System

Abstract

An object of a system according to an embodiment is to efficiently generate a menu in consideration of the preference and nutrition balance of a family and automatically order necessary food materials.SOLUTION: A system includes a data collection part, an analysis part, a menu generation part, an ordering part, and a notification part. The data collection part collects data on past menus and family reactions. The analysis unit analyzes the data collected by the data collection unit. The menu generation part generates a menu in consideration of individual preferences and nutrition balance on the basis of the data analyzed by the analysis part. The ordering part automatically orders necessary food materials on the basis of the menu generated by the menu generation part. The notifying unit notifies the user of the contents and menu ordered by the ordering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, planning a menu every day that takes into account the family's preferences and nutritional balance was a time-consuming process, and it was difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently generate menus that take into consideration the preferences and nutritional balance of family members, and to automatically order the necessary ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a menu generation unit, an ordering unit, and a notification unit. The data collection unit collects data on past menus and family reactions. The analysis unit analyzes the data collected by the data collection unit. The menu generation unit generates a menu that takes into account individual preferences and nutritional balance based on the data analyzed by the analysis unit. The ordering unit automatically orders the necessary ingredients based on the menu generated by the menu generation unit. The notification unit notifies the user of the contents ordered by the ordering unit and the menu list. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate menus that take into consideration the preferences and nutritional balance of family members and automatically order the necessary ingredients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 creation system according to an embodiment of the present invention is a system that creates a daily menu that takes into account individual preferences and nutritional balance based on past menus and family reactions, and creates a menu table. Furthermore, this system also handles ordering to fresh food delivery services in one stop. This allows the user to utilize past data and easily create an optimal menu that takes into account family preferences and nutritional balance. Furthermore, since ordering to fresh food delivery services is also automated, the system reduces the hassle of shopping and enables efficient food ingredient management.

[0029] A menu creation system according to an embodiment includes a data collection unit, an analysis unit, a menu generation unit, an ordering unit, and a notification unit. The data collection unit collects data on past menus and family reactions. For example, the data collection unit stores the types of dishes previously prepared and the family's evaluations of the dishes (e.g., "It was delicious," "I want to eat it again," "I wish it was a little saltier," etc.) in a database. The data collection unit can also analyze the family's facial expressions and voices during meals and use an emotion estimation function to evaluate the family's satisfaction with the meal in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the generation AI uses past data to create a menu that takes into account each family member's preferences and nutritional balance. The generation AI calculates the optimal combination for providing a balanced meal based on knowledge of nutrition. The menu generation unit generates a menu that takes into account each family member's preferences and nutritional balance based on the data analyzed by the analysis unit. For example, the generation AI automatically generates weekly or monthly menus, such as "teriyaki chicken and stir-fried vegetables" on Mondays and "grilled fish and salad" on Tuesdays. The ordering unit automatically orders the necessary ingredients based on the menu generated by the menu generation unit. For example, if the menu includes "teriyaki chicken," the ordering unit automatically selects the necessary ingredients, such as chicken, seasonings, and vegetables, and places an order with a delivery service. The notification unit notifies the user of the menu and the contents of the order placed by the ordering unit. For example, the notification unit may send the menu and order details to the user via a smartphone app or email, and accept instructions for corrections or additions as needed. This allows the menu creation system according to the embodiment to easily create an optimal menu that takes into account the preferences and nutritional balance of the family by utilizing past data. Furthermore, ordering with a fresh food delivery service is also automated, eliminating the need for shopping and enabling efficient ingredient management.

[0030] The data collection unit collects environmental data such as temperature, humidity, and lighting during meals, and can identify factors that affect meal satisfaction. For example, the data collection unit measures the indoor temperature and humidity during meals using a sensor and collects the data. For example, it analyzes the effect that a comfortable temperature and humidity have on satisfaction. This allows the system to identify factors that affect meal satisfaction and provide a more comfortable dining environment.

[0031] The data collection unit can collect health data such as family members' weight, blood pressure, and blood sugar levels, and track the effects of diet over the long term. For example, the data collection unit periodically measures family members' weight and blood pressure and collects the data. For example, it can analyze the effects of specific menus on weight and blood pressure. This allows for more effective health management of family members by tracking the effects of diet over the long term.

[0032] The data collection unit can analyze conversations during family meals and automatically extract feedback about the meal. For example, the data collection unit converts conversations during meals into text using voice recognition technology and extracts feedback about the meal using natural language processing. For example, comments such as "delicious" and "I wish it was a little saltier" are automatically collected. This allows for the automatic extraction of feedback about meals, making it possible to propose more accurate menus.

