340] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it can generate menus such as sea bream soufflé and vegetable puree.

[1341] The server sends the generated menu to the terminal along with a message suggesting it.

[1342] Step 4:

[1343] Menu confirmation and approval

[1344] The user reviews the suggested menu.

[1345] The user can either accept the proposed menu or request a change.

[1346] The server will suggest new menus as needed, based on the user's selection.

[1347] Step 5:

[1348] Texture adjustment decision

[1349] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[1350] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[1351] Step 6:

[1352] Cooking using a 3D food printer

[1353] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[1354] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1355] Step 7:

[1356] Food service

[1357] The terminal, a 3D food printer, dispenses the finished dish.

[1358] The user receives a custom-textured hood provided by the printer.

[1359] Step 8:

[1360] Gathering feedback

[1361] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1362] The user enters their response into the feedback form and submits it.

[1363] Step 9:

[1364] Feedback analysis and improvement

[1365] The server analyzes the feedback received from the user and records it in a database.

[1366] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[1367] (Example 1)

[1368] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1369] Traditional systems struggled to accommodate diverse user preferences and constraints, providing quickly and appropriately customized meals. Furthermore, the lack of means to adjust the texture of ingredients based on user chewing and swallowing abilities made it difficult to deliver a high-quality dining experience. Additionally, the absence of a system for effectively collecting post-meal feedback and incorporating it into future recommendations hindered improvements in service quality.

[1370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1371] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a means for receiving and analyzing prompt sentences generated using the interactive artificial intelligence means. This enables the provision of meals optimized to the individual needs of the user and continuous improvement of service quality.

[1372] "Interactive artificial intelligence means" refers to technology that collects information through dialogue with the user and generates appropriate questions and answers based on that information.

[1373] "Data analysis means" refers to technologies that analyze collected user information and select the optimal ingredients and recipes based on the user's needs and constraints.

[1374] "Instruction generation means" refers to a technology for determining specific processing methods to adjust the texture of selected ingredients and generating those instructions.

[1375] A "three-dimensional food printer" is a technology for creating and serving food by arranging and processing ingredients in three dimensions based on generated instructions.

[1376] A "feedback collection method" is a technology that collects opinions and feedback from users after a meal, analyzes that data, and uses it to improve future suggestions and adjustment methods.

[1377] A "prompt message" refers to the format of questions and instructions generated by an interactive artificial intelligence based on input text or audio information.

[1378] The system of the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides meals while maintaining visual appeal, aroma, and nutritional balance. The system includes multiple means such as interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, and feedback collection means.

[1379] The server uses conversational artificial intelligence (AI) to send questions to the user's terminal. These questions include what the user wants to eat, preferred ingredients, allergy information, and the degree of chewing and swallowing difficulties. This allows the server to collect information about the user's preferences and constraints. For example, the user might respond, "Today I want to eat a soft fish dish." Conversational AI such as IBM Watson or Google Dialogflow are used for this information collection.

[1380] Next, the server analyzes the collected user information using data analysis tools. Python libraries such as Pandas and Numpy are used for this analysis. Based on the collected information, ingredients and recipes tailored to the user's preferences are selected. For example, if the user prefers soft ingredients, a soft fish dish will be selected.

[1381] Next, the server uses an instruction generation mechanism to determine specific processing methods (such as mixing, steaming, and gelling) to adjust the texture of the selected ingredients. A recipe generation algorithm is used in this process. The generated instructions are then sent to a three-dimensional food printer.

[1382] The terminal, a 3D food printer, prints ingredients according to instructions received from the server and performs the appropriate cooking. For example, an XYZ Food Printer is used. The printer performs mixing and gelling to create a fish dish with a soft texture. The user receives the cooked dish from this printer.

[1383] Finally, the server uses a feedback collection mechanism to gather post-meal feedback from users. This feedback includes the taste, appearance, aroma, and overall satisfaction of the meal. Users input this feedback via a terminal, and it is sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Python Numpy library is used for this analysis.

[1384] Examples of prompt statements as concrete examples:

[1385] User preferences and constraints: Likes soft foods, wants to eat fish, no allergies.

[1386] Suggested menu: Soft gelled fish dish

[1387] Processing method: Mixing, gelation

[1388] Feedback: It tastes good and looks beautiful, but it would be even better if the aroma were a little stronger.

[1389] Through the above process, the system of the present invention can provide delicious and visually satisfying meals to people with reduced chewing ability or dysphagia. Furthermore, by utilizing user feedback, the quality of the service can be continuously improved.

[1390] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1391] Step 1:

[1392] User information collection

[1393] The server uses interactive artificial intelligence to send questions to the user's terminal. These questions include "What do you want to eat today?", "What are your favorite foods and allergy information?", and "What is the degree of your chewing ability and swallowing difficulties?". The user answers these questions using their terminal and inputs their preferences and constraints. The collected information includes specific data such as "I like soft foods, I want to eat fish, I have no allergies".

