System

Abstract

To provide a system for efficiently performing the stock management of food materials, the proposal of a menu and the order of necessary food materials.SOLUTION: A system includes a stock status confirmation camera, a menu / recipe providing part, and an automatic ordering part. The stock status checking camera is installed in a refrigerator or a pantry. The menu / recipe providing unit proposes a menu or a recipe in consideration of user's preference and nutrition balance on the basis of the inventory status of the food material acquired by the inventory status checking camera. The automatic ordering unit automatically orders necessary food materials based on the stock status acquired by the stock status checking camera.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that managing food inventory, proposing menus, and ordering the necessary ingredients is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently manage food ingredient inventory, propose menus, and order necessary ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes an inventory check camera, a menu and recipe providing unit, and an auto-ordering unit. The inventory check camera is installed in the refrigerator or pantry. The menu and recipe providing unit proposes menus or recipes that take into account the user's preferences and nutritional balance based on the inventory status of ingredients acquired by the inventory check camera. The auto-ordering unit automatically orders the necessary ingredients based on the inventory status acquired by the inventory check camera. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage food inventory, propose menus, and 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 meal support system according to the embodiment of the present invention is a system that performs ingredient management for a user, menu suggestions, and automatic ordering in an integrated manner. As a result, the meal support system performs ingredient management for a user, menu suggestions, and automatic ordering in an integrated manner.

[0029] A meal assistance system according to an embodiment includes an inventory camera, a menu and recipe provider, and an auto-ordering unit. The inventory camera is installed in a refrigerator or pantry and monitors the availability of ingredients in real time. For example, the camera captures images of the refrigerator, and the generation AI analyzes the images to identify the types and quantities of ingredients. Alternatively, the camera can capture images of the pantry, and the generation AI analyzes the images to determine the availability of ingredients. The generation AI receives, for example, image data of the refrigerator as input, analyzes the image data, and outputs the availability status. The menu and recipe provider proposes menus and recipes that take into account the user's preferences and nutritional balance based on the availability of ingredients acquired by the inventory camera. For example, if a user requests, "I want to eat more vegetables today," the generation AI proposes an appropriate menu based on the request. The generation AI receives, as input, information about the user's requests and nutritional balance and generates menus and recipes based on that information. The auto-ordering unit automatically orders the necessary ingredients based on the availability acquired by the inventory camera. For example, if inventory in the refrigerator is low, the generation AI automatically accesses the online store and orders the necessary ingredients. The generation AI receives data on inventory status as input, generates an order list based on that data, and sends it to the online store. This allows the meal assistance system according to the embodiment to centrally manage ingredients for the user, suggest menus, and automatically order. For example, users can save the effort of planning their daily menus and easily prepare nutritionally balanced meals. Furthermore, they can avoid forgetting to buy ingredients and the hassle of inventory management, allowing them to enjoy cooking more efficiently.

[0030] The inventory status check camera can automatically determine the freshness or expiration date of ingredients based on image data captured by the camera and notify the user. For example, the inventory status check camera analyzes the image data captured by the camera and determines freshness based on changes in the color or shape of ingredients. For example, if the color of a vegetable changes, it will determine that the freshness has decreased and notify the user. The inventory status check camera also uses image analysis technology to read the expiration date printed on the package and notify the user if the expiration date is approaching. For example, it can automatically recognize the expiration date on the package and notify the user by listing ingredients that are approaching their expiration date. In this way, by automatically determining the freshness and expiration date of ingredients and notifying the user, food waste can be reduced.

[0031] When analyzing the inventory status of ingredients, the inventory status check camera also takes into account the weight or volume of the ingredients, allowing it to provide more accurate inventory information. For example, the inventory status check camera is equipped with a sensor that measures the weight of ingredients in conjunction with image data captured by the camera, and analyzes the inventory status based on changes in weight. For example, if the weight of vegetables decreases, it is determined that inventory is low. The inventory status check camera also estimates the volume of ingredients using image analysis technology, and analyzes the inventory status based on changes in volume. For example, if the volume of an ingredient decreases, it is determined that inventory is low. In this way, by taking into account the weight and volume of ingredients, more accurate inventory information can be provided.