[0033] The data collection department will anonymize and share data from different households, allowing them to refer to the success stories of other households. For example, the data collection department will build a platform for collecting and sharing anonymized menu data and family reaction data from different households. For example, they will refer to the success stories of other households. This will enable more effective menu suggestions by referring to the success stories of other households.

[0034] The menu generation unit can analyze the family's meal history and propose a menu that takes into account seasonal changes in preferences. The menu generation unit, for example, analyzes the family's meal history and understands seasonal changes in preferences. For example, it analyzes the tendency to prefer cold dishes in the summer and hot dishes in the winter. This makes it possible to propose a menu that takes into account seasonal changes in preferences.

[0035] The menu creation unit can classify the family's food preferences in detail and reflect their preferences for specific ingredients and cooking methods. For example, the menu creation unit can classify the family's food preferences in detail and register their preferences for specific ingredients and cooking methods in a database. For example, it can record the ingredients and cooking methods that specific family members like. This makes it possible to propose menus that reflect the family's preferences in detail.

[0036] The menu generator can dynamically adjust the nutritional balance according to changes in the family's health condition and lifestyle. For example, the menu generator monitors changes in the family's health condition and lifestyle in real time and dynamically adjusts the nutritional balance. For example, if the amount of exercise increases, it will suggest a menu that is high in protein. This makes it possible to adjust the nutritional balance according to changes in the family's health condition and lifestyle.

[0037] The menu generation unit can compare the food preferences of a family with other families and propose menus for families with common preferences. The menu generation unit, for example, builds a system that compares the food preferences of a family with other families and proposes menus for families with common preferences. For example, it proposes menus for families that prefer the same ingredients and cooking methods. This makes it possible to propose menus for families with common preferences.

[0038] The menu generation unit can develop an app that supports menu selection when eating out based on the family's food preferences. The menu generation unit develops an app that supports menu selection when eating out based on the family's food preferences, for example. For example, the menu is suggested based on the ingredients and cooking methods that a particular family member likes. This makes it possible to develop an app that supports menu selection when eating out.

[0039] The menu creation unit can incorporate seasonal ingredients into the menu, allowing you to enjoy seasonal flavors. For example, the menu creation unit registers seasonal ingredients for each season in a database and builds a system that incorporates them into the menu. For example, asparagus is included in spring and tomatoes in summer. This makes it possible to propose menus that allow you to enjoy seasonal flavors by incorporating seasonal ingredients.

[0040] The menu generation unit can set a specific theme for the menu and provide variations. For example, the menu generation unit sets a specific theme for the menu and builds a system that suggests dishes based on that theme. For example, each week could have a different national cuisine theme. In this way, by setting a specific theme, it becomes possible to suggest a wide variety of menus.

[0041] The menu creation unit can reflect family requests in the menu and add menus that meet special requests. The menu creation unit, for example, collects family requests and builds a system that creates a menu based on those requests. For example, requests for specific dishes or ingredients can be reflected. By reflecting family requests, it is possible to propose a menu that will provide greater satisfaction.

[0042] The menu generation unit can share the menu with other households and refer to the menus of other households. For example, the menu generation unit builds a platform for sharing menus with other households and refers to the menus of other households. For example, it shares success stories and suggests optimal menus. In this way, by referring to the menus of other households, it becomes possible to suggest more effective menus.

[0043] The menu creation unit can link the menu table with a smart device and add a function to provide audio guidance on cooking procedures. The menu creation unit, for example, links the menu table with a smart device and adds a function to provide audio guidance on cooking procedures. For example, the cooking procedures are provided audio using a smart speaker. This improves cooking efficiency by providing audio guidance on cooking procedures.

[0044] The ordering unit can track the delivery status of the fresh food delivery service in real time and propose the optimal delivery timing. The ordering unit, for example, builds a system that tracks the delivery status of the fresh food delivery service in real time and proposes the optimal delivery timing. For example, it proposes the optimal delivery time taking into account traffic conditions and weather. In this way, by proposing the optimal delivery timing, it is possible to maintain the freshness of ingredients.

[0045] The ordering unit can collect quality data from fresh food delivery services and prioritize the selection of high-quality ingredients. The ordering unit, for example, collects quality data from fresh food delivery services and builds a system that prioritizes the selection of high-quality ingredients. For example, the ordering unit selects optimal ingredients based on past quality data. This allows the system to provide high-quality ingredients to users by prioritizing the selection of high-quality ingredients.

[0046] The ordering unit can analyze price data from fresh food delivery services and select ingredients with high cost performance. The ordering unit, for example, analyzes price data from fresh food delivery services and builds a system for selecting ingredients with high cost performance. For example, ingredients are selected taking into consideration the balance between price and quality. This allows for the selection of ingredients with high cost performance, thereby providing users with economical options.

