System

Abstract

An object of a system according to an embodiment is to propose an optimal menu in consideration of a user's preference, nutrition balance, food materials at home, and the like.SOLUTION: A system includes a user information acquisition part, a nutrition calculation part, a food ingredient management part, and a menu proposal part. The user information acquisition unit grasps what the user has recently eaten and likes and dislikes. The nutrition calculation unit considers the nutrition balance and calories based on the information acquired by the user information acquisition unit. The food ingredient management part considers the food ingredients in the house and the specials of the supermarket frequently visited based on the information calculated by the nutrition calculation part. A menu proposal part proposes an optimum menu on the basis of the information managed by the food material management part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to propose optimal menus that take into account many factors, such as the user's preferences, nutritional balance, and ingredients available at home.

[0005] The system according to the embodiment aims to propose optimal menus taking into consideration the user's preferences, nutritional balance, ingredients available at home, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information acquisition unit, a nutrition calculation unit, an ingredient management unit, and a menu suggestion unit. The user information acquisition unit keeps track of what the user has eaten recently and their likes and dislikes. The nutrition calculation unit considers nutritional balance and calories based on the information acquired by the user information acquisition unit. The ingredient management unit considers ingredients available at home and special offers at frequently visited supermarkets based on the information calculated by the nutrition calculation unit. The menu suggestion unit suggests optimal menus based on the information managed by the ingredient management unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus taking into consideration the user's preferences, nutritional balance, ingredients available at home, and the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A menu suggestion system according to an embodiment of the present invention proposes optimal menus while taking into consideration a wide range of user needs and conditions. This system proposes menus by taking into consideration various factors, such as what the user has eaten recently, their likes and dislikes, nutritional balance, calories, ingredients available at home, special offers at a favorite supermarket, the children's school lunch menu, the daily specials at the company cafeteria, weather and temperature, the season and seasonal ingredients, garbage collection day, whether or not they need to prepare lunch the next day, their likes and dislikes in cooking, their strengths and weaknesses, the amount of cooking time they have, whether they prepare food in advance, and the cooking utensils they have. This allows the menu suggestion system to propose optimal menus while taking into consideration a wide range of user needs and conditions.

[0029] A menu suggestion system according to an embodiment includes a user information acquisition unit, a nutrition calculation unit, an ingredient management unit, and a menu suggestion unit. The user information acquisition unit identifies what a user has recently eaten and their likes and dislikes. For example, the unit asks the user questions such as, "What have you eaten recently?" and "Are there any ingredients you dislike?" and understands the user's preferences based on the user's answers. The user information acquisition unit can also automatically record the user's dietary history and incorporate an algorithm that predicts preferences based on past data. The nutrition calculation unit considers nutritional balance and calories based on the information acquired by the user information acquisition unit. For example, the generation AI proposes menus that take into account nutritional balance and calories based on the user's health status and goals. The generation AI calculates the nutritional value and calories of each ingredient based on knowledge of nutrition and finds the optimal combination. The ingredient management unit considers ingredients available at home and special offers at frequently visited supermarkets based on the information calculated by the nutrition calculation unit. For example, the generation AI proposes menus taking into account ingredients available at the user's home and special offers at frequently visited supermarkets. The menu suggestion unit proposes optimal menus based on the information managed by the ingredient management unit. For example, the generation AI will propose the optimal menu while taking into consideration the wide range of requests and conditions of the user. This allows the menu proposal system to propose the optimal menu while taking into consideration the wide range of requests and conditions of the user.

[0030] The user information acquisition unit can automatically record the user's meal history and introduce an algorithm that predicts preferences based on past data. The user information acquisition unit, for example, builds a system that automatically records the details of each meal the user eats. For example, it takes photos of meals with a smartphone camera, analyzes the images, and stores the meal details in a database. The user information acquisition unit also develops an algorithm that predicts the user's preferences based on past meal data. For example, it analyzes patterns of frequently eaten ingredients and dishes and suggests menus that the user prefers. The user information acquisition unit also builds a system that updates the user's preferences in real time based on the meal history. For example, when a new ingredient or dish is tried, its evaluation is recorded and the preference data is updated. This makes it possible to more accurately understand the user's preferences and suggest optimal menus.

[0031] The user information acquisition unit can analyze a user's social media posts and photos related to food to understand their preferences. For example, the user information acquisition unit analyzes photos and comments about food posted by the user on social media to build a system to understand preferences. For example, it uses image recognition technology to identify ingredients and dishes and stores them in a database. The user information acquisition unit also analyzes text data from social media posts to extract comments and ratings about the user's food. For example, it understands preferences based on positive comments such as "It was delicious" or "I want to eat it again." The user information acquisition unit also develops an algorithm to predict a user's eating patterns and preferences based on the data from social media posts. For example, it analyzes ratings for specific dishes and ingredients and updates preference data. In this way, by analyzing a user's social media posts and photos, it is possible to more accurately understand preferences and suggest optimal menus.

