System

Abstract

An object of a system according to an embodiment is to propose an optimal menu based on a health condition of a user and a weather condition and to automatically order necessary food ingredients.SOLUTION: A system includes a health data collection part, a weather data collection part, a menu proposal part, and a food material order part. The health data collecting unit captures health examination information of a user or real-time health data collected from a wearable device. The weather data collection unit takes in weather data and season data. The menu proposal unit proposes a menu on the basis of the data collected by the health data collection unit and the weather data collection unit. The food material ordering unit orders necessary food materials based on the menu proposed by the menu proposal 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 have had the problem of not being able to adequately propose optimal menus based on the user's health status and weather conditions, and automatically order the necessary ingredients.

[0005] The system according to the embodiment aims to propose an optimal menu based on the user's health condition and weather conditions, and to automatically order the necessary ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, a weather data collection unit, a menu suggestion unit, and an ingredient ordering unit. The health data collection unit collects real-time health data collected from a user's health checkup information or a wearable device. The weather data collection unit collects weather data and seasonal data. The menu suggestion unit suggests a menu based on the data collected by the health data collection unit and the weather data collection unit. The ingredient ordering unit orders the necessary ingredients based on the menu suggested by the menu suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus based on the user's health condition and weather conditions, and automatically order the necessary ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu suggestion system according to an embodiment of the present invention is a system that incorporates real-time health data collected from a user's health checkup information and wearable devices, proposes menus that match the weather and season, and even automatically orders the necessary ingredients. As a result, the menu suggestion system can propose optimal menus based on the user's health condition and weather conditions, and automatically order the necessary ingredients.

[0029] A menu suggestion system according to an embodiment includes a health data collection unit, a weather data collection unit, a menu suggestion unit, and an ingredient ordering unit. The health data collection unit collects real-time health data collected from a user's health checkup information or a wearable device. For example, the health data collection unit collects blood pressure, heart rate, body temperature, exercise volume, sleep data, and the like. The health data collection unit can also acquire data from wearable devices such as an Apple Watch or a fitness tracker. The weather data collection unit collects weather data and seasonal data. For example, the weather data collection unit collects data such as temperature, humidity, and precipitation. The weather data collection unit can also collect seasonal data such as the four seasons or specific months or weeks. The menu suggestion unit suggests a menu based on the data collected by the health data collection unit and the weather data collection unit. For example, if a user's blood pressure is high, the menu suggestion unit suggests a low-salt menu. The menu suggestion unit suggests cold dishes and hydrating dishes in the summer and warm dishes and nutritious dishes in the winter. Furthermore, the menu suggestion unit suggests high-calorie dishes on days when the user exercises a lot, and low-calorie dishes on days when the user exercises less. The ingredient ordering unit orders the necessary ingredients based on the menu suggested by the menu suggestion unit. For example, the ingredient ordering unit lists the necessary ingredients based on the proposed menu and places the order through the user's account. The ingredient ordering unit also works in conjunction with a food delivery service such as Co-op to automatically order the ingredients needed for the proposed menu. This allows the menu suggestion system according to the embodiment to suggest an optimal menu based on the user's health condition and weather conditions and automate the ordering of the necessary ingredients. For example, the user can obtain the necessary ingredients without any hassle. The user can also check their health data and menu suggestions and confirm the ordering of the necessary ingredients via their smartphone or tablet.

[0030] The health data collection unit collects blood pressure data of the user, and the menu suggestion unit can suggest low-salt menus based on the blood pressure data. The health data collection unit, for example, collects blood pressure data of the user. For example, systolic blood pressure and diastolic blood pressure are measured and input as data. The health data collection unit can also acquire blood pressure data in real time from a wearable device. The menu suggestion unit, for example, suggests low-salt menus based on the collected blood pressure data. For example, low-salt dishes are suggested based on a guideline for daily salt intake. The menu suggestion unit can also suggest low-salt soups and salads as specific example dishes. This makes it possible to suggest appropriate menus according to the user's blood pressure.

[0031] The weather data collection unit collects temperature data, and the menu proposal unit can propose seasonal dishes based on the temperature data. The weather data collection unit, for example, collects temperature data. For example, it measures the daytime maximum temperature and the nighttime minimum temperature and inputs them as data. The weather data collection unit can also acquire weather forecast data. The menu proposal unit, for example, proposes seasonal dishes based on the collected temperature data. For example, it proposes cold dishes and dishes that can hydrate you in the summer, and proposes warm dishes and nutritious dishes in the winter. The menu proposal unit can also propose specific examples of dishes such as chilled Chinese noodles and smoothies in the summer, and hot pot dishes and soups in the winter. This makes it possible to propose appropriate dishes according to the season.

