System

Abstract

An object of a system according to an embodiment is to propose an optimal menu by integrating a user's schedule, weather, food ingredient information in a refrigerator, and electronic leaflet information of a nearby supermarket.SOLUTION: A system includes a scheduler cooperation part, a weather information cooperation part, a refrigerator cooperation part, an electronic leaflet cooperation part, and a menu suggestion part. The scheduler cooperation unit cooperates with the scheduler to acquire a schedule of the user. The weather information link unit acquires weather information. The refrigerator cooperation unit acquires food ingredient information in the refrigerator. An electronic leaflet cooperation part acquires electronic leaflet information of a neighboring supermarket. The menu proposal unit proposes a menu on the basis of the information acquired by the scheduler cooperation unit, the weather information cooperation unit, the refrigerator cooperation unit, and the electronic leaflet cooperation 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] Conventional technology does not integrate the user's schedule, weather, information about ingredients in the refrigerator, or electronic flyer information from nearby supermarkets to suggest optimal menus, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal menus by integrating the user's schedule, weather, information about ingredients in the refrigerator, and electronic flyer information from nearby supermarkets. [Means for solving the problem]

[0006] The system according to the embodiment includes a scheduler linkage unit, a weather information linkage unit, a refrigerator linkage unit, an electronic flyer linkage unit, and a menu suggestion unit. The scheduler linkage unit links with the scheduler to acquire the user's schedule. The weather information linkage unit acquires weather information. The refrigerator linkage unit acquires information about ingredients in the refrigerator. The electronic flyer linkage unit acquires electronic flyer information from nearby supermarkets. The menu suggestion unit suggests menus based on the information acquired by the scheduler linkage unit, weather information linkage unit, refrigerator linkage unit, and electronic flyer linkage unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus by integrating the user's schedule, weather, information about ingredients in the refrigerator, and electronic flyer information from nearby supermarkets. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu suggestion system according to an embodiment of the present invention is a system that proposes optimal menus by linking a user's schedule, weather, ingredients in the refrigerator, and supermarket sale information, and analyzing the information using a generation AI. This allows the menu suggestion system to easily find the optimal menu that takes into account the user's schedule, weather, ingredients in the refrigerator, and supermarket sale information. For example, on busy days, the system can suggest economical menus that are easy to make by utilizing sale information. This reduces food waste and allows for efficient meal planning.

[0029] The menu suggestion system according to the embodiment includes a scheduler linkage unit, a weather information linkage unit, a refrigerator linkage unit, an electronic flyer linkage unit, and a menu suggestion unit. The scheduler linkage unit acquires a user's schedule. For example, it links with Google Calendar or Outlook Calendar to acquire the user's schedule information. The weather information linkage unit acquires weather information. For example, it acquires weather information from data from the Japan Meteorological Agency or private weather forecast services. The refrigerator linkage unit acquires information about ingredients in the refrigerator. For example, it acquires information about ingredients in the refrigerator by scanning a barcode or manually entering it. The electronic flyer linkage unit acquires information about electronic flyers from nearby supermarkets. For example, it acquires information about special sales and new products from the supermarket's official app or website. The menu suggestion unit proposes menus based on the information acquired by the scheduler linkage unit, weather information linkage unit, refrigerator linkage unit, and electronic flyer linkage unit. For example, a generation AI analyzes schedule information, weather information, information about ingredients in the refrigerator, and information from electronic flyers to propose the optimal menu for that day and time. This allows the menu suggestion system to suggest the optimal menu taking into account the user's schedule, weather, ingredients in the refrigerator, and supermarket sales information.

[0030] The scheduler collaboration unit can analyze the user's past behavioral patterns and suggest menus based on predicted behavior. For example, the scheduler collaboration unit analyzes the user's past behavioral patterns based on scheduler information and suggests menus based on predicted behavior. For example, if the user tends to eat out every Friday, the scheduler collaboration unit suggests a menu suitable for eating out on that day. The scheduler collaboration unit also analyzes scheduler information and predicts the user's behavioral patterns. For example, if the user jogs every morning, the scheduler collaboration unit suggests a nutritional menu suitable for after that. The scheduler collaboration unit also analyzes the user's schedule and suggests menus based on the user's past behavioral patterns. For example, if the user prefers a particular ingredient on a particular day of the week, the scheduler collaboration unit suggests a menu using that ingredient. This makes it possible to suggest more appropriate menus based on the user's behavioral patterns.

