System

Abstract

An object of a system according to an embodiment is to perform food ingredient management, recipe proposal, and health advice in an integrated manner.SOLUTION: A system includes a food ingredient management part, a recipe proposal part, a health advice part, an expiration date management part, and a nutrition value calculation part. The food ingredient management unit manages information on food ingredients. The recipe proposal part proposes a recipe on the basis of the food material information managed by the food material management part. The health advice unit provides health advice based on the recipe proposed by the recipe proposing unit. A best-before date management part manages the best-before date of the food material managed by the food material management part. The nutrition value calculation unit calculates a nutrition value of the recipe proposed by the recipe proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to centrally manage ingredients, suggest recipes, and provide health advice, so there is room for improvement.

[0005] The system according to the embodiment aims to provide integrated management of ingredients, recipe suggestions, and health advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient management unit, a recipe proposal unit, a health advice unit, a best-before management unit, and a nutritional value calculation unit. The ingredient management unit manages ingredient information. The recipe proposal unit proposes recipes based on ingredient information managed by the ingredient management unit. The health advice unit provides health advice based on recipes proposed by the recipe proposal unit. The best-before management unit manages best-before dates of ingredients managed by the ingredient management unit. The nutritional value calculation unit calculates the nutritional value of recipes proposed by the recipe proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can manage ingredients, suggest recipes, and provide health advice in a unified manner. [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 CookBot HealthMate system according to an embodiment of the present invention is a system that comprehensively supports food ingredient management, recipe suggestions, health advice, expiration date management, and nutritional value calculation, thereby providing comprehensive support for the health of your family.

[0029] The CookBot HealthMate system according to the embodiment includes an ingredient management unit, a recipe suggestion unit, a health advice unit, an expiration date management unit, and a nutritional value calculation unit. The ingredient management unit manages ingredient information. For example, it registers information such as the type of ingredient, freshness, origin, and nutritional value in a database. The ingredient management unit can also analyze ingredient information entered by a user and suggest appropriate storage methods. For example, it classifies ingredients into those that require refrigeration and those that are best stored at room temperature. The recipe suggestion unit suggests recipes based on the ingredient information managed by the ingredient management unit. For example, a generation AI generates healthy recipes based on ingredients entered by a user. The recipe suggestion unit can also customize recipes taking into account the user's preferences and nutritional balance. For example, it suggests recipes taking into account the user's preferred flavor trends and cooking time. The health advice unit provides health advice based on the recipes suggested by the recipe suggestion unit. For example, the generation AI analyzes the user's health condition and dietary history and provides advice to promote health. The health advice unit can also provide advice for specific health goals. For example, it can suggest low-calorie recipes to a user on a diet. The expiration date management unit manages the expiration dates of ingredients managed by the ingredient management unit. For example, the generation AI records the expiration dates of ingredients and notifies the user when the expiration date is approaching. The expiration date management unit can also suggest recipes using ingredients with upcoming expiration dates. For example, it generates recipes that prioritize ingredients with upcoming expiration dates. The nutritional value calculation unit calculates the nutritional value of the recipes suggested by the recipe suggestion unit. For example, the generation AI calculates the nutritional value of the recipes, such as calories, protein, fat, and carbohydrates, and displays them to the user. The nutritional value calculation unit can also customize the nutritional value to match the user's health goals. For example, it suggests high-protein recipes for a user aiming to build muscle. This allows the CookBot HealthMate system according to the embodiment to provide comprehensive support for family health. For example, ingredient management and recipe suggestions can ensure healthy and balanced daily meals. Expiration date management can also reduce food waste and alleviate financial burdens.Additionally, it calculates nutritional values ​​and provides health advice to help you keep your family in optimal health.

[0030] The ingredient management unit can evaluate the freshness and quality of ingredients using image recognition technology and suggest optimal recipes based on that information. For example, the ingredient management unit can evaluate the freshness of ingredients using image recognition technology and suggest recipes that prioritize fresh ingredients. For example, the color and shape of vegetables can be analyzed to quantify freshness. The ingredient management unit can also evaluate the quality of ingredients using image recognition technology and suggest recipes that use high-quality ingredients. For example, it can analyze the marbling pattern of meat or the transparency of fish eyes. The ingredient management unit can also use image recognition technology to detect blemishes or spoilage in ingredients and suggest appropriate recipes based on that information. For example, it can suggest recipes that use ingredients with the damaged parts removed. This makes it possible to evaluate the freshness and quality of ingredients and suggest optimal recipes.