[0032] Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, allowing for overall food management. Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, creating a system for centralized food inventory management. For example, a camera in the pantry can check the inventory of canned goods and dried foods. Also, cameras installed throughout the kitchen can check the ingredients on the counter and storage shelves to understand the inventory status. This allows for more efficient inventory management by managing ingredients not only in the refrigerator, but also in the pantry and the entire kitchen.

[0033] The inventory status check camera can suggest ways to arrange or organize ingredients based on image data from the camera, supporting efficient storage. The inventory status check camera, for example, analyzes image data captured by the camera to build a system that suggests ways to arrange and organize ingredients. For example, it suggests ways to arrange vegetables and fruits efficiently. The inventory status check camera also suggests optimal storage methods based on the type of ingredient and how often it is used. For example, it suggests a method of placing ingredients that are used frequently in the front and ingredients that can be stored for a long time in the back. In this way, efficient storage can be supported by suggesting ways to arrange and organize ingredients.

[0034] The menu and recipe provider can propose individually optimized menus or recipes based on the user's past meal history or health data. For example, the menu and recipe provider can analyze the user's past meal history and build a system that proposes menus and recipes that take into account preferences and nutritional balance. For example, it can propose new menus based on dishes that were popular in the past. The menu and recipe provider can also propose menus and recipes that suit the user's health condition based on the user's health data. For example, it can propose low-carbohydrate menus to users with high blood sugar levels. This makes it possible to support the user's health management by proposing individually optimized menus and recipes based on the user's past meal history and health data.

[0035] The menu and recipe providing unit can propose ingredients or recipes according to the season or weather, and provide meals that incorporate a sense of the season. For example, the menu and recipe providing unit builds a system that considers seasonal ingredients and proposes menus and recipes that incorporate a sense of the season. For example, in spring, it proposes dishes using fresh vegetables. The menu and recipe providing unit also proposes ingredients and recipes according to the weather. For example, it proposes hot soup on cold days. In this way, by proposing ingredients and recipes according to the season and weather, it is possible to provide meals that incorporate a sense of the season.

[0036] The menu and recipe providing unit can incorporate local specialties or traditional dishes when proposing menus or recipes, thereby providing meals that reflect the region's characteristics. For example, the menu and recipe providing unit can consider local specialties and build a system that proposes menus and recipes that use those specialties. For example, it can propose dishes that use fresh local fish. The menu and recipe providing unit can also propose menus that incorporate local traditional dishes. For example, it can propose dishes that use traditional local cooking methods. In this way, by incorporating local specialties and traditional dishes, it is possible to provide meals that reflect the region's characteristics.

[0037] The menu and recipe providing unit considers the user's family structure or lifestyle when proposing menus or recipes, and can provide meals that the whole family can enjoy. The menu and recipe providing unit, for example, considers the user's family structure and builds a system that proposes menus and recipes that the whole family can enjoy. For example, it proposes menus that incorporate dishes that children like. The menu and recipe providing unit also considers the user's lifestyle and proposes dishes that are easy to make on busy days. For example, it proposes recipes that can be prepared in a short amount of time. In this way, by proposing menus and recipes that take family structure and lifestyle into consideration, it is possible to provide meals that the whole family can enjoy.

[0038] The auto-ordering unit can automatically select the optimal brand or product when placing an order, taking into consideration the user's purchase history or preferences. The auto-ordering unit, for example, analyzes the user's purchase history and builds a system that generates an optimal order list based on the brands and products purchased in the past. For example, it prioritizes ordering ingredients from brands that the user prefers. The auto-ordering unit also takes into consideration the user's preferences and orders specific brands and products. For example, if the user prefers a specific brand of milk, it prioritizes ordering that brand of milk. This allows the user's satisfaction to be improved by selecting the optimal brand or product based on the user's purchase history and preferences.