[0047] The ordering department can cooperate with fresh food delivery services to build a system for purchasing ingredients directly from local farmers. The ordering department, for example, cooperates with fresh food delivery services to build a system for purchasing ingredients directly from local farmers. For example, the ordering department can partner with local farmers to provide fresh ingredients. By purchasing ingredients directly from local farmers, the freshness of the ingredients can be maintained.

[0048] The ordering unit shares usage information of the fresh food delivery service with other households, allowing them to receive discounts through joint purchases. The ordering unit, for example, builds a system in which usage information of the fresh food delivery service is shared with other households, allowing them to receive discounts through joint purchases. For example, multiple households can receive discounts by purchasing together. This allows users to receive economic benefits by receiving discounts through joint purchases.

[0049] The notification unit can analyze the user's schedule and propose the optimal notification timing. The notification unit, for example, builds a system that analyzes the user's schedule and proposes the optimal notification timing. For example, the notification unit sends notifications during times when the user is not busy. This proposes the optimal notification timing, improving user convenience.

[0050] The notification unit can analyze the user's past revision history and reflect common revisions in advance. The notification unit, for example, analyzes the user's past revision history and builds a system that reflects common revisions in advance. For example, it automatically reflects items that the user frequently modifies. This reduces the user's effort by reflecting common revisions in advance.

[0051] The notification unit can collect user feedback and continuously improve the accuracy of the system. For example, the notification unit collects user feedback and builds a system that continuously improves the accuracy of the system. For example, the notification unit improves an algorithm based on user opinions. In this way, by collecting user feedback, the accuracy of the system can be continuously improved.

[0052] The notification unit can share the user's notification content with other households and refer to the feedback of other households. The notification unit, for example, builds a system that shares the user's notification content with other households and refers to the feedback of other households. For example, it shares success stories of other households. This makes it possible to improve the notification content by referring to the feedback of other households.

[0053] The notification unit can add a function to notify the user of the notification content by voice in cooperation with the smart device. For example, the notification unit builds a system that adds a function to notify the user of the notification content by voice in cooperation with the smart device. For example, the notification content is announced by voice using a smart speaker. This improves user convenience by notifying by voice.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The menu generation unit can propose menus that take into account allergy information for specific ingredients based on the family's dietary preferences. For example, if a family member is allergic to a specific ingredient, it can automatically generate a menu that avoids that ingredient. This allows family members with allergies to enjoy their meals with peace of mind. It can also suggest alternative ingredients based on allergy information. For example, it can suggest recipes using soy milk to someone with a milk allergy. It can also adjust the nutritional balance based on allergy information. For example, it can suggest ingredients that are rich in other protein sources to someone with a nut allergy.

[0056] The ordering department can monitor the local food production situation in real time and prioritize ordering seasonal ingredients. For example, it can collect harvest information from local farmers and prioritize the selection of seasonal vegetables and fruits. This allows it to provide menus using fresh local ingredients. Using local ingredients also contributes to revitalizing the local economy. Furthermore, the ordering department can also suggest alternative ingredients based on the local food production situation if the supply of ingredients is unstable. For example, if there is a shortage of a particular vegetable due to bad weather, it can consider supplying it from another region.

[0057] The menu generation unit can analyze the family's meal history and propose menus that take into account seasonal changes in preferences. For example, the unit analyzes the family's meal history to understand seasonal changes in preferences. For example, it analyzes the tendency to prefer cold dishes in the summer and hot dishes in the winter. This makes it possible to propose menus that take into account seasonal changes in preferences. It can also propose menus that take into account the seasons of ingredients. For example, it can propose dishes using asparagus in the spring and tomatoes in the summer. It can also propose menus that match seasonal events and occasions. For example, it can propose a special dinner for Christmas.

[0058] The notification unit can analyze the user's schedule and suggest the optimal timing for notification. For example, a system can be constructed that analyzes the user's schedule and suggests the optimal timing for notification. For example, notifications can be sent during times when the user is not busy. This improves user convenience by suggesting the optimal timing for notification. It can also adjust the timing of ordering ingredients to suit the user's schedule. For example, it can prevent ingredients from arriving while the user is out. It can also suggest recipes to shorten cooking time to suit the user's schedule. For example, it can suggest easy-to-make dishes for busy days.

[0059] The ordering department can collect quality data from fresh food delivery services and prioritize the selection of high-quality ingredients. For example, a system can be built that collects quality data from fresh food delivery services and prioritizes the selection of high-quality ingredients. For example, the optimal ingredients can be selected based on past quality data. This allows for the priority selection of high-quality ingredients, making it possible to provide high-quality ingredients to users. It is also possible to set ingredient selection criteria based on the quality data. For example, only ingredients that meet certain quality standards can be selected. It is also possible to ensure the traceability of ingredients based on the quality data. For example, information on the origin and producers of ingredients can be provided.