[0031] The weather information collaboration unit can propose menus that take into consideration the seasonality of ingredients based on weather information. The weather information collaboration unit, for example, proposes menus that take into consideration the seasonality of ingredients based on weather information. For example, in spring, it proposes salads using fresh vegetables. The weather information collaboration unit also proposes menus based on weather information, taking into consideration the seasonality of ingredients. For example, in summer, it proposes cold soups and salads. The weather information collaboration unit also analyzes weather information and proposes menus that take into consideration the seasonality of ingredients. For example, in autumn, it proposes dishes using mushrooms. In this way, it is possible to propose menus that take into consideration the seasonality of ingredients.

[0032] The refrigerator linkage unit can generate a menu that considers the expiration dates of ingredients in the refrigerator and suggests the optimal consumption order. The refrigerator linkage unit, for example, considers the expiration dates of ingredients in the refrigerator and generates a menu that considers the optimal consumption order. For example, it proposes a menu that prioritizes ingredients that are close to their expiration date. The refrigerator linkage unit also analyzes the expiration dates of ingredients and suggests a menu that uses ingredients without waste. For example, it proposes a soup that uses vegetables that are close to their expiration date. The refrigerator linkage unit also considers the expiration dates of ingredients in the refrigerator and generates a menu that considers the optimal consumption order. For example, it proposes a main dish that uses meat that is close to its expiration date. This reduces food waste and enables efficient consumption.

[0033] The electronic flyer linking unit can propose a menu with high cost performance based on the sale information in the electronic flyer. The electronic flyer linking unit, for example, proposes a menu with high cost performance based on the sale information in the electronic flyer. For example, it proposes a menu using ingredients that are on sale. The electronic flyer linking unit also analyzes the sale information and proposes a menu with high cost performance. For example, it proposes a menu using inexpensive ingredients. The electronic flyer linking unit also proposes a menu with high cost performance based on the sale information in the electronic flyer. For example, it proposes a menu using ingredients that are on sale. In this way, it is possible to propose economical menus by utilizing the sale information.

[0034] The menu suggestion unit can analyze the nutritional balance of the proposed menu and suggest a healthy meal. The menu suggestion unit, for example, analyzes the nutritional balance of the proposed menu and suggests a healthy meal. For example, it suggests a menu that is rich in vitamins and minerals. The menu suggestion unit also suggests a healthy meal based on the nutritional balance. For example, it suggests a menu with a good balance of protein, carbohydrates, and lipids. The menu suggestion unit also analyzes the nutritional balance of the proposed menu and suggests a healthy meal. For example, it suggests a menu that is low in calories and highly nutritious. This makes it possible to suggest a nutritionally balanced healthy meal.

[0035] The menu suggestion unit can suggest a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. The menu suggestion unit, for example, suggests a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. For example, on busy days, it suggests a menu that can be made in a short amount of time. The menu suggestion unit also suggests a menu that matches the user's schedule, based on the cooking time. For example, on days when there is more time, it suggests a more elaborate menu. The menu suggestion unit also suggests a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. For example, on busy days, it suggests a menu that can be made in a short amount of time. In this way, it is possible to suggest a menu that matches the user's schedule.

[0036] The menu suggestion unit automatically generates an ingredient shopping list based on the proposed menu, thereby reducing the effort required for shopping. The menu suggestion unit automatically generates an ingredient shopping list based on, for example, the proposed menu, thereby reducing the effort required for shopping. For example, it adds necessary ingredients to the list. The menu suggestion unit also analyzes the menu and automatically generates an ingredient shopping list. For example, it adds ingredients necessary for the proposed menu to the list. The menu suggestion unit also automatically generates an ingredient shopping list based on the proposed menu, thereby reducing the effort required for shopping. For example, it adds necessary ingredients to the list. This reduces the effort required for shopping.

[0037] The menu suggestion unit can provide cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. The menu suggestion unit, for example, provides cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. For example, a step-by-step cooking video is provided. The menu suggestion unit also provides cooking steps in the form of a video, allowing the user to easily cook. For example, cooking tips and points are explained in the video. The menu suggestion unit also provides cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. For example, a step-by-step cooking video is provided. This allows the user to easily cook.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The menu suggestion system can also suggest menus that take into account the user's health condition. For example, if the user is on a diet, it can suggest a low-calorie, nutritionally balanced menu. It can also suggest allergen-free menus to a user with specific allergies. It can also suggest menus that supplement specific nutrients based on the user's health checkup results. This makes it possible to suggest menus that are tailored to the user's health condition.

