System

Abstract

An object of a system according to an embodiment is to generate a daily menu on the basis of a user's food preference, allergy information, and cooking available time, and to deliver necessary food ingredients.SOLUTION: A system includes a user information setting part, a menu generation part, a food ingredient listing part, and a delivery part. The user information setting unit sets advance information such as user's food preferences, allergy information, and cooking available time for each day of the week. The menu generation part generates a daily menu on the basis of the information set by the user information setting part. The ingredient list-up unit lists up necessary ingredients based on the menu generated by the menu generation unit. The delivery unit delivers the foodstuffs listed up by the foodstuff listing unit to an address designated by the user.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 the process of automatically generating daily menus based on a user's food preferences, allergy information, and cooking times, and then delivering the necessary ingredients, is not sufficiently efficient.

[0005] The system according to the embodiment aims to create daily menus based on the user's food preferences, allergy information, and available cooking times, and to deliver the necessary ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information setting unit, a menu creation unit, an ingredient list unit, and a delivery unit. The user information setting unit sets advance information such as the user's food preferences, allergy information, and cooking times for each day of the week. The menu creation unit creates a daily menu based on the information set by the user information setting unit. The ingredient list unit lists necessary ingredients based on the menu created by the menu creation unit. The delivery unit delivers the ingredients listed by the ingredient list unit to an address specified by the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate daily menus based on the user's food preferences, allergy information, and cooking times, and deliver the necessary ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu suggestion and food delivery system according to an embodiment of the present invention is a system that suggests daily menus and delivers the necessary ingredients based on advance information such as a user's food preferences, allergy information, and cooking times for each day of the week. This allows the menu suggestion and food delivery system to support the user's lifestyle and provide healthy, balanced meals.

[0029] A menu suggestion and food delivery system according to an embodiment includes a user information setting unit, a menu generation unit, an ingredient list unit, and a delivery unit. The user information setting unit sets advance information such as the user's food preferences, allergy information, and cooking times for each day of the week. For example, the user inputs information such as "I prefer dishes that can be prepared in 30 minutes or less on Mondays," "I have a peanut allergy," and "I like Japanese food." The menu generation unit generates daily menus based on the information set by the user information setting unit. For example, the generation AI analyzes the user's advance information and makes specific suggestions such as "Japanese dishes that can be prepared in 30 minutes or less on Mondays." The ingredient list unit lists necessary ingredients based on the menu generated by the menu generation unit. For example, if the generation AI suggests "Monday's menu is chicken teriyaki and spinach ohitashi," the AI ​​lists the necessary chicken, spinach, seasonings, etc. for this. The delivery unit delivers the ingredients listed by the ingredient list unit to an address specified by the user. For example, an order may be placed with a delivery company to deliver ingredients to an address specified by the user. In this way, the menu suggestion and ingredient delivery system can support the user's lifestyle and provide healthy and balanced meals.

[0030] The user information setting unit can generate more accurate menus by analyzing the user's past meal history and health condition. For example, the user information setting unit stores the history of dishes the user has eaten in the past in a database and suggests the next menu based on that information. For example, it re-suggests dishes that were popular in the past. It also periodically records the user's health condition and analyzes that data to suggest nutritionally balanced menus. For example, it adjusts the meal contents based on blood pressure and blood sugar data. It also integrates the user's meal history and health data to generate menus that support long-term health management. For example, it suggests menus that take calorie restrictions into consideration for users who are on a diet. In this way, it is possible to generate more accurate menus by analyzing the user's past meal history and health condition.

[0031] The user information setting unit can analyze the user's lifestyle and activity level and generate a nutritionally balanced menu based on energy consumption. For example, the user information setting unit obtains the user's activity level from a smartwatch or fitness tracker, calculates energy consumption based on that data, and proposes a menu. For example, it proposes high-calorie meals on days when the user exercises a lot. It also analyzes the user's lifestyle data and proposes different nutritionally balanced menus for weekdays and weekends. For example, it proposes simple, nutritious dishes on weekdays and dishes that take more time to prepare on weekends. It also comprehensively analyzes the user's activity level and lifestyle and generates a menu that supports long-term health management. For example, it proposes low-calorie, nutritious meals for users who do a lot of desk work. In this way, it is possible to analyze the user's lifestyle and activity level and generate a nutritionally balanced menu based on energy consumption.

[0032] The user information setting unit can manage the food preferences and allergy information of all family members in one place and propose the optimal menu for each family member. For example, the user information setting unit can build a system that allows the food preferences and allergy information of all family members to be input in one go and propose the optimal menu for each family member based on that data. For example, it can prioritize the proposal of dishes that all family members can eat. It can also store the meal history of all family members in a database and propose the next menu based on that information. For example, it can re-propose dishes that were popular with all family members in the past. It can also regularly record the health status of all family members and analyze that data to propose a nutritionally balanced menu. For example, it can adjust the meal contents based on the health data of all family members. In this way, it can manage the food preferences and allergy information of all family members in one place and propose the optimal menu for each family member.

