System

Abstract

An object of a system according to an embodiment is to automatically determine the possibility of storage of a purchased product in a refrigerator and efficiently manage the product.SOLUTION: A system includes a determination unit, a registration unit, a proposal unit, and a management unit. The determination part determines the storage possibility of the purchased commodity in the refrigerator. The registration unit registers the purchased commodity determined by the determination unit in a management list in the refrigerator. The proposal unit proposes a recipe from the ingredients in the refrigerator registered by the registration unit. The management part grasps and re-manages the consumption state of the food material in the refrigerator.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] In conventional technology, the storage availability and management of purchased products in a refrigerator are done manually, which makes efficient management difficult.

[0005] The system according to the embodiment aims to automatically determine whether purchased items can be stored in a refrigerator and to efficiently manage the items. [Means for solving the problem]

[0006] The system according to the embodiment includes a determination unit, a registration unit, a suggestion unit, and a management unit. The determination unit determines whether purchased items can be stored in the refrigerator. The registration unit registers the purchased items determined by the determination unit in a refrigerator management list. The suggestion unit suggests recipes from ingredients in the refrigerator registered by the registration unit. The management unit monitors the consumption status of ingredients in the refrigerator and re-manages them. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine whether purchased items can be stored in a refrigerator and efficiently manage them. [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 refrigerator food management system according to an embodiment of the present invention is a system in which AI determines whether a product can be stored in the refrigerator at the time of purchase using code payment, and automatically registers and manages products that are determined to have a high probability of being stored in the refrigerator. As a result, the refrigerator food management system determines whether a purchased product can be stored in the refrigerator, automatically registers the product in a management list for the refrigerator, suggests recipes, and can grasp and re-manage consumption status.

[0029] A refrigerator food management system according to an embodiment includes a determination unit, a registration unit, a suggestion unit, and a management unit. The determination unit determines whether a purchased item can be stored in the refrigerator. For example, the generation AI analyzes information about the purchased item and determines whether it is likely to be stored in the refrigerator. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to determine whether the purchased item can be stored in the refrigerator. The registration unit registers the purchased item determined by the determination unit in a refrigerator management list. For example, if the generation AI determines that "milk" can be stored in the refrigerator, that information is automatically registered in the refrigerator management system. The suggestion unit suggests recipes based on the ingredients in the refrigerator registered by the registration unit. For example, if the refrigerator contains "tomatoes," "cheese," and "basil," the generation AI suggests a recipe for "Caprese" using these ingredients. The generation AI generates recipes based on the list of ingredients in the refrigerator. The management unit monitors and re-manages the consumption status of ingredients in the refrigerator. For example, the generation AI detects that "milk" has been consumed and removes it from the refrigerator management list. It also automatically adds newly purchased ingredients. As a result, the refrigerator food management system of the embodiment can determine whether purchased items can be stored in the refrigerator, automatically register them in a refrigerator management list, suggest recipes, and understand and re-manage consumption status.

[0030] The determination unit collects data on the temperature and humidity of purchased products and can determine whether they can be stored in a refrigerator based on these environmental conditions. The determination unit, for example, uses temperature and humidity sensors on the purchased products to collect environmental data on the products in real time. For example, the generation AI determines whether they can be stored in a refrigerator based on the temperature range and humidity conditions required for storage in the refrigerator. This makes it possible to determine whether purchased products can be stored in a refrigerator based on their temperature and humidity.

[0031] The determination unit analyzes the package information of the purchased product and can determine whether it can be stored in a refrigerator based on that information. For example, the determination unit scans the barcode of the purchased product, and the generation AI determines whether it can be stored in a refrigerator based on that information. For example, it analyzes the product information contained in the barcode and determines whether it needs to be stored in a refrigerator. This makes it possible to determine whether it can be stored in a refrigerator based on the package information of the purchased product.

[0032] When determining the storage possibility of a purchased item, the determination unit also takes into account other storage locations such as the freezer or pantry, and is able to suggest the optimal storage location. For example, when determining the storage possibility of a purchased item, the determination unit also takes into account other storage locations such as the freezer or pantry, and the generation AI suggests the optimal storage location. For example, if the refrigerator is full, the freezer or pantry will be suggested. This makes it possible to suggest the optimal storage location taking into account other storage locations such as the freezer or pantry.

