system

Abstract

The system according to this embodiment aims to automatically generate an optimal menu tailored to the user's budget, time, and mood. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a learning unit, and a collaboration unit. The reception unit receives input from the user regarding their budget, time, and mood. The generation unit generates a menu based on the information received by the reception unit. The provision unit provides the menu generated by the generation unit to the user. The learning unit learns the user's preferences based on the menu provided by the provision unit. The collaboration unit collaborates with the Kurashiru service.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

[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

[0007] The system according to this embodiment can automatically generate an optimal menu tailored to the user's budget, time, and mood. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The menu generation system according to an embodiment of the present invention accepts the user's budget, time, and mood input, and automatically generates menus that meet the user's needs, such as training, dieting, and meal prepping. In this system, the user inputs the budget, time, and mood using sliders, and the AI ​​generates the optimal menu based on this information. For example, if the user sets the budget to 500 yen, the time to 30 minutes, and the mood to "Japanese food," the AI ​​will suggest a Japanese food recipe that can be made in 30 minutes or less. The system also learns the user's preferences and generates more suitable menus based on previously selected recipes and ratings. Furthermore, it integrates with services such as Kurashiru to provide detailed instructions and videos for the suggested recipes. This allows the user to easily check recipes and start cooking. For example, even if the user's mood suddenly changes, the AI ​​will suggest a suitable recipe at that time. As the user continues to use the system, the AI ​​learns the user's preferences and can generate more suitable menus. In this way, the AI ​​menu agent responds to the user's various needs and supports making daily meals enjoyable and healthy. This allows the menu generation system to automatically generate and provide menus tailored to the user's budget, time, and mood.

[0029] The menu generation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a learning unit, and a linkage unit. The reception unit receives input from the user regarding their budget, time, and mood. For example, the reception unit allows the user to set their budget to 500 yen, their time to 30 minutes, and their mood to "Japanese food." The generation unit generates a menu based on the information received by the reception unit. For example, the generation unit suggests a Japanese food recipe that can be prepared in 30 minutes or less. The provision unit provides the menu generated by the generation unit to the user. For example, the provision unit provides detailed instructions and videos for the suggested recipe. The learning unit learns the user's preferences based on the menus provided by the provision unit. For example, the learning unit generates a more suitable menu based on previously selected recipes and ratings. The linkage unit links with services such as Kurashiru. For example, the linkage unit provides detailed instructions and videos for the suggested recipe. As a result, the menu generation system according to this embodiment can automatically generate and provide menus that match the user's budget, time, and mood.

[0030] The reception desk accepts user input of budget, time, and mood. For example, the reception desk allows a user to set a budget of 500 yen, a time of 30 minutes, and a mood of "Japanese food." Specifically, the reception desk is designed to allow users to easily input information through a user interface. The user interface is provided as a smartphone app and web application, and is intuitive to use. Users can set their budget and time using sliders and drop-down menus and select a type of cuisine that suits their mood. Furthermore, the reception desk has a voice input function, allowing users to input information by voice. For example, by voice inputting "My budget is 500 yen, I have 30 minutes, and I want Japanese food," the system will automatically recognize the information and make the settings. This allows users to easily request a menu that suits their preferences.

[0031] The generation unit generates menus based on information received by the reception unit. For example, the generation unit suggests Japanese recipes that can be prepared in under 30 minutes. The generation unit uses AI to select the optimal menu from a vast recipe database. Specifically, based on the user's input information, the generation unit lists ingredients that can be purchased within the budget and searches for recipes that use those ingredients. Furthermore, the AI ​​considers the user's past choices and ratings to prioritize suggesting recipes that match the user's preferences. For example, it suggests recipes with similar characteristics based on recipes that the user has previously rated as "easy and delicious." The generation unit can also consider seasonal and regional characteristics and suggest recipes that use seasonal ingredients. As a result, the generation unit can automatically generate fresh and delicious menus that meet the user's wishes.

[0032] The service provider delivers menus generated by the generation unit to the user. For example, the service provider provides detailed instructions and videos for suggested recipes. The service provider displays detailed information about the generated menu through the user interface. Specifically, this includes the recipe's ingredient list, cooking procedure, cooking time, and calorie information. The service provider also provides step-by-step photos and videos to clearly explain the cooking procedure. This allows users to visually confirm the cooking procedure as they proceed with cooking. Furthermore, the service provider also includes functions for users to save and share recipes. For example, users can add their favorite recipes to a favorites list and review them later. They can also share recipes with friends and family via social media and messaging apps. In this way, the service provider can provide users with convenient and easy-to-use menu information and support the enjoyment of cooking.

[0033] The learning unit learns user preferences based on the menus provided by the service unit. For example, the learning unit generates more suitable menus based on previously selected recipes and ratings. The learning unit analyzes user preference patterns using machine learning algorithms. Specifically, it collects data such as the type of recipe selected by the user, its rating, cooking time, and ingredients used, and models user preferences based on this data. Furthermore, the learning unit continuously updates the model by reflecting user feedback in real time. For example, if a user tries a new recipe and gives it a high rating, the learning unit learns the characteristics of that recipe and reflects them in future suggestions. The learning unit can also consider the user's eating history and health condition to suggest nutritionally balanced menus. This allows the learning unit to provide more personalized menus tailored to the user's preferences and health condition.

[0034] The integration unit will connect with services such as Kurashiru. For example, the integration unit will provide detailed instructions and videos for suggested recipes. The integration unit will connect with external recipe services and food delivery services via APIs to provide users with a wider variety of services. Specifically, the integration unit will retrieve detailed instructions and videos for suggested recipes from external services and provide them to users. In addition, the integration unit will provide an online ordering function for ingredients, making it easy for users to purchase the ingredients they need. For example, when a user selects a suggested recipe, the necessary ingredients will be automatically added to the cart and can be ordered online. Furthermore, the integration unit also has a function to manage the user's ingredient inventory and automatically list the necessary ingredients. In this way, the integration unit can provide users with a convenient and efficient cooking experience and improve the usability of the menu generation system.

