System

Abstract

An object of the system according to the embodiment is to efficiently deliver health-conscious meals to busy people.SOLUTION: A system includes a user information input unit, a delivery plan creation unit, a meal selection unit, a calorie calculation unit, and a delivery unit. The user information input unit inputs user information. The delivery plan making unit makes a delivery plan based on the user information input by the user information input unit. The meal selection unit selects a meal based on the delivery plan made by the delivery plan making unit. The calorie calculation unit calculates calories of the meal selected by the meal selection unit. The delivery part delivers the meal based on the calorie calculated by the calorie calculation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have made it difficult for busy people to plan their meals and choose healthy meals.

[0005] The system according to the embodiment aims to efficiently deliver healthy meals to busy people. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a delivery plan creation unit, a meal selection unit, a calorie calculation unit, and a delivery unit. The user information input unit inputs user information. The delivery plan creation unit creates a delivery plan based on the user information input by the user information input unit. The meal selection unit selects meals based on the delivery plan created by the delivery plan creation unit. The calorie calculation unit calculates the calories of the meals selected by the meal selection unit. The delivery unit delivers the meals based on the calories calculated by the calorie calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently deliver healthy meals to busy people. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A meal delivery system according to an embodiment of the present invention automatically creates a monthly delivery plan by having the user input their monthly fee, food preferences, and budget. The system provides delicious meals within the price range specified by the user. It also supports a healthy delivery lifestyle by allowing users to input special requirements such as diet, medical treatment, allergies, and halal food, and by calculating calories. This frees users from the hassle and time spent thinking about preparing meals every day, allowing them to maintain a healthy diet.

[0029] A meal delivery system according to an embodiment includes a user information input unit, a delivery plan creation unit, a meal selection unit, a calorie calculation unit, and a delivery unit. The user information input unit inputs user information. For example, a user can input monthly fees, food preferences, budgets, and special requirements (diet, medical treatment, allergies, halal food, etc.). The delivery plan creation unit creates a delivery plan based on the user information input by the user information input unit. For example, the generation AI analyzes the information input by the user and creates a delivery plan for one month. The meal selection unit selects meals based on the delivery plan created by the delivery plan creation unit. For example, the generation AI selects appropriate meals based on the user's preferences and special requirements. The calorie calculation unit calculates the calories of the meals selected by the meal selection unit. For example, the generation AI calculates the calories of each meal to support the user's health. The delivery unit delivers meals based on the calories calculated by the calorie calculation unit. For example, meals are delivered to the user based on the delivery plan created by the generation AI. As a result, the meal delivery system according to the embodiment can create an optimal delivery plan based on the information input by the user and provide healthy meals.

[0030] The user information input unit can analyze the user's past meal history and provide an input support function that reflects changes in preferences and health status. For example, the user information input unit stores the user's past meal history in a database, and the generation AI analyzes that data. For example, the unit can identify the user's preferences based on past menu orders and their ratings, and suggest recommended menus the next time the user inputs a menu. The unit can also track changes in the user's health status and provide input support by comparing the user's meal history. For example, the unit can suggest a meal plan based on the user's weight and blood pressure data, depending on the user's health status. The unit can also analyze the user's meal history and suggest menus according to the season or event. For example, the unit can suggest a meal plan for a special occasion based on the menus ordered for past Christmases or birthdays. This allows the unit to suggest more appropriate meal plans based on the user's past meal history.

[0031] The user information input unit can propose a meal plan based on the user's lifestyle, exercise amount, and sleep pattern. The user information input unit, for example, tracks the user's exercise amount and proposes a meal plan based on that. For example, it proposes high-calorie meals on days when the user exercises a lot and low-calorie meals on days when the user exercises less. It also analyzes the user's sleep patterns and provides a meal plan based on the quality of sleep. For example, it proposes meals to replenish energy on days when the user does not get enough sleep. It also proposes a balanced meal plan taking into account the user's entire lifestyle. For example, it provides meals based on how busy the user is at work and their stress level. This makes it possible to provide a meal plan that suits the user's lifestyle.

[0032] The user information input unit can provide an interface that allows the user to easily input information using voice input or image recognition. The user information input unit, for example, is equipped with a voice input function, allowing the user to input information simply by speaking. For example, input can be completed by simply speaking, "I like Japanese food and it costs 10,000 yen a month." Image recognition technology can also be used to allow the user to input information simply by uploading a photo of the food they want to eat. For example, the photo of the food can be analyzed and information about that food can be automatically input. An interface that combines both voice and images can also be provided, allowing the user to input information more intuitively. For example, detailed information can be input by talking about the food while uploading a photo of it. This makes it possible to provide an interface that allows the user to input information easily.

[0033] The user information input unit has an added function that allows the user to simultaneously enter information about family members or housemates, making it possible to create meal plans for multiple people at once. The user information input unit, for example, provides a form that allows the user to enter information about family members and housemates together, and creates meal plans for multiple people at once. For example, the user can enter the preferences and allergy information of all family members. The unit also provides meal plans that take into account each member's health status and special requirements. For example, if a family member is on a diet, the unit can suggest a menu that is suitable for that person. The unit also provides a balanced meal plan based on information about family members and housemates. For example, the unit can suggest menus that take into account the preferences of each member so that everyone will be satisfied. This makes it possible to create meal plans for multiple people at once.

[0034] The delivery planning unit can add a function to automatically adjust the optimal delivery timing based on the user's schedule or plans. The delivery planning unit, for example, works in conjunction with the user's calendar or schedule app to automatically adjust the optimal delivery timing. For example, deliveries can be made to avoid times when the user is busy. The delivery timing can also be flexibly adjusted to accommodate changes in the user's schedule. For example, the delivery time can be changed if there is a sudden change in plans. The delivery timing can also be suggested to suit the user's lifestyle. For example, the user can choose between delivering all at once on the weekend or delivering small amounts every day. This makes it possible to provide the optimal delivery timing according to the user's schedule.

