System

Abstract

An object of a system according to an embodiment is to utilize contents of a refrigerator and leaflet information of a nearby supermarket to select an inexpensive article and propose an appropriate menu.SOLUTION: A system includes a refrigerator, an image analysis unit, a collection unit, a selection unit, and a proposal unit. The refrigerator is provided with a camera for photographing the contents of the refrigerator. The image analysis unit analyzes an image captured by the camera. The collection part collects leaflet information of neighboring supermarkets. The selection part selects an inexpensive article on the basis of the contents of the refrigerator analyzed by the image analysis part and the leaflet information collected by the collection part. The proposal part proposes a menu on the basis of the article selected by the selection part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to efficiently utilize the contents of the refrigerator and information from flyers from nearby supermarkets to select low-priced items and suggest appropriate menus.

[0005] The system of the embodiment aims to select inexpensive items and suggest appropriate menus by utilizing the contents of the refrigerator and flyer information from nearby supermarkets. [Means for solving the problem]

[0006] The system according to the embodiment includes a refrigerator, an image analysis unit, a collection unit, a selection unit, and a suggestion unit. The refrigerator includes a camera that captures images of the contents of the refrigerator. The image analysis unit analyzes the images captured by the camera. The collection unit collects flyer information from nearby supermarkets. The selection unit selects inexpensive items based on the contents of the refrigerator analyzed by the image analysis unit and the flyer information collected by the collection unit. The suggestion unit suggests a menu based on the items selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment utilizes the contents of the refrigerator and flyer information from nearby supermarkets to select low-priced items and suggest appropriate 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 includes a refrigerator equipped with a camera that captures images of the refrigerator's contents; an image analysis unit that analyzes the images captured by the camera; a collection unit that collects flyer information from nearby supermarkets; a selection unit that selects inexpensive items based on the refrigerator contents analyzed by the image analysis unit and the flyer information collected by the collection unit; and a proposal unit that proposes a meal plan based on the items selected by the selection unit. For example, the system collects flyer information from nearby supermarkets, and a camera installed in the refrigerator photographs the refrigerator's contents and inputs the image data into a generation AI. The generation AI uses image analysis technology to recognize the refrigerator's contents and determine the current inventory status. Next, the generation AI selects inexpensive items based on the collected flyer information and the refrigerator's contents. For example, if there is low on milk in the refrigerator, the generation AI selects the cheapest milk from the flyer information from a nearby supermarket. The system also takes into account the preferences and health status of family members. For example, if a family member is lactose intolerant, lactose-free milk is selected. The generation AI then proposes a meal plan based on the selected items. For example, if there is chicken and vegetables in the refrigerator, the generative AI will suggest healthy recipes using chicken and vegetables. It also takes into account the preferences and health status of family members. For example, if someone in the family is on a diet, it will suggest low-calorie recipes. This allows the system to save money and maintain the health of the family. It also allows for efficient management of the contents of the refrigerator, contributing to reducing food waste.

[0029] A refrigerator management system according to an embodiment includes a camera that captures images of the contents of a refrigerator, an image analysis unit, a collection unit, a selection unit, and a suggestion unit. The camera captures images of the contents of the refrigerator. For example, the camera may be installed inside the refrigerator and periodically capture images. The camera may also estimate a user's emotions and adjust the timing of camera capture based on the estimated user emotions. The camera may also set optimal capture conditions taking into account the temperature and humidity inside the refrigerator. The image analysis unit analyzes images captured by the camera. For example, the image analysis unit may identify each item in the refrigerator based on barcodes, shapes, colors, etc. The image analysis unit may also evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has decreased. The collection unit collects flyer information from nearby supermarkets. For example, the collection unit may automatically acquire data from supermarket websites or apps. The collection unit may also estimate a user's emotions and adjust the frequency of flyer information collection based on the estimated user emotions. The selection unit selects inexpensive items based on the refrigerator contents analyzed by the image analysis unit and the flyer information collected by the collection unit. For example, the selection unit can select items taking into consideration the preferences and health conditions of the family members. The selection unit can also select optimal items taking into consideration the expiration dates of items in the refrigerator. The suggestion unit suggests a menu based on the items selected by the selection unit. For example, the suggestion unit can suggest a menu taking into consideration the preferences and health conditions of the family members. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting a menu based on the estimated user's emotions. As a result, the refrigerator management system according to the embodiment can efficiently manage the contents of the refrigerator, contributing to saving money and maintaining the health of the family.

[0030] The camera is installed inside the refrigerator and can periodically capture images. The camera is installed inside the refrigerator, for example, and can periodically capture images. For example, the camera can capture images at a fixed time every day. The camera can also capture images every time the refrigerator door is opened or closed. Furthermore, the camera can be set to capture images manually by the user. This allows the contents of the refrigerator to be monitored periodically.

[0031] The image analysis unit can identify each item in the refrigerator based on its barcode, shape, or color. The image analysis unit can identify each item in the refrigerator based on its barcode, for example. For example, the image analysis unit reads the barcode using a barcode reader and identifies the item. The image analysis unit can also identify items based on their shape. For example, the image analysis unit can analyze the shape of an item and identify it based on a specific pattern. The image analysis unit can also identify items based on their color. For example, the image analysis unit can analyze the color of an item and identify it based on a specific color range. This allows the items in the refrigerator to be accurately identified.

[0032] The collection unit can automatically acquire data from the supermarket's website or app. For example, the collection unit can automatically acquire data from the supermarket's website. For example, the collection unit can periodically acquire data from a specific URL. The collection unit can also acquire data from the supermarket's app. For example, the collection unit can acquire data using the app's API. Furthermore, the collection unit can also acquire data from the website using scraping technology. This makes it possible to automatically collect supermarket flyer information.

[0033] The selection unit can select items taking into consideration the preferences or health condition of the family members. The selection unit, for example, selects items taking into consideration the preferences of the family members. For example, the selection unit can select items that the family members like based on past preference data. The selection unit can also select items taking into consideration the health condition. For example, the selection unit can select items that are good for the family members based on the results of their health checkups. Furthermore, the selection unit can also select items taking into consideration allergy information. For example, the selection unit selects items that do not contain allergens based on a list of allergens for the family members. This makes it possible to select items that suit the preferences and health condition of the family members.

[0034] The suggestion unit can suggest a menu taking into consideration the preferences or health condition of the family members. The suggestion unit, for example, suggests a menu taking into consideration the preferences of the family members. For example, the suggestion unit can suggest a menu that the family members like based on past preference data. The suggestion unit can also suggest a menu taking into consideration the health condition. For example, the suggestion unit can suggest a healthy menu based on the results of the family members' health checkups. Furthermore, the suggestion unit can also suggest a menu taking into consideration allergy information. For example, the suggestion unit can suggest a menu that does not contain allergens based on the family members' allergen list. This makes it possible to suggest a menu that suits the preferences and health condition of the family members.

[0035] The camera can set optimal shooting conditions taking into account the temperature or humidity inside the refrigerator. The camera sets optimal shooting conditions taking into account, for example, the temperature inside the refrigerator. For example, if the temperature inside the refrigerator is high, the camera can adjust the exposure to acquire a clear image. The camera can also set optimal shooting conditions taking into account the humidity inside the refrigerator. For example, if the humidity inside the refrigerator is high, the camera can adjust the focus to acquire a clear image. Furthermore, if the temperature and humidity inside the refrigerator are appropriate, the camera can acquire an image under standard shooting conditions. This allows optimal shooting conditions to be set according to the environment inside the refrigerator.