[1394] Input: User preferences and constraints (text format)

[1395] Output: Database of user preferences and constraints

[1396] Step 2:

[1397] Data analysis and menu proposals

[1398] The server analyzes the collected user information using data analysis tools. Using Python libraries such as Pandas and Numpy, it analyzes the user's input data and selects appropriate ingredients and recipes. For example, if the user prefers soft ingredients, a soft fish dish will be selected. The server then generates a customized menu and sends the suggestion to the user's terminal. The user reviews the suggested menu and requests approval or modification.

[1399] Input: Database of user preferences and constraints

[1400] Data processing: Analyze user input data.

[1401] Output: Suggested menu (text format)

[1402] Step 3:

[1403] Texture customization

[1404] The server determines whether texture adjustment is necessary for the selected ingredients. A recipe generation algorithm is used to determine specific processing methods (mixing, steaming, gelling, etc.). For example, if a soft fish dish is selected, mixing and gelling will be used. The server generates detailed processing instructions and sends them to the 3D food printer.

[1405] Input: Suggested menu

[1406] Data processing: Determination of processing method using a recipe generation algorithm.

[1407] Output: Processing instructions (text format)

[1408] Step 4:

[1409] Cooking and serving using a 3D food printer.

[1410] A 3D food printer selects ingredients according to instructions received from a server, and prints them while performing processing such as mixing and gelling. For example, an XYZ Food Printer is used to create a soft, gelled fish dish. The user receives the cooked dish from the printer.

[1411] Input: Processing Instructions

[1412] Output: Cooked food

[1413] Step 5:

[1414] Feedback gathering and improvement

[1415] The server requests feedback from users after their meal. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction of the meal. Users enter their feedback, which is then sent to the server. The server analyzes this feedback and updates its database to improve future menu suggestions and texture adjustments. The Numpy library in Python is used to analyze the feedback data.

[1416] Input: User feedback (text format)

[1417] Data processing: Analysis of feedback data

[1418] Output: Improved suggestions and adjustment methods

[1419] Through these steps, the system can provide meals optimized to the individual needs of users and continuously improve the quality of service.

[1420] (Application Example 1)

[1421] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1422] In recent years, the aging population and increasing health consciousness have led to a growing demand for meals tailored to the individual preferences and health conditions of users. Furthermore, with the proliferation of food delivery services, there is a growing need for more customized menus. However, current systems generally only offer standardized menus, making it difficult to meet individual user needs. Therefore, a system is needed that provides meals based on ingredients with textures adjusted according to each user's preferences and constraints, while maintaining visual appeal, aroma, and nutritional balance, and further collecting feedback to improve future offerings.

[1423] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1424] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing the collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and a suggestion means for creating a custom menu and providing it to the food delivery service. This makes it possible to provide an optimal meal based on the individual user's preferences and constraints, and to further improve suggestions for the next time by utilizing the feedback.

[1425] "Gathering user preferences and constraints" means using conversational artificial intelligence to collect information about users' food preferences, allergies, health status, and other related information.

[1426] "Adjusting the texture of ingredients" means optimally changing the firmness and mouthfeel of ingredients according to the individual user's requests.

[1427] "Interactive artificial intelligence means" refers to a system that collects information and answers questions through dialogue with the user using natural language.

[1428] "Analyzing collected user information" means performing data analysis based on collected user preferences, constraints, and health information to select appropriate ingredients and recipes.

[1429] A "data analysis method" is a system that analyzes collected data to select the most suitable ingredients and recipes.

[1430] "Generating processing instructions for texture adjustment of selected ingredients" means creating detailed instructions on how to process the ingredients based on the results of data analysis.

[1431] "Instruction generation means" refers to a system means that automatically generates instructions regarding the processing method of food ingredients.

[1432] A "three-dimensional food printer" is a device that prints food ingredients in three dimensions based on digital instructions and then cooks them.

[1433] A "feedback collection method" is a system for collecting information such as user feedback and suggestions for improvement after a meal.

[1434] "A means of proposing the creation of a custom menu and providing it to a food delivery service" refers to a system that creates a customized menu according to the user's request and proposes providing that menu to a food delivery service.

[1435] The system for implementing the present invention listens to the user's preferences and constraints, adjusts the texture of ingredients based on them, and provides customized meals that are visually appealing and maintain aroma and nutritional balance. The system includes interactive artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and proposal means for creating custom menus and providing them to food delivery services.

[1436] The server uses conversational artificial intelligence to collect information from the user regarding their food preferences, allergies, chewing ability, and other related information. This collected information is analyzed by a data analysis system to select ingredients and recipes that match the user's requirements. It also determines whether the texture of the ingredients needs to be adjusted. Based on the selected ingredients and recipes, an instruction generation system automatically generates specific processing instructions, such as mixing, steaming, and gelling.