[0035] The delivery planning unit can propose meal plans according to the season and weather, allowing customers to enjoy a sense of the season. The delivery planning unit, for example, proposes menus using seasonal ingredients, providing meal plans that allow customers to enjoy a sense of the season. For example, dishes using cherry blossoms are proposed in spring, and cold noodles are proposed in summer. Meal plans are also provided that are according to the weather. For example, hot soups and hot pot dishes are proposed on cold days, and cold salads and desserts are proposed on hot days. Special menus are also provided to match seasonal events and occasions. For example, special dishes are proposed for Christmas and New Year's. In this way, meal plans that allow customers to enjoy a sense of the season can be provided.

[0036] The delivery plan creation unit can add a function that allows the user to flexibly change the delivery destination so that the system can be used even when the user is traveling or on a business trip. The delivery plan creation unit provides a function that allows the user to flexibly change the delivery destination so that the user can receive meals even when the user is traveling or on a business trip. For example, delivery can be made to a hotel at the travel destination or to an office at the business trip destination. In addition, a system can be built that automatically changes the delivery destination according to the user's schedule. For example, it can be linked to a calendar app to change the delivery destination according to the schedule. In addition, an interface is provided that allows the user to easily change the delivery destination. For example, the change can be completed by simply selecting the delivery destination on the app. This allows the user to receive meals even when the user is traveling or on a business trip.

[0037] The meal selection unit can select healthier and safer meals by analyzing the nutritional value and origin information of ingredients in detail. The meal selection unit, for example, analyzes the nutritional value of ingredients in detail to select healthy meals. For example, ingredients are selected based on the vitamin and mineral content. The meal selection unit also analyzes origin information of ingredients to confirm safety. For example, ingredients are selected based on the use of pesticides and production methods. The nutritional value and origin information of ingredients are also integrated to provide an optimal meal plan. For example, a menu using ingredients that are highly nutritious and have been confirmed to be safe is proposed. This makes it possible to provide healthier and safer meals.

[0038] The meal selection unit can monitor the user's health data in real time and provide a meal plan based on that. The meal selection unit, for example, monitors the user's blood pressure and blood sugar level in real time and provides a meal plan based on that. For example, if the blood sugar level is high, a low-carbohydrate menu is suggested. Also, based on the health data, a meal plan tailored to the user's physical condition is provided. For example, if the blood pressure is high, a low-salt menu is suggested. Also, the user's health data is analyzed in real time and the meal plan is adjusted based on the results. For example, a menu with adjusted nutritional balance according to the health condition is provided. In this way, a meal plan based on the user's health data can be provided.

[0039] The meal selection unit can provide a menu based on the user's cultural background or religious restrictions when selecting a meal. For example, the meal selection unit provides a menu using traditional dishes and ingredients, taking into account the user's cultural background. For example, it may suggest traditional dishes from a specific region. It may also provide special menus such as halal food or kosher food, taking into account religious restrictions. For example, it may suggest halal-certified meals to a Muslim user. It may also provide a balanced meal plan based on the user's cultural background or religious restrictions. For example, if a specific ingredient needs to be avoided, it may suggest a menu using alternative ingredients. This makes it possible to provide a menu that takes into account the user's cultural background or religious restrictions.

[0040] The meal selection unit can provide recipes and cooking videos for the user to cook by themselves when selecting a meal, thereby making cooking fun for the user. The meal selection unit, for example, provides detailed recipes so that the user can cook by themselves. For example, it may propose recipes with step-by-step cooking procedures. It may also provide cooking videos so that the user can visually learn cooking methods. For example, it may provide videos of professional chefs cooking. It may also provide recipes that include cooking tips and advice so that the user can enjoy cooking by themselves. For example, it may propose recipes that include cooking tips and variations. This allows the user to enjoy cooking by themselves.

[0041] The delivery department can introduce special packaging techniques to maintain the temperature and freshness of meals during delivery. For example, the delivery department uses packaging with heat and cold retention functions to maintain the temperature of meals. For example, hot dishes are delivered in hot packaging and cold dishes in cold packaging. Also, vacuum packing and gas exchange packaging are used to maintain the freshness of meals. For example, salads and fruits are delivered in vacuum packs. Also, special packaging techniques are used to maintain the appearance and taste of meals. For example, delicate dishes are delivered in packaging with shock absorbing material. This allows the delivery department to provide packaging techniques to maintain the temperature and freshness of meals.

[0042] The delivery department can optimize the movement of delivery personnel, shortening delivery times and increasing efficiency. For example, the delivery department uses AI to calculate routes to optimize the movement of delivery personnel. For example, it automatically calculates the shortest route, shortening delivery times. It also monitors the movement of delivery personnel in real time and adjusts routes according to traffic conditions. For example, it changes routes to avoid traffic jams. It also calculates routes that efficiently visit multiple delivery destinations to optimize the movement of delivery personnel. For example, it visits delivery destinations in the same area all at once. This shortens delivery times and increases efficiency.

[0043] The delivery unit can provide a smart lock or delivery box at the time of delivery to improve the convenience of the user when receiving the meal. The delivery unit, for example, introduces a smart lock so that the user can receive the meal even when they are not at home. For example, a delivery person operates the smart lock to place the meal in the house. In addition, a delivery box is installed so that the user can receive the meal at any time they like. For example, the meal is placed in the delivery box and the user takes it out later. In addition, the smart lock and delivery box can be linked to improve the convenience of the user when receiving the meal. For example, the smart lock is operated to place the meal in the delivery box. This improves the convenience of the user when receiving the meal.