[0031] The recipe suggestion unit uses a generation AI to analyze the flavor compatibility of ingredient combinations and customize recipes to suit the user's preferences. For example, the recipe suggestion unit uses a generation AI to analyze the flavor compatibility of ingredient combinations and customizes recipes based on the user's past preference data. For example, it learns the user's preferred flavor trends. The recipe suggestion unit also uses a generation AI to analyze the flavor compatibility of ingredient combinations and proposes new flavor combinations. For example, it generates new recipes with different ingredient combinations. The recipe suggestion unit also uses a generation AI to analyze flavor compatibility based on user feedback and continuously improves recipes. For example, it adjusts recipes to reflect the user's ratings. This allows recipes to be customized to suit the user's preferences.

[0032] The health advice unit can collect the user's health data in real time and provide health advice based on that data. The health advice unit can collect data from a fitness tracker or a smart watch in real time and provide health advice. For example, the health advice unit can provide advice based on the number of steps taken or heart rate. The health advice unit can also analyze the user's health data and provide advice based on the user's daily health condition. For example, the health advice unit can provide advice on improving sleep based on sleep data. The health advice unit can also provide advice on diet and exercise based on the health data collected in real time. For example, the health advice unit can suggest dietary recommendations based on the amount of exercise. This allows health data to be collected in real time and health advice to be provided.

[0033] The health advice unit uses generative AI to analyze the latest medical research and nutritional knowledge and provide evidence-based advice. For example, the health advice unit analyzes the latest medical research data using generative AI and provides evidence-based health advice. For example, it provides advice that reflects the effects of new nutrients. The health advice unit also analyzes nutritional knowledge using generative AI to suggest balanced meals. For example, it might suggest recipes using ingredients rich in specific nutrients. The health advice unit also provides advice tailored to individual health conditions based on the latest information in medical research and nutritional science. For example, it might suggest preventive measures for specific diseases. This makes it possible to provide evidence-based health advice.

[0034] The expiration date management unit monitors the storage environment of ingredients using sensors and can optimize it based on that data. For example, the expiration date management unit monitors the temperature and humidity inside the refrigerator using sensors and optimizes the storage environment of ingredients. For example, it issues an alert if the temperature is too high. The expiration date management unit also sets an appropriate storage environment for each ingredient and monitors it using sensors. For example, it manages separate storage environments for vegetables and meat. The expiration date management unit also predicts the expiration date of ingredients based on storage environment data and suggests the optimal storage method. For example, it notifies the user to consume ingredients early if the storage environment is poor. This makes it possible to optimize the storage environment of ingredients and manage expiration dates.

[0035] The expiration date management unit can use generation AI to automatically generate recipes using ingredients that are approaching their expiration date and notify the user. For example, the expiration date management unit can automatically detect ingredients that are approaching their expiration date and use generation AI to automatically generate recipes using those ingredients. For example, it can prioritize the use of ingredients that will expire in one week or less. The expiration date management unit can also notify the user of recipes that use ingredients that are approaching their expiration date to reduce waste. For example, it can use the notification function to suggest recipes to the user. The expiration date management unit can also automatically generate recipes using ingredients that are approaching their expiration date using generation AI and customize them to suit the user's preferences. For example, it can suggest recipes that reflect the user's past preferences. This allows ingredients that are approaching their expiration date to be used effectively.

[0036] The nutritional value calculation unit can be customized to suit the individual health goals of the user. For example, the nutritional value calculation unit customizes the calculation of nutritional value to suit the user's health goals. For example, it suggests low-calorie recipes to a user who is on a diet. The nutritional value calculation unit also calculates nutritional value based on the user's health goals and suggests appropriate meals. For example, it suggests high-protein recipes to a user who is aiming to build muscle. The nutritional value calculation unit also analyzes the user's health goals and calculates nutritional value based on them. For example, it suggests ingredients to increase specific nutrients. This allows nutritional value calculation to be tailored to the user's health goals.

[0037] The nutritional value calculation unit performs detailed calculations based on the origin and production method of ingredients, allowing for more accurate nutritional information to be provided. The nutritional value calculation unit calculates nutritional value based on, for example, information on the origin of ingredients, and provides more accurate nutritional information. For example, it calculates the nutritional value of locally grown vegetables in detail. The nutritional value calculation unit also calculates nutritional value taking into account the production method of ingredients. For example, it compares the nutritional value of organically grown ingredients with that of conventionally grown ingredients. The nutritional value calculation unit also calculates nutritional value based on the origin and production method of ingredients, and provides the result to the user. For example, it calculates the nutritional value of specialty products of a specific region in detail. This allows for more accurate nutritional information to be provided.