[0060] The menu generator can dynamically adjust the nutritional balance according to changes in the family's health condition and lifestyle. For example, it can monitor changes in the family's health condition and lifestyle in real time and dynamically adjust the nutritional balance. For example, if the amount of exercise increases, it can suggest a menu that is high in protein. This makes it possible to adjust the nutritional balance according to changes in the family's health condition and lifestyle. It can also suggest menus that supplement specific nutrients according to changes in health condition. For example, if there is a vitamin D deficiency, it can suggest a menu that uses ingredients that are high in vitamin D. It can also adjust cooking times according to changes in lifestyle. For example, it can suggest easy-to-make dishes for busy days.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data collection unit collects data on past menus and family reactions. For example, the types of dishes previously prepared and the family's evaluations of them (e.g., "It was delicious," "I want to eat it again," "I wish it was a little saltier") are stored in a database. The data collection unit can also analyze facial expressions and voices of family members while they are eating, and use an emotion estimation function to evaluate their satisfaction with the meal in real time. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the generation AI uses past data to devise a menu that takes into account the preferences and nutritional balance of each family member. Using its knowledge of nutrition, the generation AI calculates the optimal combination to provide a balanced meal. Step 3: The menu generator generates a menu that takes into account individual preferences and nutritional balance based on the data analyzed by the analyzer. For example, the generator AI automatically generates weekly or monthly menus, such as "teriyaki chicken and stir-fried vegetables" on Mondays and "grilled fish and salad" on Tuesdays. Step 4: The ordering unit automatically orders the necessary ingredients based on the menu generated by the menu generation unit. For example, if the menu includes "teriyaki chicken," the ordering unit automatically selects the necessary ingredients, such as chicken, seasonings, and vegetables, and places an order with the delivery service. Step 5: The notification unit notifies the user of the order contents and menu ordered by the ordering unit. For example, the notification unit sends the menu and order contents to the user via a smartphone app or email, and accepts instructions for corrections or additions as necessary.

[0063] (Example 2) The menu creation system according to an embodiment of the present invention is a system that creates a daily menu that takes into account individual preferences and nutritional balance based on past menus and family reactions, and creates a menu table. Furthermore, this system also handles ordering to fresh food delivery services in one stop. This allows the user to utilize past data and easily create an optimal menu that takes into account family preferences and nutritional balance. Furthermore, since ordering to fresh food delivery services is also automated, the system reduces the hassle of shopping and enables efficient food ingredient management.

[0064] A menu creation system according to an embodiment includes a data collection unit, an analysis unit, a menu generation unit, an ordering unit, and a notification unit. The data collection unit collects data on past menus and family reactions. For example, the data collection unit stores the types of dishes previously prepared and the family's evaluations of the dishes (e.g., "It was delicious," "I want to eat it again," "I wish it was a little saltier," etc.) in a database. The data collection unit can also analyze the family's facial expressions and voices during meals and use an emotion estimation function to evaluate the family's satisfaction with the meal in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the generation AI uses past data to create a menu that takes into account each family member's preferences and nutritional balance. The generation AI calculates the optimal combination for providing a balanced meal based on knowledge of nutrition. The menu generation unit generates a menu that takes into account each family member's preferences and nutritional balance based on the data analyzed by the analysis unit. For example, the generation AI automatically generates weekly or monthly menus, such as "teriyaki chicken and stir-fried vegetables" on Mondays and "grilled fish and salad" on Tuesdays. The ordering unit automatically orders the necessary ingredients based on the menu generated by the menu generation unit. For example, if the menu includes "teriyaki chicken," the ordering unit automatically selects the necessary ingredients, such as chicken, seasonings, and vegetables, and places an order with a delivery service. The notification unit notifies the user of the menu and the contents of the order placed by the ordering unit. For example, the notification unit may send the menu and order details to the user via a smartphone app or email, and accept instructions for corrections or additions as needed. This allows the menu creation system according to the embodiment to easily create an optimal menu that takes into account the preferences and nutritional balance of the family by utilizing past data. Furthermore, ordering with a fresh food delivery service is also automated, eliminating the need for shopping and enabling efficient ingredient management.

[0065] The data collection unit can analyze facial expressions and voices of family members while they are eating, and use the emotion estimation function to evaluate meal satisfaction in real time. For example, while the family is eating, the data collection unit can collect facial expressions and voices in real time using a camera and microphone, and evaluate satisfaction using an emotion estimation algorithm. For example, if there are a lot of smiles and laughter, it is judged to be a high level of satisfaction. This allows for real-time evaluation of the family's meal satisfaction, making it possible to make more accurate menu suggestions.