[0033] When determining the storage possibility of a purchased item, the determination unit can make a more accurate determination by referring to the user's past purchase history and consumption patterns. For example, when determining the storage possibility of a purchased item, the determination unit can make a more accurate determination by referring to the user's past purchase history, allowing the generation AI to make a more accurate determination. For example, if the same item was purchased in the past and stored in the refrigerator, it can be determined that it is storage possible. This allows a more accurate determination of storage possibility by referring to the user's past purchase history and consumption patterns.

[0034] The registration unit can automatically analyze the expiration dates and best-before dates of purchased products and register them in the refrigerator's management list based on that information. For example, the registration unit scans the expiration dates and best-before dates written on the packaging of purchased products, and the generation AI automatically registers them in the refrigerator's management list based on that information. For example, the information is obtained using a barcode or QR code. This allows the expiration dates and best-before dates of purchased products to be automatically analyzed and registered in the refrigerator's management list.

[0035] The registration unit can analyze the nutritional information of purchased items and register them in the refrigerator management list based on that information. For example, the registration unit scans the nutritional information written on the packaging of purchased items, and the generation AI automatically registers them in the refrigerator management list based on that information. For example, the information is obtained using a barcode or QR code. This allows the nutritional information of purchased items to be analyzed and registered in the refrigerator management list.

[0036] When automatically registering purchased items, the registration unit also works with other home appliances in the refrigerator, enabling comprehensive food ingredient management. For example, when automatically registering purchased items, the registration unit also works with other home appliances in the refrigerator (such as a microwave oven and an oven), and the generation AI performs comprehensive food ingredient management. For example, it prioritizes the registration of ingredients that can be cooked in a microwave oven. This allows comprehensive food ingredient management in cooperation with other home appliances in the refrigerator.

[0037] When automatically registering purchased items, the registration unit works in conjunction with the user's meal plan or diet plan to perform optimal food ingredient management. For example, when automatically registering purchased items, the registration unit works in conjunction with the user's meal plan or diet plan, and the generation AI performs optimal food ingredient management. For example, low-calorie ingredients are registered preferentially. This allows optimal food ingredient management to be performed in conjunction with the user's meal plan or diet plan.

[0038] The suggestion unit can analyze the combinations of ingredients in the refrigerator and automatically generate new recipes based on those combinations. For example, the suggestion unit analyzes the list of ingredients in the refrigerator, and the generation AI automatically generates new recipes based on those combinations. For example, it suggests a recipe for "Caprese" using "tomatoes," "cheese," and "basil." This makes it possible to automatically generate new recipes based on the combinations of ingredients in the refrigerator.

[0039] The suggestion unit analyzes the nutritional information of ingredients in the refrigerator and can suggest healthy recipes based on that information. For example, the suggestion unit analyzes the nutritional information of ingredients in the refrigerator and the generation AI suggests healthy recipes based on that information. For example, it suggests a recipe for a "healthy salad" using "broccoli," "chicken breast," and "olive oil." This makes it possible to suggest healthy recipes based on the nutritional information of ingredients in the refrigerator.

[0040] When suggesting a recipe, the suggestion unit can refer to the user's past eating history and preferences to suggest more personalized recipes. For example, when suggesting a recipe, the suggestion unit can refer to the user's past eating history and have the generation AI suggest more personalized recipes. For example, the suggestion unit can suggest recipes based on dishes that the user has liked to eat in the past. This makes it possible to suggest personalized recipes based on the user's past eating history and preferences.

[0041] The suggestion unit can suggest recipes according to the season and weather when suggesting recipes. For example, the suggestion unit suggests recipes according to the season when suggesting recipes. For example, the suggestion unit suggests "hiyashi chuka" (cold Chinese noodles) and "salad" in the summer, and "hot pot dishes" and "soup" in the winter. This makes it possible to suggest recipes according to the season and weather.

