System

Abstract

An object of a system according to an embodiment is to propose an optimal menu according to a mood, a season, and a budget of a user.SOLUTION: A system includes a menu proposal part, a food ingredient proposal part, a seasonal food ingredient proposal part, and a budget calorie proposal part. The menu proposal unit proposes a menu on the basis of the user's mood. The food ingredient proposal unit proposes food ingredients that can be purchased at the supermarket based on the proposed menu. The seasonal food ingredient proposal unit proposes a menu in consideration of seasonal food ingredients for each season. A budget calorie proposal part proposes a menu in consideration of budget, calorie and balance.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 technology has had the problem of making it difficult to suggest optimal menus based on a user's mood, season, and budget.

[0005] The system according to the embodiment aims to propose an optimal menu according to the user's mood, season, and budget. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu suggestion unit, an ingredient suggestion unit, a seasonal ingredient suggestion unit, and a budget calorie suggestion unit. The menu suggestion unit suggests a menu based on the user's mood. The ingredient suggestion unit suggests ingredients that can be purchased at a supermarket based on the suggested menu. The seasonal ingredient suggestion unit suggests a menu taking into account seasonal ingredients for each season. The budget calorie suggestion unit suggests a menu taking into account budget, calories, and balance. [Effects of the Invention]

[0007] The system according to the embodiment can propose the optimal menu according to the user's mood, season, and budget. [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 suggests menus based on the user's mood, suggests ingredients that can be purchased at supermarkets, takes into account seasonal ingredients, budget, calories, and balance. As a result, the menu suggestion system can suggest menus that take into account the user's mood, supermarket inventory, seasonal ingredients, budget, and calories.

[0029] A menu suggestion system according to an embodiment includes a menu suggestion unit, an ingredient suggestion unit, a seasonal ingredient suggestion unit, and a budget calorie suggestion unit. The menu suggestion unit suggests a menu based on the user's mood. For example, if a user inputs, "I want to eat Japanese food today," the generation AI suggests a balanced menu from Japanese cuisine. Furthermore, if a user inputs, "I want a light meal today," the generation AI can also suggest a lighter menu. The ingredient suggestion unit suggests ingredients available at a supermarket based on the proposed menu. For example, if a user inputs, "Please help me come up with a menu using ingredients available at a nearby supermarket," the generation AI can suggest a menu using ingredients available at that supermarket based on the supermarket's inventory information. Furthermore, if a user inputs, "Please help me come up with a menu using special sale items," the generation AI can also suggest a menu using special sale items. The seasonal ingredient suggestion unit suggests a menu taking into account seasonal ingredients for each season. For example, if a user inputs, "Please help me come up with a menu using ingredients appropriate for the current season," the generation AI can suggest a menu using ingredients that are in season for that season. Furthermore, if a user inputs, "I would like suggestions for menus using local seasonal ingredients," the generation AI can suggest menus using local seasonal ingredients. The budget / calorie suggestion unit proposes menus taking into account budget, calories, and balance. For example, if a user inputs, "I would like suggestions for menus with a budget of 1,000 yen or less and calories under 500 kcal," the generation AI proposes menus that meet those conditions. Furthermore, if a user inputs, "I would like suggestions for balanced meals," the generation AI can also propose menus that take nutritional balance into consideration. As a result, the menu suggestion system according to the embodiment can propose menus that take into account the user's mood, supermarket inventory, seasonal ingredients, budget, and calories. For example, if a user requests, "I want Japanese food today," the system can propose a Japanese menu using seasonal ingredients available at a nearby supermarket and provide recipes, allowing the user to easily enjoy cooking at home. Furthermore, by suggesting menus that take budget and calories into consideration, the system can also accommodate health-conscious users.

[0030] The menu suggestion unit analyzes the user's past meal history, learns the user's preference trends, and can propose more personalized menus. For example, the menu suggestion unit stores the user's past meal history in a database and analyzes the data to learn the user's preference trends. For example, the menu suggestion unit identifies the dishes and ingredients the user frequently chooses and proposes menus based on them. When analyzing the meal history, the menu suggestion unit also takes into account the time of day and frequency of the user's meals to grasp more detailed preference trends. For example, a user who prefers light meals for breakfast can be proposed a light breakfast menu. The menu suggestion unit also analyzes the user's meal history and tracks changes in preferences for specific ingredients and dishes. For example, a user whose preferences change with the seasons can be proposed a menu that suits the season. This makes it possible to propose personalized menus based on the user's preferences.