[1437] The 3D food printer prints ingredients based on generated instructions and cooks them appropriately. The cooked custom-textured food is validated by a feedback collection mechanism before being served to the user. After the meal, the user provides feedback on taste, appearance, aroma, etc., and this information is sent to the server to improve future suggestions and texture adjustments.

[1438] To implement this system, the following hardware and software will be used: a server, an interactive artificial intelligence engine (e.g., OpenAI API), data analysis tools, a 3D food printer (e.g., XYZprinting's food printer), and an application for collecting user feedback.

[1439] As a concrete example, if a user enters "I want to eat a soft fish dish today" into the application and indicates "shellfish allergy" as allergy information, the server will suggest a soft white fish fillet. It will also create a customized menu that includes steamed vegetables and low-sugar jelly. Once this menu is approved, the instructions are sent to a 3D food printer, and the cooked customized menu is served.

[1440] Examples of prompts for generative AI models:

[1441] User preferences: "I like soft foods and seafood."

[1442] Allergies: "Shellfish allergy"

[1443] Chewing Ability: "Decreased"

[1444] Create a customized menu that adheres to these preferences and restrictions.

[1445] As a result, we can provide optimal meals based on each user's individual preferences and constraints, and further improve meal suggestions for future visits by utilizing their feedback.

[1446] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1447] Step 1:

[1448] The server sends questions to the user through an interactive artificial intelligence system. Specifically, the server displays questions on the user's terminal such as, "What kind of food would you like to eat today?", "Do you have any preferred ingredients or allergy information?", and "What is the degree of your chewing ability or swallowing difficulties?". The input data consists of the user's preferences and constraints, and data analysis is performed based on this data.

[1449] Step 2:

[1450] Users operate a terminal to answer questions and input their preferences and constraints. For example, they might submit information such as "likes soft foods and seafood," "shellfish allergy," or "has difficulty chewing." The input data is sent to a server and becomes the basis for the next analysis step.

[1451] Step 3:

[1452] The data analysis tool analyzes the collected user information. The server selects ingredients and recipes suitable for the user based on the input preferences and constraints. This process uses a generative AI model to generate prompts and select ingredients and recipes. For example, if the input is "User Preferences: Soft dishes, likes seafood, Allergies: Shellfish allergy, Chewing Ability: Reduced, Create a customized menu that adheres to these preferences and restrictions," the analysis results will generate a custom menu such as "Soft white fish fillet," "Steamed vegetables," and "Low-sugar jelly."

[1453] Step 4:

[1454] The server generates processing instructions based on the selected ingredients and recipe. The instruction generation mechanism determines specific processing methods to adjust the texture of the ingredients. For example, it generates detailed processing instructions such as "mix and gel to soften the white fish" or "steam the vegetables." These instructions become the input data for the 3D food printer.

[1455] Step 5:

[1456] The 3D food printer system prints and cooks ingredients based on generated instructions. The 3D food printer terminal receives instructions from the server and processes the ingredients according to those instructions. The ingredients are mixed, gelled, printed, and cooked to produce a custom-textured food. The output data is the cooked custom menu.

[1457] Step 6:

[1458] Users receive custom-textured food delivered from a 3D food printer. After eating, users operate a terminal to send feedback. The feedback collection mechanism displays questions to the user such as, "How was the taste, appearance, aroma, and overall satisfaction of the meal?" and collects feedback. The input data is user feedback information, which serves as the basis for the next improvement steps.

[1459] Step 7:

[1460] The server analyzes the collected feedback and updates the database. Analyzing the feedback information allows for further improvements to future menu suggestions and texture adjustments. Ultimately, the data analysis is based on the feedback, resulting in improved quality for future custom menus. The output data consists of new database entries resulting from the analysis.

[1461] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1462] This invention is a system that improves the dining experience by listening to the user's preferences and constraints, adjusting the texture of ingredients based on them, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine.

[1463] System programming and processing

[1464] 1. User authentication and login

[1465] The server displays an authentication screen on the terminal for the user to log in.

[1466] The user enters their authentication information (user ID, password, etc.).

[1467] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[1468] 2. Gathering information on user preferences and constraints.

[1469] The server uses interactive artificial intelligence to send the following questions to the terminal.

[1470] What I want to eat today

[1471] Favorite foods and foods you dislike

[1472] Allergy Information

[1473] Degree of chewing and swallowing difficulties

[1474] The user answers these questions and enters the relevant information.

[1475] 3. Recognition of user emotions

[1476] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions.

[1477] The server collects emotional data to understand the user's mood on any given day.

[1478] 4. Data analysis and menu generation

[1479] The server analyzes the collected user responses and sentiment data using data analysis tools.