[0038] The nutritional value calculation unit can provide nutritional values ​​in a form that corresponds to specific dietary restrictions. For example, the nutritional value calculation unit calculates nutritional values ​​that correspond to specific dietary restrictions and suggests appropriate recipes. For example, it generates vegan or gluten-free recipes. The nutritional value calculation unit also calculates nutritional values ​​based on the user's dietary restrictions and suggests balanced meals. For example, it suggests low-carbohydrate recipes. The nutritional value calculation unit also calculates nutritional values ​​that correspond to specific dietary restrictions and provides them to the user. For example, it generates allergy-friendly recipes. This allows nutritional value calculations that correspond to specific dietary restrictions.

[0039] The nutritional value calculation unit can make adjustments based on the user's amount of exercise and daily activity level. For example, the nutritional value calculation unit analyzes the user's amount of exercise and calculates nutritional value based on that. For example, high-calorie recipes are suggested for users who exercise a lot. The nutritional value calculation unit also takes into account the user's daily activity level, calculates nutritional value, and suggests appropriate meals. For example, low-calorie recipes are suggested for users who do a lot of desk work. The nutritional value calculation unit also calculates nutritional value based on the user's amount of exercise and daily activity level, and suggests balanced meals. For example, ingredients suitable for post-exercise recovery are suggested. This allows nutritional value to be adjusted based on the user's amount of exercise and daily activity level.

[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 CookBot HealthMate system can also include a purchase history management unit that manages food ingredient purchase history. For example, the purchase history management unit can store a user's past purchase history in a database and refer to it the next time they make a purchase. The purchase history management unit can also analyze a user's purchasing patterns and automatically create a list of ingredients that are purchased regularly. For example, weekly purchases of milk or bread can be automatically added to the list. The purchase history management unit can also notify users when they are running low on certain ingredients based on their purchase history. For example, it can notify users when they are low on milk in the refrigerator. This allows users to efficiently manage their ingredients and reduce waste.

[0042] The CookBot HealthMate system can also include an allergy management unit that manages ingredient allergy information. For example, the allergy management unit can register information about ingredients to which a user has allergies in a database and take that information into consideration when suggesting recipes. The allergy management unit can also notify users when purchasing new ingredients whether the ingredient is related to an allergy. For example, a user with a nut allergy can be warned if they are about to purchase an ingredient containing nuts. The allergy management unit can also suggest alternative ingredients to avoid allergies based on the user's allergy information. For example, a user with a milk allergy can be suggested almond milk. This allows users to choose safe ingredients and maintain their health.

[0043] The CookBot HealthMate system can further include a cultivation method management unit that manages the cultivation methods of ingredients. For example, the cultivation method management unit can register information about the cultivation methods of ingredients in a database and provide it to users. The cultivation method management unit can also analyze the cultivation methods of ingredients purchased by users and provide advice on choosing healthy ingredients. For example, it can prioritize organically grown ingredients. The cultivation method management unit can also recommend environmentally friendly ingredients based on the cultivation method of the ingredients. For example, it can recommend ingredients with low levels of pesticides. This allows users to choose healthy ingredients and lead an environmentally conscious diet.

[0044] The CookBot HealthMate system can also include a storage method learning unit that learns food storage methods. For example, the storage method learning unit can record the storage methods actually used by the user in a database and refer to them the next time the user stores food. The storage method learning unit can also analyze the user's storage method and suggest the optimal storage method. For example, it can classify food items into those that are best stored in the refrigerator and those that are best stored in the freezer. The storage method learning unit can also predict the storage period based on the user's storage method and notify the user to consume the food at the appropriate time. For example, it can predict the storage period for refrigerated vegetables and notify the user when the expiration date is approaching. This allows the user to store food efficiently and reduce waste.

[0045] The CookBot HealthMate system can also include a real-time nutritional value analysis unit that analyzes the nutritional value of ingredients in real time. For example, the real-time nutritional value analysis unit can analyze and display the nutritional value of ingredients in real time while the user is cooking. The real-time nutritional value analysis unit can also analyze changes in nutritional value depending on the cooking method and provide information to the user. For example, if vitamins are lost through frying, the real-time nutritional value analysis unit can display this information. The real-time nutritional value analysis unit can also provide advice to maintain appropriate nutritional value during cooking based on the user's health goals. For example, if a user wants to increase their vitamin intake, the real-time nutritional value analysis unit can suggest steaming. This allows users to be conscious of nutritional value while cooking and prepare healthy meals.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The food ingredient management unit manages information about food ingredients. For example, it registers information such as the type of food ingredient, its freshness, place of origin, and nutritional value in a database. It can also analyze the food ingredient information entered by the user and suggest appropriate storage methods. For example, it can classify food ingredients into those that need to be stored in the refrigerator and those that are best stored at room temperature. Step 2: The recipe suggestion unit suggests recipes based on the ingredient information managed by the ingredient management unit. For example, the generation AI generates healthy recipes based on ingredients entered by the user. It can also customize recipes taking into account the user's preferences and nutritional balance. For example, it suggests recipes taking into account the user's preferred flavor trends and cooking times. Step 3: The health advice module provides health advice based on the recipes suggested by the recipe suggestion module. For example, the generative AI analyzes the user's health status and dietary history and provides advice to promote health. It can also provide advice for specific health goals. For example, it can suggest low-calorie recipes to a user on a diet. Step 4: The expiration date management unit manages the expiration dates of ingredients managed by the ingredient management unit. For example, the generation AI records the expiration dates of ingredients and notifies the user when the expiration date is approaching. It can also suggest recipes that use ingredients that are close to their expiration date. For example, it generates recipes that prioritize the use of ingredients that are close to their expiration date. Step 5: The nutritional value calculation unit calculates the nutritional value of the recipe suggested by the recipe suggestion unit. For example, the generation AI calculates the nutritional value of the recipe, such as calories, protein, fat, and carbohydrates, and displays it to the user. The nutritional value can also be customized to suit the user's health goals. For example, a user aiming to build muscle would be suggested high-protein recipes.

