System

Abstract

An object of a system according to an embodiment is to manage stock in a refrigerator and efficiently propose a menu.SOLUTION: A system includes a camera part, an analysis part, a proposal part, and a purchase instruction part. The camera unit takes a picture of the inside of the refrigerator. The analysis unit analyzes the photograph taken by the camera unit. The proposal part proposes a menu on the basis of the result analyzed by the analysis part. The purchase instruction part sets a regular menu, specifies the insufficient material and issues a purchase instruction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, managing refrigerator inventory and suggesting menus was a time-consuming process, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to manage the inventory in the refrigerator and efficiently suggest menus. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an analysis unit, a proposal unit, and a purchase instruction unit. The camera unit takes a photo of the inside of the refrigerator. The analysis unit analyzes the photo taken by the camera unit. The proposal unit proposes a menu based on the results of the analysis by the analysis unit. The purchase instruction unit sets a regular menu, identifies any ingredients that are lacking, and issues purchase instructions. [Effects of the Invention]

[0007] The system according to the embodiment can manage the inventory in the refrigerator and efficiently suggest menus. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention periodically takes photos of the inside of a refrigerator and analyzes them to determine what is in stock and propose a menu. This system includes a camera unit that takes photos of the inside of the refrigerator, an analysis unit (including AI generation processing) that analyzes the photos, a proposal unit (including AI generation processing) that proposes menus based on the analysis results, and a purchase instruction unit (which may also include AI generation processing) that sets regular menus, identifies missing ingredients, and issues purchase instructions. For example, photos of the inside of the refrigerator are periodically taken, and the AI ​​analyzes the photos to identify what is in stock. The AI ​​then proposes menus taking into account the user's preferences, rankings, carbohydrate restrictions, and other options. Furthermore, if a regular menu is set and any missing ingredients are found, the AI ​​can automatically issue purchase instructions. This allows the system to efficiently manage refrigerator inventory and propose menus tailored to the user's preferences. Furthermore, by preventing forgotten purchases, food waste can be reduced.

[0029] A refrigerator management system according to an embodiment includes a camera unit, an analysis unit, a proposal unit, and a purchase instruction unit. The camera unit takes photos of the interior of the refrigerator. For example, the camera unit may be installed on the refrigerator door and take photos each time the door is opened or closed. The camera unit may also take photos automatically on a periodic basis. The analysis unit uses a generation AI to analyze the photos taken by the camera unit. For example, the analysis unit may identify each item using image recognition technology. The generation AI may recognize each item in the photo and identify the items in stock in the refrigerator. The proposal unit uses the generation AI to propose a menu based on the analysis results by the analysis unit. For example, the proposal unit may propose a menu taking into account the user's preferences, rankings, carbohydrate restriction, and other options. The purchase instruction unit sets a regular menu, identifies missing ingredients, and issues purchase instructions. For example, the purchase instruction unit may identify missing ingredients based on the regular menu and automatically issue purchase instructions. This allows the refrigerator management system according to an embodiment to efficiently manage inventory in the refrigerator and propose menus tailored to the user's preferences. Furthermore, by preventing forgetting to buy ingredients, food waste can be reduced.

[0030] The camera unit is installed on the refrigerator door and can take pictures every time the door is opened or closed. The camera unit is installed on the refrigerator door, for example, and automatically takes pictures every time the door is opened or closed. Furthermore, by taking pictures when the door is opened or closed, the situation inside the refrigerator can be grasped in real time. This makes it possible to always manage the latest inventory status inside the refrigerator. Furthermore, the camera unit can automatically take pictures periodically, in addition to when the door is opened or closed. For example, it can be set to take pictures at 9:00 a.m. and 6:00 p.m. every day. This makes it possible to periodically grasp the situation inside the refrigerator. This makes it possible to grasp the situation inside the refrigerator in real time.

[0031] The analysis unit can identify each item using image recognition technology. The analysis unit, for example, uses a generation AI to analyze a photo taken by the camera unit. The analysis unit identifies each item using image recognition technology. For example, the analysis unit uses object detection technology to recognize each item in the photo. The analysis unit can also apply facial recognition technology to identify specific items. Furthermore, the analysis unit can also use pattern recognition technology to identify the type and quantity of items in the photo. This allows for accurate identification of inventory in the refrigerator. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input photo data into the generation AI and have the generation AI identify the items.

[0032] The suggestion unit can suggest a menu taking into consideration the user's preferences, rankings, and carbohydrate restriction options. The suggestion unit, for example, uses a generation AI to suggest a menu based on the results of analysis by the analysis unit. The suggestion unit suggests a menu taking into consideration the user's preferences, rankings, carbohydrate restriction options, and other options. For example, the suggestion unit suggests a menu taking into consideration the user's favorite dishes and ingredients to avoid that have been registered in advance. The suggestion unit can also suggest popular dishes based on the user's ranking information. Furthermore, the suggestion unit can suggest a menu taking into consideration carbohydrate restriction. For example, the suggestion unit suggests a menu using low-carbohydrate ingredients taking into consideration carbohydrate restriction. This makes it possible to suggest a menu tailored to the user's individual needs. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and have the generation AI execute menu suggestions.

[0033] The purchase instruction unit can identify missing ingredients based on a regular menu and automatically issue a purchase instruction. For example, the purchase instruction unit can set a regular menu, identify missing ingredients, and issue a purchase instruction. The purchase instruction unit can identify missing ingredients based on the regular menu and automatically issue a purchase instruction. For example, the purchase instruction unit can set a specific menu for every Monday and automatically issue a purchase instruction if ingredients required for that menu are missing. The purchase instruction unit can also identify missing ingredients based on a regular menu set in advance by the user and place an online order. Furthermore, the purchase instruction unit can use a notification function to prompt the user to purchase missing ingredients. This prevents users from forgetting to buy ingredients and enables efficient shopping. Some or all of the above-described processing in the purchase instruction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the purchase instruction unit can input information about missing ingredients into the generation AI and have the generation AI output a purchase instruction.

[0034] The camera unit can detect the temperature and humidity inside the refrigerator and select the appropriate timing for taking a photo. For example, the camera unit detects the temperature and humidity inside the refrigerator and selects the appropriate timing for taking a photo. For example, if the temperature inside the refrigerator rises, the camera unit immediately takes a photo to prevent food from deteriorating. The camera unit can also take a photo when the humidity inside the refrigerator increases to check for food deterioration due to humidity. The camera unit can also maintain regular photo timing when the temperature and humidity are stable. This allows the optimal timing for taking a photo to be selected depending on the environment inside the refrigerator. The temperature and humidity are detected using, for example, a sensor. The sensor may be a temperature sensor or a humidity sensor, and these sensors are used to monitor the environment inside the refrigerator. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input temperature and humidity data into the generation AI and have the generation AI select the timing for taking a photo.

[0035] The camera unit can automatically adjust the lighting conditions inside the refrigerator when taking a photo to obtain an appropriate image. For example, the camera unit automatically adjusts the lighting conditions inside the refrigerator when taking a photo to obtain an appropriate image. For example, if the lighting inside the refrigerator is dim, the camera unit automatically increases the lighting before taking a photo. If the lighting inside the refrigerator is too bright, the camera unit can adjust the lighting to obtain an appropriate brightness. If the lighting conditions are inappropriate, the camera unit can temporarily postpone taking a photo and take a photo when the lighting becomes appropriate. This allows taking a photo under optimal lighting conditions and improving image quality. The lighting conditions are adjusted using, for example, a lighting sensor. The lighting sensor detects the brightness inside the refrigerator and automatically adjusts the lighting intensity. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the camera unit can input lighting condition data into the generation AI and have the generation AI adjust the lighting.