[0032] The nutrition calculation unit can link the user's health data and suggest a nutritional balance according to the individual's health condition. The nutrition calculation unit, for example, links the user's health data and builds a system that suggests a nutritional balance according to the individual's health condition. For example, it suggests an optimal menu based on blood pressure and blood sugar levels. The nutrition calculation unit also analyzes the user's nutritional balance in real time based on the health data and develops an algorithm that adjusts the menu. For example, it suggests highly nutritious ingredients according to the health condition. The nutrition calculation unit also builds a system that suggests a menu that takes into account the individual's nutritional balance based on the user's health data. For example, if a specific nutrient is lacking, it suggests ingredients that contain that nutrient. This makes it possible to support health management by suggesting a nutritional balance according to the user's health condition.

[0033] The nutrition calculation unit updates the nutritional value of ingredients in real time and can propose menus based on the latest nutritional information. For example, the nutrition calculation unit builds a system that updates the nutritional value of ingredients in real time and proposes menus based on the latest nutritional information. For example, the nutrition value database is updated regularly. The nutrition calculation unit also analyzes the user's nutritional balance in real time based on the latest nutritional information and develops an algorithm to adjust the menu. For example, new research results are reflected. The nutrition calculation unit also builds a system that proposes menus that take into account the latest nutritional information based on the nutritional value data of ingredients. For example, it prioritizes the proposal of ingredients with high nutritional value. This makes it possible to provide healthier meals by proposing menus based on the latest nutritional information.

[0034] The ingredient management unit automatically manages the expiration dates of ingredients in the home and can suggest menus that prioritize ingredients with upcoming expiration dates. The ingredient management unit, for example, builds a system that automatically manages the expiration dates of ingredients in the home. For example, it uses a barcode scanner to register the expiration dates of ingredients in a database and suggests menus that prioritize ingredients with upcoming expiration dates. The ingredient management unit also develops an algorithm that suggests optimal menus based on ingredients with upcoming expiration dates. For example, it automatically generates recipes that prioritize ingredients with upcoming expiration dates. The ingredient management unit also updates the expiration date management system in real time and adds a function to notify the user. For example, it sends a push notification when suggesting a menu that uses ingredients with upcoming expiration dates. This allows the expiration dates of ingredients in the home to be managed, reducing waste and using ingredients efficiently.

[0035] The ingredient management unit updates supermarket sale information in real time and can suggest the most cost-effective menu. For example, the ingredient management unit builds a system that updates supermarket sale information in real time. For example, it obtains sale information from the supermarket's website or API and stores it in a database. The ingredient management unit also develops an algorithm that suggests the most cost-effective menu based on the sale information. For example, it automatically generates menus that prioritize sale items. The ingredient management unit also adds a function that updates supermarket sale information in real time and notifies the user. For example, it sends a push notification when suggesting a menu that uses sale items. This makes it possible to use supermarket sale information to suggest cost-effective menus.

[0036] The menu suggestion unit can automatically obtain children's school lunch menus and daily specials at employee cafeterias, and propose menus that avoid overlaps. The menu suggestion unit, for example, builds a system that automatically obtains menus for children's school lunches and employee cafeterias. For example, it obtains menu information from school or company websites and stores it in a database. The menu suggestion unit also develops an algorithm that proposes menus that avoid overlaps based on the menus for school lunches and employee cafeterias. For example, it automatically generates menus that avoid overlaps between the same ingredients and dishes. The menu suggestion unit also builds a system that updates menu information for school lunches and employee cafeterias in real time, and proposes menus that avoid overlaps. For example, it updates the menu when new menu information is added. This makes it possible to provide balanced meals by proposing menus that avoid overlaps with menus for children's school lunches and employee cafeterias.

[0037] The menu suggestion unit can propose safe menus by taking into account allergen information contained in school lunch and employee cafeteria menus. The menu suggestion unit, for example, builds a system that automatically acquires allergen information contained in school lunch and employee cafeteria menus. For example, it acquires allergen information from school or company websites and stores it in a database. The menu suggestion unit also develops an algorithm that proposes safe menus based on the allergen information. For example, it automatically generates menus that take into account the user's allergy information. The menu suggestion unit also builds a system that updates school lunch and employee cafeteria menu information in real time and proposes safe menus that take into account allergen information. For example, it updates the menu when new menu information is added. This makes it possible to accommodate users with allergies by proposing safe menus that take into account allergen information.