[0031] The menu suggestion unit allows the user to input the user's physical condition and activity level for that day, and can suggest an optimal menu based on that. For example, the menu suggestion unit provides an interface for the user to input the user's physical condition and activity level for that day, and suggests an optimal menu based on that information. For example, on a tired day, it can suggest meals that are easy to digest. The menu suggestion unit also obtains the user's activity level from a smartwatch or fitness tracker and suggests a menu based on that data. For example, on a day when the user exercises a lot, it can suggest a high-protein meal. The menu suggestion unit also suggests a menu that takes nutritional balance into consideration according to the user's physical condition and activity level. For example, on a day when the user has a cold, it can suggest a menu using ingredients rich in vitamin C. In this way, it is possible to suggest an optimal menu based on the user's physical condition and activity level.

[0032] The ingredient suggestion unit can suggest cost-effective menus by taking into account supermarket inventory information as well as sale information and coupon information. For example, the ingredient suggestion unit collects sale information and coupon information in addition to supermarket inventory information, and suggests cost-effective menus based on that information. For example, it suggests menus using sale items. The ingredient suggestion unit also registers coupon information that users can use in a database and suggests menus based on that information. For example, it can suggest menus using ingredients that can be discounted using coupons. The ingredient suggestion unit also updates supermarket sale information in real time and suggests menus based on that information. For example, if sale items change, it can suggest menus that reflect that change. This makes it possible to suggest cost-effective menus.

[0033] The ingredient suggestion unit updates supermarket inventory information in real time and can suggest menus based on the latest inventory status. For example, the ingredient suggestion unit builds a system that updates supermarket inventory information in real time and suggests menus based on that information. For example, it suggests menus that avoid ingredients that are in low stock. The ingredient suggestion unit also obtains supermarket inventory information via an API and suggests menus based on the latest inventory status. For example, it can obtain supermarket inventory data in real time using an API. The ingredient suggestion unit also regularly updates supermarket inventory information and suggests menus based on that information. For example, it can suggest a menu for the day based on inventory information that is updated daily. This makes it possible to suggest menus based on the latest inventory status.

[0034] The seasonal ingredient suggestion unit can propose seasonal menus for each region, taking into account ingredients specific to each season. The seasonal ingredient suggestion unit, for example, registers ingredients specific to each region for each season in a database and proposes seasonal menus for each region based on that information. For example, it proposes menus using local specialties. The seasonal ingredient suggestion unit also considers the climate and environment of each region and proposes menus using seasonal ingredients suitable for that region. For example, it can propose warm dishes in cold regions. The seasonal ingredient suggestion unit also considers the food culture and traditional dishes of the region and proposes seasonal menus that are rooted in that region. For example, it can propose menus that are adaptations of traditional dishes of the region. This makes it possible to propose menus that take into account seasonal ingredients for each region.

[0035] The seasonal ingredient suggestion unit can analyze the nutritional value and health benefits of seasonal ingredients and suggest healthy menus based on that. For example, the seasonal ingredient suggestion unit registers the nutritional value and health benefits of seasonal ingredients in a database and suggests healthy menus based on that information. For example, it can suggest a menu using seasonal fruits rich in vitamin C. The seasonal ingredient suggestion unit can also analyze the nutritional value of seasonal ingredients and suggest menus using ingredients with specific health benefits. For example, it can suggest menus using ingredients with antioxidant properties. The seasonal ingredient suggestion unit can also take into account the health benefits of seasonal ingredients and suggest menus tailored to specific health goals. For example, it can suggest menus using low-calorie seasonal ingredients to a user who is on a diet. This makes it possible to suggest healthy menus that take into account the nutritional value and health benefits of seasonal ingredients.

[0036] The budget calorie suggestion unit analyzes the user's past meal data, learns the user's budget and calorie trends, and can suggest an optimal menu. For example, the budget calorie suggestion unit stores the user's past meal data in a database and analyzes the data to learn the user's budget and calorie trends. For example, it identifies the price range and calorie range that the user frequently chooses and suggests a menu based on that. When analyzing the meal data, the budget calorie suggestion unit also takes into account the user's meal time and frequency to grasp more detailed budget and calorie trends. For example, a user who prefers high-calorie meals for dinner can be suggested a menu suitable for dinner. The budget calorie suggestion unit also analyzes the user's meal data and tracks changes in preferences for specific budgets and calories. For example, a low-calorie menu can be suggested to a user who is on a diet. In this way, the budget calorie suggestion unit can learn the user's budget and calorie trends based on the user's past meal data and suggest an optimal menu.