[0066] The data collection unit collects environmental data such as temperature, humidity, and lighting during meals, and can identify factors that affect meal satisfaction. For example, the data collection unit measures the indoor temperature and humidity during meals using a sensor and collects the data. For example, it analyzes the effect that a comfortable temperature and humidity have on satisfaction. This allows the system to identify factors that affect meal satisfaction and provide a more comfortable dining environment.

[0067] The data collection unit can collect health data such as family members' weight, blood pressure, and blood sugar levels, and track the effects of diet over the long term. For example, the data collection unit periodically measures family members' weight and blood pressure and collects the data. For example, it can analyze the effects of specific menus on weight and blood pressure. This allows for more effective health management of family members by tracking the effects of diet over the long term.

[0068] The data collection unit can analyze conversations during family meals and automatically extract feedback about the meal. For example, the data collection unit converts conversations during meals into text using voice recognition technology and extracts feedback about the meal using natural language processing. For example, comments such as "delicious" and "I wish it was a little saltier" are automatically collected. This allows for the automatic extraction of feedback about meals, making it possible to propose more accurate menus.

[0069] The data collection department will anonymize and share data from different households, allowing them to refer to the success stories of other households. For example, the data collection department will build a platform for collecting and sharing anonymized menu data and family reaction data from different households. For example, they will refer to the success stories of other households. This will enable more effective menu suggestions by referring to the success stories of other households.

[0070] The data collection unit can use the emotion estimation function to adjust the next menu based on the emotions felt by family members during a meal. For example, the data collection unit estimates the emotions of family members during a meal in real time and adjusts the next menu based on that data. For example, it prioritizes suggesting dishes that evoke a lot of positive emotions. This makes it possible to suggest a menu that will result in a higher level of satisfaction by adjusting the next menu based on the emotions of family members.

[0071] The menu generation unit can analyze the family's meal history and propose a menu that takes into account seasonal changes in preferences. The menu generation unit, for example, analyzes the family's meal history and understands seasonal changes in preferences. For example, it analyzes the tendency to prefer cold dishes in the summer and hot dishes in the winter. This makes it possible to propose a menu that takes into account seasonal changes in preferences.

[0072] The menu creation unit can classify the family's food preferences in detail and reflect their preferences for specific ingredients and cooking methods. For example, the menu creation unit can classify the family's food preferences in detail and register their preferences for specific ingredients and cooking methods in a database. For example, it can record the ingredients and cooking methods that specific family members like. This makes it possible to propose menus that reflect the family's preferences in detail.

[0073] The menu generator can dynamically adjust the nutritional balance according to changes in the family's health condition and lifestyle. For example, the menu generator monitors changes in the family's health condition and lifestyle in real time and dynamically adjusts the nutritional balance. For example, if the amount of exercise increases, it will suggest a menu that is high in protein. This makes it possible to adjust the nutritional balance according to changes in the family's health condition and lifestyle.

[0074] The menu generation unit can compare the food preferences of a family with other families and propose menus for families with common preferences. The menu generation unit, for example, builds a system that compares the food preferences of a family with other families and proposes menus for families with common preferences. For example, it proposes menus for families that prefer the same ingredients and cooking methods. This makes it possible to propose menus for families with common preferences.

[0075] The menu generation unit can develop an app that supports menu selection when eating out based on the family's food preferences. The menu generation unit develops an app that supports menu selection when eating out based on the family's food preferences, for example. For example, the menu is suggested based on the ingredients and cooking methods that a particular family member likes. This makes it possible to develop an app that supports menu selection when eating out.

[0076] The menu generation unit can use the emotion estimation function to adjust the menu based on the emotions that family members feel toward specific ingredients. For example, the menu generation unit uses the emotion estimation function to collect the emotions that family members feel toward specific ingredients in real time and adjust the menu based on that data. For example, ingredients that are associated with a lot of positive emotions can be prioritized. In this way, by adjusting the menu based on the emotions of the family members, it is possible to propose a menu that will provide greater satisfaction.

[0077] The menu creation unit can incorporate seasonal ingredients into the menu, allowing you to enjoy seasonal flavors. For example, the menu creation unit registers seasonal ingredients for each season in a database and builds a system that incorporates them into the menu. For example, asparagus is included in spring and tomatoes in summer. This makes it possible to propose menus that allow you to enjoy seasonal flavors by incorporating seasonal ingredients.

[0078] The menu generation unit can set a specific theme for the menu and provide variations. For example, the menu generation unit sets a specific theme for the menu and builds a system that suggests dishes based on that theme. For example, each week could have a different national cuisine theme. In this way, by setting a specific theme, it becomes possible to suggest a wide variety of menus.