[1480] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[1481] The server sends the generated menu to the terminal along with a message suggesting it.

[1482] 5. Menu confirmation and approval

[1483] The user reviews the suggested menu.

[1484] The user can either accept the proposed menu or request a change.

[1485] The server will suggest new menus as needed, based on the user's selection.

[1486] 6. Decision on texture adjustments

[1487] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[1488] The server converts these processing methods into specific instructions and transmits them to the three-dimensional food printer.

[1489] 7. Cooking using a 3D food printer

[1490] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[1491] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1492] 8. Serving the food

[1493] The terminal, a 3D food printer, dispenses the finished dish.

[1494] The user receives a custom-textured hood provided by the printer.

[1495] 9. Gathering Feedback

[1496] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1497] The user enters their response into the feedback form and submits it.

[1498] 10. Analysis and Improvement of Feedback

[1499] The server analyzes the feedback received from the user and records it in a database.

[1500] The server updates its algorithms to improve future suggestions and adjustment methods based on the analysis of the feedback.

[1501] Specific example

[1502] For example, if a user indicates that they are feeling a little down today, the system will take that emotional data into consideration and suggest ingredients and menu items that will help calm them down. The server uses an emotion engine to analyze the user's mood and, for example, suggest a warm vegetable soup and a mild-flavored dessert. Based on this information, the server can issue specific cooking instructions to a 3D food printer, providing a meal that takes the user's emotions into consideration.

[1503] Through the above process, the system of the present invention can provide people with reduced chewing ability or swallowing difficulties with delicious, visually satisfying meals that take their emotions into consideration.

[1504] The following describes the processing flow.

[1505] Step 1:

[1506] User authentication and login

[1507] The server displays an authentication screen on the terminal for the user to log in.

[1508] The user enters their authentication information (user ID, password, etc.).

[1509] The server verifies the entered information and, if authentication is successful, retrieves the user profile from the database.

[1510] Step 2:

[1511] Interviewing users about their preferences and constraints

[1512] The server uses interactive artificial intelligence to send the following questions to the terminal.

[1513] What I want to eat today

[1514] Favorite foods and foods you dislike

[1515] Allergy Information

[1516] Degree of chewing and swallowing difficulties

[1517] The user answers these questions and enters the relevant information.

[1518] Step 3:

[1519] Recognition of user emotions

[1520] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. For example, it analyzes the user's current mood by detecting changes in voice tone, word choice, and facial expressions.

[1521] The server collects emotional data to understand the user's mood on any given day.

[1522] Step 4:

[1523] Data analysis and menu generation

[1524] The server analyzes the collected user responses and emotional data using data analysis tools. For example, based on emotional data indicating that a user is "a little depressed," it selects menu items that have a relaxing effect.

[1525] Based on the analysis results, the server selects appropriate ingredients and recipes that match the user's preferences and constraints. For example, it might generate a menu such as sea bream soufflé and vegetable puree.

[1526] The server sends the generated menu to the terminal along with a message suggesting it.

[1527] Step 5:

[1528] Menu confirmation and approval

[1529] The user reviews the suggested menu. For example, they might see "Sea bream soufflé with vegetable puree" as a menu suggested by the server on their device.

[1530] The user can either accept the suggested menu or request a change. For example, the user might request "soup instead of vegetable puree."

[1531] The server will suggest new menu items as needed, based on the user's selection. For example, it might suggest soup instead of vegetable puree.

[1532] Step 6:

[1533] Texture adjustment decision

[1534] Based on the menu approved by the user, the server determines specific processing methods (e.g., mixing, steaming, gelling) to adjust the texture of the ingredients.

[1535] The server translates these processing methods into specific instructions and sends them to the three-dimensional food printer. For example, it generates instructions to "use a gelling agent and mix" for a sea bream soufflé.

[1536] Step 7:

[1537] Cooking using a 3D food printer

[1538] The terminal, a 3D food printer, prints ingredients and cooks them based on processing instructions received from the server.

[1539] The terminal performs a specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture. For example, it can make a sea bream soufflé soft.

[1540] Step 8:

[1541] Food service

[1542] The terminal, a 3D food printer, dispenses the finished dish.

[1543] The user receives custom-textured food provided by the printer. For example, they might receive a serving of sea bream soufflé and vegetable soup.

[1544] Step 9:

[1545] Gathering feedback

[1546] The server sends a feedback form to the user's device after the meal. The feedback includes the taste, appearance, aroma, and overall satisfaction with the meal.

[1547] The user enters their response into a feedback form and submits it. For example, they might enter, "The taste was good, but I would like the aroma to be a little stronger."

[1548] Step 10:

[1549] Feedback analysis and improvement

[1550] The server analyzes the feedback received from users and records it in a database. For example, it records feedback regarding the intensity of a scent.