[0035] The reception area allows users to input their budget, time, and mood using sliders. For example, the reception area allows users to set their budget to 500 yen, time to 30 minutes, and mood to "Japanese food." This makes it easy for users to input their budget, time, and mood. The slider can be set to ranges such as 0 yen to 10,000 yen for the budget, 0 minutes to 120 minutes for the time, and from "Japanese food" to "Western food" for the mood. The operation method and display format of the slider can be adjusted, for example, by drag and drop. The slider range and step size can be adjusted according to the user's needs.

[0036] The generation unit can generate menu patterns that meet the user's needs. For example, if the user selects dieting, the generation unit can suggest low-calorie, nutritionally balanced recipes. If the user selects training, the generation unit can also suggest high-protein, energy-replenishing recipes. Depending on the user's needs, the generation unit can also suggest, for example, make-ahead recipes or time-saving recipes. This allows the generation of menus that meet the user's needs. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input prompts to the generation AI to generate menu patterns that meet the user's needs, and the generation AI can generate menu patterns.

[0037] The service provider can provide detailed instructions and videos for the suggested recipe. For example, the service provider can display the steps in text format so that the user can check the detailed instructions for the suggested recipe. The service provider can also play a video so that the user can check the instructions for the suggested recipe. The service provider can display a list of ingredients and cooking steps so that the user can check the detailed instructions for the suggested recipe. This allows the user to check the detailed instructions and videos for the recipe. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider inputs the detailed instructions and videos for the suggested recipe into a generative AI, and the generative AI generates the detailed instructions and videos.

[0038] The learning unit can learn the user's preferences and generate more suitable menus based on previously selected recipes and ratings. For example, the learning unit collects data on recipes previously selected by the user to learn the user's preferences. The learning unit can also collect data on recipes previously rated by the user to learn the user's preferences. To learn the user's preferences, the learning unit analyzes, for example, the trends and rating patterns of recipes previously selected by the user. This allows it to generate menus tailored to the user's preferences. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, to learn the user's preferences, the learning unit inputs the user's past selection data into a generative AI, and the generative AI learns the user's preferences.

[0039] The integration unit can collaborate with services such as Kurashiru to provide detailed instructions and videos for suggested recipes. For example, the integration unit can use the Kurashiru API to obtain detailed instructions and videos for suggested recipes. The integration unit can also collaborate with services such as Kurashiru to provide detailed instructions and videos for suggested recipes. To collaborate with services such as Kurashiru, the integration unit clarifies, for example, how to use the API and the types of information provided. This enables it to collaborate with services such as Kurashiru to provide detailed instructions and videos for recipes. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs data obtained using the Kurashiru API into a generative AI, and the generative AI generates detailed instructions and videos.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display budget, time, and mood that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest, for example, the budget, time, and mood to be used during a specific time period, based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk inputs the user's past input history data into a generative AI, and the generative AI suggests the optimal input method.

[0041] The reception desk can filter the user's current lifestyle and dietary preferences when they input their budget, time, and mood. For example, if the user is busy, the reception desk will prioritize displaying recipes that can be prepared quickly. If the user is health-conscious, the reception desk can also suggest low-calorie or nutritionally balanced recipes. If the user prefers a particular ingredient, the reception desk will prioritize displaying recipes that use that ingredient. This allows for filtering tailored to the user's lifestyle and preferences. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk inputs data on the user's lifestyle and dietary preferences into a generative AI, which then performs the filtering.

[0042] The reception desk can prioritize displaying highly relevant input items when users input their budget, time, and mood, taking into account their geographical location. For example, if the reception desk is in a specific region, it will prioritize displaying recipes using local ingredients. If the user is traveling, for example, it can prioritize displaying easy-to-cook recipes. If the user is participating in a specific event (festival, seasonal event), for example, it will prioritize displaying recipes related to that event. This allows the reception desk to display highly relevant input items based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk inputs the user's geographical location information into a generative AI, and the generative AI displays highly relevant input items.

[0043] The reception desk can analyze the user's social media activity when they input their budget, time, and mood, and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on recipes the user has shared on social media. The reception desk can also suggest relevant input fields based on recipes the user follows from influencers. The reception desk can suggest relevant input fields based on recipes the user has "liked" on social media. This allows the reception desk to suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk inputs the user's social media activity data into a generative AI, and the generative AI suggests relevant input fields.

[0044] The generation unit can analyze the user's past eating history to suggest the optimal menu when generating a menu. For example, the generation unit can suggest a similar menu based on recipes the user has enjoyed eating in the past. The generation unit can also suggest a menu considering ingredients the user has avoided in the past. The generation unit can suggest a menu that considers nutritional balance based on the user's past eating history. This allows the generation unit to suggest the optimal menu based on the user's past eating history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's past eating history data into the generation AI, and the generation AI suggests the optimal menu.

[0045] The generation unit can customize menus based on the user's current health status and nutritional balance when generating menus. For example, if the user is on a diet, the generation unit can suggest a low-calorie, nutritionally balanced menu. If the user is training, the generation unit can also suggest a high-protein menu suitable for energy replenishment. If the user's goal is to maintain health, the generation unit can suggest a balanced menu. This allows the generation unit to suggest menus that are tailored to the user's health status and nutritional balance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs data on the user's health status and nutritional balance into the generation AI, and the generation AI customizes the menu.

[0046] The generation unit can propose the most suitable menu by considering the user's geographical location when generating menus. For example, if the user is in a specific region, the generation unit can propose a menu using ingredients from that region. If the user is traveling, for example, the generation unit can propose a menu using local ingredients. If the user is participating in a specific event (festival, seasonal event), for example, the generation unit can propose a menu related to that event. This allows the system to propose the most suitable menu based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI proposes the most suitable menu.

[0047] The generation unit can analyze the user's social media activity and suggest relevant menus when generating menus. For example, the generation unit can suggest relevant menus based on recipes the user has shared on social media. The generation unit can also suggest relevant menus based on recipes from influencers the user follows. The generation unit can suggest relevant menus based on recipes the user has "liked" on social media. This allows the generation unit to suggest relevant menus based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's social media activity data into a generation AI, and the generation AI suggests relevant menus.

[0048] The service provider can select the optimal service method by referring to the user's past recipe selection history when providing recipes. For example, the service provider can provide similar recipes based on recipes the user has selected in the past. The service provider can also provide recipes that take into account recipes the user has avoided in the past. The service provider can provide recipes that take nutritional balance into consideration based on the user's past recipe selection history. This allows the service provider to select the optimal service method based on the user's past recipe selection history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider inputs the user's past recipe selection history data into a generation AI, and the generation AI selects the optimal service method.