[0032] The menu suggestion unit can suggest high-calorie dishes or low-calorie dishes based on the user's exercise data. The menu suggestion unit, for example, suggests high-calorie dishes or low-calorie dishes based on the user's exercise data. For example, on days when the user exercises a lot, it suggests high-calorie dishes that can replenish energy, and on days when the user exercises less, it suggests low-calorie dishes. Furthermore, the menu suggestion unit can suggest, as specific dish examples, steak or pasta on days when the user exercises a lot, and salad or soup on days when the user exercises less. This makes it possible to suggest dishes with appropriate calories according to the user's exercise amount.

[0033] The ingredient ordering unit can list ingredients needed based on the menu proposed by the menu proposal unit and place an order through the user's account. The ingredient ordering unit, for example, lists ingredients needed based on the menu proposed by the menu proposal unit. For example, it compiles a list of the types and quantities of ingredients needed for the proposed dish. The ingredient ordering unit also places an order through the user's account. For example, it can purchase ingredients from affiliated stores using payment information registered in the user's account. This allows the necessary ingredients to be automatically ordered based on the proposed menu.

[0034] The health data collection unit estimates the user's stress level, and the menu suggestion unit can suggest ingredients and dishes that are effective in reducing stress based on the stress level. The health data collection unit, for example, analyzes the user's heart rate and sleep patterns to estimate the stress level. For example, if the heart rate is high and the quality of sleep is low, it is determined that stress is high. The health data collection unit can also estimate the user's stress hormone (cortisol) level. The menu suggestion unit, for example, suggests ingredients and dishes that are effective in reducing stress based on the stress level. For example, it can suggest herbal tea with a relaxing effect or dishes using ingredients with a sedative effect. Furthermore, the menu suggestion unit can suggest dishes using berries with antioxidant properties or fish containing omega-3 fatty acids as specific example dishes. This makes it possible to suggest ingredients and dishes appropriate for the user's stress level.

[0035] The health data collection unit automatically detects the user's allergy information, and the menu suggestion unit can suggest allergen-free menus based on the allergy information. The health data collection unit, for example, automatically detects the user's allergy information. For example, it analyzes medical records or self-reported questionnaires to obtain allergy information for specific ingredients. The health data collection unit can also obtain allergy information from a wearable device. The menu suggestion unit, for example, suggests allergen-free menus based on the allergy information. For example, it suggests dishes that do not contain nuts to a user with a nut allergy. Furthermore, the menu suggestion unit can suggest, as a specific dish example, a dessert that does not contain dairy products to a user with a dairy allergy. This makes it possible to suggest safe menus based on the user's allergy information.

[0036] The weather data collection unit can make beverage suggestions to optimize the user's fluid intake based on weather data. The weather data collection unit analyzes, for example, temperature and humidity, and makes beverage suggestions to optimize the user's fluid intake. For example, it can suggest cold drinks or sports drinks on hot days, and fruit juices that can replenish fluids on humid days. The weather data collection unit can also suggest hot drinks or soups on cold days. This makes it possible to suggest appropriate fluid intake according to weather conditions.

[0037] The weather data collection unit can propose nutritionally balanced menus based on seasonal data. For example, the weather data collection unit analyzes seasonal nutrient needs and proposes nutritionally balanced menus according to the season. For example, in winter, it proposes dishes using ingredients that are rich in vitamin D, and in summer, it proposes dishes using ingredients that are hydrating. In addition, the weather data collection unit can propose dishes using fresh vegetables and fruits in spring. This makes it possible to propose nutritionally balanced menus according to the season.

[0038] The weather data collection unit can predict the user's plans to go out based on weather data and suggest easy-to-carry meals. The weather data collection unit, for example, analyzes weather data and predicts the user's plans to go out. For example, on sunny days, it can suggest easy-to-carry sandwiches and fruit suitable for a picnic, and on rainy days, it can suggest hot soup or a boxed lunch. The weather data collection unit can also suggest light meals and snacks on windy days. This makes it possible to suggest easy-to-carry meals according to the user's plans to go out.

[0039] The weather data collection unit can suggest local foods using ingredients unique to the region based on seasonal data. The weather data collection unit, for example, suggests local foods using ingredients unique to the region based on seasonal data. For example, in autumn, it suggests dishes using chestnuts and pumpkins, which are local specialties, and in summer it suggests salads using tomatoes and cucumbers, which are local specialties. Furthermore, the weather data collection unit can suggest hot pot dishes using radishes and Chinese cabbage, which are local specialties, in winter. This makes it possible to suggest local foods using ingredients unique to the region.

[0040] The menu suggestion unit can propose individually customized menus by combining the user's past meal history and health data. The menu suggestion unit, for example, analyzes the user's past meal history and health data to propose individually customized menus. For example, it proposes menus that take into account the user's preferences and allergy information from past data, and if the user's intake of vitamins or minerals is insufficient, it proposes dishes that use ingredients that supplement them. The menu suggestion unit can also propose dishes that can replenish energy according to the user's exercise level and physical condition. This makes it possible to propose individually customized menus based on the user's past meal history and health data.