[0036] The camera unit records the position information of each shelf in the refrigerator when capturing an image, which can be used for later analysis. For example, the camera unit records the position information of each shelf in the refrigerator when capturing an image, which can be used for later analysis. For example, the camera unit records the position information of each shelf and prioritizes analysis of items on a specific shelf during analysis. The camera unit can also analyze item placement patterns based on the shelf position information, allowing for efficient inventory management. The camera unit can also record the shelf position information and prioritize checking the expiration dates of items on a specific shelf during later analysis. In this way, recording the shelf position information can improve analysis accuracy. The position information is recorded using, for example, coordinate data or shelf numbers. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input shelf position information to the generation AI and cause the generation AI to perform data processing to be used for analysis.

[0037] The camera unit can apply different shooting settings to different zones in the refrigerator when taking a photo. For example, the camera unit applies different shooting settings to different zones in the refrigerator when taking a photo. For example, the camera unit can increase the lighting in the upper zone of the refrigerator when taking a photo. The camera unit can also decrease the lighting in the lower zone of the refrigerator when taking a photo. The camera unit can also use standard lighting settings in the central zone of the refrigerator when taking a photo. This allows optimal shooting settings to be applied to different zones in the refrigerator. Application of different shooting settings includes adjustment of exposure, focus, white balance, etc. Some or all of the above-described processing in the camera unit may be performed using, or without, the generation AI. For example, the camera unit can input shooting setting data for each zone to the generation AI and have the generation AI apply the shooting settings.

[0038] The camera unit can focus on a specific item based on a voice command in the refrigerator when taking a photograph. For example, the camera unit focuses on a specific item based on a voice command in the refrigerator when taking a photograph. For example, if a user gives the voice command "milk," the camera unit automatically focuses on milk and takes a photograph. Also, if a user gives the voice command "eggs," the camera unit can automatically focus on eggs and take a photograph. Also, if a user gives the voice command "vegetables," the camera unit can automatically focus on vegetables and take a photograph. This allows a specific item to be focused on and photographed based on the voice command. Recognition of the voice command is performed using, for example, a voice command or voice recognition technology. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the camera unit can input voice command data to the generation AI and have the generation AI focus on the item.

[0039] The camera unit can record the number of times the refrigerator door is opened and closed when taking a photo, and adjust the frequency of taking photos based on the frequency of use. For example, the camera unit can record the number of times the refrigerator door is opened and closed when taking a photo, and adjust the frequency of taking photos based on the frequency of use. For example, if the number of times the door is opened and closed is frequent, the camera unit can increase the frequency of taking photos to grasp the situation inside the refrigerator in detail. Furthermore, if the number of times the door is opened and closed is infrequent, the camera unit can reduce the frequency of taking photos and take the minimum number of photos necessary. Furthermore, if the number of times the door is opened and closed is constant, the camera unit can maintain regular photo timing. This allows the frequency of taking photos to be adjusted based on the number of times the door is opened and closed. The number of times the door is opened and closed is recorded, for example, using a sensor. The sensor detects the door opening and closing and records the number of times. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input data on the number of times the door is opened and closed to the generation AI and have the generation AI adjust the frequency of taking photos.

[0040] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, uses a generation AI to optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, improves the recognition accuracy of a specific item based on the past analysis data. The analysis unit can also adjust parameters of the analysis algorithm by referring to the past analysis data. The analysis unit can also analyze the past analysis data and identify areas for improvement in the analysis algorithm. This makes it possible to optimize the analysis algorithm based on the past analysis data. The reference to the past analysis data is performed, for example, using a database or historical data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] The analysis unit can provide analysis results taking into account the expiration dates of items in the refrigerator during analysis. The analysis unit can provide analysis results taking into account the expiration dates of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit, for example, prioritizes analysis of items with an approaching expiration date and notifies the user. The analysis unit can also identify items that have passed their expiration date and suggest to the user that they be discarded. The analysis unit can also analyze items with long expiration dates and notify the user that they can be stored for a long period of time. This makes it possible to provide analysis results that take expiration dates into account. Expiration dates are taken into account using, for example, date information or best-before dates. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expiration date data into the generation AI and have the generation AI provide the analysis results.

[0042] The analysis unit can learn the item placement pattern in the refrigerator during analysis and improve the analysis accuracy. The analysis unit can learn the item placement pattern in the refrigerator during analysis, for example, using a generation AI, and improve the analysis accuracy. The analysis unit can, for example, learn the item placement pattern and improve the analysis accuracy based on the specific placement pattern. The analysis unit can also improve the item recognition accuracy based on the placement pattern. The analysis unit can also learn the placement pattern and perform efficient inventory management. In this way, the analysis accuracy can be improved by learning the item placement pattern. The placement pattern is learned using, for example, a machine learning algorithm or pattern recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the placement pattern data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] The analysis unit can provide analysis results by taking into account weight information of items in the refrigerator during analysis. The analysis unit can provide analysis results by taking into account weight information of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit, for example, accurately grasps inventory amounts based on the weight information of items. The analysis unit can also take weight information into account and prioritize analysis of items with close expiration dates. The analysis unit can also analyze item placement patterns based on weight information to perform efficient inventory management. This makes it possible to provide analysis results that take weight information into account. Weight information can be taken into account using, for example, a sensor. The sensor measures the weight of the items and uses the data in the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input weight information data to the generation AI and have the generation AI provide the analysis results.

[0044] The analysis unit can read barcode information of items in the refrigerator during analysis to improve analysis accuracy. The analysis unit can use, for example, a generation AI to read barcode information of items in the refrigerator during analysis to improve analysis accuracy. The analysis unit can obtain detailed information about items based on the barcode information to improve analysis accuracy. The analysis unit can also read barcode information and reflect expiration dates and nutritional information in the analysis. The analysis unit can also analyze item placement patterns based on the barcode information to perform efficient inventory management. This can improve analysis accuracy based on the barcode information. Barcode information is read using, for example, a barcode scanner or a two-dimensional code reader. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input barcode information data to the generation AI and have the generation AI improve analysis accuracy.

[0045] The analysis unit can provide analysis results by taking into account the nutritional information of items in the refrigerator during analysis. The analysis unit can provide analysis results by taking into account the nutritional information of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit can, for example, suggest healthy menus based on the nutritional information. The analysis unit can also take into account the nutritional information and notify the user if a specific nutrient is lacking. The analysis unit can also provide analysis results tailored to the user's health condition based on the nutritional information. This makes it possible to provide analysis results that take into account the nutritional information. The nutritional information is taken into account using data such as calories and vitamin content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input nutritional information data to the generation AI and have the generation AI provide the analysis results.

[0046] The suggestion unit can suggest an optimal menu by referring to the user's past meal history when suggesting a menu. The suggestion unit can suggest an optimal menu by referring to the user's past meal history when suggesting a menu, for example, using a generation AI. The suggestion unit can suggest a menu that suits the user's preferences, for example, based on the past meal history. The suggestion unit can also suggest a balanced menu by referring to the past meal history. The suggestion unit can also analyze the past meal history and suggest a menu that suits the user's health condition. This makes it possible to suggest an optimal menu based on the past meal history. The past meal history is referenced, for example, using a database or history data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input past meal history data into the generation AI and cause the generation AI to suggest an optimal menu.

[0047] The suggestion unit can suggest different menus depending on the season and weather when making a suggestion. The suggestion unit, for example, uses a generation AI to suggest different menus depending on the season and weather when making a suggestion. The suggestion unit, for example, suggests menus using seasonal ingredients depending on the season. The suggestion unit can also suggest hot dishes and cold dishes depending on the weather. The suggestion unit can also suggest nutritionally balanced menus taking the season and weather into consideration. This makes it possible to suggest an optimal menu depending on the season and weather. The season and weather are considered using data such as temperature, humidity, and seasonal ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input season and weather data into the generation AI and cause the generation AI to suggest an optimal menu.