[0039] The auto-ordering unit optimizes the timing of ordering to match the user's lifestyle or consumption pace, enabling efficient ordering. The auto-ordering unit, for example, analyzes the user's lifestyle and builds a system that suggests the optimal ordering timing. For example, if the user has a habit of shopping on weekends, the auto-ordering unit places orders at that timing. The auto-ordering unit also analyzes the user's consumption pace and places orders that match that consumption pace. For example, it analyzes the consumption rate of specific ingredients and prioritizes ordering ingredients that are consumed quickly. This allows for efficient ordering by optimizing the timing of ordering to match the user's lifestyle or consumption pace.

[0040] The auto-ordering unit can expand the auto-ordering function not only to food ingredients but also to seasonings or daily necessities, allowing for purchasing management for the entire household. The auto-ordering unit, for example, builds a system that expands the auto-ordering function not only to food ingredients but also to seasonings and daily necessities. For example, it automatically places an order when seasonings are running low in stock. The auto-ordering unit also analyzes the inventory status of daily necessities and automatically orders the necessary daily necessities. For example, it automatically places an order when toilet paper or detergent is running low in stock. In this way, by expanding the auto-ordering function not only to food ingredients but also to seasonings and daily necessities, purchasing management for the entire household becomes possible.

[0041] The auto ordering unit provides an option to purchase directly from local farmers or producers when placing an order, thereby supporting the local economy. The auto ordering unit builds a system that provides an option to purchase directly from local farmers or producers when placing an order, for example. For example, it prioritizes ordering fresh local vegetables and fruits. The auto ordering unit also recommends purchasing from local producers to support the local economy. For example, purchasing directly from local farmers can help revitalize the local economy. This makes it possible to support the local economy by providing an option to purchase directly from local farmers or producers.

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

[0043] The meal assistance system can also propose menus that take into account the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, the system can propose a menu that does not include that ingredient. It can also propose menus that include many of the user's favorite ingredients based on the user's past eating history. Furthermore, if the user requests to avoid a particular ingredient, the system can adjust the menu based on that request. This makes it possible to propose menus that meet the user's individual needs, thereby improving meal satisfaction.

[0044] The inventory check camera not only takes into account the weight and volume of ingredients, but also analyzes the nutritional value of ingredients and notifies the user. For example, the camera can analyze the nutritional value of ingredients based on image data captured by the camera and notify the user. If a specific nutrient is lacking, it can also suggest ingredients that contain a high amount of that nutrient. This allows users to understand the nutritional value of ingredients and prepare balanced meals.

[0045] Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, allowing for overall food management. For example, a camera in the pantry can check the inventory of canned goods and dried foods. In addition, cameras installed throughout the kitchen can check the ingredients on the counter and storage shelves to understand the inventory status. This allows for more efficient inventory management by managing ingredients not only in the refrigerator but also in the pantry and the entire kitchen.

[0046] The inventory status check camera can suggest ways to arrange or organize ingredients based on the image data from the camera, supporting efficient storage. For example, a system can be built that analyzes the image data captured by the camera and suggests ways to arrange and organize ingredients. For example, it can suggest ways to arrange vegetables and fruits efficiently. It can also suggest optimal storage methods based on the type of ingredient and how often it is used. This can support efficient storage by suggesting ways to arrange and organize ingredients.

[0047] The menu and recipe provider can propose individually optimized menus or recipes based on the user's past meal history or health data. For example, a system can be built that analyzes the user's past meal history and proposes menus and recipes that take into account preferences and nutritional balance. For example, a new menu can be proposed based on dishes that were popular in the past. It can also propose menus and recipes that suit the user's health status based on the user's health data. This makes it possible to support the user's health management by proposing individually optimized menus and recipes based on the user's past meal history and health data.

[0048] The menu and recipe provider can propose ingredients or recipes according to the season or weather, and provide meals that incorporate a sense of the season. For example, a system can be built that takes into account seasonal ingredients and proposes menus and recipes that incorporate a sense of the season. For example, in spring, dishes using fresh vegetables can be proposed. It can also propose ingredients and recipes that correspond to the weather. For example, hot soup can be proposed on cold days. In this way, by proposing ingredients and recipes that correspond to the season and weather, meals that incorporate a sense of the season can be provided.