[0038] The menu suggestion unit can acquire weather forecast data in real time and propose optimal menus according to the weather and temperature. For example, the menu suggestion unit builds a system that acquires weather forecast data in real time and proposes optimal menus according to the weather and temperature. For example, it acquires data using a weather forecast API and reflects it in menu suggestions. The menu suggestion unit also develops an algorithm that proposes optimal menus based on weather and temperature data. For example, it suggests hot soups and hot pot dishes on cold days, and cold salads and cold pasta on hot days. The menu suggestion unit also builds a system that updates weather forecast data in real time and automatically adjusts menus according to the weather and temperature. For example, if the weather suddenly changes, the menu is updated immediately. This makes it possible to propose optimal menus according to the weather and temperature, thereby providing meals that suit the season and climate.

[0039] The menu suggestion unit can propose the most nutritious menu by taking into account the nutritional value of seasonal and in-season ingredients. The menu suggestion unit, for example, builds a system that proposes the most nutritious menu by taking into account the nutritional value of seasonal and in-season ingredients. For example, it creates a database of seasonal ingredients and prioritizes proposing nutritious ingredients. The menu suggestion unit also develops an algorithm that proposes optimal menus based on the nutritional value of seasonal and in-season ingredients. For example, it automatically generates nutritionally balanced menus using seasonal ingredients. The menu suggestion unit also builds a system that updates seasonal and in-season ingredient data in real time and proposes the most nutritious menu. For example, it updates the menu when new seasonal ingredient information is added. This makes it possible to provide nutritious meals by proposing menus that take into account the nutritional value of seasonal and in-season ingredients.

[0040] The menu suggestion unit can suggest menus according to the weather and temperature to suit special situations such as outdoor activities and picnics. The menu suggestion unit, for example, builds a system that suggests menus according to the weather and temperature to suit special situations such as outdoor activities and picnics. For example, it suggests optimal outdoor menus based on weather forecast data. The menu suggestion unit also develops an algorithm that suggests menus according to special situations. For example, it automatically generates easy-to-carry menus suitable for picnics. The menu suggestion unit also builds a system that updates weather and temperature data in real time and automatically adjusts menus to suit special situations. For example, it updates the outdoor menu when the weather suddenly changes. In this way, by suggesting menus according to special situations, events such as outdoor activities and picnics can be more enjoyable.

[0041] The menu suggestion unit can suggest menus using seasonal and in-season ingredients as traditional or local cuisine of the region. The menu suggestion unit, for example, builds a system that suggests menus using seasonal and in-season ingredients as traditional or local cuisine of the region. For example, it creates a traditional cuisine database for each region and suggests menus using seasonal ingredients. The menu suggestion unit also develops an algorithm that suggests menus using seasonal and in-season ingredients based on traditional or local cuisine of the region. For example, it automatically generates menus that take into account the food culture of the region. The menu suggestion unit also builds a system that updates seasonal and in-season ingredient data in real time and suggests dishes as traditional or local cuisine of the region. For example, it updates the menu when new seasonal ingredient information is added. This makes it possible to provide a cultural dining experience by suggesting traditional or local cuisine of the region.

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

[0043] The menu suggestion system can also obtain the user's exercise data and suggest optimal menus based on the amount of exercise. For example, if the user is using a smartwatch or fitness tracker, the system can link that data and adjust calorie intake based on the amount of exercise. It can also suggest post-exercise recovery menus and pre-exercise energy replenishment menus. This makes it possible to provide nutritionally balanced menus based on the user's exercise habits.

[0044] The menu suggestion system can also obtain the user's sleep data and suggest the optimal menu based on the quality of their sleep. For example, if the user uses a sleep tracker, the system can link that data and suggest a menu using ingredients that have a relaxing effect if the user's sleep quality is poor. It can also suggest ingredients that contain nutrients that improve sleep quality. This makes it possible to provide a menu that takes the user's sleep quality into consideration.

[0045] The menu suggestion system can also monitor the user's stress level and suggest menus that are effective in reducing stress. For example, if the user is using a stress monitoring device, the system can link that data and, if the user's stress level is high, suggest menus that use ingredients that have a relaxing effect. It can also suggest ingredients that contain nutrients that are effective in reducing stress. This makes it possible to provide menus that take the user's stress level into consideration.

[0046] The menu suggestion system can also obtain information about the user's travel destination and suggest menus that incorporate local specialties and regional cuisine of the destination. For example, it can suggest menus that use local ingredients that the user can enjoy at their travel destination. It can also suggest menus that take into account the culture and eating habits of the destination. This makes it possible to provide menus that will enrich the user's travel experience.

[0047] The menu suggestion system can also consider the user's cultural background regarding food and suggest menus that suit the user's culture. For example, it can suggest menus that take into account dietary restrictions based on a particular religion or culture. It can also suggest traditional or local dishes that suit the user's cultural background. This makes it possible to provide menus that take the user's culture into consideration.