[0040] The menu suggestion system can also learn the user's food preferences and suggest individually customized menus. For example, if the user likes a particular dish, it can suggest a menu that is an adaptation of that dish. Also, if the user likes a particular ingredient, it can suggest a new menu using that ingredient. Furthermore, it can learn the user's food preferences and suggest seasonal menus. This makes it possible to suggest menus that match the user's preferences.

[0041] The menu suggestion system can also learn the user's food preferences and suggest menus with optimized ingredient combinations. For example, if the user likes a particular ingredient, the system can suggest new menu combinations using that ingredient. Also, if the user likes a particular dish, the system can suggest menus that are variations of that dish. Furthermore, the system can learn the user's food preferences and suggest ingredient combinations that are popular in each season. This allows for ingredient combinations that match the user's preferences.

[0042] The menu suggestion system can also learn the user's food preferences and suggest food preservation methods. For example, if a user frequently uses a particular ingredient, it can suggest the optimal method for preserving that ingredient. Also, if a user likes a particular dish, it can suggest the method for preserving the ingredients used in that dish. Furthermore, it can learn the user's food preferences and suggest preservation methods for ingredients that are difficult to preserve depending on the season. This reduces food waste and enables efficient preservation.

[0043] The menu suggestion system can also learn the user's food preferences and suggest cooking methods for ingredients. For example, if the user likes a particular ingredient, it can suggest a new cooking method for that ingredient. Also, if the user likes a particular dish, it can suggest a cooking method that adapts that dish. Furthermore, it can learn the user's food preferences and suggest cooking methods for ingredients that are difficult to cook depending on the season. This allows for cooking methods that match the user's preferences.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The scheduler linkage unit acquires the user's schedule. For example, it links with Google Calendar or Outlook Calendar to acquire the user's schedule information. Step 2: The weather information collaboration unit acquires weather information, for example, from data from the Japan Meteorological Agency or private weather forecast services. Step 3: The refrigerator link unit acquires information about ingredients in the refrigerator, for example, by scanning barcodes or manually entering the information. Step 4: The electronic flyer linking unit obtains electronic flyer information from nearby supermarkets, such as sale information and new product information from the supermarket's official app or website. Step 5: The menu suggestion unit proposes a menu based on the information obtained by the scheduler linkage unit, weather information linkage unit, refrigerator linkage unit, and electronic flyer linkage unit. For example, the generation AI analyzes schedule information, weather information, information about ingredients in the refrigerator, and information from electronic flyers to propose the optimal menu for that day and time.

[0046] (Example 2) The menu suggestion system according to an embodiment of the present invention is a system that proposes optimal menus by linking a user's schedule, weather, ingredients in the refrigerator, and supermarket sale information, and analyzing the information using a generation AI. This allows the menu suggestion system to easily find the optimal menu that takes into account the user's schedule, weather, ingredients in the refrigerator, and supermarket sale information. For example, on busy days, the system can suggest economical menus that are easy to make by utilizing sale information. This reduces food waste and allows for efficient meal planning.

[0047] The menu suggestion system according to the embodiment includes a scheduler linkage unit, a weather information linkage unit, a refrigerator linkage unit, an electronic flyer linkage unit, and a menu suggestion unit. The scheduler linkage unit acquires a user's schedule. For example, it links with Google Calendar or Outlook Calendar to acquire the user's schedule information. The weather information linkage unit acquires weather information. For example, it acquires weather information from data from the Japan Meteorological Agency or private weather forecast services. The refrigerator linkage unit acquires information about ingredients in the refrigerator. For example, it acquires information about ingredients in the refrigerator by scanning a barcode or manually entering it. The electronic flyer linkage unit acquires information about electronic flyers from nearby supermarkets. For example, it acquires information about special sales and new products from the supermarket's official app or website. The menu suggestion unit proposes menus based on the information acquired by the scheduler linkage unit, weather information linkage unit, refrigerator linkage unit, and electronic flyer linkage unit. For example, a generation AI analyzes schedule information, weather information, information about ingredients in the refrigerator, and information from electronic flyers to propose the optimal menu for that day and time. This allows the menu suggestion system to suggest the optimal menu taking into account the user's schedule, weather, ingredients in the refrigerator, and supermarket sales information.