[0079] The menu creation unit can reflect family requests in the menu and add menus that meet special requests. The menu creation unit, for example, collects family requests and builds a system that creates a menu based on those requests. For example, requests for specific dishes or ingredients can be reflected. By reflecting family requests, it is possible to propose a menu that will provide greater satisfaction.

[0080] The menu generation unit can share the menu with other households and refer to the menus of other households. For example, the menu generation unit builds a platform for sharing menus with other households and refers to the menus of other households. For example, it shares success stories and suggests optimal menus. In this way, by referring to the menus of other households, it becomes possible to suggest more effective menus.

[0081] The menu creation unit can link the menu table with a smart device and add a function to provide audio guidance on cooking procedures. The menu creation unit, for example, links the menu table with a smart device and adds a function to provide audio guidance on cooking procedures. For example, the cooking procedures are provided audio using a smart speaker. This improves cooking efficiency by providing audio guidance on cooking procedures.

[0082] The menu generation unit can use the emotion estimation function to adjust the menu based on the emotions that family members feel toward specific menu items. For example, the menu generation unit uses the emotion estimation function to collect emotions that family members feel toward specific menu items in real time and adjust the menu based on that data. For example, it prioritizes incorporating menu items that evoke a lot of positive emotions. In this way, adjusting the menu based on the emotions of family members makes it possible to propose menus that are more satisfying.

[0083] The ordering unit can track the delivery status of the fresh food delivery service in real time and propose the optimal delivery timing. The ordering unit, for example, builds a system that tracks the delivery status of the fresh food delivery service in real time and proposes the optimal delivery timing. For example, it proposes the optimal delivery time taking into account traffic conditions and weather. In this way, by proposing the optimal delivery timing, it is possible to maintain the freshness of ingredients.

[0084] The ordering unit can collect quality data from fresh food delivery services and prioritize the selection of high-quality ingredients. The ordering unit, for example, collects quality data from fresh food delivery services and builds a system that prioritizes the selection of high-quality ingredients. For example, the ordering unit selects optimal ingredients based on past quality data. This allows the system to provide high-quality ingredients to users by prioritizing the selection of high-quality ingredients.

[0085] The ordering unit can analyze price data from fresh food delivery services and select ingredients with high cost performance. The ordering unit, for example, analyzes price data from fresh food delivery services and builds a system for selecting ingredients with high cost performance. For example, ingredients are selected taking into consideration the balance between price and quality. This allows for the selection of ingredients with high cost performance, thereby providing users with economical options.

[0086] The ordering department can cooperate with fresh food delivery services to build a system for purchasing ingredients directly from local farmers. The ordering department, for example, cooperates with fresh food delivery services to build a system for purchasing ingredients directly from local farmers. For example, the ordering department can partner with local farmers to provide fresh ingredients. By purchasing ingredients directly from local farmers, the freshness of the ingredients can be maintained.

[0087] The ordering unit shares usage information of the fresh food delivery service with other households, allowing them to receive discounts through joint purchases. The ordering unit, for example, builds a system in which usage information of the fresh food delivery service is shared with other households, allowing them to receive discounts through joint purchases. For example, multiple households can receive discounts by purchasing together. This allows users to receive economic benefits by receiving discounts through joint purchases.

[0088] The ordering unit can use the emotion estimation function to adjust the order contents based on the emotions that family members feel toward specific ingredients. For example, the ordering unit uses the emotion estimation function to collect the emotions that family members feel toward specific ingredients in real time and adjust the order contents based on that data. For example, ingredients that are associated with a high number of positive emotions may be ordered preferentially. In this way, adjusting the order contents based on the emotions of family members enables the selection of ingredients that will provide greater satisfaction.

[0089] The notification unit can analyze the user's schedule and propose the optimal notification timing. The notification unit, for example, builds a system that analyzes the user's schedule and proposes the optimal notification timing. For example, the notification unit sends notifications during times when the user is not busy. This proposes the optimal notification timing, improving user convenience.

[0090] The notification unit can analyze the user's past revision history and reflect common revisions in advance. The notification unit, for example, analyzes the user's past revision history and builds a system that reflects common revisions in advance. For example, it automatically reflects items that the user frequently modifies. This reduces the user's effort by reflecting common revisions in advance.

[0091] The notification unit can collect user feedback and continuously improve the accuracy of the system. For example, the notification unit collects user feedback and builds a system that continuously improves the accuracy of the system. For example, the notification unit improves an algorithm based on user opinions. In this way, by collecting user feedback, the accuracy of the system can be continuously improved.

