System

Abstract

An object of a system according to an embodiment is to propose an optimal meal plan or recipe based on ingredients in a refrigerator and physical condition data of a user.SOLUTION: A system according to an embodiment includes a camera, an ingredient recognition unit, a physical condition data collection unit, and a suggestion unit. The camera captures an image of the inside of the refrigerator. The foodstuff recognition unit analyzes the image captured by the camera to grasp the type and amount of the foodstuff in the refrigerator. The physical condition data collection unit collects physical condition data of a user. The suggestion unit suggests an optimal meal plan or recipe on the basis of the types and amounts of the ingredients recognized by the ingredient recognition unit and the physical condition data of the user collected by the physical condition data collection unit.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 technologies do not adequately manage food ingredients in the refrigerator and propose meal plans based on the user's physical condition, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal meal plans and recipes based on ingredients in the refrigerator and the user's physical condition data. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an ingredient recognition unit, a physical condition data collection unit, and a suggestion unit. The camera captures images of the inside of the refrigerator. The ingredient recognition unit analyzes the images captured by the camera to determine the types and amounts of ingredients in the refrigerator. The physical condition data collection unit collects the user's physical condition data. The suggestion unit proposes optimal meal plans and recipes based on the types and amounts of ingredients determined by the ingredient recognition unit and the user's physical condition data collected by the physical condition data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal meal plans and recipes based on ingredients in the refrigerator and the user's physical condition data. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The system according to the embodiment of the present invention uses AI and cameras to assess the contents of a refrigerator, and combines this with the user's health data to propose optimal solutions. This allows the system to efficiently manage ingredients in the refrigerator and propose optimal meal plans tailored to the user's health condition.

[0029] The system according to the embodiment includes a camera, an ingredient recognition unit, a health data collection unit, and a suggestion unit. The camera captures images of the inside of the refrigerator. For example, the camera is installed inside the refrigerator and can periodically capture images. The camera has high resolution, allowing it to capture detailed images of ingredients. The ingredient recognition unit analyzes the images captured by the camera to determine the types and quantities of ingredients in the refrigerator. For example, the ingredient recognition unit uses image analysis technology to identify ingredients such as vegetables, fruits, meat, and dairy products. The ingredient recognition unit can also measure the amount of ingredients and manage inventory in the refrigerator. The health data collection unit collects the user's health data. For example, the health data collection unit acquires data such as blood pressure, body temperature, and heart rate from a smartwatch or a health management app. The health data collection unit can also collect the user's sleep data and exercise data. The suggestion unit proposes optimal meal plans and recipes based on the types and quantities of ingredients recognized by the ingredient recognition unit and the user's health data collected by the health data collection unit. For example, if a user has high blood pressure, the suggestion unit can propose low-salt recipes. The suggestion unit can also suggest recipes that utilize ingredients stored in the refrigerator, thereby reducing food waste. This allows the system according to the embodiment to suggest optimal meal plans and recipes based on the ingredients stored in the refrigerator and the user's physical condition data.

[0030] The ingredient recognition unit can evaluate the freshness of ingredients in real time and identify ingredients whose freshness has decreased. For example, the ingredient recognition unit uses AI to analyze image data taken by a camera inside the refrigerator and evaluate freshness based on changes in the color and shape of ingredients. For example, it determines that freshness has decreased if the color of vegetables changes or wrinkles appear on the surface of fruit. The ingredient recognition unit can also detect the smell of ingredients using a sensor to evaluate freshness. For example, it determines that freshness has decreased if an odor sensor detects an unusual odor in ingredients. The ingredient recognition unit can also evaluate freshness based on the length of time the ingredients have been stored. For example, it determines that the longer the storage period, the lower the freshness. This allows the freshness of ingredients to be evaluated in real time and ingredients whose freshness has decreased to be identified.

[0031] The ingredient recognition unit can analyze the nutritional value of ingredients and suggest combinations of ingredients that take nutritional balance into consideration. For example, the ingredient recognition unit uses AI to analyze the nutritional value of ingredients in the refrigerator and identify the nutritional components of each ingredient. For example, it analyzes nutritional components such as vitamins, minerals, protein, and lipids. The ingredient recognition unit also suggests ingredient combinations that take nutritional balance into consideration based on the nutritional value of the ingredients. For example, it suggests a balanced meal by combining vegetables rich in vitamin C with meat rich in protein. The ingredient recognition unit can also adjust the nutritional balance by taking into consideration the user's physical condition data. For example, if the user is iron deficient, it will prioritize suggesting ingredients that are rich in iron. This makes it possible to analyze the nutritional value of ingredients and suggest ingredient combinations that take nutritional balance into consideration.