[0033] The user information setting unit can simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on the user. For example, the user information setting unit can be equipped with a voice input function to build a system that allows users to set food preferences and allergy information simply by speaking. For example, information can be registered by simply speaking, "I have a peanut allergy." In addition, a system can be developed that uses image recognition technology to record a meal history simply by uploading a photo of the food a user has eaten. For example, the photo of the food can be analyzed to automatically extract ingredient and nutritional information. In addition, a system that combines voice input and image recognition can be built to enable users to easily set information. For example, information can be registered by simply speaking, "This is Japanese food," while taking a photo of the food. This makes it possible to simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on the user.

[0034] The menu generation unit proposes menus according to the season and weather, and can provide meals that suit the season and climate. For example, the menu generation unit registers seasonal ingredients in a database and proposes seasonal menus based on that information. For example, it proposes seasonal dishes such as sakura mochi in spring and chestnut rice in autumn. It also obtains weather data in real time and proposes menus that suit the weather based on that information. For example, it proposes dishes that suit the weather, such as chilled Chinese noodles on hot days and oden on cold days. It also builds a system that proposes optimal menus by taking into account the freshness and price of ingredients according to the season and weather. For example, it proposes cost-effective dishes using seasonal ingredients. This makes it possible to propose menus that suit the season and weather, and provide meals that suit the season and climate.

[0035] The menu generation unit can analyze the user's past feedback and propose menus that reflect changes in preferences and trends. The menu generation unit, for example, analyzes the user's past feedback data and proposes menus that reflect changes in preferences. For example, it prioritizes suggestions of dishes that have been well-received in recent feedback. It also analyzes food trends based on the user's feedback and proposes menus that fit those trends. For example, it proposes health-oriented dishes that are currently popular. It also builds a system that analyzes user feedback data over the long term and predicts changes in preferences to propose menus. For example, it proposes menus that reflect seasonal changes in preferences. This makes it possible to analyze the user's past feedback and propose menus that reflect changes in preferences and trends.

[0036] The menu generation unit can incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience. For example, the menu generation unit registers cuisine from different cultures in a database and suggests menus that offer new dining experiences based on that information. For example, it can suggest cuisine from different cultures such as Italian or Mexican. It can also build a system that adds new ingredients to the database and suggests menus using those ingredients. For example, it can suggest dishes using superfoods or rare vegetables. It can also suggest menus that incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience. For example, it can suggest fusion cuisine or ethnic cuisine. This makes it possible to incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience.

[0037] The menu generation unit can support health management by adapting to specific diets or dietary therapies. For example, the menu generation unit registers menus corresponding to specific diets or dietary therapies in a database and builds a system that makes suggestions based on that information. For example, menus corresponding to ketogenic diets or low-carbohydrate diets are proposed. Menus corresponding to specific diets or dietary therapies are also proposed according to the user's health condition and goals. For example, a calorie-restricted menu is proposed for a user aiming to lose weight. Menus corresponding to specific diets or dietary therapies are also proposed to support the user's health management. For example, low-carbohydrate menus are proposed for diabetic patients and low-salt menus are proposed for high blood pressure patients. This makes it possible to support health management by adapting to specific diets or dietary therapies.

[0038] The delivery department can monitor the freshness and quality of ingredients in real time and deliver them at the optimal time. For example, the delivery department can introduce sensors that monitor the freshness and quality of ingredients and build a system that delivers at the optimal time based on that data. For example, deliveries can be made before freshness deteriorates. The quality of ingredients can also be monitored in real time and adjustments can be made to ensure that ingredients are delivered at the optimal quality. For example, temperature and humidity can be controlled to maintain quality. A system can also be developed that analyzes food freshness and quality data and automatically calculates the optimal delivery timing. For example, delivery can be made when ingredients are at their freshest. This allows the freshness and quality of ingredients to be monitored in real time and delivered at the optimal time.

[0039] The delivery unit can consider the user's lifestyle and schedule and propose the optimal delivery time. The delivery unit, for example, analyzes the user's lifestyle and schedule and builds a system that proposes the optimal delivery time. For example, deliveries are made during times when the user is at home. A system is also developed that adjusts delivery times based on the user's schedule data. For example, deliveries are made to avoid times when the user is busy. The user's lifestyle data is also analyzed and the optimal delivery time is proposed. For example, deliveries are made during times when the user is relaxed. In this way, the optimal delivery time can be proposed taking the user's lifestyle and schedule into consideration.