[0036] The camera can automatically adjust to the lighting conditions inside the refrigerator to capture clear images. The camera automatically adjusts to the lighting conditions inside the refrigerator. For example, if it is dark inside the refrigerator, the camera flash can be automatically turned on to take a picture. Also, if it is bright inside the refrigerator, the camera flash can be turned off to take a picture. Furthermore, if the lighting inside the refrigerator is uneven, the camera exposure can be adjusted to capture an image with uniform brightness. This makes it possible to capture clear images according to the lighting conditions inside the refrigerator.

[0037] The camera can display guidelines to optimize the arrangement of items in the refrigerator. For example, the camera displays guidelines to optimize the arrangement of items in the refrigerator. For example, if the items are not properly arranged when the camera takes a picture, the camera can display guidelines to encourage the user to rearrange them. Also, if items are overlapping when the camera takes a picture, the camera can display guidelines to encourage the user to separate the items. Furthermore, if an item is difficult to see when the camera takes a picture, the camera can display guidelines to encourage the user to move the item. This makes it possible to optimize the arrangement of items in the refrigerator.

[0038] The camera can adjust the shooting conditions based on environmental information about the outside of the refrigerator. For example, the camera adjusts the shooting conditions taking into account environmental information about the outside of the refrigerator. For example, if the temperature in the kitchen is high, the exposure of the camera can be adjusted to obtain a clear image. Also, if the humidity in the kitchen is high, the focus of the camera can be adjusted to obtain a clear image. Furthermore, if the temperature and humidity in the kitchen are appropriate, an image can be obtained under standard shooting conditions. In this way, the shooting conditions can be adjusted according to environmental information about the outside of the refrigerator.

[0039] The camera can automatically detect and notify the expiration date of items in the refrigerator. The camera can automatically detect the expiration date of items in the refrigerator. For example, the camera can use image analysis technology to detect the expiration date of an item and notify the user. The camera can also prioritize notifying items that are close to their expiration date. Furthermore, the camera can automatically detect items that have passed their expiration date and warn the user. This makes it possible to automatically detect and notify the expiration date of items in the refrigerator.

[0040] The camera can measure the weight of items in a refrigerator and be used for inventory management. For example, the camera measures the weight of items in a refrigerator. For example, the camera can measure the weight of items using image analysis technology and be used for inventory management. The camera can also prioritize and notify the user of items whose weight has decreased. Furthermore, the camera can automatically detect items whose weight has increased and notify the user. In this way, measuring the weight of items in a refrigerator can be used for inventory management.

[0041] The image analysis unit can evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has deteriorated. The image analysis unit, for example, evaluates the freshness of items in the refrigerator. For example, the image analysis unit can evaluate the freshness of items based on changes in color and notify the user of items whose freshness has deteriorated. The image analysis unit can also evaluate freshness based on changes in texture. For example, the image analysis unit can analyze the texture of items and prioritize notifying the user of items whose freshness has deteriorated. Furthermore, the image analysis unit can evaluate freshness based on changes in smell. For example, the image analysis unit can analyze the smell of items using an odor sensor, automatically detect items whose freshness has deteriorated, and warn the user. This makes it possible to evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has deteriorated.

[0042] The image analysis unit can analyze the nutritional information of items in the refrigerator and use it for health management. The image analysis unit, for example, analyzes the nutritional information of items in the refrigerator. For example, the image analysis unit can analyze ingredient lists and provide the user with the nutritional information of the items. The image analysis unit can also calculate calories and make suggestions useful for health management based on the nutritional information. Furthermore, the image analysis unit can evaluate nutritional balance, automatically analyze the nutritional information, and notify the user. In this way, the nutritional information of items in the refrigerator can be analyzed and used for health management.

[0043] The image analysis unit can make suggestions to optimize the arrangement of items in the refrigerator. The image analysis unit makes suggestions to optimize the arrangement of items in the refrigerator, for example. For example, if the arrangement of items is disordered, the image analysis unit can suggest an optimal arrangement. Furthermore, if items are overlapping, the image analysis unit can suggest to the user that they separate the items. Furthermore, if an item is difficult to see, the image analysis unit can suggest to the user that they move the item. In this way, suggestions can be made to optimize the arrangement of items in the refrigerator.

[0044] The image analysis unit analyzes allergen information of items in the refrigerator and can use it to prevent allergies. The image analysis unit analyzes allergen information of items in the refrigerator, for example. For example, the image analysis unit can analyze an ingredient list and provide the user with allergen information of the items. The image analysis unit can also refer to the allergen list and make suggestions that are useful for preventing allergies. Furthermore, the image analysis unit can automatically analyze allergen information and notify the user. In this way, the allergen information of items in the refrigerator can be analyzed and used to prevent allergies.

[0045] The image analysis unit can analyze the expiration dates of items in the refrigerator and prioritize notification of items with upcoming expiration dates. The image analysis unit, for example, analyzes the expiration dates of items in the refrigerator. For example, the image analysis unit can analyze the expiration dates of items using barcode scanning technology and provide the expiration dates to the user. The image analysis unit can also prioritize notification of items with upcoming expiration dates. Furthermore, the image analysis unit can automatically detect items that have passed their expiration dates and warn the user. This makes it possible to analyze the expiration dates of items in the refrigerator and prioritize notification of items with upcoming expiration dates.

[0046] The image analysis unit can analyze the frequency of use of items in the refrigerator and prioritize displaying frequently used items. The image analysis unit, for example, analyzes the frequency of use of items in the refrigerator. For example, the image analysis unit can count the number of times an item is used and provide the user with frequently used items. The image analysis unit can also calculate the consumption rate and prioritize displaying frequently used items. Furthermore, the image analysis unit can automatically detect less frequently used items and notify the user. In this way, the frequency of use of items in the refrigerator can be analyzed and the frequently used items can be prioritized and displayed.

[0047] The collection unit can prioritize collecting and notifying supermarket sale information. The collection unit, for example, prioritizes collecting supermarket sale information. For example, the collection unit can prioritize collecting sale information from supermarket websites or apps and notify the user. In addition, when sale information is updated, the collection unit can collect the information in real time and notify the user. Furthermore, the collection unit can prioritize collecting the cheapest items based on the sale information and notify the user. In this way, supermarket sale information can be prioritized and notified.

[0048] The collection unit can prioritize collecting highly relevant flyer information in consideration of the user's purchase history. The collection unit, for example, collects flyer information in consideration of the user's purchase history. For example, the collection unit can prioritize collecting highly relevant flyer information based on past purchase data. The collection unit can also prioritize collecting flyer information related to items that the user frequently purchases. Furthermore, the collection unit can analyze the purchase history and prioritize collecting highly relevant sale information. This makes it possible to prioritize collecting highly relevant flyer information in consideration of the user's purchase history.

[0049] The collection unit can set the optimal collection timing taking into account the supermarket's business hours or location information. The collection unit sets the collection timing taking into account, for example, the supermarket's business hours. For example, the collection unit can collect flyer information during business hours. The collection unit can also preferentially collect flyer information from nearby supermarkets based on the supermarket's location information. Furthermore, the collection unit can also set the optimal collection timing taking into account the supermarket's business hours and location information. This makes it possible to set the optimal collection timing taking into account the supermarket's business hours and location information.