[0044] The delivery unit can provide entertainment elements when the user receives the meal at the time of delivery. The delivery unit, for example, provides a function to play music when the user receives the meal at the time of delivery. For example, the delivery unit can play the user's favorite music during delivery. The delivery unit also provides a function to send a message to the user during delivery. For example, the delivery unit can display a message from the delivery person or information about the meal. The delivery unit also provides entertainment elements that the user can enjoy during delivery. For example, the delivery unit can provide quizzes or games related to the meal during delivery. This can provide entertainment elements when the user receives the meal.

[0045] The special requirement response unit provides menus that have been supervised by professional nutritionists or doctors to meet special requirements. The special requirement response unit provides, for example, menus that have been supervised by professional nutritionists or doctors. For example, it proposes menus that are suitable for dieting or medical treatment. It also provides menus that incorporate the opinions of experts to meet special requirements. For example, it proposes menus that accommodate allergies or halal food. It also provides meal plans that meet the user's special requirements based on menus that have been supervised by professional nutritionists or doctors. For example, it proposes menus that take nutritional balance into consideration. This makes it possible to provide menus that have been supervised by professional nutritionists or doctors.

[0046] The special requirement response unit ensures traceability of ingredients to meet special requirements, providing users with a sense of security. The special requirement response unit, for example, ensures traceability of ingredients to meet special requirements. For example, it provides the production history of ingredients that do not contain allergens. It also ensures traceability of ingredients to provide users with a sense of security. For example, it provides the production history of halal-certified ingredients. It also ensures traceability of ingredients to meet special requirements, providing users with a sense of security. For example, it provides the production history of ingredients that are suitable for a user who is recovering from an illness. This ensures traceability of ingredients and provides users with a sense of security.

[0047] The special requirement response unit links meal plans to meet special requirements with other health management services. For example, the special requirement response unit links with a fitness app to provide meal plans based on exercise data to meet special requirements. For example, it calculates calories based on the amount of exercise. It also links with medical data to provide meal plans based on the user's health condition. For example, it adjusts menus based on blood pressure and blood sugar data. It also links with other health management services to provide comprehensive health support. For example, it integrates a fitness app with medical data to propose an optimal meal plan. This makes it possible to link with other health management services to provide meal plans that meet special requirements.

[0048] The special requirement response unit provides a function that allows users to customize meal plans to meet special requirements. The special requirement response unit, for example, provides an interface that allows users to customize meal plans themselves. For example, by excluding allergic ingredients or adding favorite ingredients. The special requirement response unit also provides templates according to special requirements, allowing users to customize based on those templates. For example, by adjusting menus based on diet templates. The unit also provides a function to customize meal plans in real time based on user feedback. For example, by adjusting menus according to the user's preferences or health condition. This makes it possible to provide meal plans that users can customize themselves.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The user information input unit can propose a meal plan based on the user's lifestyle, exercise amount, and sleep patterns. For example, it tracks the user's exercise amount and proposes a meal plan based on that. It proposes high-calorie meals on days when the user exercises a lot and low-calorie meals on days when the user exercises less. It also analyzes the user's sleep patterns and provides a meal plan based on the quality of sleep. It suggests meals to replenish energy on days when the user does not get enough sleep. It also proposes a balanced meal plan taking into account the user's entire lifestyle. It can also provide meals based on how busy the user is at work and their stress level. This makes it possible to provide a meal plan that suits the user's lifestyle.

[0051] The user information input unit can provide an interface that allows users to easily input information using voice input or image recognition. For example, a voice input function could be installed, allowing users to input information simply by speaking. For a monthly fee of 10,000 yen, users could complete input by simply saying that they like Japanese food. Image recognition technology could also be used to allow users to input information simply by uploading a photo of the food they want to eat. The photo of the food could be analyzed and information about that dish could be automatically input. Furthermore, an interface that combines both voice and images could be provided, allowing users to input information more intuitively. Detailed information could also be entered by talking about the dish while uploading a photo of it. This makes it possible to provide an interface that allows users to input information easily.

[0052] The user information input unit has added a function that allows the user to simultaneously enter information about family members or housemates, making it possible to create meal plans for multiple people at once. For example, a form is provided that allows the user to enter information about family members and housemates together, allowing meal plans for multiple people to be created at once. The user can enter the preferences and allergy information of all family members. The system also provides meal plans that take into account each member's health condition and special requirements. If a family member is on a diet, it can also suggest menus that are suitable for that person. Furthermore, the system provides balanced meal plans based on information about family members and housemates. It can also suggest menus that take into account the preferences of each member so that everyone is satisfied. This makes it possible to create meal plans for multiple people at once.

[0053] The delivery planning unit can add a function that automatically adjusts the optimal delivery timing based on the user's schedule or plans. For example, it can link with the user's calendar or schedule app to automatically adjust the optimal delivery timing. Deliveries can be made to avoid the user's busy hours. It can also flexibly adjust delivery timing to accommodate changes in the user's plans. In the event of a sudden change in plans, it can also change the delivery time. It can also suggest delivery timing that suits the user's lifestyle. The user can choose whether to deliver all at once on the weekend or in small amounts every day. This makes it possible to provide the optimal delivery timing according to the user's schedule.

[0054] The delivery planning unit can propose meal plans according to the season and weather, allowing customers to enjoy a sense of the season. For example, it can propose menus using seasonal ingredients and provide meal plans that allow customers to enjoy a sense of the season. It can propose dishes using cherry blossoms in spring and cold noodles in summer. It can also provide meal plans that suit the weather. It can propose hot soups and hot pot dishes on cold days, and cold salads and desserts on hot days. It can also provide special menus that match seasonal events and occasions. It can also propose special dishes for Christmas and New Year. In this way, it can provide meal plans that allow customers to enjoy a sense of the season.