[0049] The service provider can customize the method of providing recipes based on the user's current lifestyle when providing recipes. For example, if the user is busy, the service provider can provide recipes that can be cooked in a short time. If the user is health-conscious, the service provider can also provide low-calorie or nutritionally balanced recipes. If the user prefers a particular ingredient, the service provider can provide recipes that use that ingredient. This allows the service provider to customize the method of providing recipes according to the user's current lifestyle. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's lifestyle data into a generative AI, and the generative AI customizes the method of providing recipes.

[0050] The service provider can provide the most suitable recipe by considering the user's geographical location when providing recipes. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. If the user is traveling, the service provider can also provide a recipe using local ingredients. If the user is participating in a specific event (festival, seasonal event), the service provider can provide a recipe related to that event. This allows the service provider to provide the most suitable recipe based on the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's geographical location information into a generative AI, and the generative AI provides the most suitable recipe.

[0051] The service provider can analyze a user's social media activity and provide relevant recipes when providing recipes. For example, the service provider can provide relevant recipes based on recipes shared by the user on social media. The service provider can also provide relevant recipes based on recipes from influencers followed by the user. The service provider can provide relevant recipes based on recipes liked by the user on social media. This allows the service provider to provide relevant recipes based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's social media activity data into a generative AI, and the generative AI provides relevant recipes.

[0052] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize an algorithm that reflects user preferences based on past learning data. The learning unit can also analyze a user's eating patterns from past learning data and optimize the algorithm accordingly. The learning unit can optimize an algorithm that considers the user's nutritional balance based on past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs past learning data into a generative AI, and the generative AI optimizes the learning algorithm.

[0053] The learning unit can improve the accuracy of the training data by analyzing the user's past eating history during training. For example, the learning unit can improve the accuracy of the training data by analyzing the user's preferences based on their past eating history. The learning unit can also improve the accuracy of the training data by identifying foods to avoid based on the user's past eating history. The learning unit can create training data that considers nutritional balance based on the user's past eating history. This allows the learning unit to improve the accuracy of the training data based on the user's past eating history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's past eating history data into a generative AI, and the generative AI improves the accuracy of the training data.

[0054] The learning unit can weight the training data while considering the user's geographical location information. For example, if the user is in a specific region, the learning unit can create training data that emphasizes local ingredients. If the user is traveling, for example, the learning unit can also create training data that emphasizes local ingredients. If the user is participating in a specific event (festival, seasonal event), for example, the learning unit can create training data related to that event. This allows the learning data to be weighted based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's geographical location information into a generative AI, and the generative AI weights the training data.

[0055] The learning unit can improve the accuracy of the training data by analyzing the user's social media activity during training. For example, the learning unit can improve the accuracy of the training data based on recipes shared by the user on social media. The learning unit can also improve the accuracy of the training data based on recipes from influencers followed by the user. The learning unit can improve the accuracy of the training data based on recipes liked by the user on social media. This allows the accuracy of the training data to be improved based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's social media activity data into a generative AI, and the generative AI improves the accuracy of the training data.

[0056] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit can select the optimal integration method based on the services the user has used in the past. The integration unit can also, for example, prioritize integration with services that the user uses frequently based on the user's past integration history. The integration unit analyzes the user's past integration history and, for example, selects the most efficient integration method. This allows the integration unit to select the optimal integration method based on the user's past integration history. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit inputs the user's past integration history data into a generating AI, and the generating AI selects the optimal integration method.

[0057] The integration unit can customize the integrated services based on the user's current lifestyle when integrating. For example, if the user is busy, the integration unit will prioritize integrating services that can be used in a short amount of time. If the user is health-conscious, the integration unit can also prioritize integrating health-related services. If the user prefers a particular food, the integration unit will prioritize integrating services that use that food. This allows the integrated services to be customized according to the user's current lifestyle. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the user's lifestyle data into the generative AI, and the generative AI customizes the integrated services.

[0058] The integration unit can select the most suitable integration service by considering the user's geographical location information during integration. For example, if the user is in a specific region, the integration unit will prioritize integrating services in that region. The integration unit can also prioritize integrating local services if the user is traveling. If the integration unit is participating in a specific event (festival, seasonal event), it will prioritize integrating services related to that event. This allows the integration unit to select the most suitable integration service based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the user's geographical location information into the generative AI, and the generative AI selects the most suitable integration service.

[0059] The integration unit can improve the accuracy of the integrated service by analyzing the user's social media activity during integration. For example, the integration unit can improve the accuracy of the integrated service based on the services the user has shared on social media. The integration unit can also improve the accuracy of the integrated service based on the services of influencers the user follows. The integration unit can improve the accuracy of the integrated service based on the services the user has "liked" on social media. In this way, the accuracy of the integrated service can be improved based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit inputs the user's social media activity data into a generative AI, and the generative AI improves the accuracy of the integrated service.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can input the user's food allergy information. For example, if the reception desk has an allergy to a specific food, it will suggest recipes that do not contain that food. The reception desk can also prioritize displaying recipes that do not contain nuts, for example, if the user has a nut allergy. If the reception desk has a dairy allergy, it will suggest recipes that do not contain dairy products, for example. This allows for the suggestion of safe menus based on the user's allergy information.

[0062] The generation unit can generate menus while taking into account the user's ingredient inventory information. For example, if the user inputs ingredients they have in their refrigerator, the generation unit will suggest recipes using those ingredients. If the user inputs vegetables they have in their refrigerator, the generation unit can also prioritize generating recipes using those vegetables. If the user inputs meats they have in their refrigerator, the generation unit will suggest recipes using those meats. This allows for the generation of waste-free menus based on the user's ingredient inventory information.

[0063] The service can provide recipes according to the user's cooking skill level. For example, if the user is a beginner, the service will prioritize displaying easy recipes. If the user is an intermediate cook, the service may suggest slightly more difficult recipes. If the user is an advanced cook, the service may provide more complex recipes. This ensures that the service provides appropriate recipes according to the user's cooking skill level.