[0050] The collection unit can collect information about online shopping sites and enable comparison and consideration. The collection unit, for example, collects information about online shopping sites. For example, the collection unit can collect information about online shopping sites in addition to information from supermarket websites and apps. The collection unit can also collect sale information about online shopping sites and compare it with the sale information from supermarkets. Furthermore, the collection unit can collect review information about online shopping sites and provide it to the user. This makes it possible to collect information about online shopping sites and enable comparison and consideration.

[0051] The collection unit can collect supermarket review information and provide it to the user. The collection unit, for example, collects supermarket review information. For example, the collection unit can collect review information from supermarket websites or apps and provide it to the user. The collection unit can also suggest the best supermarket for the user based on the review information. Furthermore, the collection unit can analyze the review information and provide useful information to the user. In this way, supermarket review information can be collected and provided to the user.

[0052] The collection unit can collect supermarket point program information and notify the user. The collection unit, for example, collects supermarket point program information. For example, the collection unit can collect point program information from supermarket websites or apps and notify the user. The collection unit can also suggest the best supermarket for the user based on the point program information. Furthermore, the collection unit can analyze the point program information and provide useful information to the user. In this way, supermarket point program information can be collected and notified to the user.

[0053] The selection unit can select appropriate items taking into account the expiration dates of the items in the refrigerator. The selection unit, for example, selects items taking into account the expiration dates of the items in the refrigerator. For example, the selection unit can prioritize selecting items that are close to their expiration date. The selection unit can also automatically detect items that have passed their expiration date and warn the user. Furthermore, the selection unit can select optimal items based on the expiration date. This makes it possible to select appropriate items taking into account the expiration dates of the items in the refrigerator.

[0054] The selection unit can select items taking into consideration the health condition and allergy information of family members. The selection unit can select items taking into consideration, for example, the health condition of family members. For example, the selection unit can select healthy items based on health check results. The selection unit can also select items taking into consideration allergy information. For example, the selection unit can select items that do not contain allergens based on an allergen list. Furthermore, the selection unit can automatically analyze the health condition and allergy information and select the most appropriate item. This makes it possible to select items taking into consideration the health condition and allergy information of family members.

[0055] The selection unit can select an appropriate item by referring to the user's past purchase history. The selection unit can select an item by referring to, for example, the user's past purchase history. For example, the selection unit can select an optimal item based on past purchase data. The selection unit can also prioritize and select items that have been frequently purchased in the past. Furthermore, the selection unit can analyze the purchase history and select an optimal item. This makes it possible to select an appropriate item by referring to the user's past purchase history.

[0056] The selection unit can select an appropriate item by taking into consideration the sale information of the supermarket. The selection unit can select an item by taking into consideration the sale information of the supermarket, for example. For example, the selection unit can select the most appropriate item based on the sale information. Furthermore, when the sale information is updated, the selection unit can select the most appropriate item in real time. Furthermore, the selection unit can analyze the sale information and select the cheapest item. In this way, it is possible to select an appropriate item by taking into consideration the sale information of the supermarket.

[0057] The selection unit can select an item taking into consideration the user's dietary restrictions and diet plan. The selection unit can, for example, select an item taking into consideration the user's dietary restrictions. For example, the selection unit can select an optimal item based on calorie restrictions. The selection unit can also select an item taking into consideration the diet plan. For example, the selection unit can select a low-calorie item. Furthermore, the selection unit can automatically analyze the dietary restrictions and diet plan and select an optimal item. This makes it possible to select an item taking into consideration the user's dietary restrictions and diet plan.

[0058] The selection unit can select an item taking into consideration the user's tastes or preferences. The selection unit can, for example, select an item taking into consideration the user's tastes. For example, the selection unit can select the most suitable item based on past preference data. The selection unit can also select an item taking into consideration the user's preferences. For example, the selection unit can preferentially select a preferred item. Furthermore, the selection unit can automatically analyze tastes and preferences and select the most suitable item. This makes it possible to select an item taking into consideration the user's tastes and preferences.

[0059] The suggestion unit can suggest an appropriate menu taking into account the expiration dates of items in the refrigerator. The suggestion unit, for example, suggests a menu taking into account the expiration dates of items in the refrigerator. For example, the suggestion unit can suggest a menu that prioritizes the use of items that are close to their expiration date. The suggestion unit can also automatically detect items that have passed their expiration date and warn the user. Furthermore, the suggestion unit can suggest an optimal menu based on the expiration date. This makes it possible to suggest an appropriate menu taking into account the expiration dates of items in the refrigerator.

[0060] The suggestion unit can suggest a menu taking into consideration the health condition and allergy information of family members. The suggestion unit can suggest a menu taking into consideration, for example, the health condition of family members. For example, the suggestion unit can suggest a healthy menu based on health checkup results. The suggestion unit can also suggest a menu taking into consideration allergy information. For example, the suggestion unit can suggest a menu that does not contain allergens based on an allergen list. Furthermore, the suggestion unit can automatically analyze the health condition and allergy information to suggest an optimal menu. This makes it possible to suggest a menu taking into consideration the health condition and allergy information of family members.

[0061] The suggestion unit can suggest an appropriate menu by referring to the user's past meal history. The suggestion unit can suggest a menu by referring to the user's past meal history, for example. For example, the suggestion unit can suggest an optimal menu based on past meal data. The suggestion unit can also preferentially suggest menus that have been frequently eaten in the past. Furthermore, the suggestion unit can analyze the meal history and suggest an optimal menu. In this way, an appropriate menu can be suggested by referring to the user's past meal history.

[0062] The suggestion unit can suggest an appropriate menu taking into consideration the season or weather. The suggestion unit suggests a menu taking into consideration, for example, the season and weather. For example, the suggestion unit can suggest a menu using ingredients according to the season. The suggestion unit can also suggest a menu according to the weather. Furthermore, the suggestion unit can also suggest an optimal menu taking into consideration the season and weather. In this way, an appropriate menu can be suggested taking into consideration the season and weather.

[0063] The suggestion unit can suggest a menu taking into consideration the user's dietary restrictions and diet plan. The suggestion unit, for example, suggests a menu taking into consideration the user's dietary restrictions. For example, the suggestion unit can suggest an optimal menu based on calorie restrictions. The suggestion unit can also suggest a menu taking into consideration the diet plan. For example, the suggestion unit can suggest a low-calorie menu. Furthermore, the suggestion unit can automatically analyze the dietary restrictions and diet plan and suggest an optimal menu. In this way, it is possible to suggest a menu taking into consideration the user's dietary restrictions and diet plan.

[0064] The suggestion unit can suggest a menu taking into consideration the user's tastes or preferences. The suggestion unit, for example, suggests a menu taking into consideration the user's tastes. For example, the suggestion unit can suggest an optimal menu based on past preference data. The suggestion unit can also suggest a menu taking into consideration the user's preferences. For example, the suggestion unit can preferentially suggest a favorite menu. Furthermore, the suggestion unit can automatically analyze tastes and preferences and suggest an optimal menu. In this way, a menu can be suggested taking into consideration the user's tastes and preferences.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The refrigerator management system may further include a voice recognition unit. The voice recognition unit can analyze a user's voice commands and provide information about the contents of the refrigerator. For example, if a user asks, "Is there any milk?", the voice recognition unit can check the stock status of milk in the refrigerator by linking the camera and the image analysis unit and respond to the user by voice. The voice recognition unit can also notify the user of the expiration date of items in the refrigerator based on the user's voice commands. Furthermore, the voice recognition unit can suggest menus based on the user's voice commands. This allows the user to efficiently manage the contents of the refrigerator using voice commands.