[0032] The suggestion unit can optimize the placement of ingredients in the refrigerator to make them easier to take out. For example, the suggestion unit uses AI to analyze image data taken by a camera inside the refrigerator and optimize the placement of ingredients. For example, ingredients that are used frequently are placed in positions that make them easier to take out. The suggestion unit also adjusts the placement based on the type of ingredient and how often it is used. For example, vegetables and fruits are placed in positions that make them easier to take out, and ingredients with longer shelf lives are placed in the back. The suggestion unit can also learn the user's usage patterns and suggest the optimal placement. For example, ingredients that are frequently used by the user are placed with priority. This makes it possible to optimize the placement of ingredients in the refrigerator and make them easier to take out.

[0033] The suggestion unit can link the ingredient recognition results with other smart home appliances to automate the cooking process. For example, the suggestion unit can link the ingredient recognition results for a refrigerator with a smart oven to automate the cooking process. For example, the oven can automatically set the cooking time and temperature based on the ingredients in the refrigerator. The suggestion unit can also link with a smart rice cooker or a smart microwave to automate the cooking process. For example, the rice cooker can automatically set the cooking time, and the microwave can automatically set the heating time. The suggestion unit can also link with a smart speaker to provide audio guidance on the cooking steps. For example, the user can receive instructions without using their hands while cooking. This allows the ingredient recognition results to be linked with other smart home appliances to automate the cooking process.

[0034] The physical condition data collection unit can analyze long-term health trends based on the user's physical condition data and suggest preventative health management. The physical condition data collection unit, for example, collects the user's physical condition data over a long period of time and analyzes health trends. For example, it analyzes blood pressure fluctuation patterns based on blood pressure data from the past year. The physical condition data collection unit also suggests preventative health management based on the user's physical condition data. For example, if blood pressure is on the rise, it suggests reducing salt intake. The physical condition data collection unit can also suggest improving exercise habits based on the user's physical condition data. For example, if there is a continuing lack of exercise, it recommends moderate exercise. In this way, it is possible to analyze long-term health trends based on the user's physical condition data and suggest preventative health management.

[0035] The physical condition data collection unit can combine the physical condition data and the dietary history to evaluate the impact of meals and suggest areas for improvement. The physical condition data collection unit, for example, combines and analyzes the user's physical condition data and dietary history to evaluate the impact of meals. For example, it analyzes fluctuations in blood glucose levels after meals to evaluate the impact of meals. The physical condition data collection unit also suggests areas for improvement based on the user's physical condition data and dietary history. For example, if blood glucose levels are high after meals, it suggests reducing carbohydrates. The physical condition data collection unit can also suggest improvements to nutritional balance based on the user's physical condition data and dietary history. For example, if there is a persistent vitamin deficiency, it suggests consuming foods rich in vitamins. In this way, it is possible to combine the physical condition data and dietary history to evaluate the impact of meals and suggest areas for improvement.

[0036] The physical condition data collection unit can make suggestions for improving the user's exercise habits and lifestyle habits based on the physical condition data. The physical condition data collection unit makes suggestions for improving exercise habits based on the user's physical condition data, for example. For example, it analyzes heart rate data and suggests appropriate exercise intensity and frequency. The physical condition data collection unit also makes suggestions for improving lifestyle habits based on the user's physical condition data. For example, it analyzes sleep data and suggests appropriate sleep duration and quality. The physical condition data collection unit can also make suggestions for improving eating habits based on the user's physical condition data. For example, it suggests adjusting the timing and content of meals. In this way, it is possible to make suggestions for improving the user's exercise habits and lifestyle habits based on the physical condition data.

[0037] The health data collection unit can share health data with family members and medical institutions to support comprehensive health management. The health data collection unit, for example, shares the user's health data with family members to support health management. For example, it allows family members to check the user's health data in real time. The health data collection unit can also share the user's health data with medical institutions to support comprehensive health management. For example, doctors can provide appropriate diagnoses and treatments based on the user's health data. The health data collection unit can also store the user's health data in the cloud and make it accessible as needed. For example, the user can access the health data from different devices to manage their health. This allows health data to be shared with family members and medical institutions to support comprehensive health management.

[0038] The suggestion unit can suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season. The suggestion unit can suggest meal plans that allow the user to enjoy the feeling of the season, for example, based on seasonal ingredients. For example, it can suggest a salad using fresh vegetables in the spring, and a hot soup in the winter. The suggestion unit can also suggest meal plans according to the weather. For example, it can suggest cold dishes on hot summer days, and hot dishes on cold winter days. The suggestion unit can also suggest meal plans that match seasonal events and occasions. For example, it can suggest a special dinner for Christmas. In this way, it is possible to suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season.

[0039] The suggestion unit can incorporate local traditional cuisine and culture into meal plans and make suggestions for enjoying the diversity of food culture. The suggestion unit, for example, proposes meal plans based on the traditional cuisine of the user's region of residence. For example, it proposes recipes that incorporate local specialties and traditional cooking methods. The suggestion unit can also propose meal plans that incorporate traditional cuisine from different regions. For example, it can propose recipes that recreate dishes that the user enjoyed while traveling. The suggestion unit can also propose meal plans that match local culture and events. For example, it can propose special dishes that match local festivals and events. This makes it possible to incorporate local traditional cuisine and culture into meal plans and make suggestions for enjoying the diversity of food culture.