[0042] The management unit monitors the consumption status of ingredients in the refrigerator in real time and can re-manage based on that information. For example, the management unit monitors the consumption status of ingredients in the refrigerator in real time and the generation AI re-manages based on that information. For example, when an ingredient is consumed, it is automatically removed from the list. This allows the consumption status of ingredients in the refrigerator to be monitored in real time and re-managed based on that information.

[0043] The management unit analyzes consumption patterns of ingredients in the refrigerator and can propose the optimal re-management method based on that information. For example, the management unit analyzes consumption patterns of ingredients in the refrigerator and the generation AI proposes the optimal re-management method based on that information. For example, ingredients that are consumed frequently are given priority in management. This allows the consumption patterns of ingredients in the refrigerator to be analyzed and the optimal re-management method to be proposed based on that information.

[0044] When the management unit grasps the consumption status of ingredients in the refrigerator, it also works with other home appliances, enabling comprehensive consumption management. For example, when the management unit grasps the consumption status of ingredients in the refrigerator, it also works with other home appliances (such as a microwave oven or oven), and the generation AI performs comprehensive consumption management. For example, ingredients cooked in the microwave oven are automatically deleted from the list. This allows comprehensive consumption management in cooperation with other home appliances.

[0045] When grasping the consumption status of ingredients in the refrigerator, the management unit works in conjunction with the user's meal plan or diet plan to perform optimal consumption management. For example, when grasping the consumption status of ingredients in the refrigerator, the management unit works in conjunction with the user's meal plan, and the generation AI performs optimal consumption management. For example, the order in which ingredients should be consumed is suggested based on the user's meal plan. This allows optimal consumption management to be performed in conjunction with the user's meal plan or diet plan.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When determining the storage possibility of a purchased product, the determination unit can collect data on the product's place of origin and transportation route, and determine whether the product can be stored in a refrigerator based on that information. For example, if the product is produced in a hot and humid region, the generation AI can determine that the product needs to be stored in a refrigerator. It can also determine that if the transportation route is long, storing the product in a refrigerator is recommended to maintain the product's freshness. This makes it possible to determine whether the product can be stored in a refrigerator based on the product's place of origin and transportation route.

[0048] When determining the storage feasibility of a purchased product, the determination unit can analyze the product's ingredient information and allergen information and determine whether the product can be stored in a refrigerator based on that information. For example, the generation AI can analyze the product's ingredient information and determine whether it contains ingredients that require storage in a refrigerator. It can also determine, based on the allergen information, whether a product containing a specific allergen is recommended for storage in a refrigerator. This makes it possible to determine whether a product can be stored in a refrigerator based on its ingredient information and allergen information.

[0049] When determining the storage possibility of a purchased product, the determination unit can collect data on the product's purpose and frequency of use and determine whether the product can be stored in a refrigerator based on that information. For example, if the product's purpose is that it is used frequently, the generation AI can determine that the product needs to be stored in a refrigerator. It can also determine that for products that are used frequently, storage in a refrigerator is recommended. This makes it possible to determine whether the product can be stored in a refrigerator based on the product's purpose and frequency of use.

[0050] When determining the storage feasibility of a purchased item, the determination unit can analyze information about the product's eco-label and environmental impact, and determine whether the item can be stored in a refrigerator based on that information. For example, the generation AI can analyze the product's eco-label and determine that environmentally friendly items are appropriate for storage in a refrigerator. It can also determine that items with a high environmental impact should be stored in a refrigerator. This makes it possible to determine whether the item can be stored in a refrigerator based on the product's eco-label and environmental impact.

[0051] The suggestion unit can also take into account the user's dietary preferences and allergy information when analyzing the combinations of ingredients in the refrigerator and automatically generating new recipes based on those combinations. For example, the generation AI can suggest appropriate recipes based on the user's favorite dishes in the past and allergy information. It can also prioritize recipes that use specific ingredients depending on the user's dietary preferences. This makes it possible to automatically generate new recipes taking into account the user's dietary preferences and allergy information.