[0067] The camera can be equipped with a 3D scanning function to optimize the placement of items in the refrigerator. The 3D scanning function can grasp the position and shape of items in the refrigerator in three dimensions and suggest optimal placement. For example, if items overlap, the camera can visually guide the user using a 3D model to separate the items. The camera can also suggest placement methods to maximize the use of space in the refrigerator. Furthermore, the camera can automatically record the placement of items in the refrigerator and use this as a reference for the next time the items are placed. This allows for efficient management of the placement of items in the refrigerator.

[0068] The image analysis unit can analyze the nutritional information of items in the refrigerator and use it for health management. For example, the image analysis unit can analyze ingredient lists and provide the user with the nutritional information of the items. The image analysis unit can also calculate calories and make suggestions useful for health management based on the nutritional information. Furthermore, the image analysis unit can evaluate nutritional balance, automatically analyze the nutritional information, and notify the user. In this way, the nutritional information of items in the refrigerator can be analyzed and used for health management.

[0069] The collection unit can collect supermarket review information and provide it to the user. For example, the collection unit can collect review information from supermarket websites or apps and provide it to the user. The collection unit can also suggest the best supermarket for the user based on the review information. Furthermore, the collection unit can analyze the review information and provide useful information to the user. In this way, supermarket review information can be collected and provided to the user.

[0070] The selection unit can select an appropriate item by referring to the user's past purchase history. For example, the selection unit can select the most appropriate item based on past purchase data. The selection unit can also prioritize the selection of items that have been frequently purchased in the past. Furthermore, the selection unit can analyze the purchase history and select the most appropriate item. This makes it possible to select an appropriate item by referring to the user's past purchase history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The camera takes a picture of the contents of the refrigerator. For example, the camera is installed inside the refrigerator and can take pictures periodically. The camera can also estimate the user's emotions and adjust the timing of the camera's capture based on the estimated user emotions. Furthermore, the camera can set the optimal capture conditions taking into account the temperature and humidity inside the refrigerator. Step 2: The image analysis unit analyzes the images captured by the camera. For example, the image analysis unit can identify each item in the refrigerator based on barcodes, shapes, colors, etc. The image analysis unit can also evaluate the freshness of the items in the refrigerator and notify the user of items that have become less fresh. Step 3: The collection unit collects flyer information from nearby supermarkets. For example, the collection unit can automatically acquire data from supermarket websites or apps. The collection unit can also estimate the user's emotions and adjust the frequency of collecting flyer information based on the estimated user emotions. Step 4: The selection unit selects inexpensive items based on the contents of the refrigerator analyzed by the image analysis unit and the flyer information collected by the collection unit. For example, the selection unit can select items taking into consideration the preferences and health status of the family. The selection unit can also select optimal items taking into consideration the expiration dates of the items in the refrigerator. Step 5: The suggestion unit suggests a menu based on the items selected by the selection unit. For example, the suggestion unit can suggest a menu taking into consideration the preferences and health status of the family. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting a menu based on the estimated user's emotions.

[0073] (Example 2) A system according to an embodiment of the present invention includes a refrigerator equipped with a camera that captures images of the refrigerator's contents; an image analysis unit that analyzes the images captured by the camera; a collection unit that collects flyer information from nearby supermarkets; a selection unit that selects inexpensive items based on the refrigerator contents analyzed by the image analysis unit and the flyer information collected by the collection unit; and a proposal unit that proposes a meal plan based on the items selected by the selection unit. For example, the system collects flyer information from nearby supermarkets, and a camera installed in the refrigerator photographs the refrigerator's contents and inputs the image data into a generation AI. The generation AI uses image analysis technology to recognize the refrigerator's contents and determine the current inventory status. Next, the generation AI selects inexpensive items based on the collected flyer information and the refrigerator's contents. For example, if there is low on milk in the refrigerator, the generation AI selects the cheapest milk from the flyer information from a nearby supermarket. The system also takes into account the preferences and health status of family members. For example, if a family member is lactose intolerant, lactose-free milk is selected. The generation AI then proposes a meal plan based on the selected items. For example, if there is chicken and vegetables in the refrigerator, the generative AI will suggest healthy recipes using chicken and vegetables. It also takes into account the preferences and health status of family members. For example, if someone in the family is on a diet, it will suggest low-calorie recipes. This allows the system to save money and maintain the health of the family. It also allows for efficient management of the contents of the refrigerator, contributing to reducing food waste.

[0074] A refrigerator management system according to an embodiment includes a camera that captures images of the contents of a refrigerator, an image analysis unit, a collection unit, a selection unit, and a suggestion unit. The camera captures images of the contents of the refrigerator. For example, the camera may be installed inside the refrigerator and periodically capture images. The camera may also estimate a user's emotions and adjust the timing of camera capture based on the estimated user emotions. The camera may also set optimal capture conditions taking into account the temperature and humidity inside the refrigerator. The image analysis unit analyzes images captured by the camera. For example, the image analysis unit may identify each item in the refrigerator based on barcodes, shapes, colors, etc. The image analysis unit may also evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has decreased. The collection unit collects flyer information from nearby supermarkets. For example, the collection unit may automatically acquire data from supermarket websites or apps. The collection unit may also estimate a user's emotions and adjust the frequency of flyer information collection based on the estimated user emotions. The selection unit selects inexpensive items based on the refrigerator contents analyzed by the image analysis unit and the flyer information collected by the collection unit. For example, the selection unit can select items taking into consideration the preferences and health conditions of the family members. The selection unit can also select optimal items taking into consideration the expiration dates of items in the refrigerator. The suggestion unit suggests a menu based on the items selected by the selection unit. For example, the suggestion unit can suggest a menu taking into consideration the preferences and health conditions of the family members. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting a menu based on the estimated user's emotions. As a result, the refrigerator management system according to the embodiment can efficiently manage the contents of the refrigerator, contributing to saving money and maintaining the health of the family.

[0075] The camera is installed inside the refrigerator and can periodically capture images. The camera is installed inside the refrigerator, for example, and can periodically capture images. For example, the camera can capture images at a fixed time every day. The camera can also capture images every time the refrigerator door is opened or closed. Furthermore, the camera can be set to capture images manually by the user. This allows the contents of the refrigerator to be monitored periodically.

[0076] The image analysis unit can identify each item in the refrigerator based on its barcode, shape, or color. The image analysis unit can identify each item in the refrigerator based on its barcode, for example. For example, the image analysis unit reads the barcode using a barcode reader and identifies the item. The image analysis unit can also identify items based on their shape. For example, the image analysis unit can analyze the shape of an item and identify it based on a specific pattern. The image analysis unit can also identify items based on their color. For example, the image analysis unit can analyze the color of an item and identify it based on a specific color range. This allows the items in the refrigerator to be accurately identified.

[0077] The collection unit can automatically acquire data from the supermarket's website or app. For example, the collection unit can automatically acquire data from the supermarket's website. For example, the collection unit can periodically acquire data from a specific URL. The collection unit can also acquire data from the supermarket's app. For example, the collection unit can acquire data using the app's API. Furthermore, the collection unit can also acquire data from the website using scraping technology. This makes it possible to automatically collect supermarket flyer information.