[0040] The suggestion unit can share meal plans with other users and promote the exchange of meal ideas within the community. For example, the suggestion unit builds a platform for sharing meal plans, allowing users to share their meal plans with other users. For example, it provides a function that allows users to post photos of recipes and ingredients. The suggestion unit also creates a community where users can exchange meal ideas. For example, users can post their own meal plans and receive feedback from other users. The suggestion unit can also suggest new ideas to other users based on the user's meal plan. For example, it can suggest dishes that the user has not tried. This allows meal plans to be shared with other users, promoting the exchange of meal ideas within the community.

[0041] The suggestion unit can incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions. The suggestion unit, for example, proposes environmentally conscious meal plans based on eco-friendly ingredients. For example, it may use locally produced organic vegetables or fish caught through sustainable fishing. The suggestion unit also proposes eco-friendly recipes. For example, it may propose cooking methods that reduce food waste or recipes that use reusable ingredients. The suggestion unit can also provide information to increase the user's environmental awareness. For example, it may introduce how to select eco-friendly ingredients or environmentally conscious cooking methods. This allows the suggestion unit to incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The suggestion unit can optimize the placement of ingredients in the refrigerator to make them easier to take out. For example, the suggestion unit uses AI to analyze image data taken by a camera inside the refrigerator and optimize the placement of ingredients. For example, ingredients that are used frequently are placed in positions that make them easier to take out. The suggestion unit also adjusts the placement based on the type of ingredient and how often it is used. For example, vegetables and fruits are placed in positions that make them easier to take out, and ingredients with longer shelf lives are placed in the back. The suggestion unit can also learn the user's usage patterns and suggest the optimal placement. For example, ingredients that are frequently used by the user are placed with priority. This makes it possible to optimize the placement of ingredients in the refrigerator and make them easier to take out.

[0044] The suggestion unit can link the ingredient recognition results with other smart home appliances to automate the cooking process. For example, the suggestion unit can link the ingredient recognition results for a refrigerator with a smart oven to automate the cooking process. For example, the oven can automatically set the cooking time and temperature based on the ingredients in the refrigerator. The suggestion unit can also link with a smart rice cooker or a smart microwave to automate the cooking process. For example, the rice cooker can automatically set the cooking time, and the microwave can automatically set the heating time. The suggestion unit can also link with a smart speaker to provide audio guidance on the cooking steps. For example, the user can receive instructions without using their hands while cooking. This allows the ingredient recognition results to be linked with other smart home appliances to automate the cooking process.

[0045] The physical condition data collection unit can analyze long-term health trends based on the user's physical condition data and suggest preventative health management. The physical condition data collection unit, for example, collects the user's physical condition data over a long period of time and analyzes health trends. For example, it analyzes blood pressure fluctuation patterns based on blood pressure data from the past year. The physical condition data collection unit also suggests preventative health management based on the user's physical condition data. For example, if blood pressure is on the rise, it suggests reducing salt intake. The physical condition data collection unit can also suggest improving exercise habits based on the user's physical condition data. For example, if there is a continuing lack of exercise, it recommends moderate exercise. In this way, it is possible to analyze long-term health trends based on the user's physical condition data and suggest preventative health management.

[0046] The physical condition data collection unit can combine the physical condition data and the dietary history to evaluate the impact of meals and suggest areas for improvement. The physical condition data collection unit, for example, combines and analyzes the user's physical condition data and dietary history to evaluate the impact of meals. For example, it analyzes fluctuations in blood glucose levels after meals to evaluate the impact of meals. The physical condition data collection unit also suggests areas for improvement based on the user's physical condition data and dietary history. For example, if blood glucose levels are high after meals, it suggests reducing carbohydrates. The physical condition data collection unit can also suggest improvements to nutritional balance based on the user's physical condition data and dietary history. For example, if there is a persistent vitamin deficiency, it suggests consuming foods rich in vitamins. In this way, it is possible to combine the physical condition data and dietary history to evaluate the impact of meals and suggest areas for improvement.

[0047] The suggestion unit can suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season. The suggestion unit can suggest meal plans that allow the user to enjoy the feeling of the season, for example, based on seasonal ingredients. For example, it can suggest a salad using fresh vegetables in the spring, and a hot soup in the winter. The suggestion unit can also suggest meal plans according to the weather. For example, it can suggest cold dishes on hot summer days, and hot dishes on cold winter days. The suggestion unit can also suggest meal plans that match seasonal events and occasions. For example, it can suggest a special dinner for Christmas. In this way, it is possible to suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season.