[0041] The menu suggestion unit can select optimal ingredients based on the user's ingredient preferences and allergy information. The menu suggestion unit selects optimal ingredients, for example, taking into consideration the user's ingredient preferences and allergy information. For example, the user can register their favorite ingredients and ingredients they want to avoid in advance, and suggest menus based on that. In addition, the menu suggestion unit can suggest dishes that do not contain nuts to a user with a nut allergy, and if a user prefers a specific ingredient, can preferentially suggest dishes that use that ingredient. This allows optimal ingredients to be selected based on the user's ingredient preferences and allergy information.

[0042] The menu suggestion unit can suggest menus to reduce waste based on the user's ingredient inventory information. The menu suggestion unit suggests menus to reduce waste, for example, based on the user's ingredient inventory information. For example, it suggests dishes using ingredients remaining in the refrigerator and suggests menus that prioritize the use of ingredients with an approaching expiration date. The menu suggestion unit can also grasp the ingredient inventory status in real time and suggest menus based on that. This allows for menu suggestions to reduce waste based on the user's ingredient inventory information.

[0043] The menu suggestion unit can suggest dishes that the whole family can enjoy based on the user's family structure and meal sharing. The menu suggestion unit, for example, takes into account the user's family structure and suggests dishes that the whole family can enjoy. For example, for a family with children, it suggests a menu that combines dishes for children and dishes for adults, and suggests dishes on large platters and appetizers that can be shared. The menu suggestion unit can also suggest menus that will satisfy everyone based on family preferences and allergy information. This makes it possible to suggest dishes that the whole family can enjoy.

[0044] The ingredient ordering unit can analyze the user's purchasing history and make ingredient ordering suggestions that take into account repeat purchase trends. The ingredient ordering unit can, for example, analyze the user's purchasing history and make ingredient ordering suggestions that take into account repeat purchase trends. For example, ingredients that are purchased regularly can be automatically added to the list, and milk and bread that are purchased weekly can be automatically added to the order list. The ingredient ordering unit can also suggest brands and types of ingredients that the user prefers based on past data. This allows ingredient ordering suggestions that take into account repeat purchase trends based on the user's purchasing history.

[0045] The ingredient ordering unit can select ingredients with high cost performance based on the user's budget. The ingredient ordering unit selects ingredients with high cost performance, for example, taking into account the user's budget. For example, it lists ingredients that can be purchased within the budget and suggests dishes using inexpensive, nutritious ingredients. The ingredient ordering unit can also use sale information and discount coupons to suggest the best ingredients within the budget. This allows ingredients with high cost performance to be selected based on the user's budget.

[0046] The food ordering unit can suggest environmentally friendly food ingredients and packaging based on the user's eco-consciousness. The food ordering unit, for example, takes the user's eco-consciousness into consideration and suggests environmentally friendly food ingredients and packaging. For example, it suggests products that use organic food ingredients or recyclable packaging, and suggests products that use locally produced food ingredients or plastic-free packaging. The food ordering unit can also suggest food ingredients with a low carbon footprint or products that use eco-friendly packaging. This makes it possible to suggest environmentally friendly food ingredients and packaging based on the user's eco-consciousness.

[0047] The ingredient ordering unit prioritizes the suggestions of local specialties of the user's region, thereby revitalizing the local economy. The ingredient ordering unit, for example, prioritizes the suggestions of local specialties of the user's region, thereby revitalizing the local economy. For example, the ingredient ordering unit can suggest dishes using local agricultural products and specialties, and dishes using fresh fish caught at local fishing ports. The ingredient ordering unit can also suggest dishes using ingredients purchased directly from local farmers and producers. This allows the ingredient ordering unit to prioritize the suggestions of local specialties of the user's region, thereby revitalizing the local economy.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The menu suggestion system can also suggest individually customized menus taking into account the user's dietary preferences and allergy information. For example, if the user prefers a particular ingredient, it will preferentially suggest dishes that use that ingredient. It can also suggest dishes that do not contain allergens based on allergy information. Furthermore, it can analyze the user's past eating history and suggest menus that take nutritional balance into consideration. This makes it possible to provide optimal menus that meet the user's individual needs.

[0050] The menu suggestion system can also take into account the user's family structure to suggest dishes that the whole family can enjoy. For example, for families with children, it can suggest a menu that combines dishes for children and dishes for adults, and suggests large dishes and shareable appetizers. It can also suggest menus that will satisfy everyone based on family preferences and allergy information. This allows it to provide meals that the whole family can enjoy.

[0051] The menu suggestion system can also suggest menus to reduce waste based on the user's ingredient inventory information. For example, it can suggest dishes using ingredients remaining in the refrigerator and prioritize ingredients with an approaching expiration date. It can also grasp ingredient inventory status in real time and suggest menus based on that. This allows for menu suggestions to reduce waste based on the user's ingredient inventory information.