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

[0049] Step 1: The user information acquisition unit determines what the user has eaten recently and what they like and dislike. For example, it asks the user questions such as "What have you eaten recently?" and "Are there any ingredients you dislike?" and understands the user's preferences based on the answers. The user information acquisition unit can also automatically record the user's eating history and implement an algorithm that predicts preferences based on past data. Step 2: The nutrition calculation unit considers nutritional balance and calories based on the information acquired by the user information acquisition unit. For example, the generation AI proposes a menu that takes into account nutritional balance and calories based on the user's health condition and goals. Based on its knowledge of nutrition, the generation AI calculates the nutritional value and calories of each ingredient and finds the optimal combination. Step 3: The ingredient management unit considers ingredients available at home and special offers at frequently visited supermarkets based on the information calculated by the nutrition calculation unit. For example, the generation AI proposes a menu taking into account ingredients available at the user's home and special offers at frequently visited supermarkets. Step 4: The menu suggestion unit proposes the optimal menu based on the information managed by the ingredient management unit. For example, the generation AI proposes the optimal menu while taking into consideration the wide-ranging requests and conditions of the user. This allows the menu suggestion system to propose the optimal menu while taking into consideration the wide-ranging requests and conditions of the user.

[0050] (Example 2) A menu suggestion system according to an embodiment of the present invention proposes optimal menus while taking into consideration a wide range of user needs and conditions. This system proposes menus by taking into consideration various factors, such as what the user has eaten recently, their likes and dislikes, nutritional balance, calories, ingredients available at home, special offers at a favorite supermarket, the children's school lunch menu, the daily specials at the company cafeteria, weather and temperature, the season and seasonal ingredients, garbage collection day, whether or not they need to prepare lunch the next day, their likes and dislikes in cooking, their strengths and weaknesses, the amount of cooking time they have, whether they prepare food in advance, and the cooking utensils they have. This allows the menu suggestion system to propose optimal menus while taking into consideration a wide range of user needs and conditions.

[0051] A menu suggestion system according to an embodiment includes a user information acquisition unit, a nutrition calculation unit, an ingredient management unit, and a menu suggestion unit. The user information acquisition unit identifies what a user has recently eaten and their likes and dislikes. For example, the unit asks the user questions such as, "What have you eaten recently?" and "Are there any ingredients you dislike?" and understands the user's preferences based on the user's answers. The user information acquisition unit can also automatically record the user's dietary history and incorporate an algorithm that predicts preferences based on past data. The nutrition calculation unit considers nutritional balance and calories based on the information acquired by the user information acquisition unit. For example, the generation AI proposes menus that take into account nutritional balance and calories based on the user's health status and goals. The generation AI calculates the nutritional value and calories of each ingredient based on knowledge of nutrition and finds the optimal combination. The ingredient management unit considers ingredients available at home and special offers at frequently visited supermarkets based on the information calculated by the nutrition calculation unit. For example, the generation AI proposes menus taking into account ingredients available at the user's home and special offers at frequently visited supermarkets. The menu suggestion unit proposes optimal menus based on the information managed by the ingredient management unit. For example, the generation AI will propose the optimal menu while taking into consideration the wide range of requests and conditions of the user. This allows the menu proposal system to propose the optimal menu while taking into consideration the wide range of requests and conditions of the user.

[0052] The user information acquisition unit can automatically record the user's meal history and introduce an algorithm that predicts preferences based on past data. The user information acquisition unit, for example, builds a system that automatically records the details of each meal the user eats. For example, it takes photos of meals with a smartphone camera, analyzes the images, and stores the meal details in a database. The user information acquisition unit also develops an algorithm that predicts the user's preferences based on past meal data. For example, it analyzes patterns of frequently eaten ingredients and dishes and suggests menus that the user prefers. The user information acquisition unit also builds a system that updates the user's preferences in real time based on the meal history. For example, when a new ingredient or dish is tried, its evaluation is recorded and the preference data is updated. This makes it possible to more accurately understand the user's preferences and suggest optimal menus.

[0053] The user information acquisition unit can analyze a user's social media posts and photos related to food to understand their preferences. For example, the user information acquisition unit analyzes photos and comments about food posted by the user on social media to build a system to understand preferences. For example, it uses image recognition technology to identify ingredients and dishes and stores them in a database. The user information acquisition unit also analyzes text data from social media posts to extract comments and ratings about the user's food. For example, it understands preferences based on positive comments such as "It was delicious" or "I want to eat it again." The user information acquisition unit also develops an algorithm to predict a user's eating patterns and preferences based on the data from social media posts. For example, it analyzes ratings for specific dishes and ingredients and updates preference data. In this way, by analyzing a user's social media posts and photos, it is possible to more accurately understand preferences and suggest optimal menus.