[0048] The suggestion unit can incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions. The suggestion unit, for example, proposes environmentally conscious meal plans based on eco-friendly ingredients. For example, it may use locally produced organic vegetables or fish caught through sustainable fishing. The suggestion unit also proposes eco-friendly recipes. For example, it may propose cooking methods that reduce food waste or recipes that use reusable ingredients. The suggestion unit can also provide information to increase the user's environmental awareness. For example, it may introduce how to select eco-friendly ingredients or environmentally conscious cooking methods. This allows the suggestion unit to incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The camera takes pictures of the inside of the refrigerator. For example, the camera is installed inside the refrigerator and can take pictures periodically. The camera has high resolution and can capture even the fine details of the ingredients. Step 2: The ingredient recognition unit analyzes the images captured by the camera to determine the types and quantities of ingredients in the refrigerator. For example, the ingredient recognition unit uses image analysis technology to identify ingredients such as vegetables, fruits, meat, and dairy products. The ingredient recognition unit can also measure the amount of ingredients and manage inventory in the refrigerator. Step 3: The physical condition data collection unit collects the user's physical condition data. For example, the physical condition data collection unit obtains data such as blood pressure, body temperature, and heart rate from a smartwatch or a health management app. The physical condition data collection unit can also collect the user's sleep data and exercise data. Step 4: The suggestion unit suggests optimal meal plans and recipes based on the types and amounts of ingredients recognized by the ingredient recognition unit and the user's physical condition data collected by the physical condition data collection unit. For example, if the user has high blood pressure, the suggestion unit suggests low-salt recipes. The suggestion unit can also reduce food waste by suggesting recipes that use ingredients in the refrigerator.

[0051] (Example 2) The system according to the embodiment of the present invention uses AI and cameras to assess the contents of a refrigerator, and combines this with the user's health data to propose optimal solutions. This allows the system to efficiently manage ingredients in the refrigerator and propose optimal meal plans tailored to the user's health condition.

[0052] The system according to the embodiment includes a camera, an ingredient recognition unit, a health data collection unit, and a suggestion unit. The camera captures images of the inside of the refrigerator. For example, the camera is installed inside the refrigerator and can periodically capture images. The camera has high resolution, allowing it to capture detailed images of ingredients. The ingredient recognition unit analyzes the images captured by the camera to determine the types and quantities of ingredients in the refrigerator. For example, the ingredient recognition unit uses image analysis technology to identify ingredients such as vegetables, fruits, meat, and dairy products. The ingredient recognition unit can also measure the amount of ingredients and manage inventory in the refrigerator. The health data collection unit collects the user's health data. For example, the health data collection unit acquires data such as blood pressure, body temperature, and heart rate from a smartwatch or a health management app. The health data collection unit can also collect the user's sleep data and exercise data. The suggestion unit proposes optimal meal plans and recipes based on the types and quantities of ingredients recognized by the ingredient recognition unit and the user's health data collected by the health data collection unit. For example, if a user has high blood pressure, the suggestion unit can propose low-salt recipes. The suggestion unit can also suggest recipes that utilize ingredients stored in the refrigerator, thereby reducing food waste. This allows the system according to the embodiment to suggest optimal meal plans and recipes based on the ingredients stored in the refrigerator and the user's physical condition data.

[0053] The ingredient recognition unit can evaluate the freshness of ingredients in real time and identify ingredients whose freshness has decreased. For example, the ingredient recognition unit uses AI to analyze image data taken by a camera inside the refrigerator and evaluate freshness based on changes in the color and shape of ingredients. For example, it determines that freshness has decreased if the color of vegetables changes or wrinkles appear on the surface of fruit. The ingredient recognition unit can also detect the smell of ingredients using a sensor to evaluate freshness. For example, it determines that freshness has decreased if an odor sensor detects an unusual odor in ingredients. The ingredient recognition unit can also evaluate freshness based on the length of time the ingredients have been stored. For example, it determines that the longer the storage period, the lower the freshness. This allows the freshness of ingredients to be evaluated in real time and ingredients whose freshness has decreased to be identified.

[0054] The ingredient recognition unit can analyze the nutritional value of ingredients and suggest combinations of ingredients that take nutritional balance into consideration. For example, the ingredient recognition unit uses AI to analyze the nutritional value of ingredients in the refrigerator and identify the nutritional components of each ingredient. For example, it analyzes nutritional components such as vitamins, minerals, protein, and lipids. The ingredient recognition unit also suggests ingredient combinations that take nutritional balance into consideration based on the nutritional value of the ingredients. For example, it suggests a balanced meal by combining vegetables rich in vitamin C with meat rich in protein. The ingredient recognition unit can also adjust the nutritional balance by taking into consideration the user's physical condition data. For example, if the user is iron deficient, it will prioritize suggesting ingredients that are rich in iron. This makes it possible to analyze the nutritional value of ingredients and suggest ingredient combinations that take nutritional balance into consideration.