[0052] The menu suggestion system can also analyze a user's purchasing history and make ordering suggestions for ingredients that take into account repeat purchase trends. For example, ingredients that are purchased regularly can be automatically added to the list, and milk and bread that are purchased weekly can be automatically added to the order list. It can also suggest brands and types of ingredients that the user prefers based on past data. This allows for ordering suggestions for ingredients that take into account repeat purchase trends based on the user's purchasing history.

[0053] The menu suggestion system can also consider the user's budget and select cost-effective ingredients. For example, it can list ingredients that can be purchased within the user's budget and suggest dishes that use inexpensive, nutritious ingredients. It can also use sale information and discount coupons to suggest the best ingredients within the user's budget. This allows the system to select cost-effective ingredients based on the user's budget.

[0054] The menu suggestion system can further consider the user's eco-consciousness and suggest environmentally friendly ingredients and packaging. For example, it can suggest products that use organic ingredients or recyclable packaging, and it can also suggest products that use locally produced ingredients or plastic-free packaging. It can also suggest ingredients with a low carbon footprint or products that use eco-friendly packaging. This makes it possible to provide environmentally friendly ingredients and packaging based on the user's eco-consciousness.

[0055] The menu suggestion system can also prioritize local specialties in the user's region, helping to revitalize the local economy. For example, it can suggest dishes using local agricultural products and specialties, and dishes using fresh fish caught at local fishing ports. It can also suggest dishes using ingredients purchased directly from local farmers and producers. This allows the system to prioritize local specialties in the user's region, helping to revitalize the local economy.

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

[0057] Step 1: The health data collection unit captures the user's health checkup information or real-time health data collected from wearable devices, such as blood pressure, heart rate, body temperature, exercise volume, and sleep data, and obtains data from wearable devices such as Apple Watch and fitness trackers. Step 2: The weather data collection unit collects weather data and seasonal data. For example, data such as temperature, humidity, and precipitation can be collected, and seasonal data such as the four seasons, specific months, or weeks can be collected. Step 3: The menu suggestion unit suggests menus based on the data collected by the health data collection unit and the weather data collection unit. For example, if the user's blood pressure is high, it suggests low-salt menus, and in summer it suggests cold dishes and dishes that help with hydration. In winter it suggests warm dishes and nutritious dishes, and on days when the user exercises a lot, it suggests high-calorie dishes and low-calorie dishes on days when the user exercises less. Step 4: The ingredient ordering unit orders the ingredients needed based on the menu proposed by the menu suggestion unit. For example, it may list the ingredients needed based on the proposed menu and place an order through the user's account. Furthermore, it may link with a food delivery service such as Co-op to automatically order the ingredients needed for the proposed menu.

[0058] (Example 2) The menu suggestion system according to an embodiment of the present invention is a system that incorporates real-time health data collected from a user's health checkup information and wearable devices, proposes menus that match the weather and season, and even automatically orders the necessary ingredients. As a result, the menu suggestion system can propose optimal menus based on the user's health condition and weather conditions, and automatically order the necessary ingredients.

[0059] A menu suggestion system according to an embodiment includes a health data collection unit, a weather data collection unit, a menu suggestion unit, and an ingredient ordering unit. The health data collection unit collects real-time health data collected from a user's health checkup information or a wearable device. For example, the health data collection unit collects blood pressure, heart rate, body temperature, exercise volume, sleep data, and the like. The health data collection unit can also acquire data from wearable devices such as an Apple Watch or a fitness tracker. The weather data collection unit collects weather data and seasonal data. For example, the weather data collection unit collects data such as temperature, humidity, and precipitation. The weather data collection unit can also collect seasonal data such as the four seasons or specific months or weeks. The menu suggestion unit suggests a menu based on the data collected by the health data collection unit and the weather data collection unit. For example, if a user's blood pressure is high, the menu suggestion unit suggests a low-salt menu. The menu suggestion unit suggests cold dishes and hydrating dishes in the summer and warm dishes and nutritious dishes in the winter. Furthermore, the menu suggestion unit suggests high-calorie dishes on days when the user exercises a lot, and low-calorie dishes on days when the user exercises less. The ingredient ordering unit orders the necessary ingredients based on the menu suggested by the menu suggestion unit. For example, the ingredient ordering unit lists the necessary ingredients based on the proposed menu and places the order through the user's account. The ingredient ordering unit also works in conjunction with a food delivery service such as Co-op to automatically order the ingredients needed for the proposed menu. This allows the menu suggestion system according to the embodiment to suggest an optimal menu based on the user's health condition and weather conditions and automate the ordering of the necessary ingredients. For example, the user can obtain the necessary ingredients without any hassle. The user can also check their health data and menu suggestions and confirm the ordering of the necessary ingredients via their smartphone or tablet.