[0055] The delivery planning unit can add a function that allows the delivery destination to be changed flexibly so that the user can use the service even when traveling or on a business trip. For example, a function that allows the delivery destination to be changed flexibly is provided so that the user can receive meals even when traveling or on a business trip. Delivery can be made to a hotel at the travel destination or to an office at the business trip destination. A system that automatically changes the delivery destination according to the user's schedule is also built. It can also be linked to a calendar app to change the delivery destination according to the schedule. Furthermore, an interface is provided that allows the user to easily change the delivery destination. The change can be completed by simply selecting the delivery destination on the app. This allows the user to receive meals even when traveling or on a business trip.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user information input unit inputs user information. For example, the user can input monthly fees, food preferences, budget, special requirements (diet, medical treatment, allergies, halal food, etc.). Step 2: The delivery plan creation unit creates a delivery plan based on the user information entered by the user information input unit. For example, the generation AI analyzes the information entered by the user and creates a delivery plan for one month. Step 3: The meal selection unit selects meals based on the delivery plan created by the delivery plan creation unit. For example, the generation AI selects appropriate meals based on the user's preferences and special requirements. Step 4: The calorie calculation unit calculates the calories of the meals selected by the meal selection unit. For example, the generation AI calculates the calories of each meal to support the user's health. Step 5: The delivery unit delivers the meal based on the calories calculated by the calorie calculation unit. For example, the meal is delivered to the user based on the delivery plan created by the generation AI.

[0058] (Example 2) A meal delivery system according to an embodiment of the present invention automatically creates a monthly delivery plan by having the user input their monthly fee, food preferences, and budget. The system provides delicious meals within the price range specified by the user. It also supports a healthy delivery lifestyle by allowing users to input special requirements such as diet, medical treatment, allergies, and halal food, and by calculating calories. This frees users from the hassle and time spent thinking about preparing meals every day, allowing them to maintain a healthy diet.

[0059] A meal delivery system according to an embodiment includes a user information input unit, a delivery plan creation unit, a meal selection unit, a calorie calculation unit, and a delivery unit. The user information input unit inputs user information. For example, a user can input monthly fees, food preferences, budgets, and special requirements (diet, medical treatment, allergies, halal food, etc.). The delivery plan creation unit creates a delivery plan based on the user information input by the user information input unit. For example, the generation AI analyzes the information input by the user and creates a delivery plan for one month. The meal selection unit selects meals based on the delivery plan created by the delivery plan creation unit. For example, the generation AI selects appropriate meals based on the user's preferences and special requirements. The calorie calculation unit calculates the calories of the meals selected by the meal selection unit. For example, the generation AI calculates the calories of each meal to support the user's health. The delivery unit delivers meals based on the calories calculated by the calorie calculation unit. For example, meals are delivered to the user based on the delivery plan created by the generation AI. As a result, the meal delivery system according to the embodiment can create an optimal delivery plan based on the information input by the user and provide healthy meals.

[0060] The user information input unit can analyze the user's past meal history and provide an input support function that reflects changes in preferences and health status. For example, the user information input unit stores the user's past meal history in a database, and the generation AI analyzes that data. For example, the unit can identify the user's preferences based on past menu orders and their ratings, and suggest recommended menus the next time the user inputs a menu. The unit can also track changes in the user's health status and provide input support by comparing the user's meal history. For example, the unit can suggest a meal plan based on the user's weight and blood pressure data, depending on the user's health status. The unit can also analyze the user's meal history and suggest menus according to the season or event. For example, the unit can suggest a meal plan for a special occasion based on the menus ordered for past Christmases or birthdays. This allows the unit to suggest more appropriate meal plans based on the user's past meal history.

[0061] The user information input unit can propose a meal plan based on the user's lifestyle, exercise amount, and sleep pattern. The user information input unit, for example, tracks the user's exercise amount and proposes a meal plan based on that. For example, it proposes high-calorie meals on days when the user exercises a lot and low-calorie meals on days when the user exercises less. It also analyzes the user's sleep patterns and provides a meal plan based on the quality of sleep. For example, it proposes meals to replenish energy on days when the user does not get enough sleep. It also proposes a balanced meal plan taking into account the user's entire lifestyle. For example, it provides meals based on how busy the user is at work and their stress level. This makes it possible to provide a meal plan that suits the user's lifestyle.

[0062] The user information input unit can use the emotion estimation function to analyze the emotions of the user at the time of input and propose a meal plan according to the user's stress and fatigue level. The user information input unit, for example, analyzes the user's facial expressions and voice at the time of input and calculates an emotion score. For example, if the user is highly stressed, it proposes meals that have a relaxing effect. The emotion estimation function can also be used to measure the user's fatigue level and provide a meal plan accordingly. For example, it can propose a menu using ingredients that are effective in recovering from fatigue. It can also propose a meal plan that elicits positive emotions based on the user's emotion data. For example, if the user is feeling down, it can provide meals that have a mood-lifting effect. In this way, it is possible to provide a meal plan according to the user's emotions.

[0063] The user information input unit can provide an interface that allows the user to easily input information using voice input or image recognition. The user information input unit, for example, is equipped with a voice input function, allowing the user to input information simply by speaking. For example, input can be completed by simply speaking, "I like Japanese food and it costs 10,000 yen a month." Image recognition technology can also be used to allow the user to input information simply by uploading a photo of the food they want to eat. For example, the photo of the food can be analyzed and information about that food can be automatically input. An interface that combines both voice and images can also be provided, allowing the user to input information more intuitively. For example, detailed information can be input by talking about the food while uploading a photo of it. This makes it possible to provide an interface that allows the user to input information easily.