[0048] The suggestion unit can customize a menu taking into account the user's health condition when proposing the menu. The suggestion unit customizes a menu taking into account the user's health condition when proposing the menu, for example, using a generation AI. The suggestion unit, for example, suggests a menu containing specific nutrients according to the user's health condition. The suggestion unit can also suggest low-calorie or low-carbohydrate menus taking into account the user's health condition. The suggestion unit can also suggest a balanced menu based on the user's health condition. This makes it possible to suggest an optimal menu according to the user's health condition. The health condition is taken into account using, for example, medical data or fitness data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input health condition data into the generation AI and cause the generation AI to customize the menu.

[0049] The suggestion unit can suggest a menu taking into consideration the user's family composition when proposing the menu. The suggestion unit, for example, uses a generation AI to suggest a menu taking into consideration the user's family composition when proposing the menu. The suggestion unit, for example, suggests a menu with portion sizes tailored to the number of family members. The suggestion unit can also suggest a nutritionally balanced menu tailored to the age range of the family members. The suggestion unit can also suggest a menu taking into consideration the preferences and allergy information of the family members. This makes it possible to suggest an optimal menu tailored to the family composition. The family composition is taken into consideration using data such as the number of family members and their age range. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input family composition data into the generation AI and have the generation AI suggest an optimal menu.

[0050] The suggestion unit can suggest a menu taking into consideration the user's ingredient allergy information when suggesting the menu. The suggestion unit, for example, uses a generation AI to suggest a menu taking into consideration the user's ingredient allergy information when suggesting the menu. The suggestion unit, for example, suggests a menu that avoids ingredients to which the user is allergic. The suggestion unit can also suggest a menu that uses alternative ingredients based on the allergy information. The suggestion unit can also suggest a menu that uses safe ingredients by taking into consideration the allergy information. This makes it possible to suggest a safe menu based on the allergy information. The ingredient allergy information is taken into consideration using, for example, an allergen list or medical data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input ingredient allergy information data into the generation AI and cause the generation AI to suggest an optimal menu.

[0051] The suggestion unit can customize the menu by reflecting the user's ingredient preferences and dislikes when proposing the menu. The suggestion unit, for example, uses a generation AI to customize the menu by reflecting the user's ingredient preferences and dislikes when proposing the menu. The suggestion unit, for example, suggests a menu using ingredients that the user likes. The suggestion unit can also suggest a menu that avoids ingredients that the user dislikes. The suggestion unit can also suggest a balanced menu based on the user's preferences and dislikes. This makes it possible to suggest an optimal menu based on the user's preferences and dislikes. The ingredient preferences and dislikes are taken into consideration using, for example, a questionnaire or past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input data on ingredient preferences and dislikes into the generation AI and have the generation AI customize the menu.

[0052] The purchase instruction unit can issue optimal purchase instructions by referring to the user's past purchase history when issuing a purchase instruction. The purchase instruction unit can issue optimal purchase instructions by referring to the user's past purchase history, for example, using a generation AI. The purchase instruction unit, for example, prioritizes frequently purchased items based on the past purchase history. The purchase instruction unit can also refer to the past purchase history and issue purchase instructions for all necessary items. The purchase instruction unit can also analyze the past purchase history and create an efficient shopping list. This makes it possible to issue optimal purchase instructions based on the past purchase history. The past purchase history is referenced using, for example, a database or history data. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input past purchase history data into the generation AI and cause the generation AI to output optimal purchase instructions.

[0053] The purchase instruction unit can customize the purchase instruction taking into account the user's budget information when issuing the purchase instruction. The purchase instruction unit customizes the purchase instruction taking into account the user's budget information when issuing the purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes items that can be purchased within the user's budget. The purchase instruction unit can also efficiently issue purchase instructions for necessary items based on the budget information. The purchase instruction unit can also suggest items with high cost performance taking into account the budget information. This makes it possible to issue optimal purchase instructions based on the budget information. The budget information is taken into account using, for example, a monthly budget or expenditure history. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input budget information data into the generation AI and have the generation AI customize the purchase instruction.

[0054] The purchase instruction unit can learn the user's purchasing patterns when issuing a purchase instruction, thereby improving the accuracy of the purchase instruction. The purchase instruction unit can, for example, use a generation AI to learn the user's purchasing patterns when issuing a purchase instruction, thereby improving the accuracy of the purchase instruction. The purchase instruction unit can, for example, learn the user's purchasing patterns and prioritize frequently purchased items. The purchase instruction unit can also issue purchase instructions for all necessary items based on the purchasing patterns. The purchase instruction unit can also learn the purchasing patterns and create an efficient shopping list. In this way, the accuracy of the purchase instructions can be improved by learning the purchasing patterns. The purchasing patterns are learned using, for example, a machine learning algorithm or pattern recognition technology. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchasing pattern data into the generation AI and cause the generation AI to improve the accuracy of the purchase instructions.

[0055] The purchase instruction unit can select the optimal purchase destination by taking into account the user's geographical location information when issuing a purchase instruction. The purchase instruction unit can select the optimal purchase destination by taking into account the user's geographical location information when issuing a purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes selecting stores close to the user's current location. The purchase instruction unit can also suggest an efficient shopping route based on the geographical location information. The purchase instruction unit can also select the optimal purchase destination by taking into account the geographical location information. This makes it possible to select the optimal purchase destination based on the geographical location information. The geographical location information can be taken into account using, for example, GPS data or address information. Some or all of the above-mentioned processing in the purchase instruction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the purchase instruction unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal purchase destination.

[0056] The purchase instruction unit can analyze the user's purchase history at the time of issuing a purchase instruction and provide relevant coupons and discount information. The purchase instruction unit can analyze the user's purchase history at the time of issuing a purchase instruction, for example, using a generation AI, and provide relevant coupons and discount information. The purchase instruction unit can provide relevant coupons based on the purchase history, for example. The purchase instruction unit can also analyze the purchase history and provide discount information. The purchase instruction unit can also provide coupons and discount information that encourage efficient shopping based on the purchase history. This makes it possible to provide coupons and discount information based on the purchase history. The coupons and discount information are provided using, for example, electronic coupons or discount codes. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchase history data into the generation AI and cause the generation AI to provide coupons and discount information.

[0057] The purchase instruction unit can adjust the timing of the purchase instruction taking into account the user's purchase frequency when issuing a purchase instruction. The purchase instruction unit can adjust the timing of the purchase instruction taking into account the user's purchase frequency when issuing a purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes purchase instructions for items with high purchase frequencies. The purchase instruction unit can also issue purchase instructions for all necessary items based on the purchase frequency. The purchase instruction unit can also create an efficient shopping list taking into account the purchase frequency. This makes it possible to select the optimal timing of the purchase instruction based on the purchase frequency. The purchase frequency is taken into account using, for example, weekly or monthly purchase frequency data. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchase frequency data into the generation AI and cause the generation AI to adjust the timing of the purchase instruction.

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

[0059] The camera unit can detect the temperature of each shelf in the refrigerator and adjust the timing of image capture according to the temperature. For example, if the temperature of a specific shelf in the refrigerator rises, it can immediately take a photo to prevent food from deteriorating. Furthermore, if the temperature is stable, it can maintain regular image capture timing. Furthermore, if the temperature drops, it can reduce the frequency of image capture and only take the minimum number of photos necessary. This allows the optimal image capture timing to be selected according to the temperature inside the refrigerator. Temperature detection is performed, for example, using a temperature sensor. Some or all of the above-described processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input temperature data into the generation AI and have the generation AI adjust the image capture timing.