[0078] The selection unit can select items taking into consideration the preferences or health condition of the family members. The selection unit, for example, selects items taking into consideration the preferences of the family members. For example, the selection unit can select items that the family members like based on past preference data. The selection unit can also select items taking into consideration the health condition. For example, the selection unit can select items that are good for the family members based on the results of their health checkups. Furthermore, the selection unit can also select items taking into consideration allergy information. For example, the selection unit selects items that do not contain allergens based on a list of allergens for the family members. This makes it possible to select items that suit the preferences and health condition of the family members.

[0079] The suggestion unit can suggest a menu taking into consideration the preferences or health condition of the family members. The suggestion unit, for example, suggests a menu taking into consideration the preferences of the family members. For example, the suggestion unit can suggest a menu that the family members like based on past preference data. The suggestion unit can also suggest a menu taking into consideration the health condition. For example, the suggestion unit can suggest a healthy menu based on the results of the family members' health checkups. Furthermore, the suggestion unit can also suggest a menu taking into consideration allergy information. For example, the suggestion unit can suggest a menu that does not contain allergens based on the family members' allergen list. This makes it possible to suggest a menu that suits the preferences and health condition of the family members.

[0080] The camera can estimate the user's emotions and adjust the timing of camera capture based on the estimated user emotions. The camera, for example, estimates the user's emotions. For example, the camera can estimate the user's emotions using facial expression recognition technology. The camera can also estimate the user's emotions using voice analysis technology. Furthermore, the camera can estimate the user's emotions by analyzing behavioral patterns. The camera adjusts the timing of camera capture based on the estimated user emotions. For example, if the user is feeling stressed, the camera can reduce the frequency of camera capture to reduce the user's burden. Also, if the user is relaxed, the camera can increase the frequency of camera capture and provide detailed inventory information. Furthermore, if the user is in a hurry, the camera can temporarily stop capturing images and prioritize user operation. This makes it possible to adjust the timing of camera capture according to the user's emotions.

[0081] The camera can set optimal shooting conditions taking into account the temperature or humidity inside the refrigerator. The camera sets optimal shooting conditions taking into account, for example, the temperature inside the refrigerator. For example, if the temperature inside the refrigerator is high, the camera can adjust the exposure to acquire a clear image. The camera can also set optimal shooting conditions taking into account the humidity inside the refrigerator. For example, if the humidity inside the refrigerator is high, the camera can adjust the focus to acquire a clear image. Furthermore, if the temperature and humidity inside the refrigerator are appropriate, the camera can acquire an image under standard shooting conditions. This allows optimal shooting conditions to be set according to the environment inside the refrigerator.

[0082] The camera can automatically adjust to the lighting conditions inside the refrigerator to capture clear images. The camera automatically adjusts to the lighting conditions inside the refrigerator. For example, if it is dark inside the refrigerator, the camera flash can be automatically turned on to take a picture. Also, if it is bright inside the refrigerator, the camera flash can be turned off to take a picture. Furthermore, if the lighting inside the refrigerator is uneven, the camera exposure can be adjusted to capture an image with uniform brightness. This makes it possible to capture clear images according to the lighting conditions inside the refrigerator.

[0083] The camera can display guidelines to optimize the arrangement of items in the refrigerator. For example, the camera displays guidelines to optimize the arrangement of items in the refrigerator. For example, if the items are not properly arranged when the camera takes a picture, the camera can display guidelines to encourage the user to rearrange them. Also, if items are overlapping when the camera takes a picture, the camera can display guidelines to encourage the user to separate the items. Furthermore, if an item is difficult to see when the camera takes a picture, the camera can display guidelines to encourage the user to move the item. This makes it possible to optimize the arrangement of items in the refrigerator.

[0084] The camera can estimate the user's emotions and determine the priority of images to be captured based on the estimated user's emotions. The camera estimates the user's emotions, for example. For example, the camera can estimate the user's emotions using facial expression recognition technology. The camera can also estimate the user's emotions using voice analysis technology. Furthermore, the camera can estimate the user's emotions by analyzing behavioral patterns. The camera determines the priority of images to be captured based on the estimated user's emotions. For example, if the user is feeling stressed, the camera can prioritize capturing images of only important items. If the user is relaxed, the camera can capture all items equally. If the user is in a hurry, the camera can prioritize capturing images of items with an approaching expiration date. This makes it possible to determine the priority of images to be captured according to the user's emotions.

[0085] The camera can adjust the shooting conditions based on environmental information about the outside of the refrigerator. For example, the camera adjusts the shooting conditions taking into account environmental information about the outside of the refrigerator. For example, if the temperature in the kitchen is high, the exposure of the camera can be adjusted to obtain a clear image. Also, if the humidity in the kitchen is high, the focus of the camera can be adjusted to obtain a clear image. Furthermore, if the temperature and humidity in the kitchen are appropriate, an image can be obtained under standard shooting conditions. In this way, the shooting conditions can be adjusted according to environmental information about the outside of the refrigerator.

[0086] The camera can automatically detect and notify the expiration date of items in the refrigerator. The camera can automatically detect the expiration date of items in the refrigerator. For example, the camera can use image analysis technology to detect the expiration date of an item and notify the user. The camera can also prioritize notifying items that are close to their expiration date. Furthermore, the camera can automatically detect items that have passed their expiration date and warn the user. This makes it possible to automatically detect and notify the expiration date of items in the refrigerator.

[0087] The camera can measure the weight of items in a refrigerator and be used for inventory management. For example, the camera measures the weight of items in a refrigerator. For example, the camera can measure the weight of items using image analysis technology and be used for inventory management. The camera can also prioritize and notify the user of items whose weight has decreased. Furthermore, the camera can automatically detect items whose weight has increased and notify the user. In this way, measuring the weight of items in a refrigerator can be used for inventory management.

[0088] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user's emotions. The image analysis unit, for example, estimates the user's emotions. For example, the image analysis unit can estimate the user's emotions using facial expression recognition technology. The image analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the image analysis unit can estimate the user's emotions by analyzing behavioral patterns. The image analysis unit adjusts the accuracy of the image analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the accuracy of the image analysis can be increased to provide more accurate information. Furthermore, if the user is relaxed, the accuracy of the image analysis can be set to standard to provide balanced information. Furthermore, if the user is in a hurry, the accuracy of the image analysis can be lowered to provide information quickly. In this way, the accuracy of the image analysis can be adjusted according to the user's emotions.

[0089] The image analysis unit can evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has deteriorated. The image analysis unit, for example, evaluates the freshness of items in the refrigerator. For example, the image analysis unit can evaluate the freshness of items based on changes in color and notify the user of items whose freshness has deteriorated. The image analysis unit can also evaluate freshness based on changes in texture. For example, the image analysis unit can analyze the texture of items and prioritize notifying the user of items whose freshness has deteriorated. Furthermore, the image analysis unit can evaluate freshness based on changes in smell. For example, the image analysis unit can analyze the smell of items using an odor sensor, automatically detect items whose freshness has deteriorated, and warn the user. This makes it possible to evaluate the freshness of items in the refrigerator and notify the user of items whose freshness has deteriorated.

[0090] The image analysis unit can analyze the nutritional information of items in the refrigerator and use it for health management. The image analysis unit, for example, analyzes the nutritional information of items in the refrigerator. For example, the image analysis unit can analyze ingredient lists and provide the user with the nutritional information of the items. The image analysis unit can also calculate calories and make suggestions useful for health management based on the nutritional information. Furthermore, the image analysis unit can evaluate nutritional balance, automatically analyze the nutritional information, and notify the user. In this way, the nutritional information of items in the refrigerator can be analyzed and used for health management.