[0048] (Example 2) The CookBot HealthMate system according to an embodiment of the present invention is a system that comprehensively supports food ingredient management, recipe suggestions, health advice, expiration date management, and nutritional value calculation, thereby providing comprehensive support for the health of your family.

[0049] The CookBot HealthMate system according to the embodiment includes an ingredient management unit, a recipe suggestion unit, a health advice unit, an expiration date management unit, and a nutritional value calculation unit. The ingredient management unit manages ingredient information. For example, it registers information such as the type of ingredient, freshness, origin, and nutritional value in a database. The ingredient management unit can also analyze ingredient information entered by a user and suggest appropriate storage methods. For example, it classifies ingredients into those that require refrigeration and those that are best stored at room temperature. The recipe suggestion unit suggests recipes based on the ingredient information managed by the ingredient management unit. For example, a generation AI generates healthy recipes based on ingredients entered by a user. The recipe suggestion unit can also customize recipes taking into account the user's preferences and nutritional balance. For example, it suggests recipes taking into account the user's preferred flavor trends and cooking time. The health advice unit provides health advice based on the recipes suggested by the recipe suggestion unit. For example, the generation AI analyzes the user's health condition and dietary history and provides advice to promote health. The health advice unit can also provide advice for specific health goals. For example, it can suggest low-calorie recipes to a user on a diet. The expiration date management unit manages the expiration dates of ingredients managed by the ingredient management unit. For example, the generation AI records the expiration dates of ingredients and notifies the user when the expiration date is approaching. The expiration date management unit can also suggest recipes using ingredients with upcoming expiration dates. For example, it generates recipes that prioritize ingredients with upcoming expiration dates. The nutritional value calculation unit calculates the nutritional value of the recipes suggested by the recipe suggestion unit. For example, the generation AI calculates the nutritional value of the recipes, such as calories, protein, fat, and carbohydrates, and displays them to the user. The nutritional value calculation unit can also customize the nutritional value to match the user's health goals. For example, it suggests high-protein recipes for a user aiming to build muscle. This allows the CookBot HealthMate system according to the embodiment to provide comprehensive support for family health. For example, ingredient management and recipe suggestions can ensure healthy and balanced daily meals. Expiration date management can also reduce food waste and alleviate financial burdens.Additionally, it calculates nutritional values ​​and provides health advice to help you keep your family in optimal health.

[0050] The ingredient management unit can evaluate the freshness and quality of ingredients using image recognition technology and suggest optimal recipes based on that information. For example, the ingredient management unit can evaluate the freshness of ingredients using image recognition technology and suggest recipes that prioritize fresh ingredients. For example, the color and shape of vegetables can be analyzed to quantify freshness. The ingredient management unit can also evaluate the quality of ingredients using image recognition technology and suggest recipes that use high-quality ingredients. For example, it can analyze the marbling pattern of meat or the transparency of fish eyes. The ingredient management unit can also use image recognition technology to detect blemishes or spoilage in ingredients and suggest appropriate recipes based on that information. For example, it can suggest recipes that use ingredients with the damaged parts removed. This makes it possible to evaluate the freshness and quality of ingredients and suggest optimal recipes.

[0051] The recipe suggestion unit uses a generation AI to analyze the flavor compatibility of ingredient combinations and customize recipes to suit the user's preferences. For example, the recipe suggestion unit uses a generation AI to analyze the flavor compatibility of ingredient combinations and customizes recipes based on the user's past preference data. For example, it learns the user's preferred flavor trends. The recipe suggestion unit also uses a generation AI to analyze the flavor compatibility of ingredient combinations and proposes new flavor combinations. For example, it generates new recipes with different ingredient combinations. The recipe suggestion unit also uses a generation AI to analyze flavor compatibility based on user feedback and continuously improves recipes. For example, it adjusts recipes to reflect the user's ratings. This allows recipes to be customized to suit the user's preferences.

