System

Abstract

An object of a system according to an embodiment is to efficiently manage food ingredients in a refrigerator and reduce food waste.SOLUTION: A system includes a collection unit, an analysis unit, a notification unit, a proposal unit, and a learning unit. The collection unit collects information on food ingredients in the refrigerator. The analysis unit analyzes the state of the food ingredient on the basis of the information collected by the collection unit. The notification unit notifies the user of the food ingredient whose expiration date is approaching based on the information analyzed by the analysis unit. The proposal unit proposes a recipe suitable for the foodstuff notified by the notification unit. The learning unit learns the user's preferences and constraints based on the recipe proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, managing food in the refrigerator is a hassle, and there is a risk of food being wasted due to expiration dates.

[0005] The system according to the embodiment aims to efficiently manage food ingredients in a refrigerator and reduce food waste. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects information about ingredients in the refrigerator. The analysis unit analyzes the condition of the ingredients based on the information collected by the collection unit. The notification unit notifies the user of ingredients that are approaching their expiration date based on the information analyzed by the analysis unit. The suggestion unit suggests recipes that match the ingredients notified by the notification unit. The learning unit learns the user's preferences and constraints based on the recipes suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage food ingredients in a refrigerator and reduce food waste. [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 smart refrigerator assistant according to an embodiment of the present invention is a system that monitors ingredients in a refrigerator in real time, notifies the user when their expiration date is approaching, and suggests recipes that are suitable for those ingredients. The smart refrigerator assistant monitors ingredients in the refrigerator in real time and notifies the user when their expiration date is approaching. It also suggests recipes that are suitable for those ingredients. In doing so, it learns the user's preferences and constraints, enabling effective use of ingredients and greater meal variety. For example, the smart refrigerator assistant detects ingredients in the refrigerator using cameras and sensors, and analyzes that information using AI to determine the status of the ingredients. It then notifies the user when the expiration date is approaching. For example, if there is an ingredient with an expiration date within three days, a notification is displayed on the smartphone or refrigerator display. It also suggests recipes that are suitable for those ingredients. For example, if there is a vegetable with an upcoming expiration date, it suggests a recipe using that vegetable. The system selects recipes taking into account the user's preferences, allergies, dietary restrictions, etc. This allows the user to use ingredients efficiently and enjoy a more diverse diet. It also learns the user's preferences and constraints. For example, AI can analyze a user's past recipes and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. This allows it to suggest recipes that are best suited to the user. This allows the smart refrigerator assistant to make effective use of ingredients and increase dietary diversity. This allows the smart refrigerator assistant to use ingredients in the refrigerator without waste and allow users to enjoy a variety of recipes. In addition, by identifying ingredients that are close to their expiration date, food waste can be reduced, which is expected to have an economical effect. For example, by suggesting recipes that use ingredients that are close to their expiration date, food waste can be reduced and economical effects can be achieved.

[0029] A smart refrigerator assistant according to an embodiment includes a collection unit, an analysis unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects information about ingredients in the refrigerator. The collection unit can collect, for example, the type, quantity, and expiration date of ingredients in the refrigerator using a camera or a sensor. For example, the collection unit can take images of ingredients with a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. The collection unit can also detect the expiration date of ingredients using a sensor. For example, the expiration date of ingredients can be read using an RFID tag. The analysis unit analyzes the condition of ingredients based on the information collected by the collection unit. For example, the analysis unit can analyze collected image data to determine the freshness and degree of spoilage of ingredients. The analysis unit can also analyze the collected expiration date information to identify ingredients whose expiration date is approaching. For example, the analysis unit can evaluate the freshness of ingredients and detect signs of spoilage using image analysis technology. The notification unit notifies the user of ingredients whose expiration date is approaching based on the information analyzed by the analysis unit. For example, if there is an ingredient whose expiration date is within three days, the notification unit can display a notification on the smartphone or refrigerator display. For example, the notification unit can send a notification to the user's smartphone using a push notification. The notification unit can also display a pop-up message on the refrigerator display. The suggestion unit can suggest recipes that suit the ingredient notified by the notification unit. For example, if there is a vegetable whose expiration date is approaching, the suggestion unit can suggest a recipe using that vegetable. The suggestion unit can select a recipe taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, the suggestion unit can analyze the user's past recipe selection history and suggest recipes that suit the user's preferences. The learning unit learns the user's preferences and restrictions based on the recipes suggested by the suggestion unit. For example, the learning unit can analyze the user's past recipe selection history and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. For example, the learning unit can model the user's preferences using a machine learning algorithm and suggest optimal recipes.As a result, the smart refrigerator assistant of the embodiment can collect, analyze, notify, suggest, and learn information about ingredients in the refrigerator, thereby enabling effective use of ingredients and diversity in meals.

[0030] The collection unit can collect the type, quantity, and expiration date of ingredients in the refrigerator using a camera or sensor. For example, the collection unit can capture images of ingredients using a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. For example, the collection unit can acquire high-resolution images using a CCD camera and identify the type of ingredient. The collection unit can also measure the quantity of ingredients using an infrared sensor. For example, the infrared sensor measures the surface temperature of the ingredients and estimates the quantity of ingredients based on that data. The collection unit can also read the expiration date of ingredients using an RFID tag. For example, the collection unit can acquire expiration date information from RFID tags attached to the ingredients using an RFID reader. This allows accurate collection of information about ingredients in the refrigerator using a camera or sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input image data acquired by the camera into a generation AI and have the generation AI execute a process to identify the type and quantity of ingredients from the image data.