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

[0050] Step 1: Check inventory status. The camera is installed in the refrigerator or pantry and checks the inventory status of ingredients in real time. For example, the camera can take pictures of the inside of the refrigerator, and the generation AI can analyze those images to recognize the type and quantity of ingredients. The camera can also take pictures of the inside of the pantry, and the generation AI can analyze those images to determine the inventory status of ingredients. Step 2: The menu and recipe provider proposes menus and recipes that take into account the user's preferences and nutritional balance based on the availability of ingredients captured by the inventory check camera. For example, if a user requests, "I want to eat a lot of vegetables today," the generation AI will propose an appropriate menu based on that request. The generation AI receives information about the user's request and nutritional balance as input, and generates menus and recipes based on that information. Step 3: The auto-ordering unit automatically orders the necessary ingredients based on the inventory status acquired by the inventory status check camera. For example, if the refrigerator is low on stock, the generation AI automatically accesses the online store and orders the necessary ingredients. The generation AI receives data on the inventory status as input, generates an order list based on that data, and sends it to the online store.

[0051] (Example 2) The meal support system according to the embodiment of the present invention is a system that performs ingredient management for a user, menu suggestions, and automatic ordering in an integrated manner. As a result, the meal support system performs ingredient management for a user, menu suggestions, and automatic ordering in an integrated manner.

[0052] A meal assistance system according to an embodiment includes an inventory camera, a menu and recipe provider, and an auto-ordering unit. The inventory camera is installed in a refrigerator or pantry and monitors the availability of ingredients in real time. For example, the camera captures images of the refrigerator, and the generation AI analyzes the images to identify the types and quantities of ingredients. Alternatively, the camera can capture images of the pantry, and the generation AI analyzes the images to determine the availability of ingredients. The generation AI receives, for example, image data of the refrigerator as input, analyzes the image data, and outputs the availability status. The menu and recipe provider proposes menus and recipes that take into account the user's preferences and nutritional balance based on the availability of ingredients acquired by the inventory camera. For example, if a user requests, "I want to eat more vegetables today," the generation AI proposes an appropriate menu based on the request. The generation AI receives, as input, information about the user's requests and nutritional balance and generates menus and recipes based on that information. The auto-ordering unit automatically orders the necessary ingredients based on the availability acquired by the inventory camera. For example, if inventory in the refrigerator is low, the generation AI automatically accesses the online store and orders the necessary ingredients. The generation AI receives data on inventory status as input, generates an order list based on that data, and sends it to the online store. This allows the meal assistance system according to the embodiment to centrally manage ingredients for the user, suggest menus, and automatically order. For example, users can save the effort of planning their daily menus and easily prepare nutritionally balanced meals. Furthermore, they can avoid forgetting to buy ingredients and the hassle of inventory management, allowing them to enjoy cooking more efficiently.

[0053] The inventory status check camera can automatically determine the freshness or expiration date of ingredients based on image data captured by the camera and notify the user. For example, the inventory status check camera analyzes the image data captured by the camera and determines freshness based on changes in the color or shape of ingredients. For example, if the color of a vegetable changes, it will determine that the freshness has decreased and notify the user. The inventory status check camera also uses image analysis technology to read the expiration date printed on the package and notify the user if the expiration date is approaching. For example, it can automatically recognize the expiration date on the package and notify the user by listing ingredients that are approaching their expiration date. In this way, by automatically determining the freshness and expiration date of ingredients and notifying the user, food waste can be reduced.

[0054] When analyzing the inventory status of ingredients, the inventory status check camera also takes into account the weight or volume of the ingredients, allowing it to provide more accurate inventory information. For example, the inventory status check camera is equipped with a sensor that measures the weight of ingredients in conjunction with image data captured by the camera, and analyzes the inventory status based on changes in weight. For example, if the weight of vegetables decreases, it is determined that inventory is low. The inventory status check camera also estimates the volume of ingredients using image analysis technology, and analyzes the inventory status based on changes in volume. For example, if the volume of an ingredient decreases, it is determined that inventory is low. In this way, by taking into account the weight and volume of ingredients, more accurate inventory information can be provided.