[0052] The suggestion unit can also consider the user's health condition and nutritional balance when analyzing the nutritional information of ingredients in the refrigerator and suggesting healthy recipes based on that information. For example, the generation AI can suggest recipes using ingredients that are high in specific nutrients depending on the user's health condition. It can also suggest balanced meals taking into account the user's nutritional balance. This allows it to suggest healthy recipes that take into account the user's health condition and nutritional balance.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The judgment unit determines whether the purchased item can be stored in the refrigerator. For example, the generation AI analyzes the information about the purchased item and determines whether it is likely to be stored in the refrigerator. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to determine whether the purchased item can be stored in the refrigerator. Step 2: The registration unit registers the purchased items determined by the determination unit in the refrigerator management list. For example, if the generation AI determines that "milk" can be stored in the refrigerator, that information is automatically registered in the refrigerator management system. Step 3: The suggestion unit suggests recipes from the ingredients in the refrigerator registered by the registration unit. For example, if there are "tomatoes," "cheese," and "basil" in the refrigerator, the generation AI will suggest a "Caprese" recipe using these ingredients. The generation AI generates recipes based on the list of ingredients in the refrigerator. Step 4: The management unit monitors the consumption status of ingredients in the refrigerator and re-manages them. For example, the generation AI detects that "milk" has been consumed and removes it from the refrigerator management list. It also automatically adds newly purchased ingredients.

[0055] (Example 2) The refrigerator food management system according to an embodiment of the present invention is a system in which AI determines whether a product can be stored in the refrigerator at the time of purchase using code payment, and automatically registers and manages products that are determined to have a high probability of being stored in the refrigerator. As a result, the refrigerator food management system determines whether a purchased product can be stored in the refrigerator, automatically registers the product in a management list for the refrigerator, suggests recipes, and can grasp and re-manage consumption status.

[0056] A refrigerator food management system according to an embodiment includes a determination unit, a registration unit, a suggestion unit, and a management unit. The determination unit determines whether a purchased item can be stored in the refrigerator. For example, the generation AI analyzes information about the purchased item and determines whether it is likely to be stored in the refrigerator. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to determine whether the purchased item can be stored in the refrigerator. The registration unit registers the purchased item determined by the determination unit in a refrigerator management list. For example, if the generation AI determines that "milk" can be stored in the refrigerator, that information is automatically registered in the refrigerator management system. The suggestion unit suggests recipes based on the ingredients in the refrigerator registered by the registration unit. For example, if the refrigerator contains "tomatoes," "cheese," and "basil," the generation AI suggests a recipe for "Caprese" using these ingredients. The generation AI generates recipes based on the list of ingredients in the refrigerator. The management unit monitors and re-manages the consumption status of ingredients in the refrigerator. For example, the generation AI detects that "milk" has been consumed and removes it from the refrigerator management list. It also automatically adds newly purchased ingredients. As a result, the refrigerator food management system of the embodiment can determine whether purchased items can be stored in the refrigerator, automatically register them in a refrigerator management list, suggest recipes, and understand and re-manage consumption status.

[0057] The determination unit collects data on the temperature and humidity of purchased products and can determine whether they can be stored in a refrigerator based on these environmental conditions. The determination unit, for example, uses temperature and humidity sensors on the purchased products to collect environmental data on the products in real time. For example, the generation AI determines whether they can be stored in a refrigerator based on the temperature range and humidity conditions required for storage in the refrigerator. This makes it possible to determine whether purchased products can be stored in a refrigerator based on their temperature and humidity.

[0058] The determination unit analyzes the package information of the purchased product and can determine whether it can be stored in a refrigerator based on that information. For example, the determination unit scans the barcode of the purchased product, and the generation AI determines whether it can be stored in a refrigerator based on that information. For example, it analyzes the product information contained in the barcode and determines whether it needs to be stored in a refrigerator. This makes it possible to determine whether it can be stored in a refrigerator based on the package information of the purchased product.