[0037] The budget calorie suggestion unit allows the user to input their health condition and goals (e.g., dieting or muscle building), and can suggest an optimal menu based on that. The budget calorie suggestion unit, for example, provides an interface for the user to input their health condition and goals, and suggests an optimal menu based on that information. For example, a low-calorie menu is suggested for a user who is on a diet. The budget calorie suggestion unit also suggests a menu that takes nutritional balance into consideration according to the user's health condition and goals. For example, a high-protein menu can be suggested for a user who is aiming to build muscle. The budget calorie suggestion unit also periodically updates the user's health condition and goals, and suggests a menu based on that information. For example, if a new health goal is added, it can suggest a menu that reflects that. This makes it possible to suggest an optimal menu based on the user's health condition and goals.

[0038] The budget calorie suggestion unit allows the user to input ingredients that the user has and can suggest menus that fit within the budget using those ingredients. For example, the budget calorie suggestion unit provides an interface for the user to input ingredients that the user has and suggests menus that fit within the budget based on that information. For example, it suggests menus that fit within the budget using ingredients that are in the refrigerator. The budget calorie suggestion unit also allows the user to easily register ingredients that the user has by scanning barcodes or entering voice input. For example, the barcodes of ingredients can be scanned using a smartphone camera. The budget calorie suggestion unit also stores ingredients that the user has in a database and suggests menus that fit within the budget based on that data. For example, it can take into account the expiration dates of ingredients and prioritize suggestions of ingredients that should be used soon. This makes it possible to suggest menus that fit within the budget using ingredients that the user has.

[0039] The budget calorie suggestion unit can propose a menu that will satisfy everyone, taking into consideration the health conditions and goals of the user's family and housemates. For example, the budget calorie suggestion unit registers the health conditions and goals of the user's family and housemates in a database and proposes menus based on that information. For example, it proposes a healthy menu that will satisfy the entire family. The budget calorie suggestion unit also considers the dietary restrictions and allergy information of the family and housemates and proposes a menu that everyone can eat. For example, if there is someone with an allergy, it can propose a menu that avoids that ingredient. The budget calorie suggestion unit also regularly updates the health conditions and goals of the family and housemates and proposes a menu based on that information. For example, if a new health goal is added, it can propose a menu that reflects that. This makes it possible to propose a menu that takes into consideration the health conditions and goals of the user's family and housemates.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The menu suggestion unit allows the user to input their food preferences and allergy information, and can suggest optimal menus based on that information. For example, if the user is allergic to a particular ingredient, the unit can suggest menus that avoid that ingredient. Also, if the user likes a particular dish, the unit can suggest menus that include that dish. Furthermore, the user's food preferences and allergy information can be saved in a database and used for future menu suggestions. This makes it possible to suggest menus that meet the user's individual needs.

[0042] The menu suggestion unit allows the user to input meal times and meal frequency, and can suggest optimal menus based on this. For example, if the user wants a light breakfast, a light breakfast menu can be suggested. Also, if the user eats multiple times a day, a menu can be suggested according to the frequency of the meals. Furthermore, the user's meal times and frequency can be saved in a database and used for suggesting menus from the next time onwards. This makes it possible to suggest menus that suit the user's eating habits.

[0043] The menu suggestion unit allows the user to input their food preferences and allergy information, and can suggest optimal menus based on that information. For example, if the user is allergic to a particular ingredient, the unit can suggest menus that avoid that ingredient. Also, if the user likes a particular dish, the unit can suggest menus that include that dish. Furthermore, the user's food preferences and allergy information can be saved in a database and used for future menu suggestions. This makes it possible to suggest menus that meet the user's individual needs.

[0044] The menu suggestion unit allows the user to input the user's physical condition and activity level for that day, and can suggest the optimal menu based on that. For example, on days when the user is tired, it can suggest meals that are easy to digest. It can also obtain the user's activity level from a smartwatch or fitness tracker and suggest menus based on that data. For example, on days when the user exercises a lot, it can suggest high-protein meals. This makes it possible to suggest the optimal menu based on the user's physical condition and activity level.

[0045] The ingredient suggestion unit can suggest cost-effective menus by taking into account supermarket inventory information, sale information, and coupon information. For example, it can suggest menus using sale items. It can also register coupon information that users can use in a database and suggest menus based on that information. It can also update supermarket sale information in real time and suggest menus based on that information. This makes it possible to suggest cost-effective menus.

[0046] The seasonal ingredient suggestion unit can propose seasonal menus for each region, taking into account ingredients specific to each season. For example, it can propose menus using local specialties. It can also propose menus using seasonal ingredients suitable for each region, taking into account the climate and environment of each region. It can also propose seasonal menus that are rooted in the region, taking into account the food culture and traditional cuisine of the region. This makes it possible to propose menus that take into account seasonal ingredients for each region.