[0031] The analysis unit can analyze the collected information and determine the condition of the ingredients. For example, the analysis unit can analyze the collected image data and determine the freshness and degree of spoilage of the ingredients. For example, the analysis unit can use image analysis technology to detect discoloration or mold on the surface of the ingredients. The analysis unit can also analyze the collected expiration date information and identify ingredients whose expiration date is approaching. For example, the analysis unit can compare the expiration date information with a database and list ingredients whose expiration date is within three days. The analysis unit can also measure the moisture content of the ingredients and evaluate their freshness. For example, the analysis unit can measure the moisture content of the ingredients using near-infrared spectroscopy and evaluate their freshness based on that data. This allows the collected information to be analyzed to accurately determine the condition of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected image data into a generation AI and have the generation AI determine the freshness and degree of spoilage of the ingredients.

[0032] The notification unit can notify the user of food ingredients approaching their expiration date on a smartphone or refrigerator display. For example, if there is food ingredient with an expiration date within three days, the notification unit can display a notification on the smartphone or refrigerator display. For example, the notification unit can send a notification to the user's smartphone using a push notification. The notification unit can also display a pop-up message on the refrigerator display. For example, the notification unit can display a list of food ingredients approaching their expiration date to alert the user. The notification unit can also notify the user using an audio notification. For example, the notification unit can notify the user by audio using a speaker built into the refrigerator. This notifies the user of food ingredients approaching their expiration date, thereby reducing food waste. Some or all of the above-mentioned processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input information about food ingredients approaching their expiration date into a generation AI and cause the generation AI to generate a notification message.

[0033] The suggestion unit can suggest recipes that prioritize ingredients with upcoming expiration dates. For example, if there are vegetables with upcoming expiration dates, the suggestion unit can suggest recipes using those vegetables. For example, the suggestion unit can suggest recipes such as stir-fried vegetables or salads. The suggestion unit can also select recipes taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, the suggestion unit can analyze the user's past recipes and ingredient usage history to suggest recipes that suit the user's preferences. Furthermore, the suggestion unit can also suggest recipes that take nutritional balance into consideration. For example, the suggestion unit can analyze the nutritional value of ingredients and suggest balanced meals. This can reduce food waste by suggesting recipes that prioritize ingredients with upcoming expiration dates. Some or all of the above-mentioned processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input information about ingredients with upcoming expiration dates into the generation AI and cause the generation AI to suggest recipes.

[0034] The learning unit can learn the user's past recipe selection history and allergy information to understand the user's preferences and restrictions. For example, the learning unit can analyze the user's past recipe selection history and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. For example, the learning unit uses a machine learning algorithm to model the user's preferences and suggest optimal recipes. The learning unit can also select recipes taking into account the user's allergy information. For example, the learning unit can suggest recipes that do not include ingredients to which the user is allergic. Furthermore, the learning unit can select recipes taking into account the user's dietary restrictions. For example, if the user is on a diet, the learning unit can suggest low-calorie recipes. By learning the user's past recipe selection history and allergy information, the learning unit can suggest optimal recipes for the user. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the user's recipe selection history and allergy information into the generation AI and cause the generation AI to learn the user's preferences and restrictions.

[0035] When collecting ingredients, the collection unit can analyze the user's past ingredient use history and select the optimal collection method. For example, the collection unit can prioritize collecting ingredients that the user has used frequently in the past. The collection unit can also avoid collecting ingredients that the user has not used in the past. Furthermore, the collection unit can select the optimal collection timing based on the user's past ingredient use history. For example, the collection unit prioritizes collecting ingredients that the user has used frequently in the past and postpones ingredients that have been used less frequently. In this way, the optimal collection method can be selected by analyzing the user's past ingredient use history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's ingredient use history data into the generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can improve the accuracy of the collected data by taking into account the temperature and humidity inside the refrigerator when collecting ingredients. For example, when the temperature inside the refrigerator is high, the collection unit can have the AI ​​increase the frequency of collecting ingredients, thereby improving the accuracy of the data. Furthermore, when the humidity inside the refrigerator is high, the collection unit can have the AI ​​adjust the method of collecting ingredients, thereby improving the accuracy of the data. Furthermore, the collection unit can monitor fluctuations in temperature and humidity inside the refrigerator in real time to improve the accuracy of the collected data. For example, the collection unit can monitor the environment inside the refrigerator using a temperature sensor and a humidity sensor and adjust the collection method based on the data. In this way, the accuracy of the collected data can be improved by taking the temperature and humidity inside the refrigerator into consideration. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data from a temperature sensor and a humidity sensor into the generation AI and cause the generation AI to adjust the collection method.

[0037] When collecting ingredients, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can have the AI ​​use voice recognition technology to collect ingredient information. Furthermore, when the user uses text input, the collection unit can have the AI ​​use text analysis technology to collect ingredient information. Furthermore, when the user uses image input, the collection unit can have the AI ​​use image recognition technology to collect ingredient information. For example, the collection unit can analyze images of ingredients taken by the user with a smartphone camera and collect the information. This allows for efficient collection of ingredient information by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data, text data, and image data into the generation AI and cause the generation AI to collect ingredient information.

[0038] When collecting ingredients, the collection unit can prioritize collecting highly relevant ingredient information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting ingredient information available in that area. Furthermore, when the user is traveling, the collection unit can collect ingredient information popular in the area where the user is traveling. Furthermore, when the user is at home, the collection unit can prioritize collecting ingredient information available at nearby supermarkets. For example, the collection unit collects ingredient information based on the user's current location using the user's GPS data. This allows highly relevant ingredient information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant ingredient information.