[0064] The user information input unit has an added function that allows the user to simultaneously enter information about family members or housemates, making it possible to create meal plans for multiple people at once. The user information input unit, for example, provides a form that allows the user to enter information about family members and housemates together, and creates meal plans for multiple people at once. For example, the user can enter the preferences and allergy information of all family members. The unit also provides meal plans that take into account each member's health status and special requirements. For example, if a family member is on a diet, the unit can suggest a menu that is suitable for that person. The unit also provides a balanced meal plan based on information about family members and housemates. For example, the unit can suggest menus that take into account the preferences of each member so that everyone will be satisfied. This makes it possible to create meal plans for multiple people at once.

[0065] The user information input unit uses the emotion estimation function to provide real-time feedback on the emotions the user feels when inputting, thereby providing input assistance that elicits positive emotions. The user information input unit, for example, analyzes the user's facial expressions and voice when inputting and displays an emotion score in real time. For example, if input is proceeding smoothly, positive feedback is displayed. The emotion estimation function is also used to analyze the emotions the user feels when inputting and provide advice to elicit positive emotions. For example, an encouraging message is displayed if input is difficult. The input assistance function is also customized based on the user's emotion data. For example, music or background that helps the user relax is provided. This makes it possible to provide input assistance that is tailored to the user's emotions.

[0066] The delivery planning unit can add a function to automatically adjust the optimal delivery timing based on the user's schedule or plans. The delivery planning unit, for example, works in conjunction with the user's calendar or schedule app to automatically adjust the optimal delivery timing. For example, deliveries can be made to avoid times when the user is busy. The delivery timing can also be flexibly adjusted to accommodate changes in the user's schedule. For example, the delivery time can be changed if there is a sudden change in plans. The delivery timing can also be suggested to suit the user's lifestyle. For example, the user can choose between delivering all at once on the weekend or delivering small amounts every day. This makes it possible to provide the optimal delivery timing according to the user's schedule.

[0067] The delivery planning unit can propose meal plans according to the season and weather, allowing customers to enjoy a sense of the season. The delivery planning unit, for example, proposes menus using seasonal ingredients, providing meal plans that allow customers to enjoy a sense of the season. For example, dishes using cherry blossoms are proposed in spring, and cold noodles are proposed in summer. Meal plans are also provided that are according to the weather. For example, hot soups and hot pot dishes are proposed on cold days, and cold salads and desserts are proposed on hot days. Special menus are also provided to match seasonal events and occasions. For example, special dishes are proposed for Christmas and New Year's. In this way, meal plans that allow customers to enjoy a sense of the season can be provided.

[0068] The delivery plan planning unit uses the emotion estimation function to propose a meal plan that matches the user's mood, thereby increasing emotional satisfaction. The delivery plan planning unit proposes a meal plan that matches the user's mood, for example, based on the user's emotion data. For example, when stress is high, it proposes a meal that has a relaxing effect. In addition, the emotion estimation function is used to provide a special menu that matches the user's mood. For example, when feeling depressed, it proposes a meal that has the effect of lifting the mood. In addition, the user's emotion data is analyzed in real time, and the meal plan is adjusted based on the results. For example, when the emotion score is low, it proposes a meal that will improve the mood. In this way, it is possible to provide a meal plan that matches the user's mood.

[0069] The delivery plan creation unit can add a function that allows the user to flexibly change the delivery destination so that the system can be used even when the user is traveling or on a business trip. The delivery plan creation unit provides a function that allows the user to flexibly change the delivery destination so that the user can receive meals even when the user is traveling or on a business trip. For example, delivery can be made to a hotel at the travel destination or to an office at the business trip destination. In addition, a system can be built that automatically changes the delivery destination according to the user's schedule. For example, it can be linked to a calendar app to change the delivery destination according to the schedule. In addition, an interface is provided that allows the user to easily change the delivery destination. For example, the change can be completed by simply selecting the delivery destination on the app. This allows the user to receive meals even when the user is traveling or on a business trip.

[0070] The delivery plan planning unit uses the emotion estimation function to create a delivery plan based on the user's emotions and can provide a special meal for a specific event or anniversary. The delivery plan planning unit, for example, provides a special meal for a specific event or anniversary based on the user's emotion data. For example, it suggests a special menu for a birthday or anniversary. The emotion estimation function is also used to plan a special event according to the user's emotions. For example, it suggests a surprise event when the user is feeling down. The emotion data of the user is also analyzed in real time, and a special meal plan is provided based on the results. For example, a special dessert is provided when the emotion score is high. This makes it possible to provide a special meal for a specific event or anniversary.

[0071] The meal selection unit can select healthier and safer meals by analyzing the nutritional value and origin information of ingredients in detail. The meal selection unit, for example, analyzes the nutritional value of ingredients in detail to select healthy meals. For example, ingredients are selected based on the vitamin and mineral content. The meal selection unit also analyzes origin information of ingredients to confirm safety. For example, ingredients are selected based on the use of pesticides and production methods. The nutritional value and origin information of ingredients are also integrated to provide an optimal meal plan. For example, a menu using ingredients that are highly nutritious and have been confirmed to be safe is proposed. This makes it possible to provide healthier and safer meals.

[0072] The meal selection unit can monitor the user's health data in real time and provide a meal plan based on that. The meal selection unit, for example, monitors the user's blood pressure and blood sugar level in real time and provides a meal plan based on that. For example, if the blood sugar level is high, a low-carbohydrate menu is suggested. Also, based on the health data, a meal plan tailored to the user's physical condition is provided. For example, if the blood pressure is high, a low-salt menu is suggested. Also, the user's health data is analyzed in real time and the meal plan is adjusted based on the results. For example, a menu with adjusted nutritional balance according to the health condition is provided. In this way, a meal plan based on the user's health data can be provided.