[0047] The seasonal ingredient suggestion unit can analyze the nutritional value and health benefits of seasonal ingredients and suggest healthy menus based on that. For example, it can suggest menus using seasonal fruits rich in vitamin C. It can also suggest menus using ingredients with antioxidant properties. It can also suggest menus tailored to specific health goals. For example, it can suggest menus using low-calorie seasonal ingredients to a user on a diet. This makes it possible to suggest healthy menus that take into account the nutritional value and health benefits of seasonal ingredients.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The menu suggestion unit suggests a menu based on the user's mood. For example, if the user inputs, "I want to eat Japanese food today," the generation AI will suggest a balanced menu from Japanese cuisine. Also, if the user inputs, "I want to eat a light meal today," the generation AI can suggest a lighter menu. Step 2: The ingredient suggestion unit suggests ingredients that can be purchased at the supermarket based on the proposed menu. For example, if a user inputs, "Please help me come up with a menu using ingredients that can be purchased at a nearby supermarket," the generation AI will suggest a menu using ingredients that can be purchased based on the supermarket's inventory information. Also, if a user inputs, "Please help me come up with a menu using special sale items," the generation AI can suggest a menu using special sale items. Step 3: The seasonal ingredient suggestion unit proposes a menu taking into account seasonal ingredients. For example, if a user inputs, "I would like suggestions for a menu using ingredients that are in season," the generation AI will propose a menu using ingredients that are in season for that season. Also, if a user inputs, "I would like suggestions for a menu using ingredients that are in season in the region," the generation AI can also propose a menu using ingredients that are in season in the region. Step 4: The budget and calorie suggestion unit proposes a menu taking into account budget, calories, and balance. For example, if a user inputs, "I would like suggestions for a menu with a budget of 1,000 yen or less and calories of 500 kcal or less," the generation AI will propose a menu that meets those conditions. Also, if a user inputs, "I would like suggestions for a balanced meal," the generation AI can also propose a menu that takes nutritional balance into consideration.

[0050] (Example 2) The menu suggestion system according to an embodiment of the present invention is a system that suggests menus based on the user's mood, suggests ingredients that can be purchased at supermarkets, takes into account seasonal ingredients, budget, calories, and balance. As a result, the menu suggestion system can suggest menus that take into account the user's mood, supermarket inventory, seasonal ingredients, budget, and calories.

[0051] A menu suggestion system according to an embodiment includes a menu suggestion unit, an ingredient suggestion unit, a seasonal ingredient suggestion unit, and a budget calorie suggestion unit. The menu suggestion unit suggests a menu based on the user's mood. For example, if a user inputs, "I want to eat Japanese food today," the generation AI suggests a balanced menu from Japanese cuisine. Furthermore, if a user inputs, "I want a light meal today," the generation AI can also suggest a lighter menu. The ingredient suggestion unit suggests ingredients available at a supermarket based on the proposed menu. For example, if a user inputs, "Please help me come up with a menu using ingredients available at a nearby supermarket," the generation AI can suggest a menu using ingredients available at that supermarket based on the supermarket's inventory information. Furthermore, if a user inputs, "Please help me come up with a menu using special sale items," the generation AI can also suggest a menu using special sale items. The seasonal ingredient suggestion unit suggests a menu taking into account seasonal ingredients for each season. For example, if a user inputs, "Please help me come up with a menu using ingredients appropriate for the current season," the generation AI can suggest a menu using ingredients that are in season for that season. Furthermore, if a user inputs, "I would like suggestions for menus using local seasonal ingredients," the generation AI can suggest menus using local seasonal ingredients. The budget / calorie suggestion unit proposes menus taking into account budget, calories, and balance. For example, if a user inputs, "I would like suggestions for menus with a budget of 1,000 yen or less and calories under 500 kcal," the generation AI proposes menus that meet those conditions. Furthermore, if a user inputs, "I would like suggestions for balanced meals," the generation AI can also propose menus that take nutritional balance into consideration. As a result, the menu suggestion system according to the embodiment can propose menus that take into account the user's mood, supermarket inventory, seasonal ingredients, budget, and calories. For example, if a user requests, "I want Japanese food today," the system can propose a Japanese menu using seasonal ingredients available at a nearby supermarket and provide recipes, allowing the user to easily enjoy cooking at home. Furthermore, by suggesting menus that take budget and calories into consideration, the system can also accommodate health-conscious users.