[0040] The delivery department can work with local farmers and producers to provide fresh local ingredients. For example, the delivery department could work with local farmers and producers to build a system to provide fresh local ingredients. For example, it could prioritize the delivery of local agricultural products. It could also develop a system to purchase ingredients directly from local producers and deliver them to users while maintaining their freshness. For example, it could deliver them immediately after harvest. It could also work with local farmers and producers to suggest menus that incorporate local specialties and deliver those ingredients. For example, it could suggest dishes that use local seasonal ingredients. This would allow it to work with local farmers and producers to provide fresh local ingredients.

[0041] The delivery department can reduce the environmental burden by using ecological packaging and recyclable containers. The delivery department, for example, uses ecological packaging for the delivery of food ingredients and builds a system that reduces the environmental burden. For example, it employs packaging made from renewable materials. It also uses recyclable containers to reduce the environmental burden in the delivery of food ingredients. For example, it uses recyclable plastic or glass containers. It also builds an environmentally friendly delivery system by using ecological packaging and recyclable containers. For example, it introduces a system for collecting and reusing them after use. This allows it to use ecological packaging and recyclable containers and reduce the environmental burden.

[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 user information setting unit can simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on users. For example, a system equipped with a voice input function can be built that allows users to set food preferences and allergy information simply by speaking. For example, information can be registered by simply saying, "I have a peanut allergy." In addition, a system can be developed using image recognition technology that records a user's diet history simply by uploading a photo of the food they ate. For example, the photo of the food can be analyzed to automatically extract ingredient and nutritional information. In addition, a system that combines voice input and image recognition can be built to allow users to easily set information. For example, information can be registered by simply saying, "This is Japanese food" while taking a photo of the food. This makes it possible to simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on users.

[0044] The menu generation unit can propose menus according to the season and weather, and provide meals that suit the season and climate. For example, seasonal ingredients can be registered in a database, and seasonal menus can be proposed based on that information. For example, seasonal dishes such as sakura mochi in spring and chestnut rice in autumn can be proposed. Weather data can also be obtained in real time, and menus that suit the weather can be proposed based on that information. For example, dishes that suit the weather can be proposed such as chilled Chinese noodles on hot days and oden on cold days. A system can also be built that considers the freshness and price of ingredients according to the season and weather to propose the optimal menu. For example, cost-effective dishes using seasonal ingredients can be proposed. This makes it possible to propose menus that suit the season and weather, and provide meals that suit the season and climate.

[0045] The delivery department can work with local farmers and producers to provide fresh local ingredients. For example, it can work with local farmers and producers to build a system that provides fresh local ingredients. For example, it can prioritize the delivery of local agricultural products. It can also develop a system that purchases ingredients directly from local producers and delivers them to users while maintaining their freshness. For example, it can deliver them immediately after harvest. It can also work with local farmers and producers to propose menus that incorporate local specialties and deliver those ingredients. For example, it can propose dishes that use local seasonal ingredients. This allows it to work with local farmers and producers to provide fresh local ingredients.

[0046] The delivery department can reduce the environmental burden by using ecological packaging and recyclable containers. For example, a system can be built to use ecological packaging for the delivery of food ingredients and reduce the environmental burden. For example, packaging made from renewable materials can be used. Also, recyclable containers can be used to reduce the environmental burden of delivering food ingredients. For example, recyclable plastic and glass containers can be used. Also, by using ecological packaging and recyclable containers, an environmentally friendly delivery system can be built. For example, a system can be introduced to collect and reuse them after use. This allows the use of ecological packaging and recyclable containers and reduce the environmental burden.

[0047] The delivery unit can consider the user's lifestyle and schedule and suggest the optimal delivery time. For example, a system can be constructed that analyzes the user's lifestyle and schedule and suggests the optimal delivery time. For example, deliveries can be made during times when the user is at home. A system can also be developed that adjusts delivery times based on the user's schedule data. For example, deliveries can be made to avoid times when the user is busy. A system can also be developed that analyzes the user's lifestyle data and suggests the optimal delivery time. For example, deliveries can be made during times when the user is relaxed. This makes it possible to consider the user's lifestyle and schedule and suggest the optimal delivery time.

[0048] The menu generation unit can incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences. For example, it can register cuisine from different cultures in a database and, based on that information, suggest menus that offer new dining experiences. For example, it can suggest cuisine from different cultures such as Italian or Mexican. It can also build a system that adds new ingredients to the database and suggests menus using those ingredients. For example, it can suggest dishes using superfoods or rare vegetables. It can also suggest menus that incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences. For example, it can suggest fusion cuisine or ethnic cuisine. This makes it possible to incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences.