[0054] The user information acquisition unit can use the emotion estimation function to analyze the user's emotions toward the meal and suggest a menu that elicits positive emotions. The user information acquisition unit, for example, analyzes the user's facial expressions and voice while eating to build a system that estimates emotions. For example, it calculates the user's emotion score using a camera or microphone and suggests a menu that elicits positive emotions. The user information acquisition unit also analyzes the user's emotions toward the meal based on the emotion estimation data and updates the preference data. For example, it prioritizes suggestions of ingredients and dishes that evoke strong positive emotions. The user information acquisition unit also uses the emotion estimation function to monitor the user's emotions toward the meal in real time and make suggestions according to changes in preferences. For example, it adjusts the menu based on changes in emotions during the meal. In this way, meal satisfaction can be improved by analyzing the user's emotions and suggesting menus that elicit positive emotions.

[0055] The nutrition calculation unit can link the user's health data and suggest a nutritional balance according to the individual's health condition. The nutrition calculation unit, for example, links the user's health data and builds a system that suggests a nutritional balance according to the individual's health condition. For example, it suggests an optimal menu based on blood pressure and blood sugar levels. The nutrition calculation unit also analyzes the user's nutritional balance in real time based on the health data and develops an algorithm that adjusts the menu. For example, it suggests highly nutritious ingredients according to the health condition. The nutrition calculation unit also builds a system that suggests a menu that takes into account the individual's nutritional balance based on the user's health data. For example, if a specific nutrient is lacking, it suggests ingredients that contain that nutrient. This makes it possible to support health management by suggesting a nutritional balance according to the user's health condition.

[0056] The nutrition calculation unit updates the nutritional value of ingredients in real time and can propose menus based on the latest nutritional information. For example, the nutrition calculation unit builds a system that updates the nutritional value of ingredients in real time and proposes menus based on the latest nutritional information. For example, the nutrition value database is updated regularly. The nutrition calculation unit also analyzes the user's nutritional balance in real time based on the latest nutritional information and develops an algorithm to adjust the menu. For example, new research results are reflected. The nutrition calculation unit also builds a system that proposes menus that take into account the latest nutritional information based on the nutritional value data of ingredients. For example, it prioritizes the proposal of ingredients with high nutritional value. This makes it possible to provide healthier meals by proposing menus based on the latest nutritional information.

[0057] The nutrition calculation unit uses the emotion estimation function to analyze the user's emotions toward healthy eating and can suggest menus that elicit positive emotions. The nutrition calculation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions toward healthy eating. For example, it calculates an emotion score using a camera or microphone and suggests menus that elicit positive emotions. The nutrition calculation unit also develops an algorithm that analyzes the user's emotions toward healthy eating in real time based on the user's emotion data and adjusts the menu. For example, if the emotion score is low, it suggests a different menu. The nutrition calculation unit also builds a system that monitors the user's emotions in real time based on the emotion estimation data and suggests menus that elicit positive emotions toward healthy eating. For example, it adjusts the menu based on changes in emotions during a meal. In this way, the user's emotions are analyzed and menus that elicit positive emotions are suggested, thereby improving satisfaction with healthy eating.

[0058] The ingredient management unit automatically manages the expiration dates of ingredients in the home and can suggest menus that prioritize ingredients with upcoming expiration dates. The ingredient management unit, for example, builds a system that automatically manages the expiration dates of ingredients in the home. For example, it uses a barcode scanner to register the expiration dates of ingredients in a database and suggests menus that prioritize ingredients with upcoming expiration dates. The ingredient management unit also develops an algorithm that suggests optimal menus based on ingredients with upcoming expiration dates. For example, it automatically generates recipes that prioritize ingredients with upcoming expiration dates. The ingredient management unit also updates the expiration date management system in real time and adds a function to notify the user. For example, it sends a push notification when suggesting a menu that uses ingredients with upcoming expiration dates. This allows the expiration dates of ingredients in the home to be managed, reducing waste and using ingredients efficiently.

[0059] The ingredient management unit updates supermarket sale information in real time and can suggest the most cost-effective menu. For example, the ingredient management unit builds a system that updates supermarket sale information in real time. For example, it obtains sale information from the supermarket's website or API and stores it in a database. The ingredient management unit also develops an algorithm that suggests the most cost-effective menu based on the sale information. For example, it automatically generates menus that prioritize sale items. The ingredient management unit also adds a function that updates supermarket sale information in real time and notifies the user. For example, it sends a push notification when suggesting a menu that uses sale items. This makes it possible to use supermarket sale information to suggest cost-effective menus.