[0052] The menu suggestion unit analyzes the user's past meal history, learns the user's preference trends, and can propose more personalized menus. For example, the menu suggestion unit stores the user's past meal history in a database and analyzes the data to learn the user's preference trends. For example, the menu suggestion unit identifies the dishes and ingredients the user frequently chooses and proposes menus based on them. When analyzing the meal history, the menu suggestion unit also takes into account the time of day and frequency of the user's meals to grasp more detailed preference trends. For example, a user who prefers light meals for breakfast can be proposed a light breakfast menu. The menu suggestion unit also analyzes the user's meal history and tracks changes in preferences for specific ingredients and dishes. For example, a user whose preferences change with the seasons can be proposed a menu that suits the season. This makes it possible to propose personalized menus based on the user's preferences.

[0053] The menu suggestion unit allows the user to input the user's physical condition and activity level for that day, and can suggest an optimal menu based on that. For example, the menu suggestion unit provides an interface for the user to input the user's physical condition and activity level for that day, and suggests an optimal menu based on that information. For example, on a tired day, it can suggest meals that are easy to digest. The menu suggestion unit also obtains the user's activity level from a smartwatch or fitness tracker and suggests a menu based on that data. For example, on a day when the user exercises a lot, it can suggest a high-protein meal. The menu suggestion unit also suggests a menu that takes nutritional balance into consideration according to the user's physical condition and activity level. For example, on a day when the user has a cold, it can suggest a menu using ingredients rich in vitamin C. In this way, it is possible to suggest an optimal menu based on the user's physical condition and activity level.

[0054] The menu suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest menus that contribute to stress reduction and mood improvement. The menu suggestion unit uses, for example, facial expression recognition and voice analysis technology to analyze the user's emotional state. For example, if the user is feeling stressed, it suggests a menu using ingredients that have a relaxing effect. The menu suggestion unit also uses the emotion estimation function to monitor the user's emotional state in real time and suggest a menu based on the results. For example, if the user is feeling depressed, it can suggest a menu using ingredients that have a mood-boosting effect. The menu suggestion unit also analyzes the user's emotional state and compares it with past data to understand emotional fluctuations. For example, it can suggest a menu that is effective for a specific emotional state. This makes it possible to suggest a menu based on the user's emotional state.

[0055] The ingredient suggestion unit can suggest cost-effective menus by taking into account supermarket inventory information as well as sale information and coupon information. For example, the ingredient suggestion unit collects sale information and coupon information in addition to supermarket inventory information, and suggests cost-effective menus based on that information. For example, it suggests menus using sale items. The ingredient suggestion unit also registers coupon information that users can use in a database and suggests menus based on that information. For example, it can suggest menus using ingredients that can be discounted using coupons. The ingredient suggestion unit also updates supermarket sale information in real time and suggests menus based on that information. For example, if sale items change, it can suggest menus that reflect that change. This makes it possible to suggest cost-effective menus.

[0056] The ingredient suggestion unit updates supermarket inventory information in real time and can suggest menus based on the latest inventory status. For example, the ingredient suggestion unit builds a system that updates supermarket inventory information in real time and suggests menus based on that information. For example, it suggests menus that avoid ingredients that are in low stock. The ingredient suggestion unit also obtains supermarket inventory information via an API and suggests menus based on the latest inventory status. For example, it can obtain supermarket inventory data in real time using an API. The ingredient suggestion unit also regularly updates supermarket inventory information and suggests menus based on that information. For example, it can suggest a menu for the day based on inventory information that is updated daily. This makes it possible to suggest menus based on the latest inventory status.

[0057] The ingredient suggestion unit uses the emotion estimation function to analyze the user's emotions when making a purchase and can suggest ingredients that will increase the user's desire to buy. The ingredient suggestion unit uses, for example, facial expression recognition and voice analysis technology to analyze the user's emotions when making a purchase. For example, it prioritizes suggesting ingredients that the user is interested in. The ingredient suggestion unit also uses the emotion estimation function to suggest ingredients that will increase the user's desire to buy. For example, it can suggest ingredients that the user is feeling positive about. The ingredient suggestion unit also monitors the user's emotional state in real time and suggests ingredients that will increase the user's desire to buy based on the results. For example, if the user is feeling stressed, it can suggest ingredients that have a relaxing effect. This makes it possible to suggest ingredients that will increase the user's desire to buy.

[0058] The seasonal ingredient suggestion unit can propose seasonal menus for each region, taking into account ingredients specific to each season. The seasonal ingredient suggestion unit, for example, registers ingredients specific to each region for each season in a database and proposes seasonal menus for each region based on that information. For example, it proposes menus using local specialties. The seasonal ingredient suggestion unit also considers the climate and environment of each region and proposes menus using seasonal ingredients suitable for that region. For example, it can propose warm dishes in cold regions. The seasonal ingredient suggestion unit also considers the food culture and traditional dishes of the region and proposes seasonal menus that are rooted in that region. For example, it can propose menus that are adaptations of traditional dishes of the region. This makes it possible to propose menus that take into account seasonal ingredients for each region.