[0048] The scheduler collaboration unit can analyze the user's past behavioral patterns and suggest menus based on predicted behavior. For example, the scheduler collaboration unit analyzes the user's past behavioral patterns based on scheduler information and suggests menus based on predicted behavior. For example, if the user tends to eat out every Friday, the scheduler collaboration unit suggests a menu suitable for eating out on that day. The scheduler collaboration unit also analyzes scheduler information and predicts the user's behavioral patterns. For example, if the user jogs every morning, the scheduler collaboration unit suggests a nutritional menu suitable for after that. The scheduler collaboration unit also analyzes the user's schedule and suggests menus based on the user's past behavioral patterns. For example, if the user prefers a particular ingredient on a particular day of the week, the scheduler collaboration unit suggests a menu using that ingredient. This makes it possible to suggest more appropriate menus based on the user's behavioral patterns.

[0049] The weather information collaboration unit can propose menus that take into consideration the seasonality of ingredients based on weather information. The weather information collaboration unit, for example, proposes menus that take into consideration the seasonality of ingredients based on weather information. For example, in spring, it proposes salads using fresh vegetables. The weather information collaboration unit also proposes menus based on weather information, taking into consideration the seasonality of ingredients. For example, in summer, it proposes cold soups and salads. The weather information collaboration unit also analyzes weather information and proposes menus that take into consideration the seasonality of ingredients. For example, in autumn, it proposes dishes using mushrooms. In this way, it is possible to propose menus that take into consideration the seasonality of ingredients.

[0050] The refrigerator linkage unit can generate a menu that considers the expiration dates of ingredients in the refrigerator and suggests the optimal consumption order. The refrigerator linkage unit, for example, considers the expiration dates of ingredients in the refrigerator and generates a menu that considers the optimal consumption order. For example, it proposes a menu that prioritizes ingredients that are close to their expiration date. The refrigerator linkage unit also analyzes the expiration dates of ingredients and suggests a menu that uses ingredients without waste. For example, it proposes a soup that uses vegetables that are close to their expiration date. The refrigerator linkage unit also considers the expiration dates of ingredients in the refrigerator and generates a menu that considers the optimal consumption order. For example, it proposes a main dish that uses meat that is close to its expiration date. This reduces food waste and enables efficient consumption.

[0051] The electronic flyer linking unit can propose a menu with high cost performance based on the sale information in the electronic flyer. The electronic flyer linking unit, for example, proposes a menu with high cost performance based on the sale information in the electronic flyer. For example, it proposes a menu using ingredients that are on sale. The electronic flyer linking unit also analyzes the sale information and proposes a menu with high cost performance. For example, it proposes a menu using inexpensive ingredients. The electronic flyer linking unit also proposes a menu with high cost performance based on the sale information in the electronic flyer. For example, it proposes a menu using ingredients that are on sale. In this way, it is possible to propose economical menus by utilizing the sale information.

[0052] The menu suggestion unit can analyze the nutritional balance of the proposed menu and suggest a healthy meal. The menu suggestion unit, for example, analyzes the nutritional balance of the proposed menu and suggests a healthy meal. For example, it suggests a menu that is rich in vitamins and minerals. The menu suggestion unit also suggests a healthy meal based on the nutritional balance. For example, it suggests a menu with a good balance of protein, carbohydrates, and lipids. The menu suggestion unit also analyzes the nutritional balance of the proposed menu and suggests a healthy meal. For example, it suggests a menu that is low in calories and highly nutritious. This makes it possible to suggest a nutritionally balanced healthy meal.

[0053] The menu suggestion unit can suggest a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. The menu suggestion unit, for example, suggests a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. For example, on busy days, it suggests a menu that can be made in a short amount of time. The menu suggestion unit also suggests a menu that matches the user's schedule, based on the cooking time. For example, on days when there is more time, it suggests a more elaborate menu. The menu suggestion unit also suggests a menu that matches the user's schedule, taking into account the cooking time of the suggested menu. For example, on busy days, it suggests a menu that can be made in a short amount of time. In this way, it is possible to suggest a menu that matches the user's schedule.