[1551] The server updates its algorithms based on the analysis of feedback to improve future suggestions and adjustment methods. For example, next time, it might incorporate cooking methods that enhance the aroma into its selection.

[1552] (Example 2)

[1553] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1554] Conventional meal suggestion systems offer menus that take into account user preferences and limitations, but they fail to provide appropriate meal suggestions based on the user's emotional state. Therefore, providing meals that are sensitive to the emotional needs of users, particularly those with reduced chewing ability or swallowing difficulties, presents a challenge. Furthermore, system improvements utilizing feedback have been insufficient.

[1555] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1556] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and emotional data and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of the selected ingredients; a three-dimensional food printer means for cooking and serving the ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; and an emotion engine for recognizing emotions from the user's voice and facial expressions. This enables appropriate menu suggestions according to the user's emotional state and texture adjustments that take those emotions into consideration.

[1557] "Interactive artificial intelligence means" refers to a means of providing an interface that uses artificial intelligence to gather user preferences and constraints.

[1558] "Data analysis means" refers to methods for analyzing collected user information and sentiment data to select appropriate ingredients and recipes.

[1559] The "instruction generation means" is a means for generating specific processing instructions for adjusting the texture of food ingredients based on the analysis results.

[1560] A "three-dimensional food printer means" is a means for printing food ingredients based on generated instructions, and then cooking and serving them.

[1561] "Feedback collection methods" refer to methods for collecting feedback from users after a meal and using that feedback to improve future suggestions and adjustments.

[1562] An "emotion engine" is a means of recognizing emotions from a user's voice and facial expressions and providing that information to data analysis tools.

[1563] This invention is a system that improves the dining experience by thoroughly interviewing users about their preferences and constraints, adjusting the texture of ingredients based on that information, and further recognizing the user's emotions. The system includes multiple means such as conversational artificial intelligence means, data analysis means, instruction generation means, three-dimensional food printer means, feedback collection means, and emotion engine. The operation of each means and the hardware and software used will be described in detail below.

[1564] User authentication and login

[1565] The server displays an authentication screen on the terminal for the user to log in. This generates an HTML form containing input fields for user ID and password. Once the user submits the information, the server queries the database (e.g., MySQL) to verify that the user exists. If authentication is successful, the server reads the user profile and proceeds to the next process.

[1566] Interviewing users about their preferences and constraints

[1567] The server activates an interactive artificial intelligence (e.g., IBM Watson) to generate questions about what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of chewing and swallowing difficulties. The generated series of questions are sent to the user's terminal, and the user answers them.

[1568] Recognition of user emotions

[1569] The server utilizes an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions. The collected emotion data is stored for analysis.

[1570] Data analysis and menu generation

[1571] The server analyzes the collected user responses and sentiment data using data analysis tools (data analysis algorithms implemented in Python). Based on the analysis results, it selects appropriate ingredients and recipes (for example, sea bream soufflé and vegetable puree). The generated menu is then suggested to the user's terminal.

[1572] Menu confirmation and approval

[1573] The user reviews the suggested menu. They enter their information as requested for approval or modification. The server then either suggests a new menu or proceeds to the next process.

[1574] Texture adjustment decision

[1575] The server determines the specific processing method (e.g., mixing, steaming, gelling) based on the approved menu. These processing steps are then transmitted as specific instructions to the 3D food printer.

[1576] Cooking using a 3D food printer

[1577] The terminal, a 3D food printer, prints and cooks ingredients according to instructions received from the server. It executes the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1578] Food service

[1579] The terminal, a 3D food printer, delivers the finished dish to the user. The user receives the custom-textured food provided by the printer.

[1580] Gathering feedback

[1581] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal, where the user answers questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[1582] Feedback analysis and improvement

[1583] The server analyzes the feedback received from users and records it in a database. Furthermore, it updates its algorithms based on the feedback to improve future suggestions and adjustment methods.

[1584] Specific example

[1585] For example, if a user indicates that they are feeling a little down today, the server uses an emotion engine to analyze this information. Based on the analysis results, it suggests a warm vegetable soup and a mild-flavored dessert. The server then uses this information to send specific cooking instructions (e.g., steaming and smoothing vegetables) to a 3D food printer. As a result, it can provide users with a meal that takes their emotions into consideration.

[1586] Example of a prompt

[1587] "A user is feeling a bit down today. Please suggest a menu that will help them calm down. What ingredients and cooking methods would be appropriate to include in that menu?"

[1588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1589] Step 1:

[1590] User authentication and login

[1591] The server displays an authentication screen on the terminal for the user to log in. The screen includes fields for entering the user ID and password.

[1592] The user enters their user ID and password in the input fields and submits the form.

[1593] The server compares the received user ID and password with a database (e.g., MySQL) and retrieves the user profile if authentication is successful. If authentication is successful, the user profile is output.