[0060] The analysis unit can improve analysis accuracy by taking into account shape information of items in the refrigerator. For example, the analysis unit can accurately recognize specific items based on the item's shape information. It can also distinguish between items with similar shapes by taking into account the shape information. Furthermore, it can analyze item placement patterns based on the shape information and perform efficient inventory management. This makes it possible to provide analysis results that take shape information into account. Shape information can be taken into account using, for example, image recognition technology or pattern recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input shape information data into a generation AI and have the generation AI improve analysis accuracy.

[0061] The camera unit can detect the color of items in the refrigerator and adjust the timing of photographing based on the color. For example, if the color of a specific item in the refrigerator changes, a photograph can be taken immediately to prevent food deterioration. Furthermore, if the color remains stable, regular photographing can be maintained. Furthermore, if the color does not change, the frequency of photographing can be reduced to take only the minimum number of photographs necessary. This allows the optimal photographing timing to be selected according to the color of the items in the refrigerator. Color detection is performed, for example, using a color sensor. Some or all of the above-described processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input color data into the generation AI and have the generation AI adjust the photographing timing.

[0062] The camera unit can detect the size of items in the refrigerator and adjust the timing of photographing based on the size. For example, if the size of a specific item in the refrigerator changes, a photograph can be taken immediately to prevent food deterioration. If the size remains stable, regular photographing can be maintained. Furthermore, if the size does not change, the frequency of photographing can be reduced and only the minimum number of photographs necessary can be taken. This allows the optimal photographing timing to be selected according to the size of the items in the refrigerator. Size detection is performed, for example, using a size sensor. Some or all of the above-mentioned processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input size data into the generation AI and have the generation AI adjust the photographing timing.

[0063] The analysis unit can provide analysis results taking into account the frequency of use of items in the refrigerator. For example, the analysis unit can prioritize analysis of frequently used items and notify the user. It can also identify items that are used less frequently and suggest to the user that they be discarded. It can also analyze items that are used at a medium frequency and perform efficient inventory management. This makes it possible to provide analysis results that take into account frequency of use. The frequency of use is taken into account using, for example, usage history data or sensor information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input frequency of use data into the generation AI and have the generation AI provide the analysis results.

[0064] The suggestion unit can suggest a menu taking into consideration the user's mealtimes. For example, it can suggest a lighter menu for breakfast and a menu suitable for replenishing energy for lunch. It can also suggest a balanced menu for dinner. It can also suggest a lighter menu that is easy to digest for a late-night snack. This makes it possible to suggest an optimal menu according to the mealtimes. The mealtimes are taken into consideration using, for example, the user's schedule data and time information. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input mealtime data into the generation AI and have the generation AI suggest an optimal menu.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The camera unit takes a photo of the inside of the refrigerator. For example, the camera unit can be installed on the refrigerator door and take a photo every time the door is opened or closed. The camera unit can also take photos automatically at regular intervals. Step 2: The analysis unit uses the generation AI to analyze the photos taken by the camera unit. For example, the analysis unit uses image recognition technology to identify each item. The generation AI recognizes each item in the photo and identifies the inventory in the refrigerator. Step 3: The suggestion unit uses the generation AI to propose a menu based on the results of the analysis by the analysis unit. For example, the suggestion unit may propose a menu taking into account the user's preferences, rankings, carbohydrate restrictions, and other options. Step 4: The purchase instruction unit sets a regular menu, identifies the ingredients that are in short supply, and issues a purchase instruction. For example, the purchase instruction unit can identify the ingredients that are in short supply based on the regular menu and automatically issue a purchase instruction.

[0067] (Example 2) A system according to an embodiment of the present invention periodically takes photos of the inside of a refrigerator and analyzes them to determine what is in stock and propose a menu. This system includes a camera unit that takes photos of the inside of the refrigerator, an analysis unit (including AI generation processing) that analyzes the photos, a proposal unit (including AI generation processing) that proposes menus based on the analysis results, and a purchase instruction unit (which may also include AI generation processing) that sets regular menus, identifies missing ingredients, and issues purchase instructions. For example, photos of the inside of the refrigerator are periodically taken, and the AI ​​analyzes the photos to identify what is in stock. The AI ​​then proposes menus taking into account the user's preferences, rankings, carbohydrate restrictions, and other options. Furthermore, if a regular menu is set and any missing ingredients are found, the AI ​​can automatically issue purchase instructions. This allows the system to efficiently manage refrigerator inventory and propose menus tailored to the user's preferences. Furthermore, by preventing forgotten purchases, food waste can be reduced.

[0068] A refrigerator management system according to an embodiment includes a camera unit, an analysis unit, a proposal unit, and a purchase instruction unit. The camera unit takes photos of the interior of the refrigerator. For example, the camera unit may be installed on the refrigerator door and take photos each time the door is opened or closed. The camera unit may also take photos automatically on a periodic basis. The analysis unit uses a generation AI to analyze the photos taken by the camera unit. For example, the analysis unit may identify each item using image recognition technology. The generation AI may recognize each item in the photo and identify the items in stock in the refrigerator. The proposal unit uses the generation AI to propose a menu based on the analysis results by the analysis unit. For example, the proposal unit may propose a menu taking into account the user's preferences, rankings, carbohydrate restriction, and other options. The purchase instruction unit sets a regular menu, identifies missing ingredients, and issues purchase instructions. For example, the purchase instruction unit may identify missing ingredients based on the regular menu and automatically issue purchase instructions. This allows the refrigerator management system according to an embodiment to efficiently manage inventory in the refrigerator and propose menus tailored to the user's preferences. Furthermore, by preventing forgetting to buy ingredients, food waste can be reduced.

[0069] The camera unit is installed on the refrigerator door and can take pictures every time the door is opened or closed. The camera unit is installed on the refrigerator door, for example, and automatically takes pictures every time the door is opened or closed. Furthermore, by taking pictures when the door is opened or closed, the situation inside the refrigerator can be grasped in real time. This makes it possible to always manage the latest inventory status inside the refrigerator. Furthermore, the camera unit can automatically take pictures periodically, in addition to when the door is opened or closed. For example, it can be set to take pictures at 9:00 a.m. and 6:00 p.m. every day. This makes it possible to periodically grasp the situation inside the refrigerator. This makes it possible to grasp the situation inside the refrigerator in real time.

[0070] The analysis unit can identify each item using image recognition technology. The analysis unit, for example, uses a generation AI to analyze a photo taken by the camera unit. The analysis unit identifies each item using image recognition technology. For example, the analysis unit uses object detection technology to recognize each item in the photo. The analysis unit can also apply facial recognition technology to identify specific items. Furthermore, the analysis unit can also use pattern recognition technology to identify the type and quantity of items in the photo. This allows for accurate identification of inventory in the refrigerator. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input photo data into the generation AI and have the generation AI identify the items.

[0071] The suggestion unit can suggest a menu taking into consideration the user's preferences, rankings, and carbohydrate restriction options. The suggestion unit, for example, uses a generation AI to suggest a menu based on the results of analysis by the analysis unit. The suggestion unit suggests a menu taking into consideration the user's preferences, rankings, carbohydrate restriction options, and other options. For example, the suggestion unit suggests a menu taking into consideration the user's favorite dishes and ingredients to avoid that have been registered in advance. The suggestion unit can also suggest popular dishes based on the user's ranking information. Furthermore, the suggestion unit can suggest a menu taking into consideration carbohydrate restriction. For example, the suggestion unit suggests a menu using low-carbohydrate ingredients taking into consideration carbohydrate restriction. This makes it possible to suggest a menu tailored to the user's individual needs. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI and have the generation AI execute menu suggestions.