[0059] The determination unit can use the emotion estimation function to analyze the emotion felt by the user at the time of purchase and determine the possibility of storing the product in the refrigerator based on that emotion. For example, the determination unit analyzes the user's facial expression at the time of purchase and determines the emotion felt by the user using the emotion estimation function. For example, if the user feels a sense of security, it determines that the product can be stored in the refrigerator. In this way, the possibility of storing the product in the refrigerator can be determined based on the user's emotion.

[0060] When determining the storage possibility of a purchased item, the determination unit also takes into account other storage locations such as the freezer or pantry, and is able to suggest the optimal storage location. For example, when determining the storage possibility of a purchased item, the determination unit also takes into account other storage locations such as the freezer or pantry, and the generation AI suggests the optimal storage location. For example, if the refrigerator is full, the freezer or pantry will be suggested. This makes it possible to suggest the optimal storage location taking into account other storage locations such as the freezer or pantry.

[0061] When determining the storage possibility of a purchased item, the determination unit can make a more accurate determination by referring to the user's past purchase history and consumption patterns. For example, when determining the storage possibility of a purchased item, the determination unit can make a more accurate determination by referring to the user's past purchase history, allowing the generation AI to make a more accurate determination. For example, if the same item was purchased in the past and stored in the refrigerator, it can be determined that it is storage possible. This allows a more accurate determination of storage possibility by referring to the user's past purchase history and consumption patterns.

[0062] The determination unit can use the emotion estimation function to analyze the emotion felt by the user at the time of purchase and suggest the optimal storage location based on that emotion. For example, the determination unit analyzes the user's facial expression at the time of purchase, determines the emotion felt by the user using the emotion estimation function, and suggests the optimal storage location based on that emotion. For example, if the user feels a sense of security, the inside of the refrigerator is suggested. In this way, the optimal storage location can be suggested based on the user's emotion.

[0063] The registration unit can automatically analyze the expiration dates and best-before dates of purchased products and register them in the refrigerator's management list based on that information. For example, the registration unit scans the expiration dates and best-before dates written on the packaging of purchased products, and the generation AI automatically registers them in the refrigerator's management list based on that information. For example, the information is obtained using a barcode or QR code. This allows the expiration dates and best-before dates of purchased products to be automatically analyzed and registered in the refrigerator's management list.

[0064] The registration unit can analyze the nutritional information of purchased items and register them in the refrigerator management list based on that information. For example, the registration unit scans the nutritional information written on the packaging of purchased items, and the generation AI automatically registers them in the refrigerator management list based on that information. For example, the information is obtained using a barcode or QR code. This allows the nutritional information of purchased items to be analyzed and registered in the refrigerator management list.

[0065] The registration unit can use the emotion estimation function to analyze the emotion felt by the user at the time of purchase and register the product in the refrigerator management list based on that emotion. For example, the registration unit analyzes the user's facial expression at the time of purchase, determines the emotion felt by the user using the emotion estimation function, and registers the product in the refrigerator management list based on that emotion. For example, if the user feels a sense of security, the registration unit registers the product. In this way, the product can be registered in the refrigerator management list based on the user's emotion.

[0066] When automatically registering purchased items, the registration unit also works with other home appliances in the refrigerator, enabling comprehensive food ingredient management. For example, when automatically registering purchased items, the registration unit also works with other home appliances in the refrigerator (such as a microwave oven and an oven), and the generation AI performs comprehensive food ingredient management. For example, it prioritizes the registration of ingredients that can be cooked in a microwave oven. This allows comprehensive food ingredient management in cooperation with other home appliances in the refrigerator.

[0067] When automatically registering purchased items, the registration unit works in conjunction with the user's meal plan or diet plan to perform optimal food ingredient management. For example, when automatically registering purchased items, the registration unit works in conjunction with the user's meal plan or diet plan, and the generation AI performs optimal food ingredient management. For example, low-calorie ingredients are registered preferentially. This allows optimal food ingredient management to be performed in conjunction with the user's meal plan or diet plan.