[0091] The image analysis unit can make suggestions to optimize the arrangement of items in the refrigerator. The image analysis unit makes suggestions to optimize the arrangement of items in the refrigerator, for example. For example, if the arrangement of items is disordered, the image analysis unit can suggest an optimal arrangement. Furthermore, if items are overlapping, the image analysis unit can suggest to the user that they separate the items. Furthermore, if an item is difficult to see, the image analysis unit can suggest to the user that they move the item. In this way, suggestions can be made to optimize the arrangement of items in the refrigerator.

[0092] The image analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The image analysis unit, for example, estimates the user's emotions. For example, the image analysis unit can estimate the user's emotions using facial expression recognition technology. The image analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the image analysis unit can estimate the user's emotions by analyzing behavioral patterns. The image analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0093] The image analysis unit analyzes allergen information of items in the refrigerator and can use it to prevent allergies. The image analysis unit analyzes allergen information of items in the refrigerator, for example. For example, the image analysis unit can analyze an ingredient list and provide the user with allergen information of the items. The image analysis unit can also refer to the allergen list and make suggestions that are useful for preventing allergies. Furthermore, the image analysis unit can automatically analyze allergen information and notify the user. In this way, the allergen information of items in the refrigerator can be analyzed and used to prevent allergies.

[0094] The image analysis unit can analyze the expiration dates of items in the refrigerator and prioritize notification of items with upcoming expiration dates. The image analysis unit, for example, analyzes the expiration dates of items in the refrigerator. For example, the image analysis unit can analyze the expiration dates of items using barcode scanning technology and provide the expiration dates to the user. The image analysis unit can also prioritize notification of items with upcoming expiration dates. Furthermore, the image analysis unit can automatically detect items that have passed their expiration dates and warn the user. This makes it possible to analyze the expiration dates of items in the refrigerator and prioritize notification of items with upcoming expiration dates.

[0095] The image analysis unit can analyze the frequency of use of items in the refrigerator and prioritize displaying frequently used items. The image analysis unit, for example, analyzes the frequency of use of items in the refrigerator. For example, the image analysis unit can count the number of times an item is used and provide the user with frequently used items. The image analysis unit can also calculate the consumption rate and prioritize displaying frequently used items. Furthermore, the image analysis unit can automatically detect less frequently used items and notify the user. In this way, the frequency of use of items in the refrigerator can be analyzed and the frequently used items can be prioritized and displayed.

[0096] The collection unit can estimate the user's emotions and adjust the frequency of collecting flyer information based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions by analyzing behavioral patterns. The collection unit adjusts the frequency of collecting flyer information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection frequency can be reduced to reduce the user's burden. Furthermore, if the user is relaxed, the collection frequency can be increased to provide more detailed information. Furthermore, if the user is in a hurry, collection can be temporarily stopped to prioritize the user's operations. In this way, the frequency of collecting flyer information can be adjusted according to the user's emotions.

[0097] The collection unit can prioritize collecting and notifying supermarket sale information. The collection unit, for example, prioritizes collecting supermarket sale information. For example, the collection unit can prioritize collecting sale information from supermarket websites or apps and notify the user. In addition, when sale information is updated, the collection unit can collect the information in real time and notify the user. Furthermore, the collection unit can prioritize collecting the cheapest items based on the sale information and notify the user. In this way, supermarket sale information can be prioritized and notified.

[0098] The collection unit can prioritize collecting highly relevant flyer information in consideration of the user's purchase history. The collection unit, for example, collects flyer information in consideration of the user's purchase history. For example, the collection unit can prioritize collecting highly relevant flyer information based on past purchase data. The collection unit can also prioritize collecting flyer information related to items that the user frequently purchases. Furthermore, the collection unit can analyze the purchase history and prioritize collecting highly relevant sale information. This makes it possible to prioritize collecting highly relevant flyer information in consideration of the user's purchase history.

[0099] The collection unit can set the optimal collection timing taking into account the supermarket's business hours or location information. The collection unit sets the collection timing taking into account, for example, the supermarket's business hours. For example, the collection unit can collect flyer information during business hours. The collection unit can also preferentially collect flyer information from nearby supermarkets based on the supermarket's location information. Furthermore, the collection unit can also set the optimal collection timing taking into account the supermarket's business hours and location information. This makes it possible to set the optimal collection timing taking into account the supermarket's business hours and location information.

[0100] The collection unit can estimate the user's emotions and determine the priority of flyer information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions by analyzing behavioral patterns. The collection unit determines the priority of flyer information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, only important sale information can be collected with priority. Furthermore, if the user is relaxed, all flyer information can be collected equally. Furthermore, if the user is in a hurry, flyer information related to items with an approaching expiration date can be collected with priority. In this way, the priority of flyer information to be collected can be determined according to the user's emotions.

[0101] The collection unit can collect information about online shopping sites and enable comparison and consideration. The collection unit, for example, collects information about online shopping sites. For example, the collection unit can collect information about online shopping sites in addition to information from supermarket websites and apps. The collection unit can also collect sale information about online shopping sites and compare it with the sale information from supermarkets. Furthermore, the collection unit can collect review information about online shopping sites and provide it to the user. This makes it possible to collect information about online shopping sites and enable comparison and consideration.

[0102] The collection unit can collect supermarket review information and provide it to the user. The collection unit, for example, collects supermarket review information. For example, the collection unit can collect review information from supermarket websites or apps and provide it to the user. The collection unit can also suggest the best supermarket for the user based on the review information. Furthermore, the collection unit can analyze the review information and provide useful information to the user. In this way, supermarket review information can be collected and provided to the user.

[0103] The collection unit can collect supermarket point program information and notify the user. The collection unit, for example, collects supermarket point program information. For example, the collection unit can collect point program information from supermarket websites or apps and notify the user. The collection unit can also suggest the best supermarket for the user based on the point program information. Furthermore, the collection unit can analyze the point program information and provide useful information to the user. In this way, supermarket point program information can be collected and notified to the user.

[0104] The selection unit can estimate the user's emotion and determine the priority of items to be selected based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion. For example, the selection unit can estimate the user's emotion using facial expression recognition technology. The selection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the selection unit can estimate the user's emotion by analyzing behavioral patterns. The selection unit determines the priority of items to be selected based on the estimated user's emotion. For example, if the user is feeling stressed, only important items can be selected with priority. Furthermore, if the user is relaxed, all items can be selected equally. Furthermore, if the user is in a hurry, items with an approaching expiration date can be selected with priority. In this way, the priority of items to be selected can be determined according to the user's emotion.

[0105] The selection unit can select appropriate items taking into account the expiration dates of the items in the refrigerator. The selection unit, for example, selects items taking into account the expiration dates of the items in the refrigerator. For example, the selection unit can prioritize selecting items that are close to their expiration date. The selection unit can also automatically detect items that have passed their expiration date and warn the user. Furthermore, the selection unit can select optimal items based on the expiration date. This makes it possible to select appropriate items taking into account the expiration dates of the items in the refrigerator.

[0106] The selection unit can select items taking into consideration the health condition and allergy information of family members. The selection unit can select items taking into consideration, for example, the health condition of family members. For example, the selection unit can select healthy items based on health check results. The selection unit can also select items taking into consideration allergy information. For example, the selection unit can select items that do not contain allergens based on an allergen list. Furthermore, the selection unit can automatically analyze the health condition and allergy information and select the most appropriate item. This makes it possible to select items taking into consideration the health condition and allergy information of family members.