[0072] The purchase instruction unit can identify missing ingredients based on a regular menu and automatically issue a purchase instruction. For example, the purchase instruction unit can set a regular menu, identify missing ingredients, and issue a purchase instruction. The purchase instruction unit can identify missing ingredients based on the regular menu and automatically issue a purchase instruction. For example, the purchase instruction unit can set a specific menu for every Monday and automatically issue a purchase instruction if ingredients required for that menu are missing. The purchase instruction unit can also identify missing ingredients based on a regular menu set in advance by the user and place an online order. Furthermore, the purchase instruction unit can use a notification function to prompt the user to purchase missing ingredients. This prevents users from forgetting to buy ingredients and enables efficient shopping. Some or all of the above-described processing in the purchase instruction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the purchase instruction unit can input information about missing ingredients into the generation AI and have the generation AI output a purchase instruction.

[0073] The camera unit can detect the user's emotions and adjust the timing of taking pictures based on the detected user emotions. For example, the camera unit can detect the user's emotions and adjust the timing of taking pictures based on the detected user emotions. For example, if the user is feeling stressed, the camera unit can take pictures every time the refrigerator door is opened and closed, reducing the user's burden. Furthermore, if the user is relaxed, the camera unit can maintain regular shooting timing and accurately grasp the situation inside the refrigerator. Furthermore, if the user is in a hurry, the camera unit can reduce the frequency of shooting and take the minimum number of pictures necessary. This makes it possible to select the optimal shooting timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the camera unit can be performed using, for example, the generation AI. For example, the camera unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of taking pictures.

[0074] The camera unit can detect the temperature and humidity inside the refrigerator and select the appropriate timing for taking a photo. For example, the camera unit detects the temperature and humidity inside the refrigerator and selects the appropriate timing for taking a photo. For example, if the temperature inside the refrigerator rises, the camera unit immediately takes a photo to prevent food from deteriorating. The camera unit can also take a photo when the humidity inside the refrigerator increases to check for food deterioration due to humidity. The camera unit can also maintain regular photo timing when the temperature and humidity are stable. This allows the optimal timing for taking a photo to be selected depending on the environment inside the refrigerator. The temperature and humidity are detected using, for example, a sensor. The sensor may be a temperature sensor or a humidity sensor, and these sensors are used to monitor the environment inside the refrigerator. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input temperature and humidity data into the generation AI and have the generation AI select the timing for taking a photo.

[0075] The camera unit can automatically adjust the lighting conditions inside the refrigerator when taking a photo to obtain an appropriate image. For example, the camera unit automatically adjusts the lighting conditions inside the refrigerator when taking a photo to obtain an appropriate image. For example, if the lighting inside the refrigerator is dim, the camera unit automatically increases the lighting before taking a photo. If the lighting inside the refrigerator is too bright, the camera unit can adjust the lighting to obtain an appropriate brightness. If the lighting conditions are inappropriate, the camera unit can temporarily postpone taking a photo and take a photo when the lighting becomes appropriate. This allows taking a photo under optimal lighting conditions and improving image quality. The lighting conditions are adjusted using, for example, a lighting sensor. The lighting sensor detects the brightness inside the refrigerator and automatically adjusts the lighting intensity. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the camera unit can input lighting condition data into the generation AI and have the generation AI adjust the lighting.

[0076] The camera unit records the position information of each shelf in the refrigerator when capturing an image, which can be used for later analysis. For example, the camera unit records the position information of each shelf in the refrigerator when capturing an image, which can be used for later analysis. For example, the camera unit records the position information of each shelf and prioritizes analysis of items on a specific shelf during analysis. The camera unit can also analyze item placement patterns based on the shelf position information, allowing for efficient inventory management. The camera unit can also record the shelf position information and prioritize checking the expiration dates of items on a specific shelf during later analysis. In this way, recording the shelf position information can improve analysis accuracy. The position information is recorded using, for example, coordinate data or shelf numbers. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input shelf position information to the generation AI and cause the generation AI to perform data processing to be used for analysis.

[0077] The camera unit can detect the user's emotions and determine the priority of images to be captured based on the detected user emotions. For example, the camera unit can detect the user's emotions and determine the priority of images to be captured based on the detected user emotions. For example, when the user is feeling stressed, the camera unit can prioritize capturing important items and perform a quick analysis. Furthermore, when the user is relaxed, the camera unit can capture an overall image of the refrigerator interior and perform a detailed analysis. Furthermore, when the user is in a hurry, the camera unit can prioritize capturing only the essential items and perform a quick analysis. This allows the priority of images to be captured to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit can be performed using, for example, the generation AI. For example, the camera unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of images.

[0078] The camera unit can apply different shooting settings to different zones in the refrigerator when taking a photo. For example, the camera unit applies different shooting settings to different zones in the refrigerator when taking a photo. For example, the camera unit can increase the lighting in the upper zone of the refrigerator when taking a photo. The camera unit can also decrease the lighting in the lower zone of the refrigerator when taking a photo. The camera unit can also use standard lighting settings in the central zone of the refrigerator when taking a photo. This allows optimal shooting settings to be applied to different zones in the refrigerator. Application of different shooting settings includes adjustment of exposure, focus, white balance, etc. Some or all of the above-described processing in the camera unit may be performed using, or without, the generation AI. For example, the camera unit can input shooting setting data for each zone to the generation AI and have the generation AI apply the shooting settings.

[0079] The camera unit can focus on a specific item based on a voice command in the refrigerator when taking a photograph. For example, the camera unit focuses on a specific item based on a voice command in the refrigerator when taking a photograph. For example, if a user gives the voice command "milk," the camera unit automatically focuses on milk and takes a photograph. Also, if a user gives the voice command "eggs," the camera unit can automatically focus on eggs and take a photograph. Also, if a user gives the voice command "vegetables," the camera unit can automatically focus on vegetables and take a photograph. This allows a specific item to be focused on and photographed based on the voice command. Recognition of the voice command is performed using, for example, a voice command or voice recognition technology. Some or all of the above-described processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the camera unit can input voice command data to the generation AI and have the generation AI focus on the item.

[0080] The camera unit can record the number of times the refrigerator door is opened and closed when taking a photo, and adjust the frequency of taking photos based on the frequency of use. For example, the camera unit can record the number of times the refrigerator door is opened and closed when taking a photo, and adjust the frequency of taking photos based on the frequency of use. For example, if the number of times the door is opened and closed is frequent, the camera unit can increase the frequency of taking photos to grasp the situation inside the refrigerator in detail. Furthermore, if the number of times the door is opened and closed is infrequent, the camera unit can reduce the frequency of taking photos and take the minimum number of photos necessary. Furthermore, if the number of times the door is opened and closed is constant, the camera unit can maintain regular photo timing. This allows the frequency of taking photos to be adjusted based on the number of times the door is opened and closed. The number of times the door is opened and closed is recorded, for example, using a sensor. The sensor detects the door opening and closing and records the number of times. Some or all of the above-mentioned processing in the camera unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the camera unit can input data on the number of times the door is opened and closed to the generation AI and have the generation AI adjust the frequency of taking photos.

[0081] The analysis unit can detect the user's emotions and adjust the display method of the analysis results based on the detected user emotions. The analysis unit can detect the user's emotions using, for example, a generation AI and adjust the display method of the analysis results based on the detected user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal display method of the analysis results depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0082] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, uses a generation AI to optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, improves the recognition accuracy of a specific item based on the past analysis data. The analysis unit can also adjust parameters of the analysis algorithm by referring to the past analysis data. The analysis unit can also analyze the past analysis data and identify areas for improvement in the analysis algorithm. This makes it possible to optimize the analysis algorithm based on the past analysis data. The reference to the past analysis data is performed, for example, using a database or historical data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0083] The analysis unit can provide analysis results taking into account the expiration dates of items in the refrigerator during analysis. The analysis unit can provide analysis results taking into account the expiration dates of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit, for example, prioritizes analysis of items with an approaching expiration date and notifies the user. The analysis unit can also identify items that have passed their expiration date and suggest to the user that they be discarded. The analysis unit can also analyze items with long expiration dates and notify the user that they can be stored for a long period of time. This makes it possible to provide analysis results that take expiration dates into account. Expiration dates are taken into account using, for example, date information or best-before dates. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expiration date data into the generation AI and have the generation AI provide the analysis results.