[0055] The suggestion unit can prioritize suggesting ingredients that match the user's preferences based on the user's emotional data. The suggestion unit, for example, analyzes the user's past eating history to identify ingredients that the user likes to eat. For example, it prioritizes recognizing ingredients that the user frequently purchases or ingredients that the user has given high ratings to in the past. The suggestion unit also suggests ingredients that match the user's preferences based on the user's emotional data. For example, it suggests ingredients that the user enjoys eating or ingredients that the user likes to eat when feeling stressed. The suggestion unit can also suggest new ingredients or recipes based on the user's preference data. For example, it suggests ingredients that the user has not tried before or different cooking methods. In this way, it is possible to prioritize suggesting ingredients that match the user's preferences based on the user's emotional data.

[0056] The suggestion unit can optimize the placement of ingredients in the refrigerator to make them easier to take out. For example, the suggestion unit uses AI to analyze image data taken by a camera inside the refrigerator and optimize the placement of ingredients. For example, ingredients that are used frequently are placed in positions that make them easier to take out. The suggestion unit also adjusts the placement based on the type of ingredient and how often it is used. For example, vegetables and fruits are placed in positions that make them easier to take out, and ingredients with longer shelf lives are placed in the back. The suggestion unit can also learn the user's usage patterns and suggest the optimal placement. For example, ingredients that are frequently used by the user are placed with priority. This makes it possible to optimize the placement of ingredients in the refrigerator and make them easier to take out.

[0057] The suggestion unit can link the ingredient recognition results with other smart home appliances to automate the cooking process. For example, the suggestion unit can link the ingredient recognition results for a refrigerator with a smart oven to automate the cooking process. For example, the oven can automatically set the cooking time and temperature based on the ingredients in the refrigerator. The suggestion unit can also link with a smart rice cooker or a smart microwave to automate the cooking process. For example, the rice cooker can automatically set the cooking time, and the microwave can automatically set the heating time. The suggestion unit can also link with a smart speaker to provide audio guidance on the cooking steps. For example, the user can receive instructions without using their hands while cooking. This allows the ingredient recognition results to be linked with other smart home appliances to automate the cooking process.

[0058] The suggestion unit can analyze the emotions of the user when they open the refrigerator and adjust the placement of ingredients and the suggestions. For example, the suggestion unit uses a camera to capture the user's facial expression when they open the refrigerator and analyzes the emotions using an emotion estimation function. For example, if the user looks surprised, the suggestion unit reconsiders the placement of ingredients in the refrigerator. The suggestion unit also records the user's voice and analyzes their emotions using voice analysis technology. For example, if the user feels dissatisfied, the suggestions are adjusted. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions. For example, if the user feels stressed, the suggestion unit will suggest ingredients that have a relaxing effect. This makes it possible to analyze the emotions of the user when they open the refrigerator and adjust the placement of ingredients and the suggestions.

[0059] The physical condition data collection unit can analyze long-term health trends based on the user's physical condition data and suggest preventative health management. The physical condition data collection unit, for example, collects the user's physical condition data over a long period of time and analyzes health trends. For example, it analyzes blood pressure fluctuation patterns based on blood pressure data from the past year. The physical condition data collection unit also suggests preventative health management based on the user's physical condition data. For example, if blood pressure is on the rise, it suggests reducing salt intake. The physical condition data collection unit can also suggest improving exercise habits based on the user's physical condition data. For example, if there is a continuing lack of exercise, it recommends moderate exercise. In this way, it is possible to analyze long-term health trends based on the user's physical condition data and suggest preventative health management.

[0060] The physical condition data collection unit can combine the physical condition data and the dietary history to evaluate the impact of meals and suggest areas for improvement. The physical condition data collection unit, for example, combines and analyzes the user's physical condition data and dietary history to evaluate the impact of meals. For example, it analyzes fluctuations in blood glucose levels after meals to evaluate the impact of meals. The physical condition data collection unit also suggests areas for improvement based on the user's physical condition data and dietary history. For example, if blood glucose levels are high after meals, it suggests reducing carbohydrates. The physical condition data collection unit can also suggest improvements to nutritional balance based on the user's physical condition data and dietary history. For example, if there is a persistent vitamin deficiency, it suggests consuming foods rich in vitamins. In this way, it is possible to combine the physical condition data and dietary history to evaluate the impact of meals and suggest areas for improvement.

[0061] The physical condition data collection unit can use the emotion estimation function to propose a physical condition management plan that takes into account the user's emotional state. The physical condition data collection unit, for example, collects the user's emotional data and reflects it in the physical condition management plan. For example, for a user who is highly stressed, it can suggest foods that have a relaxing effect. The physical condition data collection unit can also adjust the physical condition management plan based on the user's emotional state. For example, if the user is tired, it can suggest that the user prioritize rest. The physical condition data collection unit can also suggest improvements to exercise habits based on the user's emotional data. For example, if the user is feeling stressed, it can recommend exercise that has a relaxing effect. In this way, the emotion estimation function can be used to propose a physical condition management plan that takes into account the user's emotional state.