[0052] The recipe suggestion unit can use the emotion estimation function to suggest recipes that match the user's mood. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's mood and suggest recipes that use ingredients that have a relaxing effect. For example, if the user is feeling stressed, herbal tea is suggested. The recipe suggestion unit also suggests recipes that use ingredients that will energize the user depending on the user's emotional state. For example, if the user is tired, ingredients that are suitable for replenishing energy are used. The recipe suggestion unit also uses the emotion estimation function to suggest dessert recipes that match the user's mood. For example, if the user is feeling down, a recipe using chocolate is suggested. In this way, recipes that match the user's mood can be suggested.

[0053] The health advice unit can collect the user's health data in real time and provide health advice based on that data. The health advice unit can collect data from a fitness tracker or a smart watch in real time and provide health advice. For example, the health advice unit can provide advice based on the number of steps taken or heart rate. The health advice unit can also analyze the user's health data and provide advice based on the user's daily health condition. For example, the health advice unit can provide advice on improving sleep based on sleep data. The health advice unit can also provide advice on diet and exercise based on the health data collected in real time. For example, the health advice unit can suggest dietary recommendations based on the amount of exercise. This allows health data to be collected in real time and health advice to be provided.

[0054] The health advice unit uses generative AI to analyze the latest medical research and nutritional knowledge and provide evidence-based advice. For example, the health advice unit analyzes the latest medical research data using generative AI and provides evidence-based health advice. For example, it provides advice that reflects the effects of new nutrients. The health advice unit also analyzes nutritional knowledge using generative AI to suggest balanced meals. For example, it might suggest recipes using ingredients rich in specific nutrients. The health advice unit also provides advice tailored to individual health conditions based on the latest information in medical research and nutritional science. For example, it might suggest preventive measures for specific diseases. This makes it possible to provide evidence-based health advice.

[0055] The health advice unit can use the emotion estimation function to provide health advice according to the user's emotional state. For example, the health advice unit uses the emotion estimation function to analyze the user's emotional state and suggest ingredients and recipes for relaxation. For example, if the user is highly stressed, the health advice unit suggests ingredients that have a relaxing effect. The health advice unit also suggests ingredients and recipes that will energize the user according to the user's emotional state. For example, if the user is tired, the health advice unit suggests ingredients that are suitable for replenishing energy. The health advice unit also uses the emotion estimation function to provide advice on exercise and relaxation according to the user's emotional state. For example, the health advice unit suggests yoga or meditation according to the user's emotional state. This makes it possible to provide health advice according to the user's emotional state.

[0056] The expiration date management unit monitors the storage environment of ingredients using sensors and can optimize it based on that data. For example, the expiration date management unit monitors the temperature and humidity inside the refrigerator using sensors and optimizes the storage environment of ingredients. For example, it issues an alert if the temperature is too high. The expiration date management unit also sets an appropriate storage environment for each ingredient and monitors it using sensors. For example, it manages separate storage environments for vegetables and meat. The expiration date management unit also predicts the expiration date of ingredients based on storage environment data and suggests the optimal storage method. For example, it notifies the user to consume ingredients early if the storage environment is poor. This makes it possible to optimize the storage environment of ingredients and manage expiration dates.

[0057] The expiration date management unit can use generation AI to automatically generate recipes using ingredients that are approaching their expiration date and notify the user. For example, the expiration date management unit can automatically detect ingredients that are approaching their expiration date and use generation AI to automatically generate recipes using those ingredients. For example, it can prioritize the use of ingredients that will expire in one week or less. The expiration date management unit can also notify the user of recipes that use ingredients that are approaching their expiration date to reduce waste. For example, it can use the notification function to suggest recipes to the user. The expiration date management unit can also automatically generate recipes using ingredients that are approaching their expiration date using generation AI and customize them to suit the user's preferences. For example, it can suggest recipes that reflect the user's past preferences. This allows ingredients that are approaching their expiration date to be used effectively.

[0058] The expiration date management unit can use the emotion estimation function to manage expiration dates according to the emotional state of the user. For example, the expiration date management unit uses the emotion estimation function to analyze the emotional state of the user and suggest recipes that are easy to prepare when they are busy. For example, it suggests recipes that can be made in a short time during times of high stress. The expiration date management unit also suggests recipes that have a relaxing effect using ingredients that are close to their expiration date according to the emotional state of the user. For example, it suggests recipes that use herbs that have a relaxing effect. The expiration date management unit also uses the emotion estimation function to manage expiration dates according to the emotional state of the user and reduce food waste. For example, it sends notifications to encourage consumption of ingredients according to the emotional state. This allows expiration dates to be managed according to the emotional state of the user.