[0059] The seasonal ingredient suggestion unit can analyze the nutritional value and health benefits of seasonal ingredients and suggest healthy menus based on that. For example, the seasonal ingredient suggestion unit registers the nutritional value and health benefits of seasonal ingredients in a database and suggests healthy menus based on that information. For example, it can suggest a menu using seasonal fruits rich in vitamin C. The seasonal ingredient suggestion unit can also analyze the nutritional value of seasonal ingredients and suggest menus using ingredients with specific health benefits. For example, it can suggest menus using ingredients with antioxidant properties. The seasonal ingredient suggestion unit can also take into account the health benefits of seasonal ingredients and suggest menus tailored to specific health goals. For example, it can suggest menus using low-calorie seasonal ingredients to a user who is on a diet. This makes it possible to suggest healthy menus that take into account the nutritional value and health benefits of seasonal ingredients.

[0060] The seasonal ingredient suggestion unit can use the emotion estimation function to analyze seasonal emotional fluctuations and suggest the optimal menu for that time of year. For example, the seasonal ingredient suggestion unit collects user emotion data to analyze seasonal emotional fluctuations and suggests the optimal menu based on that data. For example, for a user who tends to feel depressed in the winter, the unit suggests a menu using ingredients that have a mood-boosting effect. The seasonal ingredient suggestion unit can also use the emotion estimation function to monitor seasonal emotional fluctuations in real time and suggest a menu based on the results. For example, it can suggest a menu using ingredients that have a relaxing effect in spring. The seasonal ingredient suggestion unit can also analyze seasonal emotional fluctuations and compare them with past data to understand the emotional fluctuations. For example, it can suggest a menu that is effective for a specific season. This makes it possible to suggest the optimal menu taking seasonal emotional fluctuations into consideration.

[0061] The budget calorie suggestion unit analyzes the user's past meal data, learns the user's budget and calorie trends, and can suggest an optimal menu. For example, the budget calorie suggestion unit stores the user's past meal data in a database and analyzes the data to learn the user's budget and calorie trends. For example, it identifies the price range and calorie range that the user frequently chooses and suggests a menu based on that. When analyzing the meal data, the budget calorie suggestion unit also takes into account the user's meal time and frequency to grasp more detailed budget and calorie trends. For example, a user who prefers high-calorie meals for dinner can be suggested a menu suitable for dinner. The budget calorie suggestion unit also analyzes the user's meal data and tracks changes in preferences for specific budgets and calories. For example, a low-calorie menu can be suggested to a user who is on a diet. In this way, the budget calorie suggestion unit can learn the user's budget and calorie trends based on the user's past meal data and suggest an optimal menu.

[0062] The budget calorie suggestion unit allows the user to input their health condition and goals (e.g., dieting or muscle building), and can suggest an optimal menu based on that. The budget calorie suggestion unit, for example, provides an interface for the user to input their health condition and goals, and suggests an optimal menu based on that information. For example, a low-calorie menu is suggested for a user who is on a diet. The budget calorie suggestion unit also suggests a menu that takes nutritional balance into consideration according to the user's health condition and goals. For example, a high-protein menu can be suggested for a user who is aiming to build muscle. The budget calorie suggestion unit also periodically updates the user's health condition and goals, and suggests a menu based on that information. For example, if a new health goal is added, it can suggest a menu that reflects that. This makes it possible to suggest an optimal menu based on the user's health condition and goals.

[0063] The budget calorie suggestion unit can use the emotion estimation function to analyze the user's emotional response to their health goals and suggest a menu that will increase their motivation. The budget calorie suggestion unit can use, for example, facial expression recognition or voice analysis technology to analyze the user's emotional response to their health goals. For example, it can suggest a menu that matches a health goal for which the user has expressed positive emotions. The budget calorie suggestion unit can also use the emotion estimation function to calculate an emotion score for the user's health goals and suggest a menu based on that score. For example, it can suggest a menu that matches a health goal with a high emotion score. The budget calorie suggestion unit can also monitor the user's emotional response in real time and suggest a menu that will increase their motivation based on the results. For example, if the user has a positive response to a suggested menu, it can suggest a similar menu. This makes it possible to analyze the user's emotional response to their health goals and suggest a menu that will increase their motivation.