[0060] The ingredient management unit can use the emotion estimation function to analyze the emotions a user feels toward a menu using sale items and suggest a menu that elicits positive emotions. The ingredient management unit, for example, uses the emotion estimation function to build a system that analyzes the emotions a user feels toward a menu using sale items. For example, the emotion estimation function can be used to calculate an emotion score using a camera or microphone and suggest a menu that elicits positive emotions. The ingredient management unit also develops an algorithm that analyzes emotions toward a menu using sale items in real time based on the user's emotion data and adjusts the menu. For example, if the emotion score is low, a different menu is suggested. The ingredient management unit also monitors the user's emotions in real time based on the emotion estimation data and builds a system that suggests a menu that elicits positive emotions toward a menu using sale items. For example, the menu is adjusted based on changes in emotion during a meal. In this way, the satisfaction level of a menu using sale items can be improved by analyzing the user's emotions and suggesting a menu that elicits positive emotions.

[0061] The menu suggestion unit can automatically obtain children's school lunch menus and daily specials at employee cafeterias, and propose menus that avoid overlaps. The menu suggestion unit, for example, builds a system that automatically obtains menus for children's school lunches and employee cafeterias. For example, it obtains menu information from school or company websites and stores it in a database. The menu suggestion unit also develops an algorithm that proposes menus that avoid overlaps based on the menus for school lunches and employee cafeterias. For example, it automatically generates menus that avoid overlaps between the same ingredients and dishes. The menu suggestion unit also builds a system that updates menu information for school lunches and employee cafeterias in real time, and proposes menus that avoid overlaps. For example, it updates the menu when new menu information is added. This makes it possible to provide balanced meals by proposing menus that avoid overlaps with menus for children's school lunches and employee cafeterias.

[0062] The menu suggestion unit can propose safe menus by taking into account allergen information contained in school lunch and employee cafeteria menus. The menu suggestion unit, for example, builds a system that automatically acquires allergen information contained in school lunch and employee cafeteria menus. For example, it acquires allergen information from school or company websites and stores it in a database. The menu suggestion unit also develops an algorithm that proposes safe menus based on the allergen information. For example, it automatically generates menus that take into account the user's allergy information. The menu suggestion unit also builds a system that updates school lunch and employee cafeteria menu information in real time and proposes safe menus that take into account allergen information. For example, it updates the menu when new menu information is added. This makes it possible to accommodate users with allergies by proposing safe menus that take into account allergen information.

[0063] The menu suggestion unit can use the emotion estimation function to analyze the emotions that children and employees have toward the menus of school lunches and employee cafeterias, and suggest menus that elicit positive emotions. For example, the menu suggestion unit uses the emotion estimation function to build a system that analyzes the emotions that children and employees have toward the menus of school lunches and employee cafeterias. For example, it calculates an emotion score using a camera or microphone and suggests menus that elicit positive emotions. The menu suggestion unit also develops an algorithm that analyzes the emotions of children and employees toward the menus of school lunches and employee cafeterias in real time based on the emotion data of children and employees, and adjusts the menu. For example, if the emotion score is low, it suggests a different menu. The menu suggestion unit also builds a system that monitors the emotions of children and employees in real time based on the emotion estimation data, and suggests menus that elicit positive emotions toward the menus of school lunches and employee cafeterias. For example, it adjusts the menu based on changes in emotions during meals. In this way, satisfaction with school lunches and employee cafeterias can be improved by analyzing the emotions of children and employees and suggesting menus that elicit positive emotions.

[0064] The menu suggestion unit can acquire weather forecast data in real time and propose optimal menus according to the weather and temperature. For example, the menu suggestion unit builds a system that acquires weather forecast data in real time and proposes optimal menus according to the weather and temperature. For example, it acquires data using a weather forecast API and reflects it in menu suggestions. The menu suggestion unit also develops an algorithm that proposes optimal menus based on weather and temperature data. For example, it suggests hot soups and hot pot dishes on cold days, and cold salads and cold pasta on hot days. The menu suggestion unit also builds a system that updates weather forecast data in real time and automatically adjusts menus according to the weather and temperature. For example, if the weather suddenly changes, the menu is updated immediately. This makes it possible to propose optimal menus according to the weather and temperature, thereby providing meals that suit the season and climate.

[0065] The menu suggestion unit can propose the most nutritious menu by taking into account the nutritional value of seasonal and in-season ingredients. The menu suggestion unit, for example, builds a system that proposes the most nutritious menu by taking into account the nutritional value of seasonal and in-season ingredients. For example, it creates a database of seasonal ingredients and prioritizes proposing nutritious ingredients. The menu suggestion unit also develops an algorithm that proposes optimal menus based on the nutritional value of seasonal and in-season ingredients. For example, it automatically generates nutritionally balanced menus using seasonal ingredients. The menu suggestion unit also builds a system that updates seasonal and in-season ingredient data in real time and proposes the most nutritious menu. For example, it updates the menu when new seasonal ingredient information is added. This makes it possible to provide nutritious meals by proposing menus that take into account the nutritional value of seasonal and in-season ingredients.