[0039] When collecting ingredients, the collection unit can analyze the user's social media activities and collect related ingredient information. For example, the collection unit can collect related ingredient information based on recipes shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect ingredient information that may be of interest to the user. Furthermore, the collection unit can collect related ingredient information based on recipes shared by the user's friends. For example, the collection unit can analyze the user's social media accounts to collect related ingredient information. In this way, related ingredient information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related ingredient information.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting ingredients. The collection unit can, for example, adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting ingredients that the user has previously liked. Furthermore, the collection unit can avoid collecting ingredients that the user has previously avoided. For example, the collection unit analyzes the user's feedback data and customizes the collection method. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients. The analysis unit can, for example, perform a detailed analysis of ingredients that are close to their expiration date. The analysis unit can also perform a detailed analysis of ingredients that are used frequently. Furthermore, the analysis unit can also perform a detailed analysis of ingredients that are highly nutritious. By adjusting the level of detail of the analysis based on the importance of the ingredients, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of ingredient. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness to vegetables. The analysis unit can also apply an analysis algorithm that emphasizes storage conditions to meat. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes expiration dates to dairy products. In this way, by applying different analysis algorithms depending on the category of ingredient, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors from the user's past analysis results and improve accuracy. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI adjust the analysis algorithm.

[0044] During analysis, the analysis unit can determine the analysis priority based on when the ingredients were collected. For example, the analysis unit can prioritize analyzing recently collected ingredients. The analysis unit can also prioritize analyzing ingredients that are close to their expiration date. Furthermore, the analysis unit can also prioritize analyzing ingredients that are used frequently. In this way, by determining the analysis priority based on when the ingredients were collected, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on when the ingredients were collected into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ingredients. For example, the analysis unit can analyze ingredients of the same category together. The analysis unit can also prioritize the analysis of ingredients that are used frequently. Furthermore, the analysis unit can also prioritize the analysis of ingredients that are close to their expiration date. In this way, by adjusting the order of analysis based on the relevance of ingredients, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can use simple terminology. Furthermore, the analysis unit can select optimal terminology based on the user's past analysis results. This allows for adjusting the use of technical terminology in the analysis according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the expiration date of the ingredient when providing the notification. The notification unit can provide a detailed notification, for example, for ingredients whose expiration date is approaching. The notification unit can also provide a detailed notification for ingredients that are used frequently. Furthermore, the notification unit can also provide a detailed notification for ingredients that are highly nutritious. In this way, by adjusting the level of detail of the notification based on the importance of the expiration date of the ingredient, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input expiration date data of the ingredient into the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0048] The notification unit can apply different notification algorithms depending on the category of ingredient when providing notification. For example, the notification unit can apply a notification algorithm that emphasizes freshness to vegetables. The notification unit can also apply a notification algorithm that emphasizes storage status to meat. The notification unit can also apply a notification algorithm that emphasizes expiration date to dairy products. In this way, by applying different notification algorithms depending on the category of ingredient, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input ingredient category data to the generation AI and cause the generation AI to apply the notification algorithm.

[0049] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. The notification unit can, for example, adjust the notification algorithm based on the user's past notification results. The notification unit can also correct errors and improve accuracy based on the user's past notification results. Furthermore, the notification unit can analyze the user's past notification results and select the optimal notification method. This can improve the accuracy of notifications by referring to the user's past notification results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification result data into the generation AI and have the generation AI adjust the notification algorithm.

[0050] When notifying, the notification unit can determine the priority of notifications based on the proximity of the expiration date of the ingredients. For example, the notification unit can give top priority to ingredients whose expiration date is within one day. The notification unit can also give second priority to ingredients whose expiration date is within three days. Furthermore, the notification unit can also give last priority to ingredients whose expiration date is within one week. In this way, by determining the priority of notifications based on the proximity of the expiration date of the ingredients, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input expiration date data of ingredients to the generation AI and have the generation AI determine the priority of notifications.

[0051] The notification unit can adjust the order of notifications based on the relevance of ingredients when notifying. For example, the notification unit can notify ingredients of the same category together. The notification unit can also prioritize notifying ingredients that are used frequently. Furthermore, the notification unit can also prioritize notifying ingredients that are close to their expiration date. In this way, by adjusting the order of notifications based on the relevance of ingredients, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input ingredient relevance data into the generation AI and have the generation AI adjust the order of notifications.

[0052] The notification unit can adjust the use of technical terminology in notifications according to the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Alternatively, if the user does not have technical expertise, the notification unit can use simple terminology. Furthermore, the notification unit can select optimal terminology based on the user's past notification results. This allows for more appropriate notifications to be provided by adjusting the use of technical terminology in notifications according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0053] When proposing a recipe, the suggestion unit can adjust the level of detail of the proposal based on the importance of the ingredients. For example, the suggestion unit can suggest a detailed recipe for ingredients that are close to their expiration date. The suggestion unit can also suggest a detailed recipe for ingredients that are frequently used. Furthermore, the suggestion unit can also suggest a detailed recipe for ingredients that are highly nutritious. In this way, by adjusting the level of detail of the proposal based on the importance of the ingredients, more appropriate recipes can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input ingredient importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0054] When proposing a recipe, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, the suggestion unit can suggest recipes that emphasize freshness for vegetables. For meat, the suggestion unit can also suggest recipes that emphasize storage conditions. For dairy products, the suggestion unit can also suggest recipes that emphasize expiration dates. In this way, by applying different suggestion algorithms depending on the category of ingredients, more appropriate recipes can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0055] When proposing a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can, for example, adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also correct errors from the user's past suggestion results to improve accuracy. Furthermore, the suggestion unit can analyze the user's past suggestion results and select an optimal suggestion method. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to adjust the suggestion algorithm.