[0064] The budget calorie suggestion unit allows the user to input ingredients that the user has and can suggest menus that fit within the budget using those ingredients. For example, the budget calorie suggestion unit provides an interface for the user to input ingredients that the user has and suggests menus that fit within the budget based on that information. For example, it suggests menus that fit within the budget using ingredients that are in the refrigerator. The budget calorie suggestion unit also allows the user to easily register ingredients that the user has by scanning barcodes or entering voice input. For example, the barcodes of ingredients can be scanned using a smartphone camera. The budget calorie suggestion unit also stores ingredients that the user has in a database and suggests menus that fit within the budget based on that data. For example, it can take into account the expiration dates of ingredients and prioritize suggestions of ingredients that should be used soon. This makes it possible to suggest menus that fit within the budget using ingredients that the user has.

[0065] The budget calorie suggestion unit can propose a menu that will satisfy everyone, taking into consideration the health conditions and goals of the user's family and housemates. For example, the budget calorie suggestion unit registers the health conditions and goals of the user's family and housemates in a database and proposes menus based on that information. For example, it proposes a healthy menu that will satisfy the entire family. The budget calorie suggestion unit also considers the dietary restrictions and allergy information of the family and housemates and proposes a menu that everyone can eat. For example, if there is someone with an allergy, it can propose a menu that avoids that ingredient. The budget calorie suggestion unit also regularly updates the health conditions and goals of the family and housemates and proposes a menu based on that information. For example, if a new health goal is added, it can propose a menu that reflects that. This makes it possible to propose a menu that takes into consideration the health conditions and goals of the user's family and housemates.

[0066] The budget calorie suggestion unit can use the emotion estimation function to analyze the user's emotional response to the budget and calories input, and suggest a menu that elicits a positive response. The budget calorie suggestion unit, for example, analyzes the user's emotional response to the budget and calories input, and suggests a menu that elicits a positive response based on the results. For example, it suggests a menu within a budget that the user is interested in. The budget calorie suggestion unit also uses the emotion estimation function to calculate the user's emotional score, and suggests a menu based on the score. For example, it can preferentially suggest menus within a budget with a high emotional score. The budget calorie suggestion unit also monitors the user's emotional response in real time, and adjusts the menu based on the results. For example, if the user has a positive response to a suggested menu, it can suggest a similar menu. In this way, it is possible to analyze the user's emotional response to the budget and calories, and suggest a menu that elicits a positive response.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The menu suggestion unit allows the user to input their food preferences and allergy information, and can suggest optimal menus based on that information. For example, if the user is allergic to a particular ingredient, the unit can suggest menus that avoid that ingredient. Also, if the user likes a particular dish, the unit can suggest menus that include that dish. Furthermore, the user's food preferences and allergy information can be saved in a database and used for future menu suggestions. This makes it possible to suggest menus that meet the user's individual needs.

[0069] The menu suggestion unit allows the user to input meal times and meal frequency, and can suggest optimal menus based on this. For example, if the user wants a light breakfast, a light breakfast menu can be suggested. Also, if the user eats multiple times a day, a menu can be suggested according to the frequency of the meals. Furthermore, the user's meal times and frequency can be saved in a database and used for suggesting menus from the next time onwards. This makes it possible to suggest menus that suit the user's eating habits.

[0070] The menu suggestion unit allows the user to input their food preferences and allergy information, and can suggest optimal menus based on that information. For example, if the user is allergic to a particular ingredient, the unit can suggest menus that avoid that ingredient. Also, if the user likes a particular dish, the unit can suggest menus that include that dish. Furthermore, the user's food preferences and allergy information can be saved in a database and used for future menu suggestions. This makes it possible to suggest menus that meet the user's individual needs.

[0071] The menu suggestion unit allows the user to input the user's physical condition and activity level for that day, and can suggest the optimal menu based on that. For example, on days when the user is tired, it can suggest meals that are easy to digest. It can also obtain the user's activity level from a smartwatch or fitness tracker and suggest menus based on that data. For example, on days when the user exercises a lot, it can suggest high-protein meals. This makes it possible to suggest the optimal menu based on the user's physical condition and activity level.

[0072] The menu suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest menus that contribute to stress reduction and mood improvement. For example, if the user is feeling stressed, it can suggest menus using ingredients that have a relaxing effect. Also, if the user is feeling depressed, it can suggest menus using ingredients that have a mood-boosting effect. Furthermore, it can monitor the user's emotional state in real time and suggest menus based on the results. This makes it possible to suggest menus based on the user's emotional state.

[0073] The ingredient suggestion unit can suggest cost-effective menus by taking into account supermarket inventory information, sale information, and coupon information. For example, it can suggest menus using sale items. It can also register coupon information that users can use in a database and suggest menus based on that information. It can also update supermarket sale information in real time and suggest menus based on that information. This makes it possible to suggest cost-effective menus.