[0059] The nutritional value calculation unit can be customized to suit the individual health goals of the user. For example, the nutritional value calculation unit customizes the calculation of nutritional value to suit the user's health goals. For example, it suggests low-calorie recipes to a user who is on a diet. The nutritional value calculation unit also calculates nutritional value based on the user's health goals and suggests appropriate meals. For example, it suggests high-protein recipes to a user who is aiming to build muscle. The nutritional value calculation unit also analyzes the user's health goals and calculates nutritional value based on them. For example, it suggests ingredients to increase specific nutrients. This allows nutritional value calculation to be tailored to the user's health goals.

[0060] The nutritional value calculation unit performs detailed calculations based on the origin and production method of ingredients, allowing for more accurate nutritional information to be provided. The nutritional value calculation unit calculates nutritional value based on, for example, information on the origin of ingredients, and provides more accurate nutritional information. For example, it calculates the nutritional value of locally grown vegetables in detail. The nutritional value calculation unit also calculates nutritional value taking into account the production method of ingredients. For example, it compares the nutritional value of organically grown ingredients with that of conventionally grown ingredients. The nutritional value calculation unit also calculates nutritional value based on the origin and production method of ingredients, and provides the result to the user. For example, it calculates the nutritional value of specialty products of a specific region in detail. This allows for more accurate nutritional information to be provided.

[0061] The nutritional value calculation unit uses the emotion estimation function to calculate nutritional value according to the user's emotional state and can suggest meals that will increase emotional satisfaction. The nutritional value calculation unit, for example, uses the emotion estimation function to analyze the user's emotional state and suggest meals that will increase emotional satisfaction. For example, if the user is highly stressed, ingredients that have a relaxing effect are suggested. The nutritional value calculation unit also calculates nutritional value according to the user's emotional state and suggests recipes that will increase emotional satisfaction. For example, if the user is tired, ingredients that are suitable for replenishing energy are suggested. The nutritional value calculation unit also uses the emotion estimation function to calculate nutritional value according to the user's emotional state and suggest meals that will increase emotional satisfaction. For example, if the user is depressed, a recipe using chocolate is suggested. This makes it possible to calculate nutritional value and suggest meals according to the user's emotional state.

[0062] The nutritional value calculation unit can provide nutritional values ​​in a form that corresponds to specific dietary restrictions. For example, the nutritional value calculation unit calculates nutritional values ​​that correspond to specific dietary restrictions and suggests appropriate recipes. For example, it generates vegan or gluten-free recipes. The nutritional value calculation unit also calculates nutritional values ​​based on the user's dietary restrictions and suggests balanced meals. For example, it suggests low-carbohydrate recipes. The nutritional value calculation unit also calculates nutritional values ​​that correspond to specific dietary restrictions and provides them to the user. For example, it generates allergy-friendly recipes. This allows nutritional value calculations that correspond to specific dietary restrictions.

[0063] The nutritional value calculation unit can make adjustments based on the user's amount of exercise and daily activity level. For example, the nutritional value calculation unit analyzes the user's amount of exercise and calculates nutritional value based on that. For example, high-calorie recipes are suggested for users who exercise a lot. The nutritional value calculation unit also takes into account the user's daily activity level, calculates nutritional value, and suggests appropriate meals. For example, low-calorie recipes are suggested for users who do a lot of desk work. The nutritional value calculation unit also calculates nutritional value based on the user's amount of exercise and daily activity level, and suggests balanced meals. For example, ingredients suitable for post-exercise recovery are suggested. This allows nutritional value to be adjusted based on the user's amount of exercise and daily activity level.

[0064] The nutritional value calculation unit calculates nutritional values ​​taking into account the emotional states of all family members, thereby supporting the health of the entire family. For example, the nutritional value calculation unit analyzes the emotional states of all family members and calculates nutritional values ​​that will help everyone relax. For example, it suggests ingredients that have a relaxing effect during times of high stress. The nutritional value calculation unit also calculates balanced nutritional values ​​taking into account the emotional states of all family members. For example, it prioritizes the use of ingredients that the whole family can enjoy. The nutritional value calculation unit also analyzes the emotional states of all family members and calculates nutritional values ​​that will energize everyone. For example, it suggests activities that the whole family can enjoy. This makes it possible to calculate nutritional values ​​taking into account the emotional states of all family members, thereby supporting the health of the entire family.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The CookBot HealthMate system can also include a purchase history management unit that manages food ingredient purchase history. For example, the purchase history management unit can store a user's past purchase history in a database and refer to it the next time they make a purchase. The purchase history management unit can also analyze a user's purchasing patterns and automatically create a list of ingredients that are purchased regularly. For example, weekly purchases of milk or bread can be automatically added to the list. The purchase history management unit can also notify users when they are running low on certain ingredients based on their purchase history. For example, it can notify users when they are low on milk in the refrigerator. This allows users to efficiently manage their ingredients and reduce waste.