[0055] The inventory status check camera can use its emotion estimation function to analyze the user's emotions toward a particular ingredient and optimize inventory management based on those emotions. For example, the inventory status check camera can analyze the user's facial expressions in conjunction with image data captured by the camera to estimate the user's emotions toward a particular ingredient. For example, the inventory status check camera can analyze the user's facial expressions when looking at vegetables and manage more inventory if the user has positive emotions. The inventory status check camera can also use voice analysis technology to analyze the user's tone and speed of voice to estimate the user's emotions toward a particular ingredient. For example, the inventory status check camera can analyze the user's tone of voice when talking about a particular ingredient and manage more inventory if the user has positive emotions. This allows for optimizing inventory management based on the user's emotions, thereby improving user satisfaction.

[0056] Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, allowing for overall food management. Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, creating a system for centralized food inventory management. For example, a camera in the pantry can check the inventory of canned goods and dried foods. Also, cameras installed throughout the kitchen can check the ingredients on the counter and storage shelves to understand the inventory status. This allows for more efficient inventory management by managing ingredients not only in the refrigerator, but also in the pantry and the entire kitchen.

[0057] The inventory status check camera can suggest ways to arrange or organize ingredients based on image data from the camera, supporting efficient storage. The inventory status check camera, for example, analyzes image data captured by the camera to build a system that suggests ways to arrange and organize ingredients. For example, it suggests ways to arrange vegetables and fruits efficiently. The inventory status check camera also suggests optimal storage methods based on the type of ingredient and how often it is used. For example, it suggests a method of placing ingredients that are used frequently in the front and ingredients that can be stored for a long time in the back. In this way, efficient storage can be supported by suggesting ways to arrange and organize ingredients.

[0058] The inventory check camera can use its emotion estimation function to analyze the user's emotions in real time when they see a specific ingredient and suggest ingredients that will elicit positive emotions. For example, the inventory check camera can analyze the user's facial expressions in real time in conjunction with image data captured by the camera and suggest ingredients that will elicit positive emotions. For example, the inventory check camera can analyze the user's facial expressions when they see vegetables and suggest ingredients that will elicit positive emotions. The inventory check camera can also use voice analysis technology to analyze the tone and speed of the user's voice in real time and suggest ingredients that will elicit positive emotions. For example, the camera can analyze the tone of the user's voice when talking about a specific ingredient and suggest ingredients that will elicit positive emotions. This can improve meal satisfaction by suggesting ingredients that will elicit positive emotions based on the user's emotions.

[0059] The menu and recipe provider can propose individually optimized menus or recipes based on the user's past meal history or health data. For example, the menu and recipe provider can analyze the user's past meal history and build a system that proposes menus and recipes that take into account preferences and nutritional balance. For example, it can propose new menus based on dishes that were popular in the past. The menu and recipe provider can also propose menus and recipes that suit the user's health condition based on the user's health data. For example, it can propose low-carbohydrate menus to users with high blood sugar levels. This makes it possible to support the user's health management by proposing individually optimized menus and recipes based on the user's past meal history and health data.

[0060] The menu and recipe providing unit can propose ingredients or recipes according to the season or weather, and provide meals that incorporate a sense of the season. For example, the menu and recipe providing unit builds a system that considers seasonal ingredients and proposes menus and recipes that incorporate a sense of the season. For example, in spring, it proposes dishes using fresh vegetables. The menu and recipe providing unit also proposes ingredients and recipes according to the weather. For example, it proposes hot soup on cold days. In this way, by proposing ingredients and recipes according to the season and weather, it is possible to provide meals that incorporate a sense of the season.

[0061] The menu and recipe providing unit uses the emotion estimation function to suggest a menu that matches the user's current mood or emotion, thereby improving meal satisfaction. The menu and recipe providing unit, for example, uses the emotion estimation function to analyze the user's current mood or emotion, and builds a system that suggests a menu based on the results. For example, if the user wants to relax, the unit suggests dishes that use ingredients that have a relaxing effect. The menu and recipe providing unit also suggests a menu that uses ingredients that reduce stress based on the user's emotion. For example, if the user is feeling stressed, the unit suggests dishes that use ingredients that have a stress-reducing effect. In this way, meal satisfaction can be improved by suggesting a menu that matches the user's mood or emotion.