[0092] The notification unit can share the user's notification content with other households and refer to the feedback of other households. The notification unit, for example, builds a system that shares the user's notification content with other households and refers to the feedback of other households. For example, it shares success stories of other households. This makes it possible to improve the notification content by referring to the feedback of other households.

[0093] The notification unit can add a function to notify the user of the notification content by voice in cooperation with the smart device. For example, the notification unit builds a system that adds a function to notify the user of the notification content by voice in cooperation with the smart device. For example, the notification content is announced by voice using a smart speaker. This improves user convenience by notifying by voice.

[0094] The notification unit can use the emotion estimation function to adjust the notification content based on the emotion the user feels about the notification content. For example, the notification unit uses the emotion estimation function to collect the emotion the user feels about the notification content in real time and adjust the notification content based on that data. For example, notification content with a high percentage of positive emotions may be sent preferentially. This allows for more effective notification by adjusting the notification content based on the user's emotion.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The menu generation unit can propose menus that take into account allergy information for specific ingredients based on the family's dietary preferences. For example, if a family member is allergic to a specific ingredient, it can automatically generate a menu that avoids that ingredient. This allows family members with allergies to enjoy their meals with peace of mind. It can also suggest alternative ingredients based on allergy information. For example, it can suggest recipes using soy milk to someone with a milk allergy. It can also adjust the nutritional balance based on allergy information. For example, it can suggest ingredients that are rich in other protein sources to someone with a nut allergy.

[0097] The notification unit can estimate the user's emotions and, if it is determined that the user is under high stress, suggest a menu using ingredients with a relaxing effect. For example, if the user is feeling stressed at work, it can suggest a menu using chamomile tea and bananas, which have a relaxing effect. Furthermore, if it is determined that the user is tired using the emotion estimation function, it can suggest a menu using ingredients suitable for replenishing energy. For example, it can suggest a menu using ingredients rich in B vitamins, which are effective in recovering from fatigue. Furthermore, if the user is feeling positive, it can also suggest a menu to maintain that emotion using the emotion estimation function. For example, if the user is feeling happy, it can suggest a colorful dish to further enhance that mood.

[0098] The ordering department can monitor the local food production situation in real time and prioritize ordering seasonal ingredients. For example, it can collect harvest information from local farmers and prioritize the selection of seasonal vegetables and fruits. This allows it to provide menus using fresh local ingredients. Using local ingredients also contributes to revitalizing the local economy. Furthermore, the ordering department can also suggest alternative ingredients based on the local food production situation if the supply of ingredients is unstable. For example, if there is a shortage of a particular vegetable due to bad weather, it can consider supplying it from another region.

[0099] The data collection unit can analyze conversations between family members during mealtimes and automatically extract feedback about meals. For example, conversations during meals can be converted into text using voice recognition technology, and feedback about meals can be extracted using natural language processing. For example, comments such as "delicious" and "I wish it was a little saltier" can be automatically collected. By automatically extracting feedback about meals, more accurate menu suggestions can be made. In addition, the system can understand family preferences and complaints based on the conversation content and reflect this in the next menu. For example, based on a comment such as "I like spicy food," it can suggest a menu that includes a lot of spicy dishes.

[0100] The menu generation unit can analyze the family's meal history and propose menus that take into account seasonal changes in preferences. For example, the unit analyzes the family's meal history to understand seasonal changes in preferences. For example, it analyzes the tendency to prefer cold dishes in the summer and hot dishes in the winter. This makes it possible to propose menus that take into account seasonal changes in preferences. It can also propose menus that take into account the seasons of ingredients. For example, it can propose dishes using asparagus in the spring and tomatoes in the summer. It can also propose menus that match seasonal events and occasions. For example, it can propose a special dinner for Christmas.

[0101] The notification unit can analyze the user's schedule and suggest the optimal timing for notification. For example, a system can be constructed that analyzes the user's schedule and suggests the optimal timing for notification. For example, notifications can be sent during times when the user is not busy. This improves user convenience by suggesting the optimal timing for notification. It can also adjust the timing of ordering ingredients to suit the user's schedule. For example, it can prevent ingredients from arriving while the user is out. It can also suggest recipes to shorten cooking time to suit the user's schedule. For example, it can suggest easy-to-make dishes for busy days.

[0102] The menu generator can use the emotion estimation function to adjust the menu based on the emotions family members feel toward specific ingredients. For example, the emotion estimation function can be used to collect the emotions family members feel toward specific ingredients in real time and adjust the menu based on that data. For example, ingredients that are associated with a lot of positive emotions can be prioritized. By adjusting the menu based on the family members' emotions, it is possible to propose menus that are more satisfying. Also, by avoiding ingredients that are associated with a lot of negative emotions, it is possible to reduce family dissatisfaction. Furthermore, the emotion estimation function can be used to track changes in family members' emotions over the long term and respond to changes in preferences. For example, if a family member starts to like an ingredient that they previously disliked, that ingredient can be suggested again.