[0056] When proposing a recipe, the suggestion unit can determine the priority of suggestions based on the proximity of the expiration date of the ingredients. For example, the suggestion unit can give first priority to suggesting ingredients with an expiration date within one day. The suggestion unit can also give second priority to suggesting ingredients with an expiration date within three days. Furthermore, the suggestion unit can also suggest ingredients with an expiration date within one week last. In this way, by determining the priority of suggestions based on the proximity of the expiration date of the ingredients, more appropriate recipes can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input expiration date data of ingredients into the generation AI and have the generation AI determine the priority of suggestions.

[0057] When proposing a recipe, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit can suggest ingredients from the same category together. The suggestion unit can also prioritize suggesting ingredients that are used frequently. Furthermore, the suggestion unit can prioritize suggesting ingredients that are close to their expiration date. In this way, by adjusting the order of suggestions based on the relevance of ingredients, more appropriate recipes can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input ingredient relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0058] When proposing a recipe, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terms. Also, if the user does not have technical expertise, the suggestion unit can use simple terms. Furthermore, the suggestion unit can select optimal terms based on the user's past proposal results. This allows for more appropriate recipes to be proposed by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0059] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, adjust the learning algorithm based on past learning data. The learning unit can also correct errors and improve accuracy from past learning data. Furthermore, the learning unit can analyze past learning data and select an optimal learning method. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0060] During learning, the learning unit can analyze the user's recipe selection history and adjust the update frequency of the learning data. The learning unit can update the learning data based on, for example, recipes frequently selected by the user. The learning unit can also determine the optimal update frequency from the user's recipe selection history. Furthermore, the learning unit can analyze the user's recipe selection history and adjust the update timing of the learning data. In this way, the update frequency of the learning data can be adjusted by analyzing the user's recipe selection history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's recipe selection history data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0061] During learning, the learning unit can weight the learning data based on the time of submission of the recipe selection history. The learning unit can weight the learning data based on, for example, the most recent recipe selection history. The learning unit can also weight the learning data based on past recipe selection history. Furthermore, the learning unit can adjust the weighting of the learning data taking into account the time of submission of the recipe selection history. In this way, more appropriate learning can be performed by weighting the learning data based on the time of submission of the recipe selection history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input recipe selection history data to a generation AI and cause the generation AI to weight the learning data.

[0062] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit can adjust the learning algorithm based on, for example, user feedback. The learning unit can also correct errors and improve accuracy based on user feedback. Furthermore, the learning unit can analyze user feedback and select an optimal learning method. In this way, the learning algorithm can be adjusted and accuracy can be improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can analyze the user's food purchase history and prioritize collection of frequently purchased food ingredients. For example, the collection unit can identify food ingredients that the user frequently purchases and prioritize monitoring the inventory status of those food ingredients. The collection unit can also track the expiration dates of food ingredients previously purchased by the user and prioritize collection of food ingredients that are approaching their expiration date. Furthermore, the collection unit can adjust the timing of seasonal food ingredient collection based on the user's purchase history. This allows for more efficient food ingredient collection by utilizing the user's purchase history.

[0065] The analysis unit can analyze the nutritional value of ingredients and suggest ingredients based on the user's health condition. For example, the analysis unit can suggest ingredients to supplement vitamins or minerals based on the user's health data. In addition, if the user is on a diet, the analysis unit can analyze and suggest low-calorie ingredients preferentially. Furthermore, the analysis unit can take into account the user's allergy information and exclude ingredients that may cause allergies. This makes it possible to suggest ingredients based on the user's health condition.

[0066] The notification unit can adjust the timing of notifications taking into account the user's schedule. For example, the notification unit can work with the user's calendar app to send notifications that avoid busy times. The notification unit can also send notifications during times when the user is relaxing. Furthermore, if the user is traveling, the notification unit can also adjust notifications to match the time zone at the user's destination. This allows notifications to be sent at appropriate times that fit the user's schedule.

[0067] The collection unit can collect information for optimizing the arrangement of ingredients in the refrigerator. For example, the collection unit can analyze the arrangement of ingredients and suggest an arrangement method for efficiently using the space in the refrigerator. The collection unit can also optimize the temperature distribution in the refrigerator based on the arrangement of ingredients. Furthermore, the collection unit can change the arrangement of ingredients to place ingredients with an approaching expiration date in a prominent position. In this way, by optimizing the arrangement of ingredients in the refrigerator, food waste can be reduced.

[0068] The analysis unit can analyze the user's eating patterns and predict food ingredient consumption. For example, the analysis unit can predict future food ingredient consumption based on the user's past eating history. The analysis unit can also suggest the timing of food ingredient purchases based on the user's eating patterns. Furthermore, the analysis unit can analyze the user's eating patterns and predict the expiration date of food ingredients. This makes it possible to predict food ingredient consumption based on the user's eating patterns.