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

[0050] Step 1: The user information setting unit sets advance information such as the user's food preferences, allergy information, cooking times by day of the week, etc. For example, the user inputs information such as "On Mondays, I prefer dishes that can be prepared in 30 minutes or less," "I have a peanut allergy," and "I like Japanese food." Step 2: The menu generation unit generates daily menus based on the information set by the user information setting unit. For example, the generation AI analyzes the user's prior information and makes specific suggestions such as "Japanese dishes that can be made in 30 minutes or less on Mondays." Step 3: The ingredient list section lists the necessary ingredients based on the menu generated by the menu generation section. For example, if the generation AI suggests "Monday's menu is chicken teriyaki and spinach ohitashi," it will list the chicken, spinach, seasonings, etc. needed for that. Step 4: The delivery unit delivers the ingredients listed by the ingredient listing unit to the address specified by the user. For example, the delivery unit places an order with a delivery company and delivers the ingredients to the address specified by the user.

[0051] (Example 2) The menu suggestion and food delivery system according to an embodiment of the present invention is a system that suggests daily menus and delivers the necessary ingredients based on advance information such as a user's food preferences, allergy information, and cooking times for each day of the week. This allows the menu suggestion and food delivery system to support the user's lifestyle and provide healthy, balanced meals.

[0052] A menu suggestion and food delivery system according to an embodiment includes a user information setting unit, a menu generation unit, an ingredient list unit, and a delivery unit. The user information setting unit sets advance information such as the user's food preferences, allergy information, and cooking times for each day of the week. For example, the user inputs information such as "I prefer dishes that can be prepared in 30 minutes or less on Mondays," "I have a peanut allergy," and "I like Japanese food." The menu generation unit generates daily menus based on the information set by the user information setting unit. For example, the generation AI analyzes the user's advance information and makes specific suggestions such as "Japanese dishes that can be prepared in 30 minutes or less on Mondays." The ingredient list unit lists necessary ingredients based on the menu generated by the menu generation unit. For example, if the generation AI suggests "Monday's menu is chicken teriyaki and spinach ohitashi," the AI ​​lists the necessary chicken, spinach, seasonings, etc. for this. The delivery unit delivers the ingredients listed by the ingredient list unit to an address specified by the user. For example, an order may be placed with a delivery company to deliver ingredients to an address specified by the user. In this way, the menu suggestion and ingredient delivery system can support the user's lifestyle and provide healthy and balanced meals.

[0053] The user information setting unit can generate more accurate menus by analyzing the user's past meal history and health condition. For example, the user information setting unit stores the history of dishes the user has eaten in the past in a database and suggests the next menu based on that information. For example, it re-suggests dishes that were popular in the past. It also periodically records the user's health condition and analyzes that data to suggest nutritionally balanced menus. For example, it adjusts the meal contents based on blood pressure and blood sugar data. It also integrates the user's meal history and health data to generate menus that support long-term health management. For example, it suggests menus that take calorie restrictions into consideration for users who are on a diet. In this way, it is possible to generate more accurate menus by analyzing the user's past meal history and health condition.

[0054] The user information setting unit can collect the user's emotional responses to meals and use the emotion estimation function to suggest menus that elicit positive emotions. The user information setting unit, for example, builds a system in which the user inputs emotional feedback after a meal, analyzes the data, and reflects it in the next menu. For example, suggestions are made based on positive feedback such as "this dish was delicious." The emotion estimation function can also be used to analyze the emotions felt by the user while eating in real time and suggest menus that elicit positive emotions. For example, an emotion score can be calculated by analyzing the user's facial expressions and voice. The system can also analyze what emotions a specific dish elicits based on the user's emotion data and prioritize suggesting dishes that elicit positive emotions. For example, dishes that previously elicited strong emotions of joy can be re-suggested. In this way, the user's emotional responses to meals can be collected and menus that elicit positive emotions can be suggested.

[0055] The user information setting unit can analyze the user's lifestyle and activity level and generate a nutritionally balanced menu based on energy consumption. For example, the user information setting unit obtains the user's activity level from a smartwatch or fitness tracker, calculates energy consumption based on that data, and proposes a menu. For example, it proposes high-calorie meals on days when the user exercises a lot. It also analyzes the user's lifestyle data and proposes different nutritionally balanced menus for weekdays and weekends. For example, it proposes simple, nutritious dishes on weekdays and dishes that take more time to prepare on weekends. It also comprehensively analyzes the user's activity level and lifestyle and generates a menu that supports long-term health management. For example, it proposes low-calorie, nutritious meals for users who do a lot of desk work. In this way, it is possible to analyze the user's lifestyle and activity level and generate a nutritionally balanced menu based on energy consumption.