[0068] The registration unit uses the emotion estimation function to analyze the emotion felt by the user at the time of purchase and can perform optimal ingredient management based on that emotion. For example, the registration unit analyzes the user's facial expression at the time of purchase, determines the emotion felt by the user using the emotion estimation function, and performs optimal ingredient management based on that emotion. For example, if the user feels a sense of relief, ingredients are managed based on that emotion. This allows optimal ingredient management based on the user's emotion.

[0069] The suggestion unit can analyze the combinations of ingredients in the refrigerator and automatically generate new recipes based on those combinations. For example, the suggestion unit analyzes the list of ingredients in the refrigerator, and the generation AI automatically generates new recipes based on those combinations. For example, it suggests a recipe for "Caprese" using "tomatoes," "cheese," and "basil." This makes it possible to automatically generate new recipes based on the combinations of ingredients in the refrigerator.

[0070] The suggestion unit analyzes the nutritional information of ingredients in the refrigerator and can suggest healthy recipes based on that information. For example, the suggestion unit analyzes the nutritional information of ingredients in the refrigerator and the generation AI suggests healthy recipes based on that information. For example, it suggests a recipe for a "healthy salad" using "broccoli," "chicken breast," and "olive oil." This makes it possible to suggest healthy recipes based on the nutritional information of ingredients in the refrigerator.

[0071] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels while eating and suggest an optimal recipe based on that emotion. For example, the suggestion unit analyzes the user's facial expression while eating, determines the emotion the user feels using the emotion estimation function, and suggests an optimal recipe based on that emotion. For example, if the user is relaxed, the suggestion unit suggests a recipe based on that emotion. This makes it possible to suggest an optimal recipe based on the user's emotion.

[0072] When suggesting a recipe, the suggestion unit can refer to the user's past eating history and preferences to suggest more personalized recipes. For example, when suggesting a recipe, the suggestion unit can refer to the user's past eating history and have the generation AI suggest more personalized recipes. For example, the suggestion unit can suggest recipes based on dishes that the user has liked to eat in the past. This makes it possible to suggest personalized recipes based on the user's past eating history and preferences.

[0073] The suggestion unit can suggest recipes according to the season and weather when suggesting recipes. For example, the suggestion unit suggests recipes according to the season when suggesting recipes. For example, the suggestion unit suggests "hiyashi chuka" (cold Chinese noodles) and "salad" in the summer, and "hot pot dishes" and "soup" in the winter. This makes it possible to suggest recipes according to the season and weather.

[0074] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels while eating and suggest recipes that suit the season and weather based on that emotion. For example, the suggestion unit analyzes the user's facial expression while eating, determines the emotion the user feels using the emotion estimation function, and suggests recipes that suit the season and weather based on that emotion. For example, if the user is relaxed, a recipe is suggested based on that emotion. This makes it possible to suggest recipes that suit the season and weather based on the user's emotion.

[0075] The management unit monitors the consumption status of ingredients in the refrigerator in real time and can re-manage based on that information. For example, the management unit monitors the consumption status of ingredients in the refrigerator in real time and the generation AI re-manages based on that information. For example, when an ingredient is consumed, it is automatically removed from the list. This allows the consumption status of ingredients in the refrigerator to be monitored in real time and re-managed based on that information.

[0076] The management unit analyzes consumption patterns of ingredients in the refrigerator and can propose the optimal re-management method based on that information. For example, the management unit analyzes consumption patterns of ingredients in the refrigerator and the generation AI proposes the optimal re-management method based on that information. For example, ingredients that are consumed frequently are given priority in management. This allows the consumption patterns of ingredients in the refrigerator to be analyzed and the optimal re-management method to be proposed based on that information.

[0077] The management unit can use the emotion estimation function to analyze the emotion felt by the user when consuming ingredients and perform re-management based on that emotion. For example, the management unit analyzes the user's facial expression when consuming ingredients, determines the emotion felt by the user using the emotion estimation function, and performs re-management based on that emotion. For example, if the user feels satisfied, re-management is performed based on that emotion. This allows re-management to be performed based on the user's emotion.