[0069] The learning unit can analyze the user's ingredient use history and learn the ingredient use patterns. For example, the learning unit can identify ingredients that the user uses frequently and learn the ingredient use patterns. The learning unit can also learn the ingredients that the user uses in a particular season. Furthermore, the learning unit can predict the frequency of ingredient use based on the user's ingredient use history. In this way, by learning the user's ingredient use patterns, it becomes possible to suggest more appropriate ingredients.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The collection unit collects information about ingredients in the refrigerator. The collection unit can use a camera or sensor to collect the type, quantity, and expiration date of ingredients in the refrigerator. For example, the collection unit can take images of ingredients with a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. The collection unit can also detect the expiration date of ingredients using a sensor. For example, the expiration date of ingredients can be read using an RFID tag. Step 2: The analysis unit analyzes the condition of the ingredients based on the information collected by the collection unit. The analysis unit analyzes the collected image data to determine the freshness and degree of spoilage of the ingredients. It also analyzes the collected expiration date information to identify ingredients that are approaching their expiration date. For example, image analysis technology can be used to evaluate the freshness of ingredients and detect signs of spoilage. Step 3: The notification unit notifies the user of ingredients that are approaching their expiration date based on the information analyzed by the analysis unit. If there are ingredients whose expiration date is within three days, the notification unit displays a notification on the smartphone or refrigerator display. For example, the notification is sent to the user's smartphone using a push notification. It can also display a pop-up message on the refrigerator display. Step 4: The suggestion unit suggests recipes that are suitable for the ingredients notified by the notification unit. If there are vegetables that are close to their expiration date, the suggestion unit suggests recipes that use those vegetables. The suggestion unit selects recipes taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, it analyzes the user's past recipe selection history and suggests recipes that suit the user's preferences. Step 5: The learning unit learns the user's preferences and constraints based on the recipes proposed by the suggestion unit. The learning unit analyzes the user's past recipes and ingredient usage history to learn the user's preferences, allergies, and dietary constraints. For example, it uses a machine learning algorithm to model the user's preferences and suggest optimal recipes.

[0072] (Example 2) A smart refrigerator assistant according to an embodiment of the present invention is a system that monitors ingredients in a refrigerator in real time, notifies the user when their expiration date is approaching, and suggests recipes that are suitable for those ingredients. The smart refrigerator assistant monitors ingredients in the refrigerator in real time and notifies the user when their expiration date is approaching. It also suggests recipes that are suitable for those ingredients. In doing so, it learns the user's preferences and constraints, enabling effective use of ingredients and greater meal variety. For example, the smart refrigerator assistant detects ingredients in the refrigerator using cameras and sensors, and analyzes that information using AI to determine the status of the ingredients. It then notifies the user when the expiration date is approaching. For example, if there is an ingredient with an expiration date within three days, a notification is displayed on the smartphone or refrigerator display. It also suggests recipes that are suitable for those ingredients. For example, if there is a vegetable with an upcoming expiration date, it suggests a recipe using that vegetable. The system selects recipes taking into account the user's preferences, allergies, dietary restrictions, etc. This allows the user to use ingredients efficiently and enjoy a more diverse diet. It also learns the user's preferences and constraints. For example, AI can analyze a user's past recipes and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. This allows it to suggest recipes that are best suited to the user. This allows the smart refrigerator assistant to make effective use of ingredients and increase dietary diversity. This allows the smart refrigerator assistant to use ingredients in the refrigerator without waste and allow users to enjoy a variety of recipes. In addition, by identifying ingredients that are close to their expiration date, food waste can be reduced, which is expected to have an economical effect. For example, by suggesting recipes that use ingredients that are close to their expiration date, food waste can be reduced and economical effects can be achieved.

[0073] A smart refrigerator assistant according to an embodiment includes a collection unit, an analysis unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects information about ingredients in the refrigerator. The collection unit can collect, for example, the type, quantity, and expiration date of ingredients in the refrigerator using a camera or a sensor. For example, the collection unit can take images of ingredients with a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. The collection unit can also detect the expiration date of ingredients using a sensor. For example, the expiration date of ingredients can be read using an RFID tag. The analysis unit analyzes the condition of ingredients based on the information collected by the collection unit. For example, the analysis unit can analyze collected image data to determine the freshness and degree of spoilage of ingredients. The analysis unit can also analyze the collected expiration date information to identify ingredients whose expiration date is approaching. For example, the analysis unit can evaluate the freshness of ingredients and detect signs of spoilage using image analysis technology. The notification unit notifies the user of ingredients whose expiration date is approaching based on the information analyzed by the analysis unit. For example, if there is an ingredient whose expiration date is within three days, the notification unit can display a notification on the smartphone or refrigerator display. For example, the notification unit can send a notification to the user's smartphone using a push notification. The notification unit can also display a pop-up message on the refrigerator display. The suggestion unit can suggest recipes that suit the ingredient notified by the notification unit. For example, if there is a vegetable whose expiration date is approaching, the suggestion unit can suggest a recipe using that vegetable. The suggestion unit can select a recipe taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, the suggestion unit can analyze the user's past recipe selection history and suggest recipes that suit the user's preferences. The learning unit learns the user's preferences and restrictions based on the recipes suggested by the suggestion unit. For example, the learning unit can analyze the user's past recipe selection history and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. For example, the learning unit can model the user's preferences using a machine learning algorithm and suggest optimal recipes.As a result, the smart refrigerator assistant of the embodiment can collect, analyze, notify, suggest, and learn information about ingredients in the refrigerator, thereby enabling effective use of ingredients and diversity in meals.

[0074] The collection unit can collect the type, quantity, and expiration date of ingredients in the refrigerator using a camera or sensor. For example, the collection unit can capture images of ingredients using a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. For example, the collection unit can acquire high-resolution images using a CCD camera and identify the type of ingredient. The collection unit can also measure the quantity of ingredients using an infrared sensor. For example, the infrared sensor measures the surface temperature of the ingredients and estimates the quantity of ingredients based on that data. The collection unit can also read the expiration date of ingredients using an RFID tag. For example, the collection unit can acquire expiration date information from RFID tags attached to the ingredients using an RFID reader. This allows accurate collection of information about ingredients in the refrigerator using a camera or sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input image data acquired by the camera into a generation AI and have the generation AI execute a process to identify the type and quantity of ingredients from the image data.