[0062] The physical condition data collection unit can make suggestions for improving the user's exercise habits and lifestyle habits based on the physical condition data. The physical condition data collection unit makes suggestions for improving exercise habits based on the user's physical condition data, for example. For example, it analyzes heart rate data and suggests appropriate exercise intensity and frequency. The physical condition data collection unit also makes suggestions for improving lifestyle habits based on the user's physical condition data. For example, it analyzes sleep data and suggests appropriate sleep duration and quality. The physical condition data collection unit can also make suggestions for improving eating habits based on the user's physical condition data. For example, it suggests adjusting the timing and content of meals. In this way, it is possible to make suggestions for improving the user's exercise habits and lifestyle habits based on the physical condition data.

[0063] The health data collection unit can share health data with family members and medical institutions to support comprehensive health management. The health data collection unit, for example, shares the user's health data with family members to support health management. For example, it allows family members to check the user's health data in real time. The health data collection unit can also share the user's health data with medical institutions to support comprehensive health management. For example, doctors can provide appropriate diagnoses and treatments based on the user's health data. The health data collection unit can also store the user's health data in the cloud and make it accessible as needed. For example, the user can access the health data from different devices to manage their health. This allows health data to be shared with family members and medical institutions to support comprehensive health management.

[0064] The physical condition data collection unit can use the emotion estimation function to suggest relaxation methods and stress relief methods according to the user's emotional state. The physical condition data collection unit, for example, suggests relaxation methods based on the user's emotion data. For example, it may recommend deep breathing or meditation to a user who is highly stressed. The physical condition data collection unit also suggests stress relief methods based on the user's emotional state. For example, if the user is tired, it may suggest listening to music that has a relaxing effect. The physical condition data collection unit can also suggest foods that have a relaxing effect based on the user's emotion data. For example, if the user is feeling stressed, it may suggest herbal tea that has a relaxing effect. In this way, the emotion estimation function can be used to suggest relaxation methods and stress relief methods according to the user's emotional state.

[0065] The suggestion unit can suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season. The suggestion unit can suggest meal plans that allow the user to enjoy the feeling of the season, for example, based on seasonal ingredients. For example, it can suggest a salad using fresh vegetables in the spring, and a hot soup in the winter. The suggestion unit can also suggest meal plans according to the weather. For example, it can suggest cold dishes on hot summer days, and hot dishes on cold winter days. The suggestion unit can also suggest meal plans that match seasonal events and occasions. For example, it can suggest a special dinner for Christmas. In this way, it is possible to suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season.

[0066] The suggestion unit can incorporate local traditional cuisine and culture into meal plans and make suggestions for enjoying the diversity of food culture. The suggestion unit, for example, proposes meal plans based on the traditional cuisine of the user's region of residence. For example, it proposes recipes that incorporate local specialties and traditional cooking methods. The suggestion unit can also propose meal plans that incorporate traditional cuisine from different regions. For example, it can propose recipes that recreate dishes that the user enjoyed while traveling. The suggestion unit can also propose meal plans that match local culture and events. For example, it can propose special dishes that match local festivals and events. This makes it possible to incorporate local traditional cuisine and culture into meal plans and make suggestions for enjoying the diversity of food culture.

[0067] The suggestion unit uses the emotion estimation function to suggest a meal plan that matches the user's mood, thereby improving meal satisfaction. The suggestion unit suggests 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 suggests dishes that use ingredients that have a relaxing effect. The suggestion unit also makes suggestions to improve meal satisfaction based on the user's emotional state. For example, when the user is tired, it suggests dishes that are highly nutritious. The suggestion unit can also suggest new ingredients and recipes based on the user's emotion data. For example, it suggests new dishes that the user might be interested in. In this way, the emotion estimation function can suggest a meal plan that matches the user's mood, thereby improving meal satisfaction.

[0068] The suggestion unit can share meal plans with other users and promote the exchange of meal ideas within the community. For example, the suggestion unit builds a platform for sharing meal plans, allowing users to share their meal plans with other users. For example, it provides a function that allows users to post photos of recipes and ingredients. The suggestion unit also creates a community where users can exchange meal ideas. For example, users can post their own meal plans and receive feedback from other users. The suggestion unit can also suggest new ideas to other users based on the user's meal plan. For example, it can suggest dishes that the user has not tried. This allows meal plans to be shared with other users, promoting the exchange of meal ideas within the community.

[0069] The suggestion unit can incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions. The suggestion unit, for example, proposes environmentally conscious meal plans based on eco-friendly ingredients. For example, it may use locally produced organic vegetables or fish caught through sustainable fishing. The suggestion unit also proposes eco-friendly recipes. For example, it may propose cooking methods that reduce food waste or recipes that use reusable ingredients. The suggestion unit can also provide information to increase the user's environmental awareness. For example, it may introduce how to select eco-friendly ingredients or environmentally conscious cooking methods. This allows the suggestion unit to incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions.