[0084] The analysis unit can learn the item placement pattern in the refrigerator during analysis and improve the analysis accuracy. The analysis unit can learn the item placement pattern in the refrigerator during analysis, for example, using a generation AI, and improve the analysis accuracy. The analysis unit can, for example, learn the item placement pattern and improve the analysis accuracy based on the specific placement pattern. The analysis unit can also improve the item recognition accuracy based on the placement pattern. The analysis unit can also learn the placement pattern and perform efficient inventory management. In this way, the analysis accuracy can be improved by learning the item placement pattern. The placement pattern is learned using, for example, a machine learning algorithm or pattern recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the placement pattern data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0085] The analysis unit can detect the user's emotions and prioritize the analysis results based on the detected user emotions. The analysis unit can detect the user's emotions using, for example, a generation AI and prioritize the analysis results based on the detected user emotions. For example, when the user is feeling stressed, the analysis unit can prioritize displaying analysis results of important items. Furthermore, when the user is relaxed, the analysis unit can also display overall analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying the minimum necessary analysis results. This allows the prioritization of analysis results to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0086] The analysis unit can provide analysis results by taking into account weight information of items in the refrigerator during analysis. The analysis unit can provide analysis results by taking into account weight information of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit, for example, accurately grasps inventory amounts based on the weight information of items. The analysis unit can also take weight information into account and prioritize analysis of items with close expiration dates. The analysis unit can also analyze item placement patterns based on weight information to perform efficient inventory management. This makes it possible to provide analysis results that take weight information into account. Weight information can be taken into account using, for example, a sensor. The sensor measures the weight of the items and uses the data in the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input weight information data to the generation AI and have the generation AI provide the analysis results.

[0087] The analysis unit can read barcode information of items in the refrigerator during analysis to improve analysis accuracy. The analysis unit can use, for example, a generation AI to read barcode information of items in the refrigerator during analysis to improve analysis accuracy. The analysis unit can obtain detailed information about items based on the barcode information to improve analysis accuracy. The analysis unit can also read barcode information and reflect expiration dates and nutritional information in the analysis. The analysis unit can also analyze item placement patterns based on the barcode information to perform efficient inventory management. This can improve analysis accuracy based on the barcode information. Barcode information is read using, for example, a barcode scanner or a two-dimensional code reader. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input barcode information data to the generation AI and have the generation AI improve analysis accuracy.

[0088] The analysis unit can provide analysis results by taking into account the nutritional information of items in the refrigerator during analysis. The analysis unit can provide analysis results by taking into account the nutritional information of items in the refrigerator during analysis, for example, using a generation AI. The analysis unit can, for example, suggest healthy menus based on the nutritional information. The analysis unit can also take into account the nutritional information and notify the user if a specific nutrient is lacking. The analysis unit can also provide analysis results tailored to the user's health condition based on the nutritional information. This makes it possible to provide analysis results that take into account the nutritional information. The nutritional information is taken into account using data such as calories and vitamin content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input nutritional information data to the generation AI and have the generation AI provide the analysis results.

[0089] The suggestion unit can detect the user's emotions and adjust the presentation method of the menu based on the detected user's emotions. The suggestion unit can detect the user's emotions using, for example, a generation AI and adjust the presentation method of the menu based on the detected user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and highly visible menu. If the user is relaxed, the suggestion unit can also suggest a menu that includes detailed information. If the user is in a hurry, the suggestion unit can also suggest a menu that focuses on the main points. This makes it possible to provide an optimal presentation method of the menu according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the menu.

[0090] The suggestion unit can suggest an optimal menu by referring to the user's past meal history when suggesting a menu. The suggestion unit can suggest an optimal menu by referring to the user's past meal history when suggesting a menu, for example, using a generation AI. The suggestion unit can suggest a menu that suits the user's preferences, for example, based on the past meal history. The suggestion unit can also suggest a balanced menu by referring to the past meal history. The suggestion unit can also analyze the past meal history and suggest a menu that suits the user's health condition. This makes it possible to suggest an optimal menu based on the past meal history. The past meal history is referenced, for example, using a database or history data. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input past meal history data into the generation AI and cause the generation AI to suggest an optimal menu.

[0091] The suggestion unit can suggest different menus depending on the season and weather when making a suggestion. The suggestion unit, for example, uses a generation AI to suggest different menus depending on the season and weather when making a suggestion. The suggestion unit, for example, suggests menus using seasonal ingredients depending on the season. The suggestion unit can also suggest hot dishes and cold dishes depending on the weather. The suggestion unit can also suggest nutritionally balanced menus taking the season and weather into consideration. This makes it possible to suggest an optimal menu depending on the season and weather. The season and weather are considered using data such as temperature, humidity, and seasonal ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input season and weather data into the generation AI and cause the generation AI to suggest an optimal menu.

[0092] The suggestion unit can customize a menu taking into account the user's health condition when proposing the menu. The suggestion unit customizes a menu taking into account the user's health condition when proposing the menu, for example, using a generation AI. The suggestion unit, for example, suggests a menu containing specific nutrients according to the user's health condition. The suggestion unit can also suggest low-calorie or low-carbohydrate menus taking into account the user's health condition. The suggestion unit can also suggest a balanced menu based on the user's health condition. This makes it possible to suggest an optimal menu according to the user's health condition. The health condition is taken into account using, for example, medical data or fitness data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input health condition data into the generation AI and cause the generation AI to customize the menu.

[0093] The suggestion unit can detect the user's emotions and determine the priority of menu items based on the detected user emotions. The suggestion unit can detect the user's emotions using, for example, a generation AI and determine the priority of menu items based on the detected user emotions. For example, when the user is feeling stressed, the suggestion unit can prioritize menu items that are easy and simple to prepare. When the user is relaxed, the suggestion unit can also recommend menu items that can be prepared over time. When the user is in a hurry, the suggestion unit can also prioritize menu items that can be prepared in a short amount of time. This allows the priority of menu items to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of menu items.

[0094] The suggestion unit can suggest a menu taking into consideration the user's family composition when proposing the menu. The suggestion unit, for example, uses a generation AI to suggest a menu taking into consideration the user's family composition when proposing the menu. The suggestion unit, for example, suggests a menu with portion sizes tailored to the number of family members. The suggestion unit can also suggest a nutritionally balanced menu tailored to the age range of the family members. The suggestion unit can also suggest a menu taking into consideration the preferences and allergy information of the family members. This makes it possible to suggest an optimal menu tailored to the family composition. The family composition is taken into consideration using data such as the number of family members and their age range. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input family composition data into the generation AI and have the generation AI suggest an optimal menu.

[0095] The suggestion unit can suggest a menu taking into consideration the user's ingredient allergy information when suggesting the menu. The suggestion unit, for example, uses a generation AI to suggest a menu taking into consideration the user's ingredient allergy information when suggesting the menu. The suggestion unit, for example, suggests a menu that avoids ingredients to which the user is allergic. The suggestion unit can also suggest a menu that uses alternative ingredients based on the allergy information. The suggestion unit can also suggest a menu that uses safe ingredients by taking into consideration the allergy information. This makes it possible to suggest a safe menu based on the allergy information. The ingredient allergy information is taken into consideration using, for example, an allergen list or medical data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input ingredient allergy information data into the generation AI and cause the generation AI to suggest an optimal menu.