[0064] The learning unit can learn the user's eating frequency and patterns and suggest optimal menus. For example, the learning unit can learn how many times a user eats out per week and suggest menus that match the frequency of eating out. If the learning unit has a tendency to cook a particular dish on weekends, it can also suggest a menu that includes that dish. If the learning unit has a tendency to use a particular ingredient on a particular day of the week, it can suggest a menu that uses that ingredient. In this way, it can suggest optimal menus based on the user's eating frequency and patterns.

[0065] The integration unit can analyze a user's purchase history and suggest the most suitable recipe. For example, it can suggest recipes using ingredients that a user has previously purchased. It can also suggest recipes using ingredients available at a specific supermarket, based on ingredients a user has purchased at that supermarket. Furthermore, it can suggest recipes using ingredients purchased online, based on those purchases. This allows the system to suggest the most suitable recipe based on the user's purchase history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk accepts input from the user regarding their budget, time, and mood. For example, a user can set their budget to 500 yen, their time to 30 minutes, and their mood to "Japanese food." Step 2: The generation unit generates a menu based on the information received by the reception unit. For example, the generation unit suggests a Japanese meal recipe that can be prepared in 30 minutes or less. Step 3: The serving unit provides the user with the menu generated by the generation unit. For example, the serving unit provides detailed instructions and videos for the suggested recipes. Step 4: The learning unit learns the user's preferences based on the menus provided by the service unit. For example, the learning unit generates more suitable menus based on previously selected recipes and ratings. Step 5: The integration department will connect with services such as Kurashiru. For example, the integration department will provide detailed instructions and videos for the suggested recipes.

[0068] (Example of form 2) The menu generation system according to an embodiment of the present invention accepts the user's budget, time, and mood input, and automatically generates menus that meet the user's needs, such as training, dieting, and meal prepping. In this system, the user inputs the budget, time, and mood using sliders, and the AI ​​generates the optimal menu based on this information. For example, if the user sets the budget to 500 yen, the time to 30 minutes, and the mood to "Japanese food," the AI ​​will suggest a Japanese food recipe that can be made in 30 minutes or less. The system also learns the user's preferences and generates more suitable menus based on previously selected recipes and ratings. Furthermore, it integrates with services such as Kurashiru to provide detailed instructions and videos for the suggested recipes. This allows the user to easily check recipes and start cooking. For example, even if the user's mood suddenly changes, the AI ​​will suggest a suitable recipe at that time. As the user continues to use the system, the AI ​​learns the user's preferences and can generate more suitable menus. In this way, the AI ​​menu agent responds to the user's various needs and supports making daily meals enjoyable and healthy. This allows the menu generation system to automatically generate and provide menus tailored to the user's budget, time, and mood.

[0069] The menu generation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a learning unit, and a linkage unit. The reception unit receives input from the user regarding their budget, time, and mood. For example, the reception unit allows the user to set their budget to 500 yen, their time to 30 minutes, and their mood to "Japanese food." The generation unit generates a menu based on the information received by the reception unit. For example, the generation unit suggests a Japanese food recipe that can be prepared in 30 minutes or less. The provision unit provides the menu generated by the generation unit to the user. For example, the provision unit provides detailed instructions and videos for the suggested recipe. The learning unit learns the user's preferences based on the menus provided by the provision unit. For example, the learning unit generates a more suitable menu based on previously selected recipes and ratings. The linkage unit links with services such as Kurashiru. For example, the linkage unit provides detailed instructions and videos for the suggested recipe. As a result, the menu generation system according to this embodiment can automatically generate and provide menus that match the user's budget, time, and mood.

[0070] The reception desk accepts user input of budget, time, and mood. For example, the reception desk allows a user to set a budget of 500 yen, a time of 30 minutes, and a mood of "Japanese food." Specifically, the reception desk is designed to allow users to easily input information through a user interface. The user interface is provided as a smartphone app and web application, and is intuitive to use. Users can set their budget and time using sliders and drop-down menus and select a type of cuisine that suits their mood. Furthermore, the reception desk has a voice input function, allowing users to input information by voice. For example, by voice inputting "My budget is 500 yen, I have 30 minutes, and I want Japanese food," the system will automatically recognize the information and make the settings. This allows users to easily request a menu that suits their preferences.

[0071] The generation unit generates menus based on information received by the reception unit. For example, the generation unit suggests Japanese recipes that can be prepared in under 30 minutes. The generation unit uses AI to select the optimal menu from a vast recipe database. Specifically, based on the user's input information, the generation unit lists ingredients that can be purchased within the budget and searches for recipes that use those ingredients. Furthermore, the AI ​​considers the user's past choices and ratings to prioritize suggesting recipes that match the user's preferences. For example, it suggests recipes with similar characteristics based on recipes that the user has previously rated as "easy and delicious." The generation unit can also consider seasonal and regional characteristics and suggest recipes that use seasonal ingredients. As a result, the generation unit can automatically generate fresh and delicious menus that meet the user's wishes.

[0072] The service provider delivers menus generated by the generation unit to the user. For example, the service provider provides detailed instructions and videos for suggested recipes. The service provider displays detailed information about the generated menu through the user interface. Specifically, this includes the recipe's ingredient list, cooking procedure, cooking time, and calorie information. The service provider also provides step-by-step photos and videos to clearly explain the cooking procedure. This allows users to visually confirm the cooking procedure as they proceed with cooking. Furthermore, the service provider also includes functions for users to save and share recipes. For example, users can add their favorite recipes to a favorites list and review them later. They can also share recipes with friends and family via social media and messaging apps. In this way, the service provider can provide users with convenient and easy-to-use menu information and support the enjoyment of cooking.

[0073] The learning unit learns user preferences based on the menus provided by the service unit. For example, the learning unit generates more suitable menus based on previously selected recipes and ratings. The learning unit analyzes user preference patterns using machine learning algorithms. Specifically, it collects data such as the type of recipe selected by the user, its rating, cooking time, and ingredients used, and models user preferences based on this data. Furthermore, the learning unit continuously updates the model by reflecting user feedback in real time. For example, if a user tries a new recipe and gives it a high rating, the learning unit learns the characteristics of that recipe and reflects them in future suggestions. The learning unit can also consider the user's eating history and health condition to suggest nutritionally balanced menus. This allows the learning unit to provide more personalized menus tailored to the user's preferences and health condition.