[0070] The suggestion unit can use the emotion estimation function to adjust the meal plan in real time according to the user's emotional state and make optimal suggestions. The suggestion unit, for example, collects the user's emotional data in real time and adjusts the meal plan. For example, when the user is tired, the suggestion unit suggests nutritious dishes. The suggestion unit also adjusts the meal plan in real time based on the user's emotional state. For example, when the user is feeling stressed, the suggestion unit suggests dishes using ingredients that have a relaxing effect. The suggestion unit can also suggest new ingredients and recipes in real time based on the user's emotional data. For example, it suggests new dishes that the user might be interested in. In this way, the emotion estimation function can be used to adjust the meal plan in real time according to the user's emotional state and make optimal suggestions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The suggestion unit can prioritize suggesting ingredients that match the user's preferences based on the user's emotional data. The suggestion unit, for example, analyzes the user's past eating history to identify ingredients that the user likes to eat. For example, it prioritizes recognizing ingredients that the user frequently purchases or ingredients that the user has given high ratings to in the past. The suggestion unit also suggests ingredients that match the user's preferences based on the user's emotional data. For example, it suggests ingredients that the user enjoys eating or ingredients that the user likes to eat when feeling stressed. The suggestion unit can also suggest new ingredients or recipes based on the user's preference data. For example, it suggests ingredients that the user has not tried before or different cooking methods. In this way, it is possible to prioritize suggesting ingredients that match the user's preferences based on the user's emotional data.

[0073] The suggestion unit can optimize the placement of ingredients in the refrigerator to make them easier to take out. For example, the suggestion unit uses AI to analyze image data taken by a camera inside the refrigerator and optimize the placement of ingredients. For example, ingredients that are used frequently are placed in positions that make them easier to take out. The suggestion unit also adjusts the placement based on the type of ingredient and how often it is used. For example, vegetables and fruits are placed in positions that make them easier to take out, and ingredients with longer shelf lives are placed in the back. The suggestion unit can also learn the user's usage patterns and suggest the optimal placement. For example, ingredients that are frequently used by the user are placed with priority. This makes it possible to optimize the placement of ingredients in the refrigerator and make them easier to take out.

[0074] The suggestion unit can link the ingredient recognition results with other smart home appliances to automate the cooking process. For example, the suggestion unit can link the ingredient recognition results for a refrigerator with a smart oven to automate the cooking process. For example, the oven can automatically set the cooking time and temperature based on the ingredients in the refrigerator. The suggestion unit can also link with a smart rice cooker or a smart microwave to automate the cooking process. For example, the rice cooker can automatically set the cooking time, and the microwave can automatically set the heating time. The suggestion unit can also link with a smart speaker to provide audio guidance on the cooking steps. For example, the user can receive instructions without using their hands while cooking. This allows the ingredient recognition results to be linked with other smart home appliances to automate the cooking process.

[0075] The suggestion unit can analyze the emotions of the user when they open the refrigerator and adjust the placement of ingredients and the suggestions. For example, the suggestion unit uses a camera to capture the user's facial expression when they open the refrigerator and analyzes the emotions using an emotion estimation function. For example, if the user looks surprised, the suggestion unit reconsiders the placement of ingredients in the refrigerator. The suggestion unit also records the user's voice and analyzes their emotions using voice analysis technology. For example, if the user feels dissatisfied, the suggestions are adjusted. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions. For example, if the user feels stressed, the suggestion unit will suggest ingredients that have a relaxing effect. This makes it possible to analyze the emotions of the user when they open the refrigerator and adjust the placement of ingredients and the suggestions.

[0076] The physical condition data collection unit can analyze long-term health trends based on the user's physical condition data and suggest preventative health management. The physical condition data collection unit, for example, collects the user's physical condition data over a long period of time and analyzes health trends. For example, it analyzes blood pressure fluctuation patterns based on blood pressure data from the past year. The physical condition data collection unit also suggests preventative health management based on the user's physical condition data. For example, if blood pressure is on the rise, it suggests reducing salt intake. The physical condition data collection unit can also suggest improving exercise habits based on the user's physical condition data. For example, if there is a continuing lack of exercise, it recommends moderate exercise. In this way, it is possible to analyze long-term health trends based on the user's physical condition data and suggest preventative health management.

[0077] The physical condition data collection unit can combine the physical condition data and the dietary history to evaluate the impact of meals and suggest areas for improvement. The physical condition data collection unit, for example, combines and analyzes the user's physical condition data and dietary history to evaluate the impact of meals. For example, it analyzes fluctuations in blood glucose levels after meals to evaluate the impact of meals. The physical condition data collection unit also suggests areas for improvement based on the user's physical condition data and dietary history. For example, if blood glucose levels are high after meals, it suggests reducing carbohydrates. The physical condition data collection unit can also suggest improvements to nutritional balance based on the user's physical condition data and dietary history. For example, if there is a persistent vitamin deficiency, it suggests consuming foods rich in vitamins. In this way, it is possible to combine the physical condition data and dietary history to evaluate the impact of meals and suggest areas for improvement.