[0056] The user information setting unit can manage the food preferences and allergy information of all family members in one place and propose the optimal menu for each family member. For example, the user information setting unit can build a system that allows the food preferences and allergy information of all family members to be input in one go and propose the optimal menu for each family member based on that data. For example, it can prioritize the proposal of dishes that all family members can eat. It can also store the meal history of all family members in a database and propose the next menu based on that information. For example, it can re-propose dishes that were popular with all family members in the past. It can also regularly record the health status of all family members and analyze that data to propose a nutritionally balanced menu. For example, it can adjust the meal contents based on the health data of all family members. In this way, it can manage the food preferences and allergy information of all family members in one place and propose the optimal menu for each family member.

[0057] The user information setting unit can simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on the user. For example, the user information setting unit can be equipped with a voice input function to build a system that allows users to set food preferences and allergy information simply by speaking. For example, information can be registered by simply speaking, "I have a peanut allergy." In addition, a system can be developed that uses image recognition technology to record a meal history simply by uploading a photo of the food a user has eaten. For example, the photo of the food can be analyzed to automatically extract ingredient and nutritional information. In addition, a system that combines voice input and image recognition can be built to enable users to easily set information. For example, information can be registered by simply speaking, "This is Japanese food," while taking a photo of the food. This makes it possible to simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on the user.

[0058] The user information setting unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time, and provide an interface that reduces stress. The user information setting unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering information in real time, and provide an interface that reduces stress. For example, relaxing music is played when the user is feeling stressed. The user's emotional state is also analyzed, and the interface is adjusted to allow input to proceed smoothly. For example, the input procedure is simplified when the user is irritated. The emotion estimation data is also used to provide feedback on the emotion of the user when entering information in real time, and provide an interface that elicits positive emotions. For example, an encouraging message is displayed each time input is completed. In this way, the emotion estimation function can be used to analyze the emotion of the user when entering information in real time, and an interface that reduces stress can be provided.

[0059] The menu generation unit proposes menus according to the season and weather, and can provide meals that suit the season and climate. For example, the menu generation unit registers seasonal ingredients in a database and proposes seasonal menus based on that information. For example, it proposes seasonal dishes such as sakura mochi in spring and chestnut rice in autumn. It also obtains weather data in real time and proposes menus that suit the weather based on that information. For example, it proposes dishes that suit the weather, such as chilled Chinese noodles on hot days and oden on cold days. It also builds a system that proposes optimal menus by taking into account the freshness and price of ingredients according to the season and weather. For example, it proposes cost-effective dishes using seasonal ingredients. This makes it possible to propose menus that suit the season and weather, and provide meals that suit the season and climate.

[0060] The menu generation unit can analyze the user's past feedback and propose menus that reflect changes in preferences and trends. The menu generation unit, for example, analyzes the user's past feedback data and proposes menus that reflect changes in preferences. For example, it prioritizes suggestions of dishes that have been well-received in recent feedback. It also analyzes food trends based on the user's feedback and proposes menus that fit those trends. For example, it proposes health-oriented dishes that are currently popular. It also builds a system that analyzes user feedback data over the long term and predicts changes in preferences to propose menus. For example, it proposes menus that reflect seasonal changes in preferences. This makes it possible to analyze the user's past feedback and propose menus that reflect changes in preferences and trends.

[0061] The menu generation unit uses the emotion estimation function to suggest a menu that matches the user's mood, thereby improving meal satisfaction. The menu generation unit, for example, uses the emotion estimation function to analyze the user's mood in real time and suggest a menu that matches that mood. For example, if the user is tired, it suggests dishes that will help the user relax. Furthermore, a system is constructed that suggests ingredients and cooking methods that match the user's mood based on the user's emotion data. For example, it suggests dishes that use ingredients that have the effect of reducing stress. Furthermore, it suggests a menu that matches the user's mood based on the emotion estimation data, thereby improving meal satisfaction. For example, if the user is feeling happy, it suggests celebratory dishes. In this way, it is possible to suggest a menu that matches the user's mood using the emotion estimation function, thereby improving meal satisfaction.

[0062] The menu generation unit can incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience. For example, the menu generation unit registers cuisine from different cultures in a database and suggests menus that offer new dining experiences based on that information. For example, it can suggest cuisine from different cultures such as Italian or Mexican. It can also build a system that adds new ingredients to the database and suggests menus using those ingredients. For example, it can suggest dishes using superfoods or rare vegetables. It can also suggest menus that incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience. For example, it can suggest fusion cuisine or ethnic cuisine. This makes it possible to incorporate cuisine from different cultures and new ingredients to provide users with a new dining experience.