[0062] The menu and recipe providing unit can incorporate local specialties or traditional dishes when proposing menus or recipes, thereby providing meals that reflect the region's characteristics. For example, the menu and recipe providing unit can consider local specialties and build a system that proposes menus and recipes that use those specialties. For example, it can propose dishes that use fresh local fish. The menu and recipe providing unit can also propose menus that incorporate local traditional dishes. For example, it can propose dishes that use traditional local cooking methods. In this way, by incorporating local specialties and traditional dishes, it is possible to provide meals that reflect the region's characteristics.

[0063] The menu and recipe providing unit considers the user's family structure or lifestyle when proposing menus or recipes, and can provide meals that the whole family can enjoy. The menu and recipe providing unit, for example, considers the user's family structure and builds a system that proposes menus and recipes that the whole family can enjoy. For example, it proposes menus that incorporate dishes that children like. The menu and recipe providing unit also considers the user's lifestyle and proposes dishes that are easy to make on busy days. For example, it proposes recipes that can be prepared in a short amount of time. In this way, by proposing menus and recipes that take family structure and lifestyle into consideration, it is possible to provide meals that the whole family can enjoy.

[0064] The menu and recipe providing unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients or dishes, and suggest recipes based on those emotions. For example, the menu and recipe providing unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients or dishes, and build a system that suggests recipes based on the results. For example, it can suggest dishes that use ingredients for which the user has positive emotions. The menu and recipe providing unit can also suggest specific dishes based on the user's emotions. For example, if the user has positive emotions toward a specific dish, it can suggest that dish. In this way, it can improve meal satisfaction by suggesting recipes based on the user's emotions.

[0065] The auto-ordering unit can automatically select the optimal brand or product when placing an order, taking into consideration the user's purchase history or preferences. The auto-ordering unit, for example, analyzes the user's purchase history and builds a system that generates an optimal order list based on the brands and products purchased in the past. For example, it prioritizes ordering ingredients from brands that the user prefers. The auto-ordering unit also takes into consideration the user's preferences and orders specific brands and products. For example, if the user prefers a specific brand of milk, it prioritizes ordering that brand of milk. This allows the user's satisfaction to be improved by selecting the optimal brand or product based on the user's purchase history and preferences.

[0066] The auto-ordering unit optimizes the timing of ordering to match the user's lifestyle or consumption pace, enabling efficient ordering. The auto-ordering unit, for example, analyzes the user's lifestyle and builds a system that suggests the optimal ordering timing. For example, if the user has a habit of shopping on weekends, the auto-ordering unit places orders at that timing. The auto-ordering unit also analyzes the user's consumption pace and places orders that match that consumption pace. For example, it analyzes the consumption rate of specific ingredients and prioritizes ordering ingredients that are consumed quickly. This allows for efficient ordering by optimizing the timing of ordering to match the user's lifestyle or consumption pace.

[0067] The auto-ordering unit can use the emotion estimation function to analyze the emotion a user has toward a specific brand or product and place an order based on that emotion. For example, the auto-ordering unit uses the emotion estimation function to analyze the emotion a user has toward a specific brand or product, and builds a system that places an order based on the results. For example, it prioritizes ordering products from brands for which the user has positive emotions. The auto-ordering unit also orders specific products based on the user's emotions. For example, if the user has positive emotions toward a specific product, it prioritizes ordering that product. In this way, by placing an order based on the user's emotions, it is possible to improve user satisfaction.

[0068] The auto-ordering unit can expand the auto-ordering function not only to food ingredients but also to seasonings or daily necessities, allowing for purchasing management for the entire household. The auto-ordering unit, for example, builds a system that expands the auto-ordering function not only to food ingredients but also to seasonings and daily necessities. For example, it automatically places an order when seasonings are running low in stock. The auto-ordering unit also analyzes the inventory status of daily necessities and automatically orders the necessary daily necessities. For example, it automatically places an order when toilet paper or detergent is running low in stock. In this way, by expanding the auto-ordering function not only to food ingredients but also to seasonings and daily necessities, purchasing management for the entire household becomes possible.