[0073] The meal selection unit uses the emotion estimation function to select meals according to the user's emotions, thereby improving meal satisfaction. The meal selection unit, for example, uses the emotion estimation function to select meals according to the user's emotions. For example, when stress is high, meals that have a relaxing effect are suggested. Furthermore, based on the user's emotion data, a meal plan tailored to the user's mood is provided. For example, when the user is feeling depressed, meals that have a mood-boosting effect are suggested. Furthermore, using the emotion estimation function, a special menu is provided based on the user's emotions. For example, a special dessert is provided when the emotion score is high. In this way, meals are selected according to the user's emotions, improving satisfaction.

[0074] The meal selection unit can provide a menu based on the user's cultural background or religious restrictions when selecting a meal. For example, the meal selection unit provides a menu using traditional dishes and ingredients, taking into account the user's cultural background. For example, it may suggest traditional dishes from a specific region. It may also provide special menus such as halal food or kosher food, taking into account religious restrictions. For example, it may suggest halal-certified meals to a Muslim user. It may also provide a balanced meal plan based on the user's cultural background or religious restrictions. For example, if a specific ingredient needs to be avoided, it may suggest a menu using alternative ingredients. This makes it possible to provide a menu that takes into account the user's cultural background or religious restrictions.

[0075] The meal selection unit can provide recipes and cooking videos for the user to cook by themselves when selecting a meal, thereby making cooking fun for the user. The meal selection unit, for example, provides detailed recipes so that the user can cook by themselves. For example, it may propose recipes with step-by-step cooking procedures. It may also provide cooking videos so that the user can visually learn cooking methods. For example, it may provide videos of professional chefs cooking. It may also provide recipes that include cooking tips and advice so that the user can enjoy cooking by themselves. For example, it may propose recipes that include cooking tips and variations. This allows the user to enjoy cooking by themselves.

[0076] The meal selection unit can use the emotion estimation function to select meals based on the user's emotions and provide meals that will elicit a specific emotion. The meal selection unit, for example, uses the emotion estimation function to select meals based on the user's emotions. For example, it suggests meals that have a relaxing effect. It also provides a meal plan that will elicit a specific emotion based on the user's emotion data. For example, it suggests meals that have a mood-boosting effect. It also uses the emotion estimation function to provide a special menu according to the user's emotions. For example, it provides a special dessert when the emotion score is high. In this way, it provides meals based on the user's emotions and elicits a specific emotion.

[0077] The delivery department can introduce special packaging techniques to maintain the temperature and freshness of meals during delivery. For example, the delivery department uses packaging with heat and cold retention functions to maintain the temperature of meals. For example, hot dishes are delivered in hot packaging and cold dishes in cold packaging. Also, vacuum packing and gas exchange packaging are used to maintain the freshness of meals. For example, salads and fruits are delivered in vacuum packs. Also, special packaging techniques are used to maintain the appearance and taste of meals. For example, delicate dishes are delivered in packaging with shock absorbing material. This allows the delivery department to provide packaging techniques to maintain the temperature and freshness of meals.

[0078] The delivery department can optimize the movement of delivery personnel, shortening delivery times and increasing efficiency. For example, the delivery department uses AI to calculate routes to optimize the movement of delivery personnel. For example, it automatically calculates the shortest route, shortening delivery times. It also monitors the movement of delivery personnel in real time and adjusts routes according to traffic conditions. For example, it changes routes to avoid traffic jams. It also calculates routes that efficiently visit multiple delivery destinations to optimize the movement of delivery personnel. For example, it visits delivery destinations in the same area all at once. This shortens delivery times and increases efficiency.

[0079] The delivery unit can use the emotion estimation function to analyze the user's emotions during delivery and improve the delivery person's response. The delivery unit, for example, analyzes the user's facial expressions and voice during delivery and calculates an emotion score. For example, if the user is dissatisfied, the delivery person is notified of this information. The emotion estimation function also provides feedback to improve the delivery person's response. For example, if the user is satisfied, positive feedback is provided to the delivery person. The delivery person's response is also customized based on the user's emotion data. For example, if the user is feeling stressed, the delivery person responds in a way that helps them relax. This makes it possible to improve the delivery person's response based on the user's emotions during delivery.

[0080] The delivery unit can provide a smart lock or delivery box at the time of delivery to improve the convenience of the user when receiving the meal. The delivery unit, for example, introduces a smart lock so that the user can receive the meal even when they are not at home. For example, a delivery person operates the smart lock to place the meal in the house. In addition, a delivery box is installed so that the user can receive the meal at any time they like. For example, the meal is placed in the delivery box and the user takes it out later. In addition, the smart lock and delivery box can be linked to improve the convenience of the user when receiving the meal. For example, the smart lock is operated to place the meal in the delivery box. This improves the convenience of the user when receiving the meal.

[0081] The delivery unit can provide entertainment elements when the user receives the meal at the time of delivery. The delivery unit, for example, provides a function to play music when the user receives the meal at the time of delivery. For example, the delivery unit can play the user's favorite music during delivery. The delivery unit also provides a function to send a message to the user during delivery. For example, the delivery unit can display a message from the delivery person or information about the meal. The delivery unit also provides entertainment elements that the user can enjoy during delivery. For example, the delivery unit can provide quizzes or games related to the meal during delivery. This can provide entertainment elements when the user receives the meal.

[0082] The delivery unit uses the emotion estimation function to monitor the user's emotions during delivery in real time and optimize the delivery person's response. The delivery unit, for example, analyzes the user's facial expressions and voice during delivery and monitors the emotion score in real time. For example, if the user is dissatisfied, the delivery person is notified of this information. The emotion estimation function also provides feedback to optimize the delivery person's response. For example, if the user is satisfied, positive feedback is provided to the delivery person. The delivery person's response is also customized based on the user's emotion data. For example, if the user is stressed, the delivery person responds in a way that helps them relax. This makes it possible to optimize the delivery person's response based on the user's emotions during delivery.