[0078] When the management unit grasps the consumption status of ingredients in the refrigerator, it also works with other home appliances, enabling comprehensive consumption management. For example, when the management unit grasps the consumption status of ingredients in the refrigerator, it also works with other home appliances (such as a microwave oven or oven), and the generation AI performs comprehensive consumption management. For example, ingredients cooked in the microwave oven are automatically deleted from the list. This allows comprehensive consumption management in cooperation with other home appliances.

[0079] When grasping the consumption status of ingredients in the refrigerator, the management unit works in conjunction with the user's meal plan or diet plan to perform optimal consumption management. For example, when grasping the consumption status of ingredients in the refrigerator, the management unit works in conjunction with the user's meal plan, and the generation AI performs optimal consumption management. For example, the order in which ingredients should be consumed is suggested based on the user's meal plan. This allows optimal consumption management to be performed in conjunction with the user's meal plan or diet plan.

[0080] The management unit uses the emotion estimation function to analyze the emotion felt by the user when consuming ingredients, and can perform optimal consumption management based on that emotion. For example, the management unit analyzes the user's facial expression when consuming ingredients, determines the emotion felt by the user using the emotion estimation function, and performs optimal consumption management based on that emotion. For example, if the user feels satisfied, consumption management is performed based on that emotion. This allows optimal consumption management to be performed based on the user's emotion.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When determining the storage possibility of a purchased product, the determination unit can collect data on the product's place of origin and transportation route, and determine whether the product can be stored in a refrigerator based on that information. For example, if the product is produced in a hot and humid region, the generation AI can determine that the product needs to be stored in a refrigerator. It can also determine that if the transportation route is long, storing the product in a refrigerator is recommended to maintain the product's freshness. This makes it possible to determine whether the product can be stored in a refrigerator based on the product's place of origin and transportation route.

[0083] When determining the storage feasibility of a purchased product, the determination unit can analyze the product's ingredient information and allergen information and determine whether the product can be stored in a refrigerator based on that information. For example, the generation AI can analyze the product's ingredient information and determine whether it contains ingredients that require storage in a refrigerator. It can also determine, based on the allergen information, whether a product containing a specific allergen is recommended for storage in a refrigerator. This makes it possible to determine whether a product can be stored in a refrigerator based on its ingredient information and allergen information.

[0084] When determining the storage possibility of a purchased product, the determination unit can collect data on the product's purpose and frequency of use and determine whether the product can be stored in a refrigerator based on that information. For example, if the product's purpose is that it is used frequently, the generation AI can determine that the product needs to be stored in a refrigerator. It can also determine that for products that are used frequently, storage in a refrigerator is recommended. This makes it possible to determine whether the product can be stored in a refrigerator based on the product's purpose and frequency of use.

[0085] The determination unit can also use the emotion estimation function to analyze the emotion felt by the user at the time of purchase, predict the expiration date of the product based on that emotion, and determine whether the product can be stored in the refrigerator. For example, if the user feels excitement or anticipation at the time of purchase, it can determine that the product is likely to be consumed early and that storing it in the refrigerator is appropriate. Also, if the user feels anxiety or worry at the time of purchase, it can determine that the product is likely to be stored for a long period of time and that storing it in the refrigerator is recommended. In this way, the expiration date of the product can be predicted based on the user's emotion, and whether the product can be stored in the refrigerator can be determined.

[0086] The determination unit can also use the emotion estimation function to analyze the emotion the user feels when making a purchase and suggest a method for storing the product based on that emotion. For example, if the user feels a sense of security when making a purchase, it can suggest that the product should be stored in the refrigerator. Also, if the user feels excitement or anticipation when making a purchase, it can suggest that the product should be stored in the freezer. In this way, it is possible to suggest a method for storing the product based on the user's emotion.

[0087] When determining the storage feasibility of a purchased item, the determination unit can analyze information about the product's eco-label and environmental impact, and determine whether the item can be stored in a refrigerator based on that information. For example, the generation AI can analyze the product's eco-label and determine that environmentally friendly items are appropriate for storage in a refrigerator. It can also determine that items with a high environmental impact should be stored in a refrigerator. This makes it possible to determine whether the item can be stored in a refrigerator based on the product's eco-label and environmental impact.