[0069] The auto ordering unit provides an option to purchase directly from local farmers or producers when placing an order, thereby supporting the local economy. The auto ordering unit builds a system that provides an option to purchase directly from local farmers or producers when placing an order, for example. For example, it prioritizes ordering fresh local vegetables and fruits. The auto ordering unit also recommends purchasing from local producers to support the local economy. For example, purchasing directly from local farmers can help revitalize the local economy. This makes it possible to support the local economy by providing an option to purchase directly from local farmers or producers.

[0070] The auto-ordering unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients or products in real time and suggest products that elicit positive emotions. For example, the auto-ordering unit uses the emotion estimation function to analyze the emotions a user has toward specific ingredients or products in real time and build a system that suggests products that elicit positive emotions based on the results. For example, the auto-ordering unit preferentially suggests products that the user has positive emotions about. The auto-ordering unit also suggests specific products based on the user's emotions. For example, if the user has positive emotions about a specific product, the auto-ordering unit preferentially suggests that product. In this way, by suggesting products that elicit positive emotions based on the user's emotions, user satisfaction can be improved.

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

[0072] The meal assistance system can also propose menus that take into account the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, the system can propose a menu that does not include that ingredient. It can also propose menus that include many of the user's favorite ingredients based on the user's past eating history. Furthermore, if the user requests to avoid a particular ingredient, the system can adjust the menu based on that request. This makes it possible to propose menus that meet the user's individual needs, thereby improving meal satisfaction.

[0073] The inventory status check camera uses its emotion estimation function to analyze the emotions a user has toward a particular ingredient and optimize inventory management based on that emotion. For example, it can analyze the user's facial expression when looking at vegetables and manage more inventory if the user has a positive emotion. It can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate the emotion toward a particular ingredient. This can improve user satisfaction by optimizing inventory management based on the user's emotions.

[0074] The inventory check camera not only takes into account the weight and volume of ingredients, but also analyzes the nutritional value of ingredients and notifies the user. For example, the camera can analyze the nutritional value of ingredients based on image data captured by the camera and notify the user. If a specific nutrient is lacking, it can also suggest ingredients that contain a high amount of that nutrient. This allows users to understand the nutritional value of ingredients and prepare balanced meals.

[0075] The inventory check camera uses its emotion estimation function to analyze the user's emotions in real time when looking at specific ingredients and can suggest ingredients that elicit positive emotions. For example, it can analyze the user's facial expression when looking at vegetables and suggest ingredients that elicit positive emotions. It can also use voice analysis technology to analyze the tone and speed of the user's voice in real time and suggest ingredients that elicit positive emotions. This can improve meal satisfaction by suggesting ingredients that elicit positive emotions based on the user's emotions.

[0076] Inventory status check cameras can be installed not only in the refrigerator, but also in the pantry or the entire kitchen, allowing for overall food management. For example, a camera in the pantry can check the inventory of canned goods and dried foods. In addition, cameras installed throughout the kitchen can check the ingredients on the counter and storage shelves to understand the inventory status. This allows for more efficient inventory management by managing ingredients not only in the refrigerator but also in the pantry and the entire kitchen.

[0077] The inventory status check camera can suggest ways to arrange or organize ingredients based on the image data from the camera, supporting efficient storage. For example, a system can be built that analyzes the image data captured by the camera and suggests ways to arrange and organize ingredients. For example, it can suggest ways to arrange vegetables and fruits efficiently. It can also suggest optimal storage methods based on the type of ingredient and how often it is used. This can support efficient storage by suggesting ways to arrange and organize ingredients.

[0078] The menu and recipe provider can propose individually optimized menus or recipes based on the user's past meal history or health data. For example, a system can be built that analyzes the user's past meal history and proposes menus and recipes that take into account preferences and nutritional balance. For example, a new menu can be proposed based on dishes that were popular in the past. It can also propose menus and recipes that suit the user's health status based on the user's health data. This makes it possible to support the user's health management by proposing individually optimized menus and recipes based on the user's past meal history and health data.