[0063] The menu generation unit can support health management by adapting to specific diets or dietary therapies. For example, the menu generation unit registers menus corresponding to specific diets or dietary therapies in a database and builds a system that makes suggestions based on that information. For example, menus corresponding to ketogenic diets or low-carbohydrate diets are proposed. Menus corresponding to specific diets or dietary therapies are also proposed according to the user's health condition and goals. For example, a calorie-restricted menu is proposed for a user aiming to lose weight. Menus corresponding to specific diets or dietary therapies are also proposed to support the user's health management. For example, low-carbohydrate menus are proposed for diabetic patients and low-salt menus are proposed for high blood pressure patients. This makes it possible to support health management by adapting to specific diets or dietary therapies.

[0064] The menu generation unit can use the emotion estimation function to suggest a special menu to suit a specific event or anniversary to the user. The menu generation unit, for example, uses the emotion estimation function to build a system that suggests a special menu to suit a specific event or anniversary to the user. For example, celebratory dishes are suggested for birthdays or wedding anniversaries. Furthermore, a menu to suit a special event or anniversary is suggested based on the user's emotion data. For example, special dishes are suggested for Christmas or Valentine's Day. Furthermore, a special menu to suit a specific event or anniversary to the user is suggested based on the emotion estimation data, thereby improving meal satisfaction. For example, celebratory dishes are suggested if the user is feeling joyful. In this way, the emotion estimation function can be used to suggest a special menu to suit a specific event or anniversary to the user.

[0065] The delivery department can monitor the freshness and quality of ingredients in real time and deliver them at the optimal time. For example, the delivery department can introduce sensors that monitor the freshness and quality of ingredients and build a system that delivers at the optimal time based on that data. For example, deliveries can be made before freshness deteriorates. The quality of ingredients can also be monitored in real time and adjustments can be made to ensure that ingredients are delivered at the optimal quality. For example, temperature and humidity can be controlled to maintain quality. A system can also be developed that analyzes food freshness and quality data and automatically calculates the optimal delivery timing. For example, delivery can be made when ingredients are at their freshest. This allows the freshness and quality of ingredients to be monitored in real time and delivered at the optimal time.

[0066] The delivery unit can consider the user's lifestyle and schedule and propose the optimal delivery time. The delivery unit, for example, analyzes the user's lifestyle and schedule and builds a system that proposes the optimal delivery time. For example, deliveries are made during times when the user is at home. A system is also developed that adjusts delivery times based on the user's schedule data. For example, deliveries are made to avoid times when the user is busy. The user's lifestyle data is also analyzed and the optimal delivery time is proposed. For example, deliveries are made during times when the user is relaxed. In this way, the optimal delivery time can be proposed taking the user's lifestyle and schedule into consideration.

[0067] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the food, thereby improving the delivery experience. The delivery unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving ingredients in real time, thereby building a system that improves the delivery experience. For example, it suggests a delivery method that makes the user feel happy. Furthermore, based on the user's emotion data, it collects feedback to improve the delivery experience and reflects it in the next delivery. For example, it adjusts the delivery time and method to satisfy the user. Furthermore, based on the emotion estimation data, it analyzes the emotions of the user when receiving the food, thereby improving the delivery experience. For example, it makes deliveries during times when the user is relaxed. In this way, it is possible to use the emotion estimation function to analyze the emotions of the user when receiving the food, thereby improving the delivery experience.

[0068] The delivery department can work with local farmers and producers to provide fresh local ingredients. For example, the delivery department could work with local farmers and producers to build a system to provide fresh local ingredients. For example, it could prioritize the delivery of local agricultural products. It could also develop a system to purchase ingredients directly from local producers and deliver them to users while maintaining their freshness. For example, it could deliver them immediately after harvest. It could also work with local farmers and producers to suggest menus that incorporate local specialties and deliver those ingredients. For example, it could suggest dishes that use local seasonal ingredients. This would allow it to work with local farmers and producers to provide fresh local ingredients.

[0069] The delivery department can reduce the environmental burden by using ecological packaging and recyclable containers. The delivery department, for example, uses ecological packaging for the delivery of food ingredients and builds a system that reduces the environmental burden. For example, it employs packaging made from renewable materials. It also uses recyclable containers to reduce the environmental burden in the delivery of food ingredients. For example, it uses recyclable plastic or glass containers. It also builds an environmentally friendly delivery system by using ecological packaging and recyclable containers. For example, it introduces a system for collecting and reusing them after use. This allows it to use ecological packaging and recyclable containers and reduce the environmental burden.