[0096] The suggestion unit can customize the menu by reflecting the user's ingredient preferences and dislikes when proposing the menu. The suggestion unit, for example, uses a generation AI to customize the menu by reflecting the user's ingredient preferences and dislikes when proposing the menu. The suggestion unit, for example, suggests a menu using ingredients that the user likes. The suggestion unit can also suggest a menu that avoids ingredients that the user dislikes. The suggestion unit can also suggest a balanced menu based on the user's preferences and dislikes. This makes it possible to suggest an optimal menu based on the user's preferences and dislikes. The ingredient preferences and dislikes are taken into consideration using, for example, a questionnaire or past selection history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input data on ingredient preferences and dislikes into the generation AI and have the generation AI customize the menu.

[0097] The purchase instruction unit can detect the user's emotions and adjust the timing of the purchase instruction based on the detected user emotions. The purchase instruction unit can detect the user's emotions using, for example, a generation AI and adjust the timing of the purchase instruction based on the detected user emotions. For example, if the user is feeling stressed, the purchase instruction unit can quickly issue a purchase instruction to reduce the burden of shopping. Furthermore, if the user is relaxed, the purchase instruction unit can periodically issue purchase instructions to encourage planned shopping. Furthermore, if the user is in a hurry, the purchase instruction unit can issue the minimum necessary purchase instructions to quickly complete shopping. This makes it possible to select the optimal timing of the purchase instruction based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the purchase instruction unit can be performed using, for example, a generation AI, or without a generation AI. For example, the purchase instruction unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of the purchase instruction.

[0098] The purchase instruction unit can issue optimal purchase instructions by referring to the user's past purchase history when issuing a purchase instruction. The purchase instruction unit can issue optimal purchase instructions by referring to the user's past purchase history, for example, using a generation AI. The purchase instruction unit, for example, prioritizes frequently purchased items based on the past purchase history. The purchase instruction unit can also refer to the past purchase history and issue purchase instructions for all necessary items. The purchase instruction unit can also analyze the past purchase history and create an efficient shopping list. This makes it possible to issue optimal purchase instructions based on the past purchase history. The past purchase history is referenced using, for example, a database or history data. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input past purchase history data into the generation AI and cause the generation AI to output optimal purchase instructions.

[0099] The purchase instruction unit can customize the purchase instruction taking into account the user's budget information when issuing the purchase instruction. The purchase instruction unit customizes the purchase instruction taking into account the user's budget information when issuing the purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes items that can be purchased within the user's budget. The purchase instruction unit can also efficiently issue purchase instructions for necessary items based on the budget information. The purchase instruction unit can also suggest items with high cost performance taking into account the budget information. This makes it possible to issue optimal purchase instructions based on the budget information. The budget information is taken into account using, for example, a monthly budget or expenditure history. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input budget information data into the generation AI and have the generation AI customize the purchase instruction.

[0100] The purchase instruction unit can learn the user's purchasing patterns when issuing a purchase instruction, thereby improving the accuracy of the purchase instruction. The purchase instruction unit can, for example, use a generation AI to learn the user's purchasing patterns when issuing a purchase instruction, thereby improving the accuracy of the purchase instruction. The purchase instruction unit can, for example, learn the user's purchasing patterns and prioritize frequently purchased items. The purchase instruction unit can also issue purchase instructions for all necessary items based on the purchasing patterns. The purchase instruction unit can also learn the purchasing patterns and create an efficient shopping list. In this way, the accuracy of the purchase instructions can be improved by learning the purchasing patterns. The purchasing patterns are learned using, for example, a machine learning algorithm or pattern recognition technology. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchasing pattern data into the generation AI and cause the generation AI to improve the accuracy of the purchase instructions.

[0101] The purchase instruction unit can detect the user's emotions and determine the priority of purchase instructions based on the detected user emotions. The purchase instruction unit can detect the user's emotions using, for example, a generation AI and determine the priority of purchase instructions based on the detected user emotions. For example, if the user is feeling stressed, the purchase instruction unit can prioritize purchase instructions for important items. Furthermore, if the user is relaxed, the purchase instruction unit can also prioritize general purchase instructions. Furthermore, if the user is in a hurry, the purchase instruction unit can prioritize minimum necessary purchase instructions. This allows the priority of purchase instructions to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the purchase instruction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the purchase instruction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of purchase instructions.

[0102] The purchase instruction unit can select the optimal purchase destination by taking into account the user's geographical location information when issuing a purchase instruction. The purchase instruction unit can select the optimal purchase destination by taking into account the user's geographical location information when issuing a purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes selecting stores close to the user's current location. The purchase instruction unit can also suggest an efficient shopping route based on the geographical location information. The purchase instruction unit can also select the optimal purchase destination by taking into account the geographical location information. This makes it possible to select the optimal purchase destination based on the geographical location information. The geographical location information can be taken into account using, for example, GPS data or address information. Some or all of the above-mentioned processing in the purchase instruction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the purchase instruction unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal purchase destination.

[0103] The purchase instruction unit can analyze the user's purchase history at the time of issuing a purchase instruction and provide relevant coupons and discount information. The purchase instruction unit can analyze the user's purchase history at the time of issuing a purchase instruction, for example, using a generation AI, and provide relevant coupons and discount information. The purchase instruction unit can provide relevant coupons based on the purchase history, for example. The purchase instruction unit can also analyze the purchase history and provide discount information. The purchase instruction unit can also provide coupons and discount information that encourage efficient shopping based on the purchase history. This makes it possible to provide coupons and discount information based on the purchase history. The coupons and discount information are provided using, for example, electronic coupons or discount codes. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchase history data into the generation AI and cause the generation AI to provide coupons and discount information.

[0104] The purchase instruction unit can adjust the timing of the purchase instruction taking into account the user's purchase frequency when issuing a purchase instruction. The purchase instruction unit can adjust the timing of the purchase instruction taking into account the user's purchase frequency when issuing a purchase instruction, for example, using a generation AI. The purchase instruction unit, for example, prioritizes purchase instructions for items with high purchase frequencies. The purchase instruction unit can also issue purchase instructions for all necessary items based on the purchase frequency. The purchase instruction unit can also create an efficient shopping list taking into account the purchase frequency. This makes it possible to select the optimal timing of the purchase instruction based on the purchase frequency. The purchase frequency is taken into account using, for example, weekly or monthly purchase frequency data. Some or all of the above-mentioned processing in the purchase instruction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input purchase frequency data into the generation AI and cause the generation AI to adjust the timing of the purchase instruction. === Hard Collateral 1-1 === Each of the multiple elements, including the camera unit, analysis unit, suggestion unit, and purchase instruction unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the camera unit can take a photo of the inside of the refrigerator using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the taken photo using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the analysis results using a generation AI. The purchase instruction unit is realized by the specific processing unit 290 of the data processing device 12 and can identify missing ingredients using a generation AI and issue a purchase instruction. === Hard Collateral 1-2 === Each of the multiple elements, including the camera unit, analysis unit, suggestion unit, and purchase instruction unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the camera unit can take a photo of the inside of the refrigerator using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the taken photo using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the analysis results using a generation AI. The purchase instruction unit is realized by the specific processing unit 290 of the data processing device 12 and can identify missing ingredients using a generation AI and issue a purchase instruction. === Hard Collateral 1-3 === Each of the multiple elements including the camera unit, analysis unit, suggestion unit, and purchase instruction unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the camera unit can take a photo of the inside of the refrigerator using the camera 42 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the taken photo using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the analysis results using a generation AI. The purchase instruction unit is realized by the specific processing unit 290 of the data processing device 12 and can identify missing ingredients using a generation AI and issue a purchase instruction. === Hard Collateral 1-4 === Each of the multiple elements including the camera unit, analysis unit, suggestion unit, and purchase instruction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the camera unit can take a photo of the inside of the refrigerator using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the taken photo using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a menu based on the analysis results using a generation AI. The purchase instruction unit is realized by the specific processing unit 290 of the data processing device 12 and can identify missing ingredients using a generation AI and issue a purchase instruction.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The camera unit can detect the temperature of each shelf in the refrigerator and adjust the timing of image capture according to the temperature. For example, if the temperature of a specific shelf in the refrigerator rises, it can immediately take a photo to prevent food from deteriorating. Furthermore, if the temperature is stable, it can maintain regular image capture timing. Furthermore, if the temperature drops, it can reduce the frequency of image capture and only take the minimum number of photos necessary. This allows the optimal image capture timing to be selected according to the temperature inside the refrigerator. Temperature detection is performed, for example, using a temperature sensor. Some or all of the above-described processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input temperature data into the generation AI and have the generation AI adjust the image capture timing.