[0074] The ingredient suggestion unit uses the emotion estimation function to analyze the user's emotions when making a purchase and suggest ingredients that will increase the user's desire to buy. For example, it can prioritize suggestions of ingredients that the user has shown interest in. It can also monitor the user's emotional state in real time and suggest ingredients that will increase the user's desire to buy based on the results. It can also suggest ingredients that the user has shown positive emotions about. This makes it possible to suggest ingredients that will increase the user's desire to buy.

[0075] The seasonal ingredient suggestion unit can propose seasonal menus for each region, taking into account ingredients specific to each season. For example, it can propose menus using local specialties. It can also propose menus using seasonal ingredients suitable for each region, taking into account the climate and environment of each region. It can also propose seasonal menus that are rooted in the region, taking into account the food culture and traditional cuisine of the region. This makes it possible to propose menus that take into account seasonal ingredients for each region.

[0076] The seasonal ingredient suggestion unit can analyze the nutritional value and health benefits of seasonal ingredients and suggest healthy menus based on that. For example, it can suggest menus using seasonal fruits rich in vitamin C. It can also suggest menus using ingredients with antioxidant properties. It can also suggest menus tailored to specific health goals. For example, it can suggest menus using low-calorie seasonal ingredients to a user on a diet. This makes it possible to suggest healthy menus that take into account the nutritional value and health benefits of seasonal ingredients.

[0077] The seasonal ingredient suggestion unit uses the emotion estimation function to analyze seasonal emotional fluctuations and suggest the optimal menu for that time of year. For example, for a user who tends to feel depressed in the winter, it can suggest a menu using ingredients that have a mood-boosting effect. It can also suggest a menu using ingredients that have a relaxing effect in the spring. It can also monitor seasonal emotional fluctuations in real time and suggest menus based on the results. This makes it possible to suggest the optimal menu that takes seasonal emotional fluctuations into consideration.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The menu suggestion unit suggests a menu based on the user's mood. For example, if the user inputs, "I want to eat Japanese food today," the generation AI will suggest a balanced menu from Japanese cuisine. Also, if the user inputs, "I want to eat a light meal today," the generation AI can suggest a lighter menu. Step 2: The ingredient suggestion unit suggests ingredients that can be purchased at the supermarket based on the proposed menu. For example, if a user inputs, "Please help me come up with a menu using ingredients that can be purchased at a nearby supermarket," the generation AI will suggest a menu using ingredients that can be purchased based on the supermarket's inventory information. Also, if a user inputs, "Please help me come up with a menu using special sale items," the generation AI can suggest a menu using special sale items. Step 3: The seasonal ingredient suggestion unit proposes a menu taking into account seasonal ingredients. For example, if a user inputs, "I would like suggestions for a menu using ingredients that are in season," the generation AI will propose a menu using ingredients that are in season for that season. Also, if a user inputs, "I would like suggestions for a menu using ingredients that are in season in the region," the generation AI can also propose a menu using ingredients that are in season in the region. Step 4: The budget and calorie suggestion unit proposes a menu taking into account budget, calories, and balance. For example, if a user inputs, "I would like suggestions for a menu with a budget of 1,000 yen or less and calories of 500 kcal or less," the generation AI will propose a menu that meets those conditions. Also, if a user inputs, "I would like suggestions for a balanced meal," the generation AI can also propose a menu that takes nutritional balance into consideration.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 menu suggestion unit that suggests a menu based on the user's mood; an ingredient suggestion section that suggests ingredients that can be purchased at supermarkets based on the proposed menu; The seasonal ingredients proposal department proposes menus taking into account the ingredients that are in season, A budget and calorie suggestion section that suggests menus taking into account budget, calories, and balance. A system characterized by:

2. The menu suggestion unit Analyze the user's past meal history, learn their preferences, and propose more personalized menus.

2. The system of claim 1.

3. The menu suggestion unit The system prompts the user to input their physical condition and activity level for that day, and then suggests the optimal menu based on that information.

2. The system of claim 1.

4. The menu suggestion unit Analyzing the user's emotional state and suggesting menus that contribute to reducing stress and improving mood 2. The system of claim 1.

5. The ingredient suggestion unit In addition to the supermarket's inventory information, the system also takes into account sales and coupon information to propose cost-effective menus.

2. The system of claim 1.

6. The ingredient suggestion unit The supermarket's inventory information is updated in real time, and menu suggestions are made based on the latest inventory status.

2. The system of claim 1.

7. The ingredient suggestion unit Analyze the user's emotions when making a purchase and suggest ingredients that will increase their purchasing motivation 2. The system of claim 1.

8. The seasonal food recommendation unit Considering the ingredients specific to each region in each season, we propose seasonal menus for each region.

2. The system of claim 1.

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