[0074] The integration unit will connect with services such as Kurashiru. For example, the integration unit will provide detailed instructions and videos for suggested recipes. The integration unit will connect with external recipe services and food delivery services via APIs to provide users with a wider variety of services. Specifically, the integration unit will retrieve detailed instructions and videos for suggested recipes from external services and provide them to users. In addition, the integration unit will provide an online ordering function for ingredients, making it easy for users to purchase the ingredients they need. For example, when a user selects a suggested recipe, the necessary ingredients will be automatically added to the cart and can be ordered online. Furthermore, the integration unit also has a function to manage the user's ingredient inventory and automatically list the necessary ingredients. In this way, the integration unit can provide users with a convenient and efficient cooking experience and improve the usability of the menu generation system.

[0075] The reception area allows users to input their budget, time, and mood using sliders. For example, the reception area allows users to set their budget to 500 yen, time to 30 minutes, and mood to "Japanese food." This makes it easy for users to input their budget, time, and mood. The slider can be set to ranges such as 0 yen to 10,000 yen for the budget, 0 minutes to 120 minutes for the time, and from "Japanese food" to "Western food" for the mood. The operation method and display format of the slider can be adjusted, for example, by drag and drop. The slider range and step size can be adjusted according to the user's needs.

[0076] The generation unit can generate menu patterns that meet the user's needs. For example, if the user selects dieting, the generation unit can suggest low-calorie, nutritionally balanced recipes. If the user selects training, the generation unit can also suggest high-protein, energy-replenishing recipes. Depending on the user's needs, the generation unit can also suggest, for example, make-ahead recipes or time-saving recipes. This allows the generation of menus that meet the user's needs. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input prompts to the generation AI to generate menu patterns that meet the user's needs, and the generation AI can generate menu patterns.

[0077] The service provider can provide detailed instructions and videos for the suggested recipe. For example, the service provider can display the steps in text format so that the user can check the detailed instructions for the suggested recipe. The service provider can also play a video so that the user can check the instructions for the suggested recipe. The service provider can display a list of ingredients and cooking steps so that the user can check the detailed instructions for the suggested recipe. This allows the user to check the detailed instructions and videos for the recipe. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider inputs the detailed instructions and videos for the suggested recipe into a generative AI, and the generative AI generates the detailed instructions and videos.

[0078] The learning unit can learn the user's preferences and generate more suitable menus based on previously selected recipes and ratings. For example, the learning unit collects data on recipes previously selected by the user to learn the user's preferences. The learning unit can also collect data on recipes previously rated by the user to learn the user's preferences. To learn the user's preferences, the learning unit analyzes, for example, the trends and rating patterns of recipes previously selected by the user. This allows it to generate menus tailored to the user's preferences. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, to learn the user's preferences, the learning unit inputs the user's past selection data into a generative AI, and the generative AI learns the user's preferences.

[0079] The integration unit can collaborate with services such as Kurashiru to provide detailed instructions and videos for suggested recipes. For example, the integration unit can use the Kurashiru API to obtain detailed instructions and videos for suggested recipes. The integration unit can also collaborate with services such as Kurashiru to provide detailed instructions and videos for suggested recipes. To collaborate with services such as Kurashiru, the integration unit clarifies, for example, how to use the API and the types of information provided. This enables it to collaborate with services such as Kurashiru to provide detailed instructions and videos for recipes. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs data obtained using the Kurashiru API into a generative AI, and the generative AI generates detailed instructions and videos.

[0080] The reception desk can estimate the user's emotions and adjust the input method for budget, time, and mood based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick input of budget, time, and mood. This allows the input method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk inputs the user's emotion data into a generative AI, which estimates the emotion and adjusts the input method.

[0081] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display budget, time, and mood that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest, for example, the budget, time, and mood to be used during a specific time period, based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk inputs the user's past input history data into a generative AI, and the generative AI suggests the optimal input method.

[0082] The reception desk can filter the user's current lifestyle and dietary preferences when they input their budget, time, and mood. For example, if the user is busy, the reception desk will prioritize displaying recipes that can be prepared quickly. If the user is health-conscious, the reception desk can also suggest low-calorie or nutritionally balanced recipes. If the user prefers a particular ingredient, the reception desk will prioritize displaying recipes that use that ingredient. This allows for filtering tailored to the user's lifestyle and preferences. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk inputs data on the user's lifestyle and dietary preferences into a generative AI, which then performs the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of input items based on the estimated emotions. For example, if the user is tired, the reception desk may prioritize inputting the most important items (budget, time). If the user is relaxed, the reception desk may prioritize inputting detailed items (mood, food preferences). If the user is in a hurry, the reception desk may adjust the input to require only the minimum necessary items. This allows the priority of input items to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk inputs the user's emotion data into a generative AI, which estimates the emotion and determines the priority of input items.

[0084] The reception desk can prioritize displaying highly relevant input items when users input their budget, time, and mood, taking into account their geographical location. For example, if the reception desk is in a specific region, it will prioritize displaying recipes using local ingredients. If the user is traveling, for example, it can prioritize displaying easy-to-cook recipes. If the user is participating in a specific event (festival, seasonal event), for example, it will prioritize displaying recipes related to that event. This allows the reception desk to display highly relevant input items based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk inputs the user's geographical location information into a generative AI, and the generative AI displays highly relevant input items.

[0085] The reception desk can analyze the user's social media activity when they input their budget, time, and mood, and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on recipes the user has shared on social media. The reception desk can also suggest relevant input fields based on recipes the user follows from influencers. The reception desk can suggest relevant input fields based on recipes the user has "liked" on social media. This allows the reception desk to suggest relevant input fields based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk inputs the user's social media activity data into a generative AI, and the generative AI suggests relevant input fields.

[0086] The generation unit can estimate the user's emotions and adjust the menu generation method based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a menu that is easy and quick to prepare. If the user is relaxed, the generation unit can also generate a menu that can be enjoyed at a leisurely pace. If the user is in a hurry, the generation unit can generate a menu that can be prepared in a short time. This allows the menu generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs user emotion data into a generation AI, the generation AI estimates the emotions, and adjusts the menu generation method.