[0060] The health data collection unit collects blood pressure data of the user, and the menu suggestion unit can suggest low-salt menus based on the blood pressure data. The health data collection unit, for example, collects blood pressure data of the user. For example, systolic blood pressure and diastolic blood pressure are measured and input as data. The health data collection unit can also acquire blood pressure data in real time from a wearable device. The menu suggestion unit, for example, suggests low-salt menus based on the collected blood pressure data. For example, low-salt dishes are suggested based on a guideline for daily salt intake. The menu suggestion unit can also suggest low-salt soups and salads as specific example dishes. This makes it possible to suggest appropriate menus according to the user's blood pressure.

[0061] The weather data collection unit collects temperature data, and the menu proposal unit can propose seasonal dishes based on the temperature data. The weather data collection unit, for example, collects temperature data. For example, it measures the daytime maximum temperature and the nighttime minimum temperature and inputs them as data. The weather data collection unit can also acquire weather forecast data. The menu proposal unit, for example, proposes seasonal dishes based on the collected temperature data. For example, it proposes cold dishes and dishes that can hydrate you in the summer, and proposes warm dishes and nutritious dishes in the winter. The menu proposal unit can also propose specific examples of dishes such as chilled Chinese noodles and smoothies in the summer, and hot pot dishes and soups in the winter. This makes it possible to propose appropriate dishes according to the season.

[0062] The menu suggestion unit can suggest high-calorie dishes or low-calorie dishes based on the user's exercise data. The menu suggestion unit, for example, suggests high-calorie dishes or low-calorie dishes based on the user's exercise data. For example, on days when the user exercises a lot, it suggests high-calorie dishes that can replenish energy, and on days when the user exercises less, it suggests low-calorie dishes. Furthermore, the menu suggestion unit can suggest, as specific dish examples, steak or pasta on days when the user exercises a lot, and salad or soup on days when the user exercises less. This makes it possible to suggest dishes with appropriate calories according to the user's exercise amount.

[0063] The ingredient ordering unit can list ingredients needed based on the menu proposed by the menu proposal unit and place an order through the user's account. The ingredient ordering unit, for example, lists ingredients needed based on the menu proposed by the menu proposal unit. For example, it compiles a list of the types and quantities of ingredients needed for the proposed dish. The ingredient ordering unit also places an order through the user's account. For example, it can purchase ingredients from affiliated stores using payment information registered in the user's account. This allows the necessary ingredients to be automatically ordered based on the proposed menu.

[0064] The health data collection unit estimates the user's stress level, and the menu suggestion unit can suggest ingredients and dishes that are effective in reducing stress based on the stress level. The health data collection unit, for example, analyzes the user's heart rate and sleep patterns to estimate the stress level. For example, if the heart rate is high and the quality of sleep is low, it is determined that stress is high. The health data collection unit can also estimate the user's stress hormone (cortisol) level. The menu suggestion unit, for example, suggests ingredients and dishes that are effective in reducing stress based on the stress level. For example, it can suggest herbal tea with a relaxing effect or dishes using ingredients with a sedative effect. Furthermore, the menu suggestion unit can suggest dishes using berries with antioxidant properties or fish containing omega-3 fatty acids as specific example dishes. This makes it possible to suggest ingredients and dishes appropriate for the user's stress level.

[0065] The health data collection unit automatically detects the user's allergy information, and the menu suggestion unit can suggest allergen-free menus based on the allergy information. The health data collection unit, for example, automatically detects the user's allergy information. For example, it analyzes medical records or self-reported questionnaires to obtain allergy information for specific ingredients. The health data collection unit can also obtain allergy information from a wearable device. The menu suggestion unit, for example, suggests allergen-free menus based on the allergy information. For example, it suggests dishes that do not contain nuts to a user with a nut allergy. Furthermore, the menu suggestion unit can suggest, as a specific dish example, a dessert that does not contain dairy products to a user with a dairy allergy. This makes it possible to suggest safe menus based on the user's allergy information.

[0066] The weather data collection unit can make beverage suggestions to optimize the user's fluid intake based on weather data. The weather data collection unit analyzes, for example, temperature and humidity, and makes beverage suggestions to optimize the user's fluid intake. For example, it can suggest cold drinks or sports drinks on hot days, and fruit juices that can replenish fluids on humid days. The weather data collection unit can also suggest hot drinks or soups on cold days. This makes it possible to suggest appropriate fluid intake according to weather conditions.

[0067] The weather data collection unit can propose nutritionally balanced menus based on seasonal data. For example, the weather data collection unit analyzes seasonal nutrient needs and proposes nutritionally balanced menus according to the season. For example, in winter, it proposes dishes using ingredients that are rich in vitamin D, and in summer, it proposes dishes using ingredients that are hydrating. In addition, the weather data collection unit can propose dishes using fresh vegetables and fruits in spring. This makes it possible to propose nutritionally balanced menus according to the season.