[0075] The analysis unit can analyze the collected information and determine the condition of the ingredients. For example, the analysis unit can analyze the collected image data and determine the freshness and degree of spoilage of the ingredients. For example, the analysis unit can use image analysis technology to detect discoloration or mold on the surface of the ingredients. The analysis unit can also analyze the collected expiration date information and identify ingredients whose expiration date is approaching. For example, the analysis unit can compare the expiration date information with a database and list ingredients whose expiration date is within three days. The analysis unit can also measure the moisture content of the ingredients and evaluate their freshness. For example, the analysis unit can measure the moisture content of the ingredients using near-infrared spectroscopy and evaluate their freshness based on that data. This allows the collected information to be analyzed to accurately determine the condition of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected image data into a generation AI and have the generation AI determine the freshness and degree of spoilage of the ingredients.

[0076] The notification unit can notify the user of food ingredients approaching their expiration date on a smartphone or refrigerator display. For example, if there is food ingredient with an expiration date within three days, the notification unit can display a notification on the smartphone or refrigerator display. For example, the notification unit can send a notification to the user's smartphone using a push notification. The notification unit can also display a pop-up message on the refrigerator display. For example, the notification unit can display a list of food ingredients approaching their expiration date to alert the user. The notification unit can also notify the user using an audio notification. For example, the notification unit can notify the user by audio using a speaker built into the refrigerator. This notifies the user of food ingredients approaching their expiration date, thereby reducing food waste. Some or all of the above-mentioned processing by the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input information about food ingredients approaching their expiration date into a generation AI and cause the generation AI to generate a notification message.

[0077] The suggestion unit can suggest recipes that prioritize ingredients with upcoming expiration dates. For example, if there are vegetables with upcoming expiration dates, the suggestion unit can suggest recipes using those vegetables. For example, the suggestion unit can suggest recipes such as stir-fried vegetables or salads. The suggestion unit can also select recipes taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, the suggestion unit can analyze the user's past recipes and ingredient usage history to suggest recipes that suit the user's preferences. Furthermore, the suggestion unit can also suggest recipes that take nutritional balance into consideration. For example, the suggestion unit can analyze the nutritional value of ingredients and suggest balanced meals. This can reduce food waste by suggesting recipes that prioritize ingredients with upcoming expiration dates. Some or all of the above-mentioned processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input information about ingredients with upcoming expiration dates into the generation AI and cause the generation AI to suggest recipes.

[0078] The learning unit can learn the user's past recipe selection history and allergy information to understand the user's preferences and restrictions. For example, the learning unit can analyze the user's past recipe selection history and ingredient usage history to learn the user's preferences, allergies, and dietary restrictions. For example, the learning unit uses a machine learning algorithm to model the user's preferences and suggest optimal recipes. The learning unit can also select recipes taking into account the user's allergy information. For example, the learning unit can suggest recipes that do not include ingredients to which the user is allergic. Furthermore, the learning unit can select recipes taking into account the user's dietary restrictions. For example, if the user is on a diet, the learning unit can suggest low-calorie recipes. By learning the user's past recipe selection history and allergy information, the learning unit can suggest optimal recipes for the user. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the user's recipe selection history and allergy information into the generation AI and cause the generation AI to learn the user's preferences and restrictions.

[0079] The collection unit can estimate the user's emotions and adjust the timing of collecting ingredients based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can delay the timing of collecting ingredients and collect them when the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can quickly collect ingredients and immediately provide the necessary information. Furthermore, if the user is relaxed, the AI ​​can collect detailed ingredient information and provide it to the user. This allows the timing of collecting ingredients to be adjusted according to the user's emotions, allowing ingredients to be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] When collecting ingredients, the collection unit can analyze the user's past ingredient use history and select the optimal collection method. For example, the collection unit can prioritize collecting ingredients that the user has used frequently in the past. The collection unit can also avoid collecting ingredients that the user has not used in the past. Furthermore, the collection unit can select the optimal collection timing based on the user's past ingredient use history. For example, the collection unit prioritizes collecting ingredients that the user has used frequently in the past and postpones ingredients that have been used less frequently. In this way, the optimal collection method can be selected by analyzing the user's past ingredient use history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's ingredient use history data into the generation AI and cause the generation AI to select the optimal collection method.

[0081] The collection unit can improve the accuracy of the collected data by taking into account the temperature and humidity inside the refrigerator when collecting ingredients. For example, when the temperature inside the refrigerator is high, the collection unit can have the AI ​​increase the frequency of collecting ingredients, thereby improving the accuracy of the data. Furthermore, when the humidity inside the refrigerator is high, the collection unit can have the AI ​​adjust the method of collecting ingredients, thereby improving the accuracy of the data. Furthermore, the collection unit can monitor fluctuations in temperature and humidity inside the refrigerator in real time to improve the accuracy of the collected data. For example, the collection unit can monitor the environment inside the refrigerator using a temperature sensor and a humidity sensor and adjust the collection method based on the data. In this way, the accuracy of the collected data can be improved by taking the temperature and humidity inside the refrigerator into consideration. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data from a temperature sensor and a humidity sensor into the generation AI and cause the generation AI to adjust the collection method.