[0083] The special requirement response unit provides menus that have been supervised by professional nutritionists or doctors to meet special requirements. The special requirement response unit provides, for example, menus that have been supervised by professional nutritionists or doctors. For example, it proposes menus that are suitable for dieting or medical treatment. It also provides menus that incorporate the opinions of experts to meet special requirements. For example, it proposes menus that accommodate allergies or halal food. It also provides meal plans that meet the user's special requirements based on menus that have been supervised by professional nutritionists or doctors. For example, it proposes menus that take nutritional balance into consideration. This makes it possible to provide menus that have been supervised by professional nutritionists or doctors.

[0084] The special requirement response unit ensures traceability of ingredients to meet special requirements, providing users with a sense of security. The special requirement response unit, for example, ensures traceability of ingredients to meet special requirements. For example, it provides the production history of ingredients that do not contain allergens. It also ensures traceability of ingredients to provide users with a sense of security. For example, it provides the production history of halal-certified ingredients. It also ensures traceability of ingredients to meet special requirements, providing users with a sense of security. For example, it provides the production history of ingredients that are suitable for a user who is recovering from an illness. This ensures traceability of ingredients and provides users with a sense of security.

[0085] The special requirement response unit uses the emotion estimation function to analyze the user's emotions when responding to special requirements and proposes a more appropriate menu. The special requirement response unit, for example, uses the emotion estimation function to analyze the user's emotions when responding to special requirements. For example, it analyzes the user's emotions regarding allergy-friendly menus. It also proposes menus that meet special requirements based on the user's emotion data. For example, it proposes menus with high emotion scores to a user who is recovering from an illness. It also uses the emotion estimation function to monitor the user's emotions when responding to special requirements in real time and provide an optimal menu. For example, it adjusts the menu according to changes in the user's emotions. This makes it possible to analyze the user's emotions when responding to special requirements and propose a more appropriate menu.

[0086] The special requirement response unit links meal plans to meet special requirements with other health management services. For example, the special requirement response unit links with a fitness app to provide meal plans based on exercise data to meet special requirements. For example, it calculates calories based on the amount of exercise. It also links with medical data to provide meal plans based on the user's health condition. For example, it adjusts menus based on blood pressure and blood sugar data. It also links with other health management services to provide comprehensive health support. For example, it integrates a fitness app with medical data to propose an optimal meal plan. This makes it possible to link with other health management services to provide meal plans that meet special requirements.

[0087] The special requirement response unit provides a function that allows users to customize meal plans to meet special requirements. The special requirement response unit, for example, provides an interface that allows users to customize meal plans themselves. For example, by excluding allergic ingredients or adding favorite ingredients. The special requirement response unit also provides templates according to special requirements, allowing users to customize based on those templates. For example, by adjusting menus based on diet templates. The unit also provides a function to customize meal plans in real time based on user feedback. For example, by adjusting menus according to the user's preferences or health condition. This makes it possible to provide meal plans that users can customize themselves.

[0088] The special requirement response unit uses an emotion estimation function to monitor the user's emotions in real time when responding to special requirements, and provides an optimal menu. The special requirement response unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when responding to special requirements. For example, it analyzes the user's emotions regarding an allergy-friendly menu. Furthermore, it adjusts the menu to respond to special requirements in real time based on the user's emotion data. For example, it suggests a menu with a high emotion score to a user who is recovering from an illness. Furthermore, it uses the emotion estimation function to monitor the user's emotions in real time when responding to special requirements, and provides an optimal menu. For example, it adjusts the menu according to changes in the user's emotions. In this way, it is possible to monitor the user's emotions in real time when responding to special requirements, and provide an optimal menu.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The user information input unit can propose a meal plan based on the user's lifestyle, exercise amount, and sleep patterns. For example, it tracks the user's exercise amount and proposes a meal plan based on that. It proposes high-calorie meals on days when the user exercises a lot and low-calorie meals on days when the user exercises less. It also analyzes the user's sleep patterns and provides a meal plan based on the quality of sleep. It suggests meals to replenish energy on days when the user does not get enough sleep. It also proposes a balanced meal plan taking into account the user's entire lifestyle. It can also provide meals based on how busy the user is at work and their stress level. This makes it possible to provide a meal plan that suits the user's lifestyle.

[0091] The user information input unit can use the emotion estimation function to analyze the emotions of the user when entering information and suggest a meal plan according to the user's stress and fatigue level. For example, it can analyze the user's facial expressions and voice when entering information and calculate an emotion score. If the user is highly stressed, it can suggest meals that have a relaxing effect. It can also use the emotion estimation function to measure the user's fatigue level and suggest a meal plan that matches that level. It can also suggest menus that use ingredients that are effective in recovering from fatigue. Furthermore, it can suggest a meal plan that elicits positive emotions based on the user's emotion data. If the user is feeling down, it can also suggest meals that have a mood-boosting effect. This makes it possible to provide a meal plan that matches the user's emotions.

[0092] The user information input unit can provide an interface that allows users to easily input information using voice input or image recognition. For example, a voice input function could be installed, allowing users to input information simply by speaking. For a monthly fee of 10,000 yen, users could complete input by simply saying that they like Japanese food. Image recognition technology could also be used to allow users to input information simply by uploading a photo of the food they want to eat. The photo of the food could be analyzed and information about that dish could be automatically input. Furthermore, an interface that combines both voice and images could be provided, allowing users to input information more intuitively. Detailed information could also be entered by talking about the dish while uploading a photo of it. This makes it possible to provide an interface that allows users to input information easily.