[0068] The weather data collection unit can make meal suggestions based on changes in the user's emotions as the seasons change. The weather data collection unit, for example, uses an emotion estimation function to analyze changes in the user's emotions as the seasons change and makes meal suggestions based on the analysis. For example, at the change of seasons in spring, dishes using ingredients that refresh the mood are suggested, and at the change of seasons in autumn, dishes using ingredients that have a calming effect are suggested. Furthermore, the weather data collection unit can suggest warm dishes and nutritious dishes at the change of seasons in winter. This allows for appropriate meal suggestions that take into account changes in the user's emotions as the seasons change.

[0069] The weather data collection unit can predict the user's plans to go out based on weather data and suggest easy-to-carry meals. The weather data collection unit, for example, analyzes weather data and predicts the user's plans to go out. For example, on sunny days, it can suggest easy-to-carry sandwiches and fruit suitable for a picnic, and on rainy days, it can suggest hot soup or a boxed lunch. The weather data collection unit can also suggest light meals and snacks on windy days. This makes it possible to suggest easy-to-carry meals according to the user's plans to go out.

[0070] The weather data collection unit can suggest local foods using ingredients unique to the region based on seasonal data. The weather data collection unit, for example, suggests local foods using ingredients unique to the region based on seasonal data. For example, in autumn, it suggests dishes using chestnuts and pumpkins, which are local specialties, and in summer it suggests salads using tomatoes and cucumbers, which are local specialties. Furthermore, the weather data collection unit can suggest hot pot dishes using radishes and Chinese cabbage, which are local specialties, in winter. This makes it possible to suggest local foods using ingredients unique to the region.

[0071] The weather data collection unit can propose special menus tailored to seasonal events and occasions. The weather data collection unit can propose special menus tailored to seasonal events and occasions, for example, by using an emotion estimation function. For example, the weather data collection unit can propose special dinners and desserts for Christmas and street food-style dishes and desserts for summer festivals. The weather data collection unit can also propose themed dishes and desserts for Halloween. This makes it possible to propose special menus tailored to seasonal events and occasions.

[0072] The menu suggestion unit can propose individually customized menus by combining the user's past meal history and health data. The menu suggestion unit, for example, analyzes the user's past meal history and health data to propose individually customized menus. For example, it proposes menus that take into account the user's preferences and allergy information from past data, and if the user's intake of vitamins or minerals is insufficient, it proposes dishes that use ingredients that supplement them. The menu suggestion unit can also propose dishes that can replenish energy according to the user's exercise level and physical condition. This makes it possible to propose individually customized menus based on the user's past meal history and health data.

[0073] The menu suggestion unit can select optimal ingredients based on the user's ingredient preferences and allergy information. The menu suggestion unit selects optimal ingredients, for example, taking into consideration the user's ingredient preferences and allergy information. For example, the user can register their favorite ingredients and ingredients they want to avoid in advance, and suggest menus based on that. In addition, the menu suggestion unit can suggest dishes that do not contain nuts to a user with a nut allergy, and if a user prefers a specific ingredient, can preferentially suggest dishes that use that ingredient. This allows optimal ingredients to be selected based on the user's ingredient preferences and allergy information.

[0074] The menu suggestion unit can use the emotion estimation function to suggest the appearance and presentation of food that matches the user's mood. The menu suggestion unit, for example, uses the emotion estimation function to suggest the appearance and presentation of food that matches the user's mood. For example, the menu suggestion unit can suggest colorful presentation when the user is in a happy mood, and simple and calm presentation when the user wants to relax. The menu suggestion unit can also suggest luxurious presentation for special occasions. This makes it possible to suggest the appearance and presentation of food that matches the user's mood.

[0075] The menu suggestion unit can suggest menus to reduce waste based on the user's ingredient inventory information. The menu suggestion unit suggests menus to reduce waste, for example, based on the user's ingredient inventory information. For example, it suggests dishes using ingredients remaining in the refrigerator and suggests menus that prioritize the use of ingredients with an approaching expiration date. The menu suggestion unit can also grasp the ingredient inventory status in real time and suggest menus based on that. This allows for menu suggestions to reduce waste based on the user's ingredient inventory information.

[0076] The menu suggestion unit can suggest dishes that the whole family can enjoy based on the user's family structure and meal sharing. The menu suggestion unit, for example, takes into account the user's family structure and suggests dishes that the whole family can enjoy. For example, for a family with children, it suggests a menu that combines dishes for children and dishes for adults, and suggests dishes on large platters and appetizers that can be shared. The menu suggestion unit can also suggest menus that will satisfy everyone based on family preferences and allergy information. This makes it possible to suggest dishes that the whole family can enjoy.

[0077] The menu suggestion unit can use the emotion estimation function to suggest music and lighting settings according to the user's emotional state, thereby improving the atmosphere of the meal. The menu suggestion unit can, for example, use the emotion estimation function to suggest music and lighting settings according to the user's emotional state. For example, calm music and soft lighting can be suggested when the user wants to relax, and bright music and colorful lighting can be suggested when the user is in a happy mood. The menu suggestion unit can also suggest romantic music and candlelight for a special occasion. This makes it possible to suggest music and lighting settings according to the user's emotional state, thereby improving the atmosphere of the meal.