[0087] The generation unit can analyze the user's past eating history to suggest the optimal menu when generating a menu. For example, the generation unit can suggest a similar menu based on recipes the user has enjoyed eating in the past. The generation unit can also suggest a menu considering ingredients the user has avoided in the past. The generation unit can suggest a menu that considers nutritional balance based on the user's past eating history. This allows the generation unit to suggest the optimal menu based on the user's past eating history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's past eating history data into the generation AI, and the generation AI suggests the optimal menu.

[0088] The generation unit can customize menus based on the user's current health status and nutritional balance when generating menus. For example, if the user is on a diet, the generation unit can suggest a low-calorie, nutritionally balanced menu. If the user is training, the generation unit can also suggest a high-protein menu suitable for energy replenishment. If the user's goal is to maintain health, the generation unit can suggest a balanced menu. This allows the generation unit to suggest menus that are tailored to the user's health status and nutritional balance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs data on the user's health status and nutritional balance into the generation AI, and the generation AI customizes the menu.

[0089] The generation unit can estimate the user's emotions and determine the priority of menus based on the estimated emotions. For example, if the user is tired, the generation unit will prioritize suggesting easy-to-prepare menus. If the user is relaxed, the generation unit can also prioritize suggesting menus that can be enjoyed at a leisurely pace. If the user is in a hurry, the generation unit will prioritize suggesting menus that can be prepared quickly. This allows the system to prioritize menus according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of menus.

[0090] The generation unit can propose the most suitable menu by considering the user's geographical location when generating menus. For example, if the user is in a specific region, the generation unit can propose a menu using ingredients from that region. If the user is traveling, for example, the generation unit can propose a menu using local ingredients. If the user is participating in a specific event (festival, seasonal event), for example, the generation unit can propose a menu related to that event. This allows the system to propose the most suitable menu based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI proposes the most suitable menu.

[0091] The generation unit can analyze the user's social media activity and suggest relevant menus when generating menus. For example, the generation unit can suggest relevant menus based on recipes the user has shared on social media. The generation unit can also suggest relevant menus based on recipes from influencers the user follows. The generation unit can suggest relevant menus based on recipes the user has "liked" on social media. This allows the generation unit to suggest relevant menus based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's social media activity data into a generation AI, and the generation AI suggests relevant menus.

[0092] The service provider can estimate the user's emotions and adjust the recipe delivery method based on the estimated emotions. For example, if the user is stressed, the service provider can provide a recipe with a simple interface. If the user is relaxed, for example, the service provider can provide detailed recipe information. If the user is in a hurry, for example, the service provider can provide a concise recipe that gets straight to the point. This allows the service provider to adjust the recipe delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the recipe delivery method.

[0093] The service provider can select the optimal service method by referring to the user's past recipe selection history when providing recipes. For example, the service provider can provide similar recipes based on recipes the user has selected in the past. The service provider can also provide recipes that take into account recipes the user has avoided in the past. The service provider can provide recipes that take nutritional balance into consideration based on the user's past recipe selection history. This allows the service provider to select the optimal service method based on the user's past recipe selection history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider inputs the user's past recipe selection history data into a generation AI, and the generation AI selects the optimal service method.

[0094] The service provider can customize the method of providing recipes based on the user's current lifestyle when providing recipes. For example, if the user is busy, the service provider can provide recipes that can be cooked in a short time. If the user is health-conscious, the service provider can also provide low-calorie or nutritionally balanced recipes. If the user prefers a particular ingredient, the service provider can provide recipes that use that ingredient. This allows the service provider to customize the method of providing recipes according to the user's current lifestyle. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's lifestyle data into a generative AI, and the generative AI customizes the method of providing recipes.

[0095] The service provider can estimate the user's emotions and prioritize recipes based on those emotions. For example, if the user is tired, the service provider will prioritize easy-to-make recipes. If the user is relaxed, the service provider may also prioritize recipes that can be enjoyed at a leisurely pace. If the user is in a hurry, the service provider may prioritize recipes that can be prepared quickly. This allows the service provider to prioritize recipes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of recipes.

[0096] The service provider can provide the most suitable recipe by considering the user's geographical location when providing recipes. For example, if the user is in a specific region, the service provider can provide a recipe using ingredients from that region. If the user is traveling, the service provider can also provide a recipe using local ingredients. If the user is participating in a specific event (festival, seasonal event), the service provider can provide a recipe related to that event. This allows the service provider to provide the most suitable recipe based on the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's geographical location information into a generative AI, and the generative AI provides the most suitable recipe.

[0097] The service provider can analyze a user's social media activity and provide relevant recipes when providing recipes. For example, the service provider can provide relevant recipes based on recipes shared by the user on social media. The service provider can also provide relevant recipes based on recipes from influencers followed by the user. The service provider can provide relevant recipes based on recipes liked by the user on social media. This allows the service provider to provide relevant recipes based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the user's social media activity data into a generative AI, and the generative AI provides relevant recipes.

[0098] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select recipes that help reduce stress as training data. If the user is relaxed, the learning unit can also select recipes that have a relaxing effect as training data. If the user is in a hurry, the learning unit can select recipes that can be cooked quickly as training data. This allows for the selection of training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit inputs the user's emotion data into a generative AI, the generative AI estimates the emotions, and selects training data.

[0099] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize an algorithm that reflects user preferences based on past learning data. The learning unit can also analyze a user's eating patterns from past learning data and optimize the algorithm accordingly. The learning unit can optimize an algorithm that considers the user's nutritional balance based on past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs past learning data into a generative AI, and the generative AI optimizes the learning algorithm.

[0100] The learning unit can improve the accuracy of the training data by analyzing the user's past eating history during training. For example, the learning unit can improve the accuracy of the training data by analyzing the user's preferences based on their past eating history. The learning unit can also improve the accuracy of the training data by identifying foods to avoid based on the user's past eating history. The learning unit can create training data that considers nutritional balance based on the user's past eating history. This allows the learning unit to improve the accuracy of the training data based on the user's past eating history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's past eating history data into a generative AI, and the generative AI improves the accuracy of the training data.

[0101] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. If the user is relaxed, for example, the learning unit can increase the learning frequency to collect more data. If the user is in a hurry, for example, the learning unit can adjust the learning frequency to collect data quickly. This allows the learning frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the learning frequency.