[0054] The menu suggestion unit automatically generates an ingredient shopping list based on the proposed menu, thereby reducing the effort required for shopping. The menu suggestion unit automatically generates an ingredient shopping list based on, for example, the proposed menu, thereby reducing the effort required for shopping. For example, it adds necessary ingredients to the list. The menu suggestion unit also analyzes the menu and automatically generates an ingredient shopping list. For example, it adds ingredients necessary for the proposed menu to the list. The menu suggestion unit also automatically generates an ingredient shopping list based on the proposed menu, thereby reducing the effort required for shopping. For example, it adds necessary ingredients to the list. This reduces the effort required for shopping.

[0055] The menu suggestion unit can provide cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. The menu suggestion unit, for example, provides cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. For example, a step-by-step cooking video is provided. The menu suggestion unit also provides cooking steps in the form of a video, allowing the user to easily cook. For example, cooking tips and points are explained in the video. The menu suggestion unit also provides cooking steps for the proposed menu in the form of a video, allowing the user to easily cook. For example, a step-by-step cooking video is provided. This allows the user to easily cook.

[0056] The menu suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the proposed menu and adjust the menu based on that emotion. For example, the menu suggestion unit can use the emotion estimation function to analyze the emotion the user has toward the proposed menu and adjust the menu based on that emotion. For example, it can preferentially suggest menus that make the user happy. The menu suggestion unit can also analyze the user's emotion and adjust the proposed menu. For example, it can suggest menus that avoid ingredients that the user dislikes. The menu suggestion unit can also use the emotion estimation function to analyze the emotion the user has toward the proposed menu and adjust the menu based on that emotion. For example, if the user has positive emotions toward a particular ingredient, it can suggest a menu that uses that ingredient. In this way, the menu can be adjusted based on the user's emotion.

[0057] The menu suggestion unit uses the emotion estimation function to analyze the emotions felt by all family members toward the proposed menu and can suggest a menu that will satisfy everyone. The menu suggestion unit, for example, uses the emotion estimation function to analyze the emotions felt by all family members toward the proposed menu and suggest a menu that will satisfy everyone. For example, it suggests a menu that uses ingredients that all family members like. The menu suggestion unit also analyzes the emotions of all family members and adjusts the proposed menu. For example, it suggests a menu that avoids ingredients that all family members dislike. The menu suggestion unit also uses the emotion estimation function to analyze the emotions felt by all family members toward the proposed menu and suggest a menu that will satisfy everyone. For example, if all family members have positive emotions toward a particular ingredient, it suggests a menu that uses that ingredient. In this way, it is possible to suggest a menu that will satisfy the whole family.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The menu suggestion system can also analyze the user's meal history and re-suggest menus that were popular in the past. For example, it can prioritize suggesting menus that the user has given high ratings to in the past. It can also suggest menus that use ingredients that the user particularly likes based on the meal history. It can also analyze the user's meal history and suggest menus that are popular according to the season. This makes it possible to suggest menus that are tailored to the user's preferences.

[0060] The menu suggestion system can also suggest menus that take into account the user's health condition. For example, if the user is on a diet, it can suggest a low-calorie, nutritionally balanced menu. It can also suggest allergen-free menus to a user with specific allergies. It can also suggest menus that supplement specific nutrients based on the user's health checkup results. This makes it possible to suggest menus that are tailored to the user's health condition.

[0061] The menu suggestion system can also estimate the user's emotions and suggest menus that will reduce stress and have a relaxing effect. For example, if the user is feeling stressed, it can suggest herbal tea or soup that has a relaxing effect. If the user is tired, it can also suggest menus that are suitable for replenishing energy. Furthermore, if the user feels like relaxing, it can also suggest menus that use ingredients that have a relaxing effect. This makes it possible to suggest menus that match the user's emotions.

[0062] The menu suggestion system can also learn the user's food preferences and suggest individually customized menus. For example, if the user likes a particular dish, it can suggest a menu that is an adaptation of that dish. Also, if the user likes a particular ingredient, it can suggest a new menu using that ingredient. Furthermore, it can learn the user's food preferences and suggest seasonal menus. This makes it possible to suggest menus that match the user's preferences.

[0063] The menu suggestion system can also estimate the user's emotions and suggest menus tailored to special events or anniversaries. For example, if a user is celebrating a birthday or anniversary, the system can suggest a special menu suitable for that day. Also, if the user is planning a special event, the system can suggest a menu tailored to that event. Furthermore, the system can analyze the user's emotions and suggest a menu that will be enjoyed on a special day. This makes it possible to suggest menus tailored to special events and anniversaries.