[0078] The physical condition data collection unit can use the emotion estimation function to propose a physical condition management plan that takes into account the user's emotional state. The physical condition data collection unit, for example, collects the user's emotional data and reflects it in the physical condition management plan. For example, for a user who is highly stressed, it can suggest foods that have a relaxing effect. The physical condition data collection unit can also adjust the physical condition management plan based on the user's emotional state. For example, if the user is tired, it can suggest that the user prioritize rest. The physical condition data collection unit can also suggest improvements to exercise habits based on the user's emotional data. For example, if the user is feeling stressed, it can recommend exercise that has a relaxing effect. In this way, the emotion estimation function can be used to propose a physical condition management plan that takes into account the user's emotional state.

[0079] The suggestion unit can suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season. The suggestion unit can suggest meal plans that allow the user to enjoy the feeling of the season, for example, based on seasonal ingredients. For example, it can suggest a salad using fresh vegetables in the spring, and a hot soup in the winter. The suggestion unit can also suggest meal plans according to the weather. For example, it can suggest cold dishes on hot summer days, and hot dishes on cold winter days. The suggestion unit can also suggest meal plans that match seasonal events and occasions. For example, it can suggest a special dinner for Christmas. In this way, it is possible to suggest meal plans according to the season and weather, and provide meals that allow the user to enjoy the feeling of the season.

[0080] The suggestion unit uses the emotion estimation function to suggest a meal plan that matches the user's mood, thereby improving meal satisfaction. The suggestion unit suggests 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 suggests dishes that use ingredients that have a relaxing effect. The suggestion unit also makes suggestions to improve meal satisfaction based on the user's emotional state. For example, when the user is tired, it suggests dishes that are highly nutritious. The suggestion unit can also suggest new ingredients and recipes based on the user's emotion data. For example, it suggests new dishes that the user might be interested in. In this way, the emotion estimation function can suggest a meal plan that matches the user's mood, thereby improving meal satisfaction.

[0081] The suggestion unit can incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions. The suggestion unit, for example, proposes environmentally conscious meal plans based on eco-friendly ingredients. For example, it may use locally produced organic vegetables or fish caught through sustainable fishing. The suggestion unit also proposes eco-friendly recipes. For example, it may propose cooking methods that reduce food waste or recipes that use reusable ingredients. The suggestion unit can also provide information to increase the user's environmental awareness. For example, it may introduce how to select eco-friendly ingredients or environmentally conscious cooking methods. This allows the suggestion unit to incorporate eco-friendly ingredients and recipes into meal plans to make environmentally conscious suggestions.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The camera takes pictures of the inside of the refrigerator. For example, the camera is installed inside the refrigerator and can take pictures periodically. The camera has high resolution and can capture even the fine details of the ingredients. Step 2: The ingredient recognition unit analyzes the images captured by the camera to determine the types and quantities of ingredients in the refrigerator. For example, the ingredient recognition unit uses image analysis technology to identify ingredients such as vegetables, fruits, meat, and dairy products. The ingredient recognition unit can also measure the amount of ingredients and manage inventory in the refrigerator. Step 3: The physical condition data collection unit collects the user's physical condition data. For example, the physical condition data collection unit obtains data such as blood pressure, body temperature, and heart rate from a smartwatch or a health management app. The physical condition data collection unit can also collect the user's sleep data and exercise data. Step 4: The suggestion unit suggests optimal meal plans and recipes based on the types and amounts of ingredients recognized by the ingredient recognition unit and the user's physical condition data collected by the physical condition data collection unit. For example, if the user has high blood pressure, the suggestion unit suggests low-salt recipes. The suggestion unit can also reduce food waste by suggesting recipes that use ingredients in the refrigerator.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 camera that takes images of the inside of the refrigerator; an ingredient recognition unit that analyzes the images taken by the camera to identify the types and amounts of ingredients in the refrigerator; a physical condition data collection unit that collects physical condition data of a user; a suggestion unit that suggests optimal meal plans and recipes based on the types and amounts of ingredients recognized by the ingredient recognition unit and the user's physical condition data collected by the physical condition data collection unit. A system characterized by:

2. The ingredient recognition unit Evaluating the freshness of the food material in real time and identifying the food material whose freshness has decreased 2. The system of claim 1.

3. The proposal unit Optimizing the arrangement of the ingredients in the refrigerator to improve ease of taking them out 2. The system of claim 1.

4. The physical condition data collection unit Based on the user's physical condition data, the system analyzes long-term health trends and proposes preventative health management.

2. The system of claim 1.

5. The proposal unit Propose meal plans according to the season and weather, and provide meals that allow you to enjoy the seasonal flavor.

2. The system of claim 1.

6. The proposal unit Based on the emotion data of the user, the food ingredients that match the preferences of the user are preferentially suggested.

2. The system of claim 1.

Images