[0082] When collecting ingredients, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can have the AI ​​use voice recognition technology to collect ingredient information. Furthermore, when the user uses text input, the collection unit can have the AI ​​use text analysis technology to collect ingredient information. Furthermore, when the user uses image input, the collection unit can have the AI ​​use image recognition technology to collect ingredient information. For example, the collection unit can analyze images of ingredients taken by the user with a smartphone camera and collect the information. This allows for efficient collection of ingredient information by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data, text data, and image data into the generation AI and cause the generation AI to collect ingredient information.

[0083] The collection unit can estimate the user's emotions and determine the priority of ingredients to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can cause the AI ​​to prioritize collecting ingredients that have a relaxing effect. Furthermore, if the user is in a hurry, the collection unit can cause the AI ​​to prioritize collecting ingredients that require a short cooking time. Furthermore, if the user is relaxed, the collection unit can cause the AI ​​to prioritize collecting ingredients that are highly nutritious. This allows for more appropriate ingredients to be collected by prioritizing ingredients to be collected according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0084] When collecting ingredients, the collection unit can prioritize collecting highly relevant ingredient information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting ingredient information available in that area. Furthermore, when the user is traveling, the collection unit can collect ingredient information popular in the area where the user is traveling. Furthermore, when the user is at home, the collection unit can prioritize collecting ingredient information available at nearby supermarkets. For example, the collection unit collects ingredient information based on the user's current location using the user's GPS data. This allows highly relevant ingredient information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant ingredient information.

[0085] When collecting ingredients, the collection unit can analyze the user's social media activities and collect related ingredient information. For example, the collection unit can collect related ingredient information based on recipes shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect ingredient information that may be of interest to the user. Furthermore, the collection unit can collect related ingredient information based on recipes shared by the user's friends. For example, the collection unit can analyze the user's social media accounts to collect related ingredient information. In this way, related ingredient information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related ingredient information.

[0086] The collection unit can customize the collection method by reflecting the user's past feedback when collecting ingredients. The collection unit can, for example, adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting ingredients that the user has previously liked. Furthermore, the collection unit can avoid collecting ingredients that the user has previously avoided. For example, the collection unit analyzes the user's feedback data and customizes the collection method. This allows the collection method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0087] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the ingredients. The analysis unit can, for example, perform a detailed analysis of ingredients that are close to their expiration date. The analysis unit can also perform a detailed analysis of ingredients that are used frequently. Furthermore, the analysis unit can also perform a detailed analysis of ingredients that are highly nutritious. By adjusting the level of detail of the analysis based on the importance of the ingredients, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of ingredient. For example, the analysis unit can apply an analysis algorithm that emphasizes freshness to vegetables. The analysis unit can also apply an analysis algorithm that emphasizes storage conditions to meat. Furthermore, the analysis unit can apply an analysis algorithm that emphasizes expiration dates to dairy products. In this way, by applying different analysis algorithms depending on the category of ingredient, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors from the user's past analysis results and improve accuracy. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI adjust the analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] During analysis, the analysis unit can determine the analysis priority based on when the ingredients were collected. For example, the analysis unit can prioritize analyzing recently collected ingredients. The analysis unit can also prioritize analyzing ingredients that are close to their expiration date. Furthermore, the analysis unit can also prioritize analyzing ingredients that are used frequently. In this way, by determining the analysis priority based on when the ingredients were collected, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on when the ingredients were collected into the generation AI and have the generation AI determine the analysis priority.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ingredients. For example, the analysis unit can analyze ingredients of the same category together. The analysis unit can also prioritize the analysis of ingredients that are used frequently. Furthermore, the analysis unit can also prioritize the analysis of ingredients that are close to their expiration date. In this way, by adjusting the order of analysis based on the relevance of ingredients, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ingredient relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0094] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can use simple terminology. Furthermore, the analysis unit can select optimal terminology based on the user's past analysis results. This allows for adjusting the use of technical terminology in the analysis according to the user's level of expertise, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0095] The notification unit can estimate the user's emotions and adjust the notification display method based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide a simple, highly visible notification. Furthermore, if the user is relaxed, the notification unit can provide a notification with detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a notification that focuses on the main points. This allows for more appropriate notifications to be provided by adjusting the notification display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0096] The notification unit can adjust the level of detail of the notification based on the importance of the expiration date of the ingredient when providing the notification. The notification unit can provide a detailed notification, for example, for ingredients whose expiration date is approaching. The notification unit can also provide a detailed notification for ingredients that are used frequently. Furthermore, the notification unit can also provide a detailed notification for ingredients that are highly nutritious. In this way, by adjusting the level of detail of the notification based on the importance of the expiration date of the ingredient, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without AI. For example, the notification unit can input expiration date data of the ingredient into the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0097] The notification unit can apply different notification algorithms depending on the category of ingredient when providing notification. For example, the notification unit can apply a notification algorithm that emphasizes freshness to vegetables. The notification unit can also apply a notification algorithm that emphasizes storage status to meat. The notification unit can also apply a notification algorithm that emphasizes expiration date to dairy products. In this way, by applying different notification algorithms depending on the category of ingredient, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input ingredient category data to the generation AI and cause the generation AI to apply the notification algorithm.

[0098] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. The notification unit can, for example, adjust the notification algorithm based on the user's past notification results. The notification unit can also correct errors and improve accuracy based on the user's past notification results. Furthermore, the notification unit can analyze the user's past notification results and select the optimal notification method. This can improve the accuracy of notifications by referring to the user's past notification results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's past notification result data into the generation AI and have the generation AI adjust the notification algorithm.