[0070] The delivery unit can use the emotion estimation function to provide feedback on the user's emotions regarding the delivered ingredients and reflect them in the next delivery. The delivery unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the delivered ingredients in real time and build a system that reflects the data in the next delivery. For example, ingredients that the user is satisfied with are delivered preferentially. Furthermore, feedback regarding the delivered ingredients is collected based on the user's emotion data and reflected in the next delivery. For example, ingredients that the user likes are delivered preferentially. Furthermore, the emotion estimation data is used to analyze the user's emotions regarding the delivered ingredients and reflect them in the next delivery. For example, ingredients that the user felt happy about are delivered again. In this way, the emotion estimation function can be used to provide feedback on the user's emotions regarding the delivered ingredients and reflect them in the next delivery.

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

[0072] The user information setting unit can collect the user's emotional responses to meals and use the emotion estimation function to suggest menus that elicit positive emotions. For example, a system can be built in which the user inputs emotional feedback after a meal, and the data can be analyzed and reflected in the next menu. For example, suggestions can be made based on positive feedback such as "this dish was delicious." The emotion estimation function can also be used to analyze the emotions felt by the user while eating in real time and suggest menus that elicit positive emotions. For example, an emotion score can be calculated by analyzing the user's facial expressions and voice. Furthermore, based on the user's emotion data, the system can analyze what emotions a particular dish elicits and prioritize suggesting dishes that elicit positive emotions. For example, dishes that previously elicited strong emotions of joy can be re-suggested. In this way, the user's emotional responses to meals can be collected and menus that elicit positive emotions can be suggested.

[0073] The user information setting unit can simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on users. For example, a system equipped with a voice input function can be built that allows users to set food preferences and allergy information simply by speaking. For example, information can be registered by simply saying, "I have a peanut allergy." In addition, a system can be developed using image recognition technology that records a user's diet history simply by uploading a photo of the food they ate. For example, the photo of the food can be analyzed to automatically extract ingredient and nutritional information. In addition, a system that combines voice input and image recognition can be built to allow users to easily set information. For example, information can be registered by simply saying, "This is Japanese food" while taking a photo of the food. This makes it possible to simplify the setting of user information using voice input and image recognition technology, thereby reducing the burden on users.

[0074] The menu generation unit can propose menus according to the season and weather, and provide meals that suit the season and climate. For example, seasonal ingredients can be registered in a database, and seasonal menus can be proposed based on that information. For example, seasonal dishes such as sakura mochi in spring and chestnut rice in autumn can be proposed. Weather data can also be obtained in real time, and menus that suit the weather can be proposed based on that information. For example, dishes that suit the weather can be proposed such as chilled Chinese noodles on hot days and oden on cold days. A system can also be built that considers the freshness and price of ingredients according to the season and weather to propose the optimal menu. For example, cost-effective dishes using seasonal ingredients can be proposed. This makes it possible to propose menus that suit the season and weather, and provide meals that suit the season and climate.

[0075] The menu generation unit can use the emotion estimation function to suggest a menu that matches the user's mood, thereby improving meal satisfaction. For example, the emotion estimation function can be used to analyze the user's mood in real time and suggest a menu that matches that mood. For example, if the user is tired, dishes that will help them relax can be suggested. Furthermore, a system can be constructed that suggests ingredients and cooking methods that match the user's mood based on the user's emotion data. For example, dishes that use ingredients that have the effect of reducing stress can be suggested. Furthermore, a menu that matches the user's mood can be suggested based on the emotion estimation data, thereby improving meal satisfaction. For example, if the user is feeling happy, celebratory dishes can be suggested. In this way, the emotion estimation function can be used to suggest a menu that matches the user's mood, thereby improving meal satisfaction.

[0076] The delivery department can work with local farmers and producers to provide fresh local ingredients. For example, it can work with local farmers and producers to build a system that provides fresh local ingredients. For example, it can prioritize the delivery of local agricultural products. It can also develop a system that purchases ingredients directly from local producers and delivers them to users while maintaining their freshness. For example, it can deliver them immediately after harvest. It can also work with local farmers and producers to propose menus that incorporate local specialties and deliver those ingredients. For example, it can propose dishes that use local seasonal ingredients. This allows it to work with local farmers and producers to provide fresh local ingredients.

[0077] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the food, thereby improving the delivery experience. For example, the emotion estimation function can be used to analyze the emotions of the user when receiving ingredients in real time, thereby building a system that improves the delivery experience. For example, a delivery method that makes the user feel happy can be suggested. Furthermore, based on the user's emotion data, feedback to improve the delivery experience can be collected and reflected in the next delivery. For example, the delivery time and method can be adjusted to satisfy the user. Furthermore, based on the emotion estimation data, the emotion of the user when receiving the food can be analyzed, thereby improving the delivery experience. For example, delivery can be made during a time when the user is relaxed. In this way, the emotion estimation function can be used to analyze the emotions of the user when receiving the food, thereby improving the delivery experience.

[0078] The delivery department can reduce the environmental burden by using ecological packaging and recyclable containers. For example, a system can be built to use ecological packaging for the delivery of food ingredients and reduce the environmental burden. For example, packaging made from renewable materials can be used. Also, recyclable containers can be used to reduce the environmental burden of delivering food ingredients. For example, recyclable plastic and glass containers can be used. Also, by using ecological packaging and recyclable containers, an environmentally friendly delivery system can be built. For example, a system can be introduced to collect and reuse them after use. This allows the use of ecological packaging and recyclable containers and reduce the environmental burden.

[0079] The delivery unit can consider the user's lifestyle and schedule and suggest the optimal delivery time. For example, a system can be constructed that analyzes the user's lifestyle and schedule and suggests the optimal delivery time. For example, deliveries can be made during times when the user is at home. A system can also be developed that adjusts delivery times based on the user's schedule data. For example, deliveries can be made to avoid times when the user is busy. A system can also be developed that analyzes the user's lifestyle data and suggests the optimal delivery time. For example, deliveries can be made during times when the user is relaxed. This makes it possible to consider the user's lifestyle and schedule and suggest the optimal delivery time.

[0080] The delivery unit can use the emotion estimation function to provide feedback on the user's emotions regarding delivered ingredients and reflect them in the next delivery. For example, a system can be constructed that uses the emotion estimation function to analyze the user's emotions regarding delivered ingredients in real time and reflect the data in the next delivery. For example, ingredients that the user is satisfied with can be delivered with priority. Furthermore, feedback regarding delivered ingredients can be collected based on the user's emotion data and reflected in the next delivery. For example, ingredients that the user likes can be delivered with priority. Furthermore, the emotion estimation data can be used to analyze the user's emotions regarding delivered ingredients and reflect them in the next delivery. For example, ingredients that the user felt happy about can be delivered again. In this way, the emotion estimation function can be used to provide feedback on the user's emotions regarding delivered ingredients and reflect them in the next delivery.

[0081] The menu generation unit can incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences. For example, it can register cuisine from different cultures in a database and, based on that information, suggest menus that offer new dining experiences. For example, it can suggest cuisine from different cultures such as Italian or Mexican. It can also build a system that adds new ingredients to the database and suggests menus using those ingredients. For example, it can suggest dishes using superfoods or rare vegetables. It can also suggest menus that incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences. For example, it can suggest fusion cuisine or ethnic cuisine. This makes it possible to incorporate cuisine from different cultures and new ingredients to provide users with new dining experiences.

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

[0083] Step 1: The user information setting unit sets advance information such as the user's food preferences, allergy information, cooking times by day of the week, etc. For example, the user inputs information such as "On Mondays, I prefer dishes that can be prepared in 30 minutes or less," "I have a peanut allergy," and "I like Japanese food." Step 2: The menu generation unit generates daily menus based on the information set by the user information setting unit. For example, the generation AI analyzes the user's prior information and makes specific suggestions such as "Japanese dishes that can be made in 30 minutes or less on Mondays." Step 3: The ingredient list section lists the necessary ingredients based on the menu generated by the menu generation section. For example, if the generation AI suggests "Monday's menu is chicken teriyaki and spinach ohitashi," it will list the chicken, spinach, seasonings, etc. needed for that. Step 4: The delivery unit delivers the ingredients listed by the ingredient listing unit to the address specified by the user. For example, the delivery unit places an order with a delivery company and delivers the ingredients to the address specified by the user.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0112] 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.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following 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.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information setting unit for setting advance information such as the user's food preferences, allergy information, and cooking times for each day of the week; a menu creation unit that creates daily menus based on the information set by the user information setting unit; an ingredient list unit that lists ingredients required based on the menu generated by the menu generation unit; a delivery unit that delivers the ingredients listed by the ingredient listing unit to an address designated by the user. A system characterized by:

2. The user information setting unit Analyzes the user's past eating history and health status to generate more accurate menus 2. The system of claim 1.

3. The user information setting unit Collects users' emotional reactions to meals and suggests menus that evoke positive emotions 2. The system of claim 1.

4. The user information setting unit Analyzes the user's lifestyle and activity level and generates nutritionally balanced meals based on energy expenditure 2. The system of claim 1.

5. The user information setting unit Manage all family members' food preferences and allergy information in one place and propose the best menu for the whole family 2. The system of claim 1.

6. The user information setting unit Using voice input and image recognition technology, the system simplifies the process of setting user information, reducing the burden on users.

2. The system of claim 1.

7. The user information setting unit Analyzes emotions in real time as users input, providing a stress-reducing interface 2. The system of claim 1.

8. The menu generation unit We propose menus according to the season and weather, and provide meals that suit the season and climate.

2. The system of claim 1.

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