[0107] The selection unit can select an appropriate item by referring to the user's past purchase history. The selection unit can select an item by referring to, for example, the user's past purchase history. For example, the selection unit can select an optimal item based on past purchase data. The selection unit can also prioritize and select items that have been frequently purchased in the past. Furthermore, the selection unit can analyze the purchase history and select an optimal item. This makes it possible to select an appropriate item by referring to the user's past purchase history.

[0108] The selection unit can estimate the user's emotion and adjust the display method of the selected item based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion. For example, the selection unit can estimate the user's emotion using facial expression recognition technology. The selection unit can also estimate the user's emotion using voice analysis technology. Furthermore, the selection unit can estimate the user's emotion by analyzing behavioral patterns. The selection unit adjusts the display method of the selected item based on the estimated user's emotion. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the selected item according to the user's emotion.

[0109] The selection unit can select an appropriate item by taking into consideration the sale information of the supermarket. The selection unit can select an item by taking into consideration the sale information of the supermarket, for example. For example, the selection unit can select the most appropriate item based on the sale information. Furthermore, when the sale information is updated, the selection unit can select the most appropriate item in real time. Furthermore, the selection unit can analyze the sale information and select the cheapest item. In this way, it is possible to select an appropriate item by taking into consideration the sale information of the supermarket.

[0110] The selection unit can select an item taking into consideration the user's dietary restrictions and diet plan. The selection unit can, for example, select an item taking into consideration the user's dietary restrictions. For example, the selection unit can select an optimal item based on calorie restrictions. The selection unit can also select an item taking into consideration the diet plan. For example, the selection unit can select a low-calorie item. Furthermore, the selection unit can automatically analyze the dietary restrictions and diet plan and select an optimal item. This makes it possible to select an item taking into consideration the user's dietary restrictions and diet plan.

[0111] The selection unit can select an item taking into consideration the user's tastes or preferences. The selection unit can, for example, select an item taking into consideration the user's tastes. For example, the selection unit can select the most suitable item based on past preference data. The selection unit can also select an item taking into consideration the user's preferences. For example, the selection unit can preferentially select a preferred item. Furthermore, the selection unit can automatically analyze tastes and preferences and select the most suitable item. This makes it possible to select an item taking into consideration the user's tastes and preferences.

[0112] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. The suggestion unit can also estimate the user's emotions using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotions by analyzing behavioral patterns. The suggestion unit adjusts the menu suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible menu can be suggested. Also, if the user is relaxed, a menu including detailed information can be suggested. Furthermore, if the user is in a hurry, a menu that focuses on the main points can be suggested. In this way, the menu suggestion method can be adjusted according to the user's emotions.

[0113] The suggestion unit can suggest an appropriate menu taking into account the expiration dates of items in the refrigerator. The suggestion unit, for example, suggests a menu taking into account the expiration dates of items in the refrigerator. For example, the suggestion unit can suggest a menu that prioritizes the use of items that are close to their expiration date. The suggestion unit can also automatically detect items that have passed their expiration date and warn the user. Furthermore, the suggestion unit can suggest an optimal menu based on the expiration date. This makes it possible to suggest an appropriate menu taking into account the expiration dates of items in the refrigerator.

[0114] The suggestion unit can suggest a menu taking into consideration the health condition and allergy information of family members. The suggestion unit can suggest a menu taking into consideration, for example, the health condition of family members. For example, the suggestion unit can suggest a healthy menu based on health checkup results. The suggestion unit can also suggest a menu taking into consideration allergy information. For example, the suggestion unit can suggest a menu that does not contain allergens based on an allergen list. Furthermore, the suggestion unit can automatically analyze the health condition and allergy information to suggest an optimal menu. This makes it possible to suggest a menu taking into consideration the health condition and allergy information of family members.

[0115] The suggestion unit can suggest an appropriate menu by referring to the user's past meal history. The suggestion unit can suggest a menu by referring to the user's past meal history, for example. For example, the suggestion unit can suggest an optimal menu based on past meal data. The suggestion unit can also preferentially suggest menus that have been frequently eaten in the past. Furthermore, the suggestion unit can analyze the meal history and suggest an optimal menu. In this way, an appropriate menu can be suggested by referring to the user's past meal history.

[0116] The suggestion unit can estimate the user's emotions and adjust the menu display method based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. The suggestion unit can also estimate the user's emotions using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotions by analyzing behavioral patterns. The suggestion unit adjusts the menu display method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the menu display method according to the user's emotions.

[0117] The suggestion unit can suggest an appropriate menu taking into consideration the season or weather. The suggestion unit suggests a menu taking into consideration, for example, the season and weather. For example, the suggestion unit can suggest a menu using ingredients according to the season. The suggestion unit can also suggest a menu according to the weather. Furthermore, the suggestion unit can also suggest an optimal menu taking into consideration the season and weather. In this way, an appropriate menu can be suggested taking into consideration the season and weather.

[0118] The suggestion unit can suggest a menu taking into consideration the user's dietary restrictions and diet plan. The suggestion unit, for example, suggests a menu taking into consideration the user's dietary restrictions. For example, the suggestion unit can suggest an optimal menu based on calorie restrictions. The suggestion unit can also suggest a menu taking into consideration the diet plan. For example, the suggestion unit can suggest a low-calorie menu. Furthermore, the suggestion unit can automatically analyze the dietary restrictions and diet plan and suggest an optimal menu. In this way, it is possible to suggest a menu taking into consideration the user's dietary restrictions and diet plan.

[0119] The suggestion unit can suggest a menu taking into consideration the user's tastes or preferences. The suggestion unit, for example, suggests a menu taking into consideration the user's tastes. For example, the suggestion unit can suggest an optimal menu based on past preference data. The suggestion unit can also suggest a menu taking into consideration the user's preferences. For example, the suggestion unit can preferentially suggest a favorite menu. Furthermore, the suggestion unit can automatically analyze tastes and preferences and suggest an optimal menu. In this way, a menu can be suggested taking into consideration the user's tastes and preferences. === Hard Collateral 1-1 === Each of the multiple elements, including the camera, image analysis unit, collection unit, selection unit, and suggestion unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the camera is implemented by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the image analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes image data acquired from the camera 42. For example, the collection unit is implemented by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12 and collects flyer information from nearby supermarkets. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects inexpensive items based on the collected flyer information and the contents of the refrigerator. For example, the suggestion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and suggests a menu based on the selected items. === Hard Collateral 1-2 === Each of the multiple elements, including the camera, image analysis unit, collection unit, selection unit, and suggestion 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 is realized by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the image analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes image data acquired from the camera 42. For example, the collection unit is realized by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12 and collects flyer information from nearby supermarkets. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects inexpensive items based on the collected flyer information and the contents of the refrigerator. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests a menu based on the selected items. === Hard Collateral 1-3 === Each of the multiple elements, including the camera, image analysis unit, collection unit, selection unit, and suggestion unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the camera is implemented by the camera 42 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the image analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes image data acquired from the camera 42. For example, the collection unit is implemented by the communication I / F 44 of the headset terminal 314 or the communication I / F 26 of the data processing device 12 and collects flyer information from nearby supermarkets. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects inexpensive items based on the collected flyer information and the contents of the refrigerator. For example, the suggestion unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and suggests a menu based on the selected items. === Hard Collateral 1-4 === Each of the multiple elements, including the camera, image analysis unit, collection unit, selection unit, and suggestion 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 is realized by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the image analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes image data acquired from the camera 42. For example, the collection unit is realized by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12 and collects flyer information from nearby supermarkets. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects inexpensive items based on the collected flyer information and the contents of the refrigerator. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and suggests a menu based on the selected items.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The refrigerator management system may further include a voice recognition unit. The voice recognition unit can analyze a user's voice commands and provide information about the contents of the refrigerator. For example, if a user asks, "Is there any milk?", the voice recognition unit can check the stock status of milk in the refrigerator by linking the camera and the image analysis unit and respond to the user by voice. The voice recognition unit can also notify the user of the expiration date of items in the refrigerator based on the user's voice commands. Furthermore, the voice recognition unit can suggest menus based on the user's voice commands. This allows the user to efficiently manage the contents of the refrigerator using voice commands.