[1594] Input: User ID, Password

[1595] Output: User Profile

[1596] Step 2:

[1597] Interviewing users about their preferences and constraints

[1598] The server activates an interactive artificial intelligence tool (e.g., IBM Watson) to generate the following questions: what you want to eat today, your favorite and disliked ingredients, allergy information, and the degree of your chewing and swallowing difficulties.

[1599] The server sends the generated question to the user's terminal.

[1600] The user answers the questions displayed on the device and enters the required information.

[1601] The server receives and stores the user's response.

[1602] Input: Questions about user preferences and constraints

[1603] Output: User response data

[1604] Step 3:

[1605] Recognition of user emotions

[1606] The server uses an emotion engine (for example, Microsoft Azure Cognitive Services) to collect user voice and facial expression data and recognize emotions.

[1607] The server analyzes emotional data and stores it to understand the user's mood on that day.

[1608] Input: User voice data, facial expression data

[1609] Output: Recognized emotion data

[1610] Step 4:

[1611] Data analysis and menu generation

[1612] The server analyzes the collected user response data and sentiment data using data analysis tools (data analysis algorithms implemented in Python).

[1613] Based on the analysis results, the server selects appropriate ingredients and recipes (e.g., sea bream soufflé and vegetable puree) that match the user's preferences and constraints.

[1614] The server sends the generated menu to the user's terminal along with a suggestion message.

[1615] Input: User response data, sentiment data

[1616] Output: Analysis results, suggested menu

[1617] Step 5:

[1618] Menu confirmation and approval

[1619] The user checks the suggested menu on their device.

[1620] The user either approves the menu or requests a change.

[1621] The server may also suggest new menu options based on the user's selection.

[1622] Input: User approval or modification request for the suggested menu.

[1623] Output: Approved menu, or menu to be re-proposed.

[1624] Step 6:

[1625] Texture adjustment decision

[1626] The server determines the specific processing method (mixing, steaming, gelling) based on the approved menu.

[1627] The server converts these processing methods into instructions and transmits them to the three-dimensional food printer.

[1628] Input: Approved menu

[1629] Output: Specific processing instructions

[1630] Step 7:

[1631] Cooking using a 3D food printer

[1632] The terminal, a 3D food printer, prints selected ingredients and prepares them based on instructions received from the server.

[1633] The terminal performs the specified process (e.g., mixing and gelling fish) to create a dish with the optimal texture.

[1634] Input: Specific processing instructions

[1635] Output: Cooked custom textured food

[1636] Step 8:

[1637] Food service

[1638] The terminal, a 3D food printer, dispenses the finished dish.

[1639] The user receives a custom-textured hood provided by the printer.

[1640] Input: Cooked custom textured food

[1641] Output: Served dishes

[1642] Step 9:

[1643] Gathering feedback

[1644] The server sends a feedback form (e.g., Google Forms) to the user's device after the meal. The feedback includes questions about the taste, appearance, aroma, and overall satisfaction of the meal.

[1645] The user enters their response into the feedback form and submits it.

[1646] The server receives and stores the feedback.

[1647] Input: Questions from the feedback form, user responses

[1648] Output: Feedback data

[1649] Step 10:

[1650] Feedback analysis and improvement

[1651] The server analyzes the received feedback data and records it in the database.

[1652] The server updates its algorithms based on feedback to improve future suggestions and adjustment methods.

[1653] Input: Feedback data

[1654] Output: Updated algorithm and adjustments reflected in the next proposal.

[1655] (Application Example 2)

[1656] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1657] Traditional meal delivery systems offer customization based on user preferences and constraints, but they do not consider the user's emotional state when suggesting menus or preparing meals. Furthermore, they lack features that allow users to select ingredients and review menus through interactive experiences in virtual restaurants. Therefore, the quality of the user experience is limited, and providing meals that are sensitive to the user's emotions presents a significant challenge.

[1658] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1659] In this invention, the server includes: an interactive artificial intelligence means for listening to the user's preferences and constraints and adjusting the texture of ingredients; a data analysis means for analyzing collected user information and selecting appropriate ingredients and recipes; an instruction generation means for generating processing instructions for adjusting the texture of selected ingredients; a three-dimensional food printer means for cooking and serving ingredients based on the generated instructions; a feedback collection means for collecting feedback from the user after the meal and improving suggestions and adjustment methods for the next time; an emotion recognition means for identifying the user's emotions and suggesting menus according to their emotional state; and an interactive interface means for accessing a virtual store and interactively selecting ingredients and checking menus. This makes it possible to provide more personalized meals that take into account the user's emotional state.

[1660] "Gathering information about user preferences and constraints" involves collecting information about the foods users are looking for, allergy information, ingredient preferences, and physical limitations.

[1661] "Adjusting the texture of ingredients" means changing the physical texture and shape of ingredients according to the user's preferences and constraints.