[0067] The CookBot HealthMate system can also include an allergy management unit that manages ingredient allergy information. For example, the allergy management unit can register information about ingredients to which a user has allergies in a database and take that information into consideration when suggesting recipes. The allergy management unit can also notify users when purchasing new ingredients whether the ingredient is related to an allergy. For example, a user with a nut allergy can be warned if they are about to purchase an ingredient containing nuts. The allergy management unit can also suggest alternative ingredients to avoid allergies based on the user's allergy information. For example, a user with a milk allergy can be suggested almond milk. This allows users to choose safe ingredients and maintain their health.

[0068] The CookBot HealthMate system can further include a cultivation method management unit that manages the cultivation methods of ingredients. For example, the cultivation method management unit can register information about the cultivation methods of ingredients in a database and provide it to users. The cultivation method management unit can also analyze the cultivation methods of ingredients purchased by users and provide advice on choosing healthy ingredients. For example, it can prioritize organically grown ingredients. The cultivation method management unit can also recommend environmentally friendly ingredients based on the cultivation method of the ingredients. For example, it can recommend ingredients with low levels of pesticides. This allows users to choose healthy ingredients and lead an environmentally conscious diet.

[0069] The CookBot HealthMate system can also include a storage method learning unit that learns food storage methods. For example, the storage method learning unit can record the storage methods actually used by the user in a database and refer to them the next time the user stores food. The storage method learning unit can also analyze the user's storage method and suggest the optimal storage method. For example, it can classify food items into those that are best stored in the refrigerator and those that are best stored in the freezer. The storage method learning unit can also predict the storage period based on the user's storage method and notify the user to consume the food at the appropriate time. For example, it can predict the storage period for refrigerated vegetables and notify the user when the expiration date is approaching. This allows the user to store food efficiently and reduce waste.

[0070] The CookBot HealthMate system can also include a real-time nutritional value analysis unit that analyzes the nutritional value of ingredients in real time. For example, the real-time nutritional value analysis unit can analyze and display the nutritional value of ingredients in real time while the user is cooking. The real-time nutritional value analysis unit can also analyze changes in nutritional value depending on the cooking method and provide information to the user. For example, if vitamins are lost through frying, the real-time nutritional value analysis unit can display this information. The real-time nutritional value analysis unit can also provide advice to maintain appropriate nutritional value during cooking based on the user's health goals. For example, if a user wants to increase their vitamin intake, the real-time nutritional value analysis unit can suggest steaming. This allows users to be conscious of nutritional value while cooking and prepare healthy meals.

[0071] The CookBot HealthMate system can further include an emotional state ingredient selection unit that supports ingredient selection based on the user's emotional state. The emotional state ingredient selection unit can, for example, analyze the user's emotional state and suggest ingredients with a relaxing effect. For example, if the user is highly stressed, it can suggest chamomile tea. The emotional state ingredient selection unit can also suggest ingredients that will energize the user based on the user's emotional state. For example, if the user is tired, it can suggest bananas. The emotional state ingredient selection unit can also advise the user to avoid ingredients that trigger certain emotions based on the user's emotional state. For example, if the user is easily irritated, it can suggest avoiding caffeine. This allows the user to select ingredients that match their emotional state and maintain a balance between mind and body.

[0072] The CookBot HealthMate system can further include an emotional state recipe adjustment unit that adjusts the difficulty of recipes based on the user's emotional state. For example, the emotional state recipe adjustment unit can analyze the user's emotional state and suggest easy recipes if the user is under high stress. For example, it can suggest recipes that require little time and effort to prepare. The emotional state recipe adjustment unit can also suggest recipes with a relaxing effect based on the user's emotional state. For example, it can suggest recipes that use herbs for relaxation. The emotional state recipe adjustment unit can also advise the user to avoid ingredients that trigger certain emotions based on the user's emotional state. For example, it can suggest avoiding caffeine if the user is easily irritated. This allows the user to choose recipes that suit their emotional state and maintain a balance between mind and body.