[0122] The camera can be equipped with a 3D scanning function to optimize the placement of items in the refrigerator. The 3D scanning function can grasp the position and shape of items in the refrigerator in three dimensions and suggest optimal placement. For example, if items overlap, the camera can visually guide the user using a 3D model to separate the items. The camera can also suggest placement methods to maximize the use of space in the refrigerator. Furthermore, the camera can automatically record the placement of items in the refrigerator and use this as a reference for the next time the items are placed. This allows for efficient management of the placement of items in the refrigerator.

[0123] The image analysis unit can analyze the nutritional information of items in the refrigerator and use it for health management. For example, the image analysis unit can analyze ingredient lists and provide the user with the nutritional information of the items. The image analysis unit can also calculate calories and make suggestions useful for health management based on the nutritional information. Furthermore, the image analysis unit can evaluate nutritional balance, automatically analyze the nutritional information, and notify the user. In this way, the nutritional information of items in the refrigerator can be analyzed and used for health management.

[0124] The collection unit can collect supermarket review information and provide it to the user. For example, the collection unit can collect review information from supermarket websites or apps and provide it to the user. The collection unit can also suggest the best supermarket for the user based on the review information. Furthermore, the collection unit can analyze the review information and provide useful information to the user. In this way, supermarket review information can be collected and provided to the user.

[0125] The selection unit can select an appropriate item by referring to the user's past purchase history. For example, the selection unit can select the most appropriate item based on past purchase data. The selection unit can also prioritize the selection of items that have been frequently purchased in the past. Furthermore, the selection unit can analyze the purchase history and select the most appropriate item. This makes it possible to select an appropriate item by referring to the user's past purchase history.

[0126] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. The suggestion unit can also estimate the user's emotions using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotions by analyzing behavioral patterns. The suggestion unit adjusts the menu suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible menu can be suggested. If the user is relaxed, a menu including detailed information can be suggested. Furthermore, if the user is in a hurry, a menu that focuses on the main points can be suggested. In this way, the menu suggestion method can be adjusted according to the user's emotions.

[0127] The camera can estimate the user's emotions and prioritize the images to be captured based on the estimated user's emotions. For example, the camera can estimate the user's emotions using facial expression recognition technology. The camera can also estimate the user's emotions using voice analysis technology. Furthermore, the camera can estimate the user's emotions by analyzing behavioral patterns. Based on the estimated user's emotions, the camera prioritizes the images to be captured. For example, if the user is feeling stressed, the camera can prioritize capturing only important items. If the user is relaxed, the camera can capture all items equally. If the user is in a hurry, the camera can prioritize capturing items with an approaching expiration date. This makes it possible to prioritize the images to be captured according to the user's emotions.

[0128] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated user's emotions. For example, the image analysis unit can estimate the user's emotions using facial expression recognition technology. The image analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the image analysis unit can estimate the user's emotions by analyzing behavioral patterns. The image analysis unit adjusts the accuracy of the image analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the accuracy of the image analysis can be increased to provide more accurate information. If the user is relaxed, the accuracy of the image analysis can be set to standard to provide balanced information. If the user is in a hurry, the accuracy of the image analysis can be lowered to provide information quickly. This makes it possible to adjust the accuracy of the image analysis according to the user's emotions.

[0129] The collection unit can estimate the user's emotions and determine the priority of flyer information to be collected based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions by analyzing behavioral patterns. The collection unit determines the priority of flyer information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, only important sale information can be collected with priority. If the user is relaxed, all flyer information can be collected equally. Furthermore, if the user is in a hurry, flyer information about items with an approaching expiration date can be collected with priority. In this way, the priority of flyer information to be collected can be determined according to the user's emotions.

[0130] The suggestion unit can estimate the user's emotions and adjust the menu display method based on the estimated user's emotions. For example, the suggestion unit can estimate the user's emotions using facial expression recognition technology. The suggestion unit can also estimate the user's emotions using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotions by analyzing behavioral patterns. The suggestion unit adjusts the menu display method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the menu display method according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The camera takes a picture of the contents of the refrigerator. For example, the camera is installed inside the refrigerator and can take pictures periodically. The camera can also estimate the user's emotions and adjust the timing of the camera's capture based on the estimated user emotions. Furthermore, the camera can set the optimal capture conditions taking into account the temperature and humidity inside the refrigerator. Step 2: The image analysis unit analyzes the images captured by the camera. For example, the image analysis unit can identify each item in the refrigerator based on barcodes, shapes, colors, etc. The image analysis unit can also evaluate the freshness of the items in the refrigerator and notify the user of items that have become less fresh. Step 3: The collection unit collects flyer information from nearby supermarkets. For example, the collection unit can automatically acquire data from supermarket websites or apps. The collection unit can also estimate the user's emotions and adjust the frequency of collecting flyer information based on the estimated user emotions. Step 4: The selection unit selects inexpensive items based on the contents of the refrigerator analyzed by the image analysis unit and the flyer information collected by the collection unit. For example, the selection unit can select items taking into consideration the preferences and health status of the family. The selection unit can also select optimal items taking into consideration the expiration dates of the items in the refrigerator. Step 5: The suggestion unit suggests a menu based on the items selected by the selection unit. For example, the suggestion unit can suggest a menu taking into consideration the preferences and health status of the family. The suggestion unit can also estimate the user's emotions and adjust the method of suggesting a menu based on the estimated user's emotions.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 refrigerator equipped with a camera that takes pictures of the contents of the refrigerator; an image analysis unit that analyzes the image captured by the camera; A collection department that collects flyer information from nearby supermarkets, a selection unit that selects a low-priced item based on the contents of the refrigerator analyzed by the image analysis unit and the flyer information collected by the collection unit; a suggestion unit that suggests a menu based on the items selected by the selection unit A system characterized by:

2. The camera is It is installed inside the refrigerator and takes pictures periodically.

2. The system of claim 1.

3. The image analysis unit Identify each item in the refrigerator based on its barcode, shape, or color 2. The system of claim 1.

4. The collecting unit Automatically pull data from supermarket websites or apps 2. The system of claim 1.

5. The selection unit Select items based on family preferences or health conditions 2. The system of claim 1.

6. The proposal unit Suggest menus taking into account family preferences or health conditions 2. The system of claim 1.

7. The camera is The system estimates the user's emotions and adjusts the timing of camera capture based on the estimated user emotions.

2. The system of claim 1.

8. The camera is Set the optimal shooting conditions by taking into account the temperature or humidity inside the refrigerator 2. The system of claim 1.

Images