[0088] The determination unit can also use the emotion estimation function to analyze the emotion felt by the user at the time of purchase, determine the consumption priority of the product based on that emotion, and register the product in a management list in the refrigerator. For example, if the user feels excited or expectant at the time of purchase, it can determine that the product is likely to be consumed early and register the product at a higher position in the management list. Also, if the user feels anxious or worried at the time of purchase, it can determine that the product is likely to be stored for a long time and register the product at a lower position in the management list. In this way, the consumption priority of the product can be determined based on the user's emotion and registered in a management list.

[0089] The suggestion unit can also take into account the user's dietary preferences and allergy information when analyzing the combinations of ingredients in the refrigerator and automatically generating new recipes based on those combinations. For example, the generation AI can suggest appropriate recipes based on the user's favorite dishes in the past and allergy information. It can also prioritize recipes that use specific ingredients depending on the user's dietary preferences. This makes it possible to automatically generate new recipes taking into account the user's dietary preferences and allergy information.

[0090] The suggestion unit can also consider the user's health condition and nutritional balance when analyzing the nutritional information of ingredients in the refrigerator and suggesting healthy recipes based on that information. For example, the generation AI can suggest recipes using ingredients that are high in specific nutrients depending on the user's health condition. It can also suggest balanced meals taking into account the user's nutritional balance. This allows it to suggest healthy recipes that take into account the user's health condition and nutritional balance.

[0091] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels while eating and suggest combinations of ingredients based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest recipes that combine ingredients that have a relaxing effect based on that emotion. Also, if the user is feeling stressed, the suggestion unit can suggest recipes that combine ingredients that have a stress-reducing effect based on that emotion. In this way, it is possible to suggest combinations of ingredients based on the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The judgment unit determines whether the purchased item can be stored in the refrigerator. For example, the generation AI analyzes the information about the purchased item and determines whether it is likely to be stored in the refrigerator. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to determine whether the purchased item can be stored in the refrigerator. Step 2: The registration unit registers the purchased items determined by the determination unit in the refrigerator management list. For example, if the generation AI determines that "milk" can be stored in the refrigerator, that information is automatically registered in the refrigerator management system. Step 3: The suggestion unit suggests recipes from the ingredients in the refrigerator registered by the registration unit. For example, if there are "tomatoes," "cheese," and "basil" in the refrigerator, the generation AI will suggest a "Caprese" recipe using these ingredients. The generation AI generates recipes based on the list of ingredients in the refrigerator. Step 4: The management unit monitors the consumption status of ingredients in the refrigerator and re-manages them. For example, the generation AI detects that "milk" has been consumed and removes it from the refrigerator management list. It also automatically adds newly purchased ingredients.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a determination unit that determines whether the purchased product can be stored in a refrigerator; a registration unit that registers the purchased product determined by the determination unit in a management list in the refrigerator; a suggestion unit that suggests recipes from ingredients stored in the refrigerator and registered by the registration unit; A management unit is provided for grasping and remanaging the consumption status of ingredients in the refrigerator. A system characterized by:

2. The determination unit When determining the storage potential of the purchased items, consider other storage locations such as the freezer or pantry and suggest the best storage location 2. The system of claim 1.

3. The registration unit The expiration date and expiry date of the purchased product are automatically analyzed, and the information is registered in the management list of the refrigerator.

2. The system of claim 1.

4. The proposal unit Analyze the combination of ingredients in the refrigerator and automatically generate new recipes based on that combination.

2. The system of claim 1.

5. The management unit The consumption status of ingredients in the refrigerator is monitored in real time, and the re-management is carried out based on that information.

2. The system of claim 1.

6. The determination unit The emotion felt by the user at the time of purchase is analyzed, and the possibility of storing the product in the refrigerator is determined based on the emotion.

2. The system of claim 1.

7. The registration unit The emotions felt by the user at the time of purchase are analyzed, and the items are registered in the management list of the refrigerator based on the emotions.

2. The system of claim 1.

8. The proposal unit Analyzes the emotions users feel while eating and suggests optimal recipes based on those emotions 2. The system of claim 1.

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