[0107] The analysis unit can improve analysis accuracy by taking into account shape information of items in the refrigerator. For example, the analysis unit can accurately recognize specific items based on the item's shape information. It can also distinguish between items with similar shapes by taking into account the shape information. Furthermore, it can analyze item placement patterns based on the shape information and perform efficient inventory management. This makes it possible to provide analysis results that take shape information into account. Shape information can be taken into account using, for example, image recognition technology or pattern recognition technology. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input shape information data into a generation AI and have the generation AI improve analysis accuracy.

[0108] The suggestion unit can detect the user's emotions and adjust the difficulty of the menu based on the detected user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a menu that is simple and easy to make. If the user is relaxed, the suggestion unit can suggest a menu that can be made over time. Furthermore, if the user is in a hurry, the suggestion unit can suggest a menu that can be made in a short amount of time. This makes it possible to provide the optimal menu difficulty level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the difficulty level of the menu.

[0109] The purchase instruction unit can detect the user's emotions and adjust the content of the purchase instruction based on the detected user's emotions. For example, if the user is feeling stressed, the purchase instruction unit can issue a purchase instruction for the bare minimum of items. Also, if the user is relaxed, the purchase instruction unit can issue detailed purchase instructions to encourage planned shopping. Furthermore, if the user is in a hurry, the purchase instruction unit can prioritize items that can be purchased quickly. This allows the optimal content of the purchase instruction to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-described processing in the purchase instruction unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the purchase instruction unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the purchase instruction.

[0110] The camera unit can detect the color of items in the refrigerator and adjust the timing of photographing based on the color. For example, if the color of a specific item in the refrigerator changes, a photograph can be taken immediately to prevent food deterioration. Furthermore, if the color remains stable, regular photographing can be maintained. Furthermore, if the color does not change, the frequency of photographing can be reduced to take only the minimum number of photographs necessary. This allows the optimal photographing timing to be selected according to the color of the items in the refrigerator. Color detection is performed, for example, using a color sensor. Some or all of the above-described processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input color data into the generation AI and have the generation AI adjust the photographing timing.

[0111] The analysis unit can detect the user's emotions and adjust the notification method of the analysis results based on the detected user emotions. For example, if the user is feeling stressed, a simple, highly visible notification method can be provided. If the user is relaxed, a notification method including detailed information can be provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points can be provided. This makes it possible to provide the optimal notification method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0112] The suggestion unit can detect the user's emotions and adjust the frequency of menu suggestions based on the detected user emotions. For example, if the user is feeling stressed, the suggestion frequency can be reduced to reduce the burden. Also, if the user is relaxed, the suggestion frequency can be increased to provide a variety of options. Furthermore, if the user is in a hurry, the minimum number of suggestions can be made to enable quick selection. This makes it possible to provide the optimal menu suggestion frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the suggestion frequency.

[0113] The camera unit can detect the size of items in the refrigerator and adjust the timing of photographing based on the size. For example, if the size of a specific item in the refrigerator changes, a photograph can be taken immediately to prevent food deterioration. If the size remains stable, regular photographing can be maintained. Furthermore, if the size does not change, the frequency of photographing can be reduced and only the minimum number of photographs necessary can be taken. This allows the optimal photographing timing to be selected according to the size of the items in the refrigerator. Size detection is performed, for example, using a size sensor. Some or all of the above-mentioned processing in the camera unit may be performed using or without the generation AI. For example, the camera unit can input size data into the generation AI and have the generation AI adjust the photographing timing.

[0114] The analysis unit can provide analysis results taking into account the frequency of use of items in the refrigerator. For example, the analysis unit can prioritize analysis of frequently used items and notify the user. It can also identify items that are used less frequently and suggest to the user that they be discarded. It can also analyze items that are used at a medium frequency and perform efficient inventory management. This makes it possible to provide analysis results that take into account frequency of use. The frequency of use is taken into account using, for example, usage history data or sensor information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input frequency of use data into the generation AI and have the generation AI provide the analysis results.

[0115] The suggestion unit can suggest a menu taking into consideration the user's mealtimes. For example, it can suggest a lighter menu for breakfast and a menu suitable for replenishing energy for lunch. It can also suggest a balanced menu for dinner. It can also suggest a lighter menu that is easy to digest for a late-night snack. This makes it possible to suggest an optimal menu according to the mealtimes. The mealtimes are taken into consideration using, for example, the user's schedule data and time information. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input mealtime data into the generation AI and have the generation AI suggest an optimal menu.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The camera unit takes a photo of the inside of the refrigerator. For example, the camera unit can be installed on the refrigerator door and take a photo every time the door is opened or closed. The camera unit can also take photos automatically at regular intervals. Step 2: The analysis unit uses the generation AI to analyze the photos taken by the camera unit. For example, the analysis unit uses image recognition technology to identify each item. The generation AI recognizes each item in the photo and identifies the inventory in the refrigerator. Step 3: The suggestion unit uses the generation AI to propose a menu based on the results of the analysis by the analysis unit. For example, the suggestion unit may propose a menu taking into account the user's preferences, rankings, carbohydrate restrictions, and other options. Step 4: The purchase instruction unit sets a regular menu, identifies the ingredients that are in short supply, and issues a purchase instruction. For example, the purchase instruction unit can identify the ingredients that are in short supply based on the regular menu and automatically issue a purchase instruction.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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, in order to avoid confusion and to 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 camera unit that takes photos of the inside of the refrigerator; an analysis unit that analyzes the photograph taken by the camera unit; a suggestion unit that suggests a menu based on the results of the analysis by the analysis unit; A purchase instruction unit that sets a regular menu, identifies shortages of ingredients, and issues purchase instructions. A system characterized by:

2. The camera unit includes: It is installed on the refrigerator door and takes a photo every time the door is opened or closed.

2. The system of claim 1.

3. The analysis unit Identify each item using image recognition technology 2. The system of claim 1.

4. The proposal unit It proposes menus taking into account user preferences, rankings, and carbohydrate restriction options.

2. The system of claim 1.

5. The purchase instruction unit Identify shortages of ingredients based on regular menus and automatically issue purchasing instructions 2. The system of claim 1.

6. The camera unit includes: Detects user emotions and adjusts the timing of shooting based on the detected user emotions 2. The system of claim 1.

7. The camera unit includes: Detects the temperature and humidity inside the refrigerator and selects the appropriate timing for taking photos 2. The system of claim 1.

8. The camera unit includes: Automatically adjusts lighting conditions inside the refrigerator to capture optimal images 2. The system of claim 1.

Images