[0066] The menu suggestion unit uses the emotion estimation function to analyze the emotions a user feels toward a menu using seasonal or in-season ingredients and can suggest menus that elicit positive emotions. The menu suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the emotions a user feels toward a menu using seasonal or in-season ingredients. For example, it calculates an emotion score using a camera or microphone and suggests menus that elicit positive emotions. The menu suggestion unit also analyzes the emotions toward a menu using seasonal or in-season ingredients in real time based on the user's emotion data and develops an algorithm to adjust the menu. For example, if the emotion score is low, it suggests a different menu. The menu suggestion unit also monitors the user's emotions in real time based on the emotion estimation data and builds a system that suggests menus that elicit positive emotions toward a menu using seasonal or in-season ingredients. For example, it adjusts the menu based on changes in emotion during a meal. In this way, by analyzing the user's emotions and suggesting menus that elicit positive emotions, it is possible to improve satisfaction with menus using seasonal or in-season ingredients.

[0067] The menu suggestion unit can suggest menus according to the weather and temperature to suit special situations such as outdoor activities and picnics. The menu suggestion unit, for example, builds a system that suggests menus according to the weather and temperature to suit special situations such as outdoor activities and picnics. For example, it suggests optimal outdoor menus based on weather forecast data. The menu suggestion unit also develops an algorithm that suggests menus according to special situations. For example, it automatically generates easy-to-carry menus suitable for picnics. The menu suggestion unit also builds a system that updates weather and temperature data in real time and automatically adjusts menus to suit special situations. For example, it updates the outdoor menu when the weather suddenly changes. In this way, by suggesting menus according to special situations, events such as outdoor activities and picnics can be more enjoyable.

[0068] The menu suggestion unit can suggest menus using seasonal and in-season ingredients as traditional or local cuisine of the region. The menu suggestion unit, for example, builds a system that suggests menus using seasonal and in-season ingredients as traditional or local cuisine of the region. For example, it creates a traditional cuisine database for each region and suggests menus using seasonal ingredients. The menu suggestion unit also develops an algorithm that suggests menus using seasonal and in-season ingredients based on traditional or local cuisine of the region. For example, it automatically generates menus that take into account the food culture of the region. The menu suggestion unit also builds a system that updates seasonal and in-season ingredient data in real time and suggests dishes as traditional or local cuisine of the region. For example, it updates the menu when new seasonal ingredient information is added. This makes it possible to provide a cultural dining experience by suggesting traditional or local cuisine of the region.

[0069] The menu suggestion unit uses the emotion estimation function to monitor in real time the emotions a user feels toward menus using seasonal or in-season ingredients and make suggestions according to changes in preferences. The menu suggestion unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions a user feels toward menus using seasonal or in-season ingredients. For example, it calculates an emotion score using a camera or microphone and makes suggestions according to changes in preferences. The menu suggestion unit also analyzes in real time the emotions toward menus using seasonal or in-season ingredients based on the user's emotion data and develops an algorithm to adjust the menu. For example, if the emotion score is low, it suggests a different menu. The menu suggestion unit also monitors the user's emotions in real time based on the emotion estimation data and builds a system that suggests menus that elicit positive emotions toward menus using seasonal or in-season ingredients. For example, it adjusts the menu based on changes in emotions during a meal. In this way, by monitoring the user's emotions in real time and making suggestions according to changes in preferences, it is possible to improve satisfaction with menus using seasonal or in-season ingredients.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The menu suggestion system can also obtain the user's exercise data and suggest optimal menus based on the amount of exercise. For example, if the user is using a smartwatch or fitness tracker, the system can link that data and adjust calorie intake based on the amount of exercise. It can also suggest post-exercise recovery menus and pre-exercise energy replenishment menus. This makes it possible to provide nutritionally balanced menus based on the user's exercise habits.

[0072] The menu suggestion system can also obtain the user's sleep data and suggest the optimal menu based on the quality of their sleep. For example, if the user uses a sleep tracker, the system can link that data and suggest a menu using ingredients that have a relaxing effect if the user's sleep quality is poor. It can also suggest ingredients that contain nutrients that improve sleep quality. This makes it possible to provide a menu that takes the user's sleep quality into consideration.

[0073] The menu suggestion system can also monitor the user's stress level and suggest menus that are effective in reducing stress. For example, if the user is using a stress monitoring device, the system can link that data and, if the user's stress level is high, suggest menus that use ingredients that have a relaxing effect. It can also suggest ingredients that contain nutrients that are effective in reducing stress. This makes it possible to provide menus that take the user's stress level into consideration.