[0099] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user emotion. For example, if the user is in a hurry, the notification unit can provide a short, to-the-point notification. Furthermore, if the user is relaxed, the notification unit can provide a longer notification with detailed information. Furthermore, if the user is excited, the notification unit can provide a visually stimulating notification. By adjusting the length of the notification according to the user's emotion, more appropriate notifications can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] When notifying, the notification unit can determine the priority of notifications based on the proximity of the expiration date of the ingredients. For example, the notification unit can give top priority to ingredients whose expiration date is within one day. The notification unit can also give second priority to ingredients whose expiration date is within three days. Furthermore, the notification unit can also give last priority to ingredients whose expiration date is within one week. In this way, by determining the priority of notifications based on the proximity of the expiration date of the ingredients, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input expiration date data of ingredients to the generation AI and have the generation AI determine the priority of notifications.

[0101] The notification unit can adjust the order of notifications based on the relevance of ingredients when notifying. For example, the notification unit can notify ingredients of the same category together. The notification unit can also prioritize notifying ingredients that are used frequently. Furthermore, the notification unit can also prioritize notifying ingredients that are close to their expiration date. In this way, by adjusting the order of notifications based on the relevance of ingredients, more appropriate notifications can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input ingredient relevance data into the generation AI and have the generation AI adjust the order of notifications.

[0102] The notification unit can adjust the use of technical terminology in notifications according to the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Alternatively, if the user does not have technical expertise, the notification unit can use simple terminology. Furthermore, the notification unit can select optimal terminology based on the user's past notification results. This allows for more appropriate notifications to be provided by adjusting the use of technical terminology in notifications according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0103] The suggestion unit can estimate the user's emotions and adjust the way in which recipe suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can suggest a simple, highly visible recipe. Furthermore, if the user is relaxed, the suggestion unit can suggest a detailed recipe. Furthermore, if the user is in a hurry, the suggestion unit can suggest a recipe that focuses on the main points. By adjusting the way in which recipe suggestions are presented according to the user's emotions, more appropriate recipes can be proposed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] When proposing a recipe, the suggestion unit can adjust the level of detail of the proposal based on the importance of the ingredients. For example, the suggestion unit can suggest a detailed recipe for ingredients that are close to their expiration date. The suggestion unit can also suggest a detailed recipe for ingredients that are frequently used. Furthermore, the suggestion unit can also suggest a detailed recipe for ingredients that are highly nutritious. In this way, by adjusting the level of detail of the proposal based on the importance of the ingredients, more appropriate recipes can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input ingredient importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0105] When proposing a recipe, the suggestion unit can apply different suggestion algorithms depending on the category of ingredients. For example, the suggestion unit can suggest recipes that emphasize freshness for vegetables. For meat, the suggestion unit can also suggest recipes that emphasize storage conditions. For dairy products, the suggestion unit can also suggest recipes that emphasize expiration dates. In this way, by applying different suggestion algorithms depending on the category of ingredients, more appropriate recipes can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input ingredient category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0106] When proposing a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can, for example, adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also correct errors from the user's past suggestion results to improve accuracy. Furthermore, the suggestion unit can analyze the user's past suggestion results and select an optimal suggestion method. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to adjust the suggestion algorithm.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the recipe suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can suggest short, concise recipes. Furthermore, if the user is relaxed, the suggestion unit can suggest detailed recipes. Furthermore, if the user is excited, the suggestion unit can suggest visually stimulating recipes. By adjusting the length of the recipe suggestions according to the user's emotions, more appropriate recipes can be suggested. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0108] When proposing a recipe, the suggestion unit can determine the priority of suggestions based on the proximity of the expiration date of the ingredients. For example, the suggestion unit can give first priority to suggesting ingredients with an expiration date within one day. The suggestion unit can also give second priority to suggesting ingredients with an expiration date within three days. Furthermore, the suggestion unit can also suggest ingredients with an expiration date within one week last. In this way, by determining the priority of suggestions based on the proximity of the expiration date of the ingredients, more appropriate recipes can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input expiration date data of ingredients into the generation AI and have the generation AI determine the priority of suggestions.

[0109] When proposing a recipe, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit can suggest ingredients from the same category together. The suggestion unit can also prioritize suggesting ingredients that are used frequently. Furthermore, the suggestion unit can prioritize suggesting ingredients that are close to their expiration date. In this way, by adjusting the order of suggestions based on the relevance of ingredients, more appropriate recipes can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input ingredient relevance data into the generation AI and cause the generation AI to adjust the order of suggestions.

[0110] When proposing a recipe, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terms. Also, if the user does not have technical expertise, the suggestion unit can use simple terms. Furthermore, the suggestion unit can select optimal terms based on the user's past proposal results. This allows for more appropriate recipes to be proposed by adjusting the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0111] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This allows for more appropriate training data to be selected by selecting training data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0112] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, adjust the learning algorithm based on past learning data. The learning unit can also correct errors and improve accuracy from past learning data. Furthermore, the learning unit can analyze past learning data and select an optimal learning method. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0113] During learning, the learning unit can analyze the user's recipe selection history and adjust the update frequency of the learning data. The learning unit can update the learning data based on, for example, recipes frequently selected by the user. The learning unit can also determine the optimal update frequency from the user's recipe selection history. Furthermore, the learning unit can analyze the user's recipe selection history and adjust the update timing of the learning data. In this way, the update frequency of the learning data can be adjusted by analyzing the user's recipe selection history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's recipe selection history data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0114] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. This allows for more appropriate learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0115] During learning, the learning unit can weight the learning data based on the time of submission of the recipe selection history. The learning unit can weight the learning data based on, for example, the