[0102] The learning unit can weight the training data while considering the user's geographical location information. For example, if the user is in a specific region, the learning unit can create training data that emphasizes local ingredients. If the user is traveling, for example, the learning unit can also create training data that emphasizes local ingredients. If the user is participating in a specific event (festival, seasonal event), for example, the learning unit can create training data related to that event. This allows the learning data to be weighted based on the user's geographical location information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's geographical location information into a generative AI, and the generative AI weights the training data.

[0103] The learning unit can improve the accuracy of the training data by analyzing the user's social media activity during training. For example, the learning unit can improve the accuracy of the training data based on recipes shared by the user on social media. The learning unit can also improve the accuracy of the training data based on recipes from influencers followed by the user. The learning unit can improve the accuracy of the training data based on recipes liked by the user on social media. This allows the accuracy of the training data to be improved based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit inputs the user's social media activity data into a generative AI, and the generative AI improves the accuracy of the training data.

[0104] The integration unit can estimate the user's emotions and select services to integrate with based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize services that have a relaxing effect. If the user is relaxed, the integration unit can also prioritize services that are enjoyable. If the user is in a hurry, the integration unit will prioritize services that can be used quickly. This allows for the selection of services to integrate with according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using a generative AI, or not using a generative AI. For example, the integration unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and selects services to integrate with.

[0105] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit can select the optimal integration method based on the services the user has used in the past. The integration unit can also, for example, prioritize integration with services that the user uses frequently based on the user's past integration history. The integration unit analyzes the user's past integration history and, for example, selects the most efficient integration method. This allows the integration unit to select the optimal integration method based on the user's past integration history. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the integration unit inputs the user's past integration history data into a generating AI, and the generating AI selects the optimal integration method.

[0106] The integration unit can customize the integrated services based on the user's current lifestyle when integrating. For example, if the user is busy, the integration unit will prioritize integrating services that can be used in a short amount of time. If the user is health-conscious, the integration unit can also prioritize integrating health-related services. If the user prefers a particular food, the integration unit will prioritize integrating services that use that food. This allows the integrated services to be customized according to the user's current lifestyle. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the user's lifestyle data into the generative AI, and the generative AI customizes the integrated services.

[0107] The integration unit can estimate the user's emotions and determine the priority of integrated services based on the estimated emotions. For example, if the user is tired, the integration unit will prioritize services that have a relaxing effect. If the user is relaxed, the integration unit may also prioritize services that are enjoyable. If the user is in a hurry, the integration unit will prioritize services that can be used quickly. This allows the priority of integrated services to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using a generative AI, or not using a generative AI. For example, the integration unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and determines the priority of integrated services.

[0108] The integration unit can select the most suitable integration service by considering the user's geographical location information during integration. For example, if the user is in a specific region, the integration unit will prioritize integrating services in that region. The integration unit can also prioritize integrating local services if the user is traveling. If the integration unit is participating in a specific event (festival, seasonal event), it will prioritize integrating services related to that event. This allows the integration unit to select the most suitable integration service based on the user's geographical location information. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the user's geographical location information into the generative AI, and the generative AI selects the most suitable integration service.

[0109] The integration unit can improve the accuracy of the integrated service by analyzing the user's social media activity during integration. For example, the integration unit can improve the accuracy of the integrated service based on the services the user has shared on social media. The integration unit can also improve the accuracy of the integrated service based on the services of influencers the user follows. The integration unit can improve the accuracy of the integrated service based on the services the user has "liked" on social media. In this way, the accuracy of the integrated service can be improved based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit inputs the user's social media activity data into a generative AI, and the generative AI improves the accuracy of the integrated service.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can input the user's food allergy information. For example, if the reception desk has an allergy to a specific food, it will suggest recipes that do not contain that food. The reception desk can also prioritize displaying recipes that do not contain nuts, for example, if the user has a nut allergy. If the reception desk has a dairy allergy, it will suggest recipes that do not contain dairy products, for example. This allows for the suggestion of safe menus based on the user's allergy information.

[0112] The generation unit can generate menus while taking into account the user's ingredient inventory information. For example, if the user inputs ingredients they have in their refrigerator, the generation unit will suggest recipes using those ingredients. If the user inputs vegetables they have in their refrigerator, the generation unit can also prioritize generating recipes using those vegetables. If the user inputs meats they have in their refrigerator, the generation unit will suggest recipes using those meats. This allows for the generation of waste-free menus based on the user's ingredient inventory information.

[0113] The service can provide recipes according to the user's cooking skill level. For example, if the user is a beginner, the service will prioritize displaying easy recipes. If the user is an intermediate cook, the service may suggest slightly more difficult recipes. If the user is an advanced cook, the service may provide more complex recipes. This ensures that the service provides appropriate recipes according to the user's cooking skill level.

[0114] The learning unit can learn the user's eating frequency and patterns and suggest optimal menus. For example, the learning unit can learn how many times a user eats out per week and suggest menus that match the frequency of eating out. If the learning unit has a tendency to cook a particular dish on weekends, it can also suggest a menu that includes that dish. If the learning unit has a tendency to use a particular ingredient on a particular day of the week, it can suggest a menu that uses that ingredient. In this way, it can suggest optimal menus based on the user's eating frequency and patterns.

[0115] The integration unit can analyze a user's purchase history and suggest the most suitable recipe. For example, it can suggest recipes using ingredients that a user has previously purchased. It can also suggest recipes using ingredients available at a specific supermarket, based on ingredients a user has purchased at that supermarket. Furthermore, it can suggest recipes using ingredients purchased online, based on those purchases. This allows the system to suggest the most suitable recipe based on the user's purchase history.

[0116] The reception desk can estimate the user's emotions and suggest menu themes based on those estimates. For example, if the user is feeling stressed, the reception desk can suggest a menu with a relaxing effect. If the user is relaxed, the reception desk can also suggest a menu that is enjoyable. If the user is in a hurry, the reception desk can suggest a menu that can be prepared quickly. In this way, menu themes can be suggested according to the user's emotions.

[0117] The generation unit can estimate the user's emotions and suggest menu variations based on those emotions. For example, if the user is stressed, the generation unit will suggest a simple menu. If the user is relaxed, for example, the generation unit can suggest a varied menu. If the user is in a hurry, for example, the generation unit will suggest a menu that can be prepared in a short time. In this way, menu variations can be suggested according to the user's emotions.