[0074] The menu suggestion system can further estimate the user's emotions and suggest menus that correspond to specific emotions. For example, if the user is feeling sad, the system can suggest menus that use ingredients that have the effect of brightening the mood. Also, if the user is tired, the system can suggest menus that use ingredients that have the effect of replenishing energy. In this way, it is possible to provide menus that correspond to the user's emotions.

[0075] The menu suggestion system can further estimate the user's emotions and suggest menus that correspond to specific emotions. For example, if the user is feeling stressed, the system can suggest a menu that uses ingredients that have a relaxing effect. Also, if the user is excited, the system can suggest a menu that uses ingredients that have a calming effect. This makes it possible to provide a menu that matches the user's emotions.

[0076] The menu suggestion system can also estimate the user's emotions and suggest menus that correspond to specific emotions. For example, if the user is tired, the system can suggest menus that use ingredients that have the effect of replenishing energy. Also, if the user wants to relax, the system can suggest menus that use ingredients that have a relaxing effect. This makes it possible to provide menus that correspond to the user's emotions.

[0077] The menu suggestion system can further estimate the user's emotions and suggest menus that correspond to specific emotions. For example, if the user is feeling happy, the system can suggest menus that use ingredients that will help maintain that mood. Also, if the user is feeling depressed, the system can suggest menus that use ingredients that have the effect of lifting the user's mood. This makes it possible to provide menus that correspond to the user's emotions.

[0078] The menu suggestion system can also estimate the user's emotions and suggest menus that correspond to specific emotions. For example, if the user is feeling nervous, the system can suggest menus that use ingredients that have a relaxing effect. Also, if the user wants to improve their concentration, the system can suggest menus that use ingredients that help with concentration. This makes it possible to provide menus that correspond to the user's emotions.

[0079] The menu suggestion system can also obtain information about the user's travel destination and suggest menus that incorporate local specialties and regional cuisine of the destination. For example, it can suggest menus that use local ingredients that the user can enjoy at their travel destination. It can also suggest menus that take into account the culture and eating habits of the destination. This makes it possible to provide menus that will enrich the user's travel experience.

[0080] The menu suggestion system can also consider the user's cultural background regarding food and suggest menus that suit the user's culture. For example, it can suggest menus that take into account dietary restrictions based on a particular religion or culture. It can also suggest traditional or local dishes that suit the user's cultural background. This makes it possible to provide menus that take the user's culture into consideration.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The user information acquisition unit determines what the user has eaten recently and what they like and dislike. For example, it asks the user questions such as "What have you eaten recently?" and "Are there any ingredients you dislike?" and understands the user's preferences based on the answers. The user information acquisition unit can also automatically record the user's eating history and implement an algorithm that predicts preferences based on past data. Step 2: The nutrition calculation unit considers nutritional balance and calories based on the information acquired by the user information acquisition unit. For example, the generation AI proposes a menu that takes into account nutritional balance and calories based on the user's health condition and goals. Based on its knowledge of nutrition, the generation AI calculates the nutritional value and calories of each ingredient and finds the optimal combination. Step 3: The ingredient management unit considers ingredients available at home and special offers at frequently visited supermarkets based on the information calculated by the nutrition calculation unit. For example, the generation AI proposes a menu taking into account ingredients available at the user's home and special offers at frequently visited supermarkets. Step 4: The menu suggestion unit proposes the optimal menu based on the information managed by the ingredient management unit. For example, the generation AI proposes the optimal menu while taking into consideration the wide-ranging requests and conditions of the user. This allows the menu suggestion system to propose the optimal menu while taking into consideration the wide-ranging requests and conditions of the user.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information acquisition unit that acquires information about what the user has recently eaten and what they like and dislike; a nutrition calculation unit that considers nutritional balance and calories based on the information acquired by the user information acquisition unit; an ingredient management unit that considers ingredients available at home and special offers at a frequently visited supermarket based on the information calculated by the nutrition calculation unit; a menu suggestion unit that suggests an optimal menu based on the information managed by the food ingredient management unit. A system characterized by:

2. The user information acquisition unit The system automatically records the user's dietary history and uses past data to implement an algorithm that predicts preferences.

2. The system of claim 1.

3. The user information acquisition unit Analyzing the user's SNS posts and photos related to meals to understand their preferences 2. The system of claim 1.

4. The user information acquisition unit Analyze the user's feelings about food and suggest menus that elicit positive emotions 2. The system of claim 1.

5. The nutrition calculation unit Linking the health data of the user and proposing the nutritional balance according to the individual health condition 2. The system of claim 1.

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