[0078] The ingredient ordering unit can analyze the user's purchasing history and make ingredient ordering suggestions that take into account repeat purchase trends. The ingredient ordering unit can, for example, analyze the user's purchasing history and make ingredient ordering suggestions that take into account repeat purchase trends. For example, ingredients that are purchased regularly can be automatically added to the list, and milk and bread that are purchased weekly can be automatically added to the order list. The ingredient ordering unit can also suggest brands and types of ingredients that the user prefers based on past data. This allows ingredient ordering suggestions that take into account repeat purchase trends based on the user's purchasing history.

[0079] The ingredient ordering unit can select ingredients with high cost performance based on the user's budget. The ingredient ordering unit selects ingredients with high cost performance, for example, taking into account the user's budget. For example, it lists ingredients that can be purchased within the budget and suggests dishes using inexpensive, nutritious ingredients. The ingredient ordering unit can also use sale information and discount coupons to suggest the best ingredients within the budget. This allows ingredients with high cost performance to be selected based on the user's budget.

[0080] The ingredient ordering unit can use the emotion estimation function to suggest promotions and discount information to increase the user's motivation to buy. The ingredient ordering unit can, for example, use the emotion estimation function to suggest promotions and discount information to increase the user's motivation to buy. For example, the ingredient ordering unit can display sale information that the user might be interested in and suggest a campaign in which a discount is applied when a specific ingredient is purchased. The ingredient ordering unit can also provide special offers and coupons depending on the user's emotional state. This makes it possible to suggest promotions and discount information to increase the user's motivation to buy.

[0081] The food ordering unit can suggest environmentally friendly food ingredients and packaging based on the user's eco-consciousness. The food ordering unit, for example, takes the user's eco-consciousness into consideration and suggests environmentally friendly food ingredients and packaging. For example, it suggests products that use organic food ingredients or recyclable packaging, and suggests products that use locally produced food ingredients or plastic-free packaging. The food ordering unit can also suggest food ingredients with a low carbon footprint or products that use eco-friendly packaging. This makes it possible to suggest environmentally friendly food ingredients and packaging based on the user's eco-consciousness.

[0082] The ingredient ordering unit prioritizes the suggestions of local specialties of the user's region, thereby revitalizing the local economy. The ingredient ordering unit, for example, prioritizes the suggestions of local specialties of the user's region, thereby revitalizing the local economy. For example, the ingredient ordering unit can suggest dishes using local agricultural products and specialties, and dishes using fresh fish caught at local fishing ports. The ingredient ordering unit can also suggest dishes using ingredients purchased directly from local farmers and producers. This allows the ingredient ordering unit to prioritize the suggestions of local specialties of the user's region, thereby revitalizing the local economy.

[0083] The ingredient ordering unit can use the emotion estimation function to suggest special gifts or surprises according to the user's emotional state. For example, the ingredient ordering unit can use the emotion estimation function to suggest special gifts or surprises according to the user's emotional state. For example, when the user is happy, the unit can suggest special desserts or gifts, and when the user is stressed, the unit can suggest relaxing herbal tea or aroma candles. Furthermore, the ingredient ordering unit can suggest special dishes or gifts to celebrate anniversaries on special days. This makes it possible to suggest special gifts or surprises according to the user's emotional state.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The menu suggestion system can also suggest individually customized menus taking into account the user's dietary preferences and allergy information. For example, if the user prefers a particular ingredient, it will preferentially suggest dishes that use that ingredient. It can also suggest dishes that do not contain allergens based on allergy information. Furthermore, it can analyze the user's past eating history and suggest menus that take nutritional balance into consideration. This makes it possible to provide optimal menus that meet the user's individual needs.

[0086] The menu suggestion system can also suggest meal appearances and presentation methods based on the user's emotional state. For example, it can suggest colorful presentations when the user is in a happy mood, and simple and calm presentations when the user wants to relax. It can also suggest luxurious presentations for special occasions. This makes it possible to provide meal appearances and presentation methods that correspond to the user's emotional state.

[0087] The menu suggestion system can also take into account the user's family structure to suggest dishes that the whole family can enjoy. For example, for families with children, it can suggest a menu that combines dishes for children and dishes for adults, and suggests large dishes and shareable appetizers. It can also suggest menus that will satisfy everyone based on family preferences and allergy information. This allows it to provide meals that the whole family can enjoy.

[0088] The menu suggestion system can also suggest menus to reduce waste based on the user's ingredient inventory information. For example, it can suggest dishes using ingredients remaining in the refrigerator and prioritize ingredients with an approaching expiration date. It can also grasp ingredient inventory status in real time and suggest menus based on that. This allows for menu suggestions to reduce waste based on the user's ingredient inventory information.