[0079] The menu and recipe provider can propose ingredients or recipes according to the season or weather, and provide meals that incorporate a sense of the season. For example, a system can be built that takes into account seasonal ingredients and proposes menus and recipes that incorporate a sense of the season. For example, in spring, dishes using fresh vegetables can be proposed. It can also propose ingredients and recipes that correspond to the weather. For example, hot soup can be proposed on cold days. In this way, by proposing ingredients and recipes that correspond to the season and weather, meals that incorporate a sense of the season can be provided.

[0080] The menu and recipe provider can use the emotion estimation function to suggest a menu that matches the user's current mood or emotion, thereby improving meal satisfaction. For example, a system can be constructed that uses the emotion estimation function to analyze the user's current mood or emotion and suggest a menu based on the results. For example, if the user feels like relaxing, it can suggest dishes that use ingredients that have a relaxing effect. It can also suggest a menu that uses ingredients that reduce stress based on the user's emotion. This makes it possible to improve meal satisfaction by suggesting a menu that matches the user's mood or emotion.

[0081] The menu and recipe provider can use the emotion estimation function to analyze the emotions a user has toward specific ingredients or dishes and suggest recipes based on those emotions. For example, a system can be constructed that uses the emotion estimation function to analyze the emotions a user has toward specific ingredients or dishes and suggest recipes based on the results. For example, it can suggest dishes using ingredients that the user has positive emotions about. It can also suggest specific dishes based on the user's emotions. This can improve meal satisfaction by suggesting recipes based on the user's emotions.

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

[0083] Step 1: Check inventory status. The camera is installed in the refrigerator or pantry and checks the inventory status of ingredients in real time. For example, the camera can take pictures of the inside of the refrigerator, and the generation AI can analyze those images to recognize the type and quantity of ingredients. The camera can also take pictures of the inside of the pantry, and the generation AI can analyze those images to determine the inventory status of ingredients. Step 2: The menu and recipe provider proposes menus and recipes that take into account the user's preferences and nutritional balance based on the availability of ingredients captured by the inventory check camera. For example, if a user requests, "I want to eat a lot of vegetables today," the generation AI will propose an appropriate menu based on that request. The generation AI receives information about the user's request and nutritional balance as input, and generates menus and recipes based on that information. Step 3: The auto-ordering unit automatically orders the necessary ingredients based on the inventory status acquired by the inventory status check camera. For example, if the refrigerator is low on stock, the generation AI automatically accesses the online store and orders the necessary ingredients. The generation AI receives data on the inventory status as input, generates an order list based on that data, and sends it to the online store.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0112] 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.

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

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

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

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

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

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

[0128] 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.

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

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

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

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

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

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

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

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

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

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

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

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

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

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

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

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

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

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

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

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

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

Claims

1. an inventory check camera installed in the refrigerator or pantry; a menu and recipe providing unit that proposes a menu or recipe that takes into consideration the user's preferences and nutritional balance based on the inventory status of ingredients acquired by the inventory status confirmation camera; and an auto-ordering unit that automatically orders necessary ingredients based on the inventory status acquired by the inventory status confirmation camera. A system characterized by:

2. The inventory status confirmation camera is Based on the image data captured by the camera, the freshness or expiration date of the food material is automatically determined and notified to the user. The system of claim 1 .

3. The inventory status confirmation camera is It is installed not only in the refrigerator but also in the pantry or the entire kitchen to manage ingredients comprehensively. The system of claim 1 .

4. The menu and recipe providing unit Proposing the menu or recipe that is individually optimized based on the user's past meal history or health data The system of claim 1 .

5. The auto ordering unit When placing the order, the most suitable brand or product is automatically selected taking into consideration the user's purchasing history or preferences. The system of claim 1 .

6. The inventory status confirmation camera is Using emotion estimation function, the emotion that the user has towards a specific ingredient is analyzed, and inventory management is optimized based on that emotion. The system of claim 1 .

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