[0118] The system can estimate the user's emotions and adjust how recipes are displayed based on those emotions. For example, if the user is stressed, the system will display recipes with a simple interface. If the user is relaxed, the system may display detailed recipe information. If the user is in a hurry, the system may display concise recipes that get straight to the point. This allows the system to adjust how recipes are displayed according to the user's emotions.

[0119] The learning unit can estimate the user's emotions and prioritize training data based on those estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning recipes that help reduce stress. If the user is relaxed, the learning unit can also prioritize learning recipes that have a relaxing effect. If the user is in a hurry, the learning unit will prioritize learning recipes that can be cooked quickly. This allows the learning unit to prioritize training data according to the user's emotions.

[0120] The integration unit can estimate the user's emotions and select the type of service to integrate based on those emotions. For example, if the user is feeling stressed, the integration unit will prioritize integrating services that have a relaxing effect. If the user is relaxed, the integration unit can also prioritize integrating services that are enjoyable. If the user is in a hurry, the integration unit will prioritize integrating services that can be used quickly. This allows the system to select the type of service to integrate according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk accepts input from the user regarding their budget, time, and mood. For example, a user can set their budget to 500 yen, their time to 30 minutes, and their mood to "Japanese food." Step 2: The generation unit generates a menu based on the information received by the reception unit. For example, the generation unit suggests a Japanese meal recipe that can be prepared in 30 minutes or less. Step 3: The serving unit provides the user with the menu generated by the generation unit. For example, the serving unit provides detailed instructions and videos for the suggested recipes. Step 4: The learning unit learns the user's preferences based on the menus provided by the service unit. For example, the learning unit generates more suitable menus based on previously selected recipes and ratings. Step 5: The integration department will connect with services such as Kurashiru. For example, the integration department will provide detailed instructions and videos for the suggested recipes.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, and collaboration unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives input from the user regarding their budget, time, and mood using the touch panel 38A and microphone 38B of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a menu based on the information received from the reception unit. The provision unit provides the generated menu to the user using the display 40A and speaker 40B of the smart device 14. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's preferences based on the menu provided by the provision unit. The collaboration unit collaborates with services such as Kurashiru via the communication I / F 26 of the data processing unit 12 and provides detailed instructions and videos for the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives input from the user regarding their budget, time, and mood using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a menu based on the information received from the reception unit. The provision unit provides the generated menu to the user using the speaker 240 of the smart glasses 214. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's preferences based on the menu provided by the provision unit. The collaboration unit collaborates with services such as Kurashiru via the communication I / F 26 of the data processing unit 12 and provides detailed instructions and videos for the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives input from the user regarding their budget, time, and mood using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a menu based on the information received from the reception unit. The provision unit provides the generated menu to the user using the speaker 240 of the headset terminal 314. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's preferences based on the menu provided by the provision unit. The collaboration unit collaborates with services such as Kurashiru via the communication I / F 26 of the data processing unit 12 and provides detailed instructions and videos for the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, and collaboration unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives input from the user regarding their budget, time, and mood using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a menu based on the information received from the reception unit. The provision unit provides the generated menu to the user using the speaker 240 of the robot 414. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's preferences based on the menu provided by the provision unit. The collaboration unit collaborates with services such as Kurashiru via the communication I / F 26 of the data processing unit 12 and provides detailed instructions and videos for the suggested recipes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception desk that accepts user input regarding budget, time, and mood, A generation unit that generates a menu based on the information received by the reception unit, A supply unit that provides the menu generated by the generation unit to the user, A learning unit that learns the user's preferences based on the menu provided by the aforementioned supply unit, It includes a collaboration unit that works in conjunction with Kurashiru's services. A system characterized by the following features. (Note 2) The aforementioned reception unit is The user inputs their budget, time, and mood using sliders. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate menu patterns tailored to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide detailed instructions and videos for the suggested recipes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It learns the user's preferences and generates more suitable menus based on previously selected recipes and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, In collaboration with Kurashiru's service, it provides detailed instructions and videos for the suggested recipes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how budget, time, and mood are entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input their budget, time, and mood, the system filters the results based on their current lifestyle and food preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input fields based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their budget, time, and mood, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their budget, time, and mood, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the menu generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating menus, the system analyzes the user's past meal history to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a menu, the menu is customized based on the user's current health status and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of the menu based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating menus, the system takes the user's geographical location into consideration to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating menus, the system analyzes the user's social media activity and suggests relevant menus. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the recipe delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing recipes, the system will refer to the user's past recipe selection history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing recipes, customize the method of providing recipes based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes recipes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing recipes, we take the user's geographical location into consideration to provide the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing recipes, we analyze users' social media activity to provide relevant recipes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the system analyzes the user's past meal history to improve the accuracy of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, we analyze users' social media activity to improve the accuracy of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, It estimates the user's emotions and selects services to integrate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, During integration, the system selects the optimal integration method by referring to the user's past integration history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, During integration, the integrated services are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates user sentiment and prioritizes linked services based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, When integrating, the system selects the most suitable integration service by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, During integration, we analyze users' social media activity to improve the accuracy of the integrated service. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user input regarding budget, time, and mood, A generation unit that generates a menu based on the information received by the reception unit, A supply unit that provides the menu generated by the generation unit to the user, A learning unit that learns the user's preferences based on the menu provided by the aforementioned supply unit, It includes a collaboration unit that works in conjunction with Kurashiru's services. A system characterized by the following features.

2. The aforementioned reception unit is The user inputs their budget, time, and mood using sliders. The system according to feature 1.

3. The generating unit is Generate menu patterns tailored to user needs. The system according to feature 1.

4. The aforementioned supply unit is, Provide detailed instructions and videos for the suggested recipes. The system according to feature 1.

5. The aforementioned learning unit, It learns the user's preferences and generates more suitable menus based on previously selected recipes and ratings. The system according to feature 1.

6. The aforementioned linkage unit is, In collaboration with Kurashiru's service, it provides detailed instructions and videos for the suggested recipes. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how budget, time, and mood are entered based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users input their budget, time, and mood, the system filters the results based on their current lifestyle and food preferences. The system according to feature 1.

Images