[0089] The menu suggestion system can also suggest music and lighting settings according to the user's emotional state to enhance the dining atmosphere. For example, it can suggest calming music and soft lighting when you want to relax, and bright music and colorful lighting when you are in a happy mood. It can also suggest romantic music and candlelight for special occasions. This allows the system to provide music and lighting settings according to the user's emotional state to enhance the dining atmosphere.

[0090] The menu suggestion system can also analyze a user's purchasing history and make ordering suggestions for ingredients that take into account repeat purchase trends. For example, ingredients that are purchased regularly can be automatically added to the list, and milk and bread that are purchased weekly can be automatically added to the order list. It can also suggest brands and types of ingredients that the user prefers based on past data. This allows for ordering suggestions for ingredients that take into account repeat purchase trends based on the user's purchasing history.

[0091] The menu suggestion system can also consider the user's budget and select cost-effective ingredients. For example, it can list ingredients that can be purchased within the user's budget and suggest dishes that use inexpensive, nutritious ingredients. It can also use sale information and discount coupons to suggest the best ingredients within the user's budget. This allows the system to select cost-effective ingredients based on the user's budget.

[0092] The menu suggestion system can also suggest special gifts or surprises according to the user's emotional state. For example, when the user is happy, it can suggest a special dessert or gift, and when the user is stressed, it can suggest relaxing herbal tea or aromatic candles. It can also suggest special dishes or gifts to celebrate anniversaries on special occasions. This makes it possible to provide special gifts or surprises according to the user's emotional state.

[0093] The menu suggestion system can further consider the user's eco-consciousness and suggest environmentally friendly ingredients and packaging. For example, it can suggest products that use organic ingredients or recyclable packaging, and it can also suggest products that use locally produced ingredients or plastic-free packaging. It can also suggest ingredients with a low carbon footprint or products that use eco-friendly packaging. This makes it possible to provide environmentally friendly ingredients and packaging based on the user's eco-consciousness.

[0094] The menu suggestion system can also prioritize local specialties in the user's region, helping to revitalize the local economy. For example, it can suggest dishes using local agricultural products and specialties, and dishes using fresh fish caught at local fishing ports. It can also suggest dishes using ingredients purchased directly from local farmers and producers. This allows the system to prioritize local specialties in the user's region, helping to revitalize the local economy.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The health data collection unit captures the user's health checkup information or real-time health data collected from wearable devices, such as blood pressure, heart rate, body temperature, exercise volume, and sleep data, and obtains data from wearable devices such as Apple Watch and fitness trackers. Step 2: The weather data collection unit collects weather data and seasonal data. For example, data such as temperature, humidity, and precipitation can be collected, and seasonal data such as the four seasons, specific months, or weeks can be collected. Step 3: The menu suggestion unit suggests menus based on the data collected by the health data collection unit and the weather data collection unit. For example, if the user's blood pressure is high, it suggests low-salt menus, and in summer it suggests cold dishes and dishes that help with hydration. In winter it suggests warm dishes and nutritious dishes, and on days when the user exercises a lot, it suggests high-calorie dishes and low-calorie dishes on days when the user exercises less. Step 4: The ingredient ordering unit orders the ingredients needed based on the menu proposed by the menu suggestion unit. For example, it may list the ingredients needed based on the proposed menu and place an order through the user's account. Furthermore, it may link with a food delivery service such as Co-op to automatically order the ingredients needed for the proposed menu.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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 AI 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 AI 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 health data collection unit that acquires real-time health data collected from a user's health checkup information or a wearable device; a weather data collection unit that captures weather data and seasonal data; a menu suggestion unit that suggests a menu based on the data collected by the health data collection unit and the meteorological data collection unit; and an ingredient ordering unit that orders ingredients necessary based on the menu proposed by the menu proposal unit. A system characterized by:

2. The health data collection unit: Collecting blood pressure data of the user; The menu suggestion unit Suggest low-salt menus based on the blood pressure data 2. The system of claim 1.

3. The meteorological data collection unit Collecting temperature data The menu suggestion unit Recommend seasonal dishes based on the temperature data 2. The system of claim 1.

4. The menu suggestion unit Recommend high- or low-calorie dishes based on the user's exercise data 2. The system of claim 1.

5. The ingredient ordering unit A list of necessary ingredients is made based on the menu proposed by the menu proposal unit; Place an order through said user's account 2. The system of claim 1.

6. The health data collection unit: Estimate the user's stress level; The menu suggestion unit Based on the stress level, it suggests ingredients and dishes that are effective in reducing stress.

2. The system of claim 1.

7. The health data collection unit: Automatically detects user allergy information, The menu suggestion unit Suggest allergen-free menus based on the allergy information 2. The system of claim 1.

8. The meteorological data collection unit Providing beverage suggestions to optimize the user's water intake based on the weather data 2. The system of claim 1.

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