[1662] "Interactive artificial intelligence means" refers to technologies that interact with users using natural language to collect information and provide guidance.

[1663] This refers to "collected user information," which includes data such as preferences, constraints, and emotional states obtained from users.

[1664] "Data analysis methods" refer to technologies that analyze patterns and trends based on collected user information to select appropriate ingredients and recipes.

[1665] "Instruction generation means" refers to a technology that generates specific cooking instructions based on analyzed data.

[1666] A "three-dimensional food printer" is a device that, based on generated instructions, stacks ingredients in a three-dimensional manner to create the final dish.

[1667] "Feedback collection methods" refer to technologies used to collect user feedback and evaluations to inform future adjustments and improvements.

[1668] "Emotion recognition means" refers to technology that analyzes the user's voice and facial expressions to identify their current emotional state.

[1669] An "interactive interface means" is a technology that allows users to interactively select ingredients and check menus in a virtual store.

[1670] This invention is a system that provides individually customized meals using a three-dimensional food printer, taking into account the user's preferences, constraints, and emotional state. Specifically, it is implemented by three entities: a server, a terminal, and a user.

[1671] First, the server uses conversational artificial intelligence to gather information about the user's preferences and constraints. The server collects information such as what the user wants to eat today, their favorite and disliked ingredients, allergy information, and the degree of chewing and swallowing difficulties. This information is stored as text data.

[1672] Next, the server uses an emotion engine to recognize emotions from the user's voice and facial expressions. This emotion data is analyzed and used to understand the user's current state of mind. The data obtained through emotion recognition is crucial when selecting menu items.

[1673] Based on collected user information and sentiment data, the server uses data analysis tools to select the optimal ingredients and recipes. Based on the analysis results, the server generates a specific menu and proposes it to the user. This proposal is presented to the user in text format.

[1674] After the user reviews and approves the proposed menu, the server generates specific processing instructions for adjusting the texture of the ingredients. These instructions include methods such as mixing, steaming, and gelling. The generated instructions are then sent to a three-dimensional food printer.

[1675] The terminal, a 3D food printer, cooks and serves ingredients based on received instructions. The 3D food printer executes the specified process to complete a dish with the optimal texture. This dish is then served to the user.

[1676] After the meal, the server uses feedback collection tools to gather feedback from the user. This includes taste, appearance, aroma, and overall satisfaction. The collected feedback is stored in a database and used to improve future suggestions and adjustments.

[1677] Furthermore, the server provides an interactive interface to enable users to interactively select ingredients and check menus within the virtual store. Users can access and operate the virtual store using smartphones, smart glasses, head-mounted displays, etc. Within the virtual store, users can receive real-time feedback on the ingredients and menus they select.

[1678] Specific example

[1679] For example, if a user is feeling "a little tired today," the server, through emotion recognition, will understand that emotion and suggest foods and menu items that will soothe the user. For instance, it could suggest a warm vegetable soup and a mild-flavored dessert. This information is then sent to a 3D food printer for preparation.

[1680] Example of a prompt

[1681] "What do you want to eat today?"

[1682] "Do you have any favorite or least favorite foods?"

[1683] "Please provide allergy information."

[1684] "Please tell us about your chewing ability and the degree of your swallowing difficulties."

[1685] This invention makes it possible to provide more personalized meals that take into account a variety of information, including the user's emotional state.

[1686] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1687] Step 1:

[1688] The server displays an authentication screen on the user's smartphone or smart glasses for login. The user enters their authentication information (user ID and password) and sends it to the server. The server compares this information with the database, and if authentication is successful, retrieves the user profile and proceeds to the next step. The input is the user ID and password, and the output is the user profile.

[1689] Step 2:

[1690] The server uses conversational artificial intelligence to send questions to the user. These questions cover topics such as what the user wants to eat today, their favorite and disliked foods, allergy information, and the degree of their chewing and swallowing difficulties. The user answers these questions and sends the information to the server. The input is the user's answers to the questions, and the output is the collected user information.

[1691] Step 3:

[1692] The server uses an emotion engine to recognize emotions from the user's voice and facial expressions. It analyzes the user's voice and image data to identify their emotional state. The input is the user's voice and image data, and the output is data of the recognized emotional state.

[1693] Step 4:

[1694] The server analyzes collected user information and recognized sentiment data using data analysis tools. Through this analysis, it selects appropriate ingredients and recipes based on the user's preferences and constraints. The input is user information and sentiment data, and the output is data on the selected menu.

[1695] Step 5:

[1696] The server proposes a menu to the user based on the analysis results. The user reviews the proposed menu and requests approval or modification. The input is the data of the analyzed menu, and the output is the user's approval or modification request.