[0093] The user information input unit has added a function that allows the user to simultaneously enter information about family members or housemates, making it possible to create meal plans for multiple people at once. For example, a form is provided that allows the user to enter information about family members and housemates together, allowing meal plans for multiple people to be created at once. The user can enter the preferences and allergy information of all family members. The system also provides meal plans that take into account each member's health condition and special requirements. If a family member is on a diet, it can also suggest menus that are suitable for that person. Furthermore, the system provides balanced meal plans based on information about family members and housemates. It can also suggest menus that take into account the preferences of each member so that everyone is satisfied. This makes it possible to create meal plans for multiple people at once.

[0094] The user information input unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels when entering text, providing input assistance that draws out positive emotions. For example, it can analyze the user's facial expressions and voice when entering text and display an emotion score in real time. If the entry is progressing smoothly, it can display positive feedback. It can also use the emotion estimation function to analyze the emotions the user feels when entering text and provide advice to draw out positive emotions. It can also display encouraging messages if the entry is difficult. It can also customize the input assistance function based on the user's emotion data. It can also provide music or backgrounds that help the user relax. This makes it possible to provide input assistance that matches the user's emotions.

[0095] The delivery planning unit can add a function that automatically adjusts the optimal delivery timing based on the user's schedule or plans. For example, it can link with the user's calendar or schedule app to automatically adjust the optimal delivery timing. Deliveries can be made to avoid the user's busy hours. It can also flexibly adjust delivery timing to accommodate changes in the user's plans. In the event of a sudden change in plans, it can also change the delivery time. It can also suggest delivery timing that suits the user's lifestyle. The user can choose whether to deliver all at once on the weekend or in small amounts every day. This makes it possible to provide the optimal delivery timing according to the user's schedule.

[0096] The delivery planning unit can propose meal plans according to the season and weather, allowing customers to enjoy a sense of the season. For example, it can propose menus using seasonal ingredients and provide meal plans that allow customers to enjoy a sense of the season. It can propose dishes using cherry blossoms in spring and cold noodles in summer. It can also provide meal plans that suit the weather. It can propose hot soups and hot pot dishes on cold days, and cold salads and desserts on hot days. It can also provide special menus that match seasonal events and occasions. It can also propose special dishes for Christmas and New Year. In this way, it can provide meal plans that allow customers to enjoy a sense of the season.

[0097] The delivery plan planning unit can use the emotion estimation function to suggest a meal plan that matches the user's mood and increase emotional satisfaction. For example, it can suggest a meal plan that matches the user's mood based on the user's emotion data. When stress is high, it can suggest meals that have a relaxing effect. The emotion estimation function can also be used to provide a special menu that matches the user's mood. When feeling depressed, it can also suggest meals that have a mood-boosting effect. Furthermore, it analyzes the user's emotion data in real time and adjusts the meal plan based on the results. When the emotion score is low, it can also suggest meals that will improve the mood. This makes it possible to provide a meal plan that matches the user's mood.

[0098] The delivery planning unit can add a function that allows the delivery destination to be changed flexibly so that the user can use the service even when traveling or on a business trip. For example, a function that allows the delivery destination to be changed flexibly is provided so that the user can receive meals even when traveling or on a business trip. Delivery can be made to a hotel at the travel destination or to an office at the business trip destination. A system that automatically changes the delivery destination according to the user's schedule is also built. It can also be linked to a calendar app to change the delivery destination according to the schedule. Furthermore, an interface is provided that allows the user to easily change the delivery destination. The change can be completed by simply selecting the delivery destination on the app. This allows the user to receive meals even when traveling or on a business trip.

[0099] The delivery plan planning unit uses the emotion estimation function to create a delivery plan based on the user's emotions and can provide a special meal for a specific event or anniversary. For example, a special meal can be provided for a specific event or anniversary based on the user's emotion data. A special menu can be suggested for birthdays and anniversaries. The emotion estimation function can also be used to plan a special event based on the user's emotions. A surprise event can also be suggested when the user is feeling down. Furthermore, the user's emotion data can be analyzed in real time, and a special meal plan can be provided based on the results. A special dessert can also be provided when the emotion score is high. This makes it possible to provide a special meal for a specific event or anniversary.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The user information input unit inputs user information. For example, the user can input monthly fees, food preferences, budget, special requirements (diet, medical treatment, allergies, halal food, etc.). Step 2: The delivery plan creation unit creates a delivery plan based on the user information entered by the user information input unit. For example, the generation AI analyzes the information entered by the user and creates a delivery plan for one month. Step 3: The meal selection unit selects meals based on the delivery plan created by the delivery plan creation unit. For example, the generation AI selects appropriate meals based on the user's preferences and special requirements. Step 4: The calorie calculation unit calculates the calories of the meals selected by the meal selection unit. For example, the generation AI calculates the calories of each meal to support the user's health. Step 5: The delivery unit delivers the meal based on the calories calculated by the calorie calculation unit. For example, the meal is delivered to the user based on the delivery plan created by the generation AI.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information input unit for inputting user information; a delivery plan creation unit that creates a delivery plan based on the user information input by the user information input unit; a meal selection unit that selects meals based on the delivery plan formulated by the delivery plan formulation unit; a calorie calculation unit that calculates the calories of the meal selected by the meal selection unit; a delivery unit that delivers the meal based on the calories calculated by the calorie calculation unit. A system characterized by:

2. The user information input unit Analyzes the user's past dietary history and provides an input support function that reflects changes in preferences and health status 2. The system of claim 1.

3. The user information input unit Based on the user's lifestyle, the app suggests meal plans based on exercise and sleep patterns.

2. The system of claim 1.

4. The user information input unit Analyzes the user's emotions when inputting information and suggests meal plans based on stress and fatigue levels 2. The system of claim 1.

5. The user information input unit Provide an interface that allows users to easily input information using voice input or image recognition 2. The system of claim 1.

6. The user information input unit Add a function that allows you to enter information about family members or housemates at the same time, allowing you to create meal plans for multiple people at once.

2. The system of claim 1.

Images