[0073] The CookBot HealthMate system may further include an emotional state meal timing suggestion unit that suggests meal times based on the user's emotional state. For example, the emotional state meal timing suggestion unit may analyze the user's emotional state and suggest meals at times when the user is most relaxed if the user is under a lot of stress. For example, it may suggest a light meal in the evening to relax. The emotional state meal timing suggestion unit may also suggest meals at times when the user needs to replenish energy, based on the user's emotional state. For example, if the user is tired, it may suggest replenishing energy in the afternoon. The emotional state meal timing suggestion unit may also advise the user to avoid foods that trigger certain emotions based on the user's emotional state. For example, if the user is easily irritated, it may suggest avoiding caffeine. This allows the user to choose meal times based on their emotional state and maintain a balance between mind and body.

[0074] The CookBot HealthMate system may further include an emotional state preservation method suggestion unit that suggests food preservation methods based on the user's emotional state. For example, the emotional state preservation method suggestion unit may analyze the user's emotional state and suggest easy food preservation methods when the user is under high stress. For example, it may suggest food that is easy to freeze. The emotional state preservation method suggestion unit may also suggest food preservation methods that have a relaxing effect based on the user's emotional state. For example, it may suggest a preservation method using herbs. The emotional state preservation method suggestion unit may also advise the user to avoid foods that trigger specific emotions based on the user's emotional state. For example, it may suggest avoiding caffeine if the user is easily irritated. This allows the user to choose a food preservation method that suits their emotional state and maintain a balance between mind and body.

[0075] The CookBot HealthMate system may further include an emotional state purchasing support unit that supports food purchases based on the user's emotional state. For example, the emotional state purchasing support unit may analyze the user's emotional state and recommend foods with a relaxing effect if the user is under high stress. For example, it may recommend chamomile tea. The emotional state purchasing support unit may also recommend foods that will energize the user based on the user's emotional state. For example, it may recommend bananas. The emotional state purchasing support unit may also advise the user to avoid foods that trigger certain emotions based on the user's emotional state. For example, it may suggest avoiding caffeine if the user is easily irritated. This allows the user to select foods that match their emotional state and maintain a balance between mind and body.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The food ingredient management unit manages information about food ingredients. For example, it registers information such as the type of food ingredient, its freshness, place of origin, and nutritional value in a database. It can also analyze the food ingredient information entered by the user and suggest appropriate storage methods. For example, it can classify food ingredients into those that need to be stored in the refrigerator and those that are best stored at room temperature. Step 2: The recipe suggestion unit suggests recipes based on the ingredient information managed by the ingredient management unit. For example, the generation AI generates healthy recipes based on ingredients entered by the user. It can also customize recipes taking into account the user's preferences and nutritional balance. For example, it suggests recipes taking into account the user's preferred flavor trends and cooking times. Step 3: The health advice module provides health advice based on the recipes suggested by the recipe suggestion module. For example, the generative AI analyzes the user's health status and dietary history and provides advice to promote health. It can also provide advice for specific health goals. For example, it can suggest low-calorie recipes to a user on a diet. Step 4: The expiration date management unit manages the expiration dates of ingredients managed by the ingredient management unit. For example, the generation AI records the expiration dates of ingredients and notifies the user when the expiration date is approaching. It can also suggest recipes that use ingredients that are close to their expiration date. For example, it generates recipes that prioritize the use of ingredients that are close to their expiration date. Step 5: The nutritional value calculation unit calculates the nutritional value of the recipe suggested by the recipe suggestion unit. For example, the generation AI calculates the nutritional value of the recipe, such as calories, protein, fat, and carbohydrates, and displays it to the user. The nutritional value can also be customized to suit the user's health goals. For example, a user aiming to build muscle would be suggested high-protein recipes.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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 food ingredient management department that manages information on ingredients; a recipe suggestion unit that suggests recipes based on the ingredient information managed by the ingredient management unit; a health advice unit that provides health advice based on the recipes suggested by the recipe suggestion unit; an expiration date management unit that manages the expiration dates of the ingredients managed by the ingredient management unit; a nutritional value calculation unit that calculates the nutritional value of the recipe proposed by the recipe suggestion unit. A system characterized by:

2. The ingredient management unit Using image recognition technology to evaluate the freshness and quality of ingredients, the system suggests optimal recipes based on that information.

2. The system of claim 1.

3. The recipe suggestion unit Using AI to analyze the flavor compatibility of ingredient combinations, recipes can be customized to suit the user's preferences.

2. The system of claim 1.

4. The recipe suggestion unit Suggest recipes that match the user's mood 2. The system of claim 1.

5. The health advice unit Collecting users' health data in real time and providing health advice based on that data 2. The system of claim 1.

6. The health advice unit Analyze the latest medical research and nutritional knowledge using generative AI to provide scientifically based advice 2. The system of claim 1.

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