[1697] Step 6:

[1698] The server determines specific processing methods to adjust the texture of the ingredients based on the menu approved by the user. It generates specific processing instructions and sends them to a 3D food printer. The input is the data of the approved menu, and the output is the specific processing instructions.

[1699] Step 7:

[1700] The terminal, a 3D food printer, prints and cooks ingredients based on processing instructions received from the server. The input is the processing instructions from the server, and the output is the final cooked dish.

[1701] Step 8:

[1702] A 3D food printer delivers a finished dish to the user. The user receives the dish from the printer and enjoys the meal. The input is the cooked food, and the output is the user's dining experience.

[1703] Step 9:

[1704] After the meal, the server collects feedback from the user using questionnaires and conversational AI. This feedback includes aspects such as the taste, appearance, aroma, and overall satisfaction with the meal. The input is the user's feedback, and the output is the collected feedback data.

[1705] Step 10:

[1706] The server analyzes the feedback collected using the feedback collection mechanism and updates the algorithm to improve future proposals and adjustment methods. The input is feedback data, and the output is the improved proposal and adjustment algorithm.

[1707] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1708] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1709] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1710] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1711] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1712] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1713] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1714] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1715] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1716] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1717] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1718] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1719] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1720] 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.

[1721] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1722] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1723] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1724] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1725] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1726] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1727] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1728] The following is further disclosed regarding the embodiments described above.

[1729] (Claim 1)

[1730] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients,

[1731] A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes,

[1732] An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients,

[1733] A three-dimensional food printer means that cooks and serves ingredients based on generated instructions,

[1734] A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal,

[1735] A system that includes this.

[1736] (Claim 2)

[1737] The system according to claim 1, further comprising a menu generation means for analyzing collected user information and generating a customized menu.

[1738] (Claim 3)

[1739] The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment.

[1740] "Example 1"

[1741] (Claim 1)

[1742] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients,

[1743] A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes,

[1744] An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients,

[1745] A three-dimensional food printer means that cooks and serves ingredients based on generated instructions,

[1746] A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal,

[1747] A means for receiving and analyzing prompt sentences generated using interactive artificial intelligence means,

[1748] A system that includes this.

[1749] (Claim 2)

[1750] The system according to claim 1, further comprising a menu generation means for analyzing collected user information and generating a customized menu.

[1751] (Claim 3)

[1752] The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment.

[1753] "Application Example 1"

[1754] (Claim 1)

[1755] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients,

[1756] A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes,

[1757] An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients,

[1758] A three-dimensional food printer means that cooks and serves ingredients based on generated instructions,

[1759] A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal,

[1760] A proposal method for creating custom menus and providing them to food delivery services,

[1761] A system that includes this.

[1762] (Claim 2)

[1763] The system according to claim 1, further comprising a menu generation means for analyzing collected user information and generating a customized menu.

[1764] (Claim 3)

[1765] The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment.

[1766] "Example 2 of combining an emotion engine"

[1767] (Claim 1)

[1768] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients,

[1769] A data analysis method that analyzes collected user information and sentiment data to select appropriate ingredients and recipes,

[1770] An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients,

[1771] A three-dimensional food printer means that cooks and serves ingredients based on generated instructions,

[1772] A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal,

[1773] An emotion engine that recognizes emotions from the user's voice and facial expressions,

[1774] A system that includes this.

[1775] (Claim 2)

[1776] The system according to claim 1, further comprising a menu generation means for analyzing collected user information and sentiment data to generate a customized menu.

[1777] (Claim 3)

[1778] The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment.

[1779] "Application example 2 when combining with an emotional engine"

[1780] (Claim 1)

[1781] An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients,

[1782] A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes,

[1783] An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients,

[1784] A three-dimensional food printer means that cooks and serves ingredients based on generated instructions,

[1785] A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal,

[1786] An emotion recognition means that identifies the user's emotions and suggests a menu according to their emotional state,

[1787] An interactive interface means for accessing a virtual store and interactively selecting ingredients and checking menus,

[1788] A system that includes this.

[1789] (Claim 2)

[1790] The system according to claim 1, further comprising a menu generation means for analyzing collected user information and generating a customized menu.

[1791] (Claim 3)

[1792] The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment. [Explanation of Symbols]

[1793] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An interactive artificial intelligence system that listens to user preferences and constraints and adjusts the texture of ingredients, A data analysis method that analyzes collected user information and selects appropriate ingredients and recipes, An instruction generation means for generating processing instructions for adjusting the texture of selected ingredients, A three-dimensional food printer means that cooks and serves ingredients based on generated instructions, A feedback collection method that gathers user feedback after meals to improve suggestions and adjustments for the next meal, A system that includes this.

2. The system according to claim 1, further comprising a menu generation means for analyzing collected user information and generating a customized menu.

3. The system according to claim 1, further comprising means for determining a specific processing method for texture adjustment.

Images