[0064] The menu suggestion system can also learn the user's food preferences and suggest menus with optimized ingredient combinations. For example, if the user likes a particular ingredient, the system can suggest new menu combinations using that ingredient. Also, if the user likes a particular dish, the system can suggest menus that are variations of that dish. Furthermore, the system can learn the user's food preferences and suggest ingredient combinations that are popular in each season. This allows for ingredient combinations that match the user's preferences.

[0065] The menu suggestion system can also estimate the user's emotions and suggest menus tailored to mealtimes. For example, if the user wants to feel refreshed at breakfast, fresh fruit and yogurt can be suggested. If the user wants to replenish their energy at lunchtime, nutritious sandwiches and salads can be suggested. Furthermore, if the user wants to relax at dinnertime, herbal teas and soups with a relaxing effect can be suggested. This allows for menu suggestions tailored to mealtimes.

[0066] The menu suggestion system can also learn the user's food preferences and suggest food preservation methods. For example, if a user frequently uses a particular ingredient, it can suggest the optimal method for preserving that ingredient. Also, if a user likes a particular dish, it can suggest the method for preserving the ingredients used in that dish. Furthermore, it can learn the user's food preferences and suggest preservation methods for ingredients that are difficult to preserve depending on the season. This reduces food waste and enables efficient preservation.

[0067] The menu suggestion system can also estimate the user's emotions and suggest menus that match the atmosphere of the meal. For example, if the user feels like relaxing, it can suggest relaxing herbal tea or soup. If the user feels like replenishing their energy, it can suggest nutritious sandwiches or salads. Furthermore, if the user is planning a special event, it can suggest a menu that matches the event. This makes it possible to suggest menus that match the atmosphere of the meal.

[0068] The menu suggestion system can also learn the user's food preferences and suggest cooking methods for ingredients. For example, if the user likes a particular ingredient, it can suggest a new cooking method for that ingredient. Also, if the user likes a particular dish, it can suggest a cooking method that adapts that dish. Furthermore, it can learn the user's food preferences and suggest cooking methods for ingredients that are difficult to cook depending on the season. This allows for cooking methods that match the user's preferences.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The scheduler linkage unit acquires the user's schedule. For example, it links with Google Calendar or Outlook Calendar to acquire the user's schedule information. Step 2: The weather information collaboration unit acquires weather information, for example, from data from the Japan Meteorological Agency or private weather forecast services. Step 3: The refrigerator link unit acquires information about ingredients in the refrigerator, for example, by scanning barcodes or manually entering the information. Step 4: The electronic flyer linking unit obtains electronic flyer information from nearby supermarkets, such as sale information and new product information from the supermarket's official app or website. Step 5: The menu suggestion unit proposes a menu based on the information obtained by the scheduler linkage unit, weather information linkage unit, refrigerator linkage unit, and electronic flyer linkage unit. For example, the generation AI analyzes schedule information, weather information, information about ingredients in the refrigerator, and information from electronic flyers to propose the optimal menu for that day and time.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] 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.

[0086] 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.

[0087] 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 AI 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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 AI 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 AI 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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 scheduler linking unit that links with a scheduler and acquires a user's schedule; a weather information linking unit that acquires weather information; a refrigerator linkage unit that acquires information about ingredients in the refrigerator; An electronic flyer linkage department that acquires electronic flyer information from nearby supermarkets, a menu suggestion unit that suggests a menu based on information acquired by the scheduler linkage unit, the weather information linkage unit, the refrigerator linkage unit, and the electronic flyer linkage unit. A system characterized by:

2. The scheduler cooperation unit Analyzing the user's past behavioral patterns and proposing the menu based on predicted behavior 2. The system of claim 1.

3. The weather information linking unit Based on the weather information, the menu is proposed taking into consideration the seasonality of ingredients.

2. The system of claim 1.

4. The refrigerator link unit is The menu is generated by taking into consideration the expiration dates of the ingredients in the refrigerator and proposing the optimal consumption order.

2. The system of claim 1.

5. The electronic flyer linking unit Based on the sale information in the electronic flyer, we will propose the above-mentioned menu with high cost performance.

2. The system of claim 1.

Images