[0103] The ordering department can collect quality data from fresh food delivery services and prioritize the selection of high-quality ingredients. For example, a system can be built that collects quality data from fresh food delivery services and prioritizes the selection of high-quality ingredients. For example, the optimal ingredients can be selected based on past quality data. This allows for the priority selection of high-quality ingredients, making it possible to provide high-quality ingredients to users. It is also possible to set ingredient selection criteria based on the quality data. For example, only ingredients that meet certain quality standards can be selected. It is also possible to ensure the traceability of ingredients based on the quality data. For example, information on the origin and producers of ingredients can be provided.

[0104] The notification unit can use the emotion estimation function to adjust the notification content based on the emotion the user feels toward the notification content. For example, the emotion estimation function can be used to collect the emotion the user feels toward the notification content in real time and adjust the notification content based on that data. For example, notification content that evokes a lot of positive emotion can be sent preferentially. This allows for more effective notifications by adjusting the notification content based on the user's emotion. Also, avoiding notification content that evokes a lot of negative emotion can reduce user stress. Furthermore, the emotion estimation function can be used to track changes in the user's emotion over the long term and optimize the notification content. For example, if a user has positive emotion during a specific time period, notifications can be sent during that time period.

[0105] The menu generator can dynamically adjust the nutritional balance according to changes in the family's health condition and lifestyle. For example, it can monitor changes in the family's health condition and lifestyle in real time and dynamically adjust the nutritional balance. For example, if the amount of exercise increases, it can suggest a menu that is high in protein. This makes it possible to adjust the nutritional balance according to changes in the family's health condition and lifestyle. It can also suggest menus that supplement specific nutrients according to changes in health condition. For example, if there is a vitamin D deficiency, it can suggest a menu that uses ingredients that are high in vitamin D. It can also adjust cooking times according to changes in lifestyle. For example, it can suggest easy-to-make dishes for busy days.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The data collection unit collects data on past menus and family reactions. For example, the types of dishes previously prepared and the family's evaluations of them (e.g., "It was delicious," "I want to eat it again," "I wish it was a little saltier") are stored in a database. The data collection unit can also analyze facial expressions and voices of family members while they are eating, and use an emotion estimation function to evaluate their satisfaction with the meal in real time. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the generation AI uses past data to devise a menu that takes into account the preferences and nutritional balance of each family member. Using its knowledge of nutrition, the generation AI calculates the optimal combination to provide a balanced meal. Step 3: The menu generator generates a menu that takes into account individual preferences and nutritional balance based on the data analyzed by the analyzer. For example, the generator AI automatically generates weekly or monthly menus, such as "teriyaki chicken and stir-fried vegetables" on Mondays and "grilled fish and salad" on Tuesdays. Step 4: The ordering unit automatically orders the necessary ingredients based on the menu generated by the menu generation unit. For example, if the menu includes "teriyaki chicken," the ordering unit automatically selects the necessary ingredients, such as chicken, seasonings, and vegetables, and places an order with the delivery service. Step 5: The notification unit notifies the user of the order contents and menu ordered by the ordering unit. For example, the notification unit sends the menu and order contents to the user via a smartphone app or email, and accepts instructions for corrections or additions as necessary.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 data collection department that collects data on past menus and family reactions; an analysis unit that analyzes the data collected by the data collection unit; a menu creation unit that creates a menu that takes into consideration individual preferences and nutritional balance based on the data analyzed by the analysis unit; an ordering unit that automatically orders necessary ingredients based on the menu generated by the menu generating unit; A notification unit that notifies the user of the contents and menu ordered by the ordering unit. A system characterized by:

2. The data collection unit The system analyzes the facial expressions and voices of the family members while they are eating, and uses emotion estimation to evaluate their satisfaction with the meal in real time.

2. The system of claim 1.

3. The data collection unit Analyzing the conversations of the family members during the meal and automatically extracting feedback about the meal 2. The system of claim 1.

4. The menu generation unit Analyzing the family's meal history and proposing the menu taking into account seasonal changes in preferences 2. The system of claim 1.

5. The ordering unit Tracking delivery status of fresh produce delivery services in real time and suggesting optimal delivery times 2. The system of claim 1.

6. The notification unit Add a function to link the user's notification content with the smart device and notify them by voice.

2. The system of claim 1.

7. The data collection unit Using an emotion estimation function, the next menu is adjusted based on the emotions felt by the family during the meal.

2. The system of claim 1.

8. The notification unit Using an emotion estimation function, the notification content is adjusted based on the emotion the user feels about the notification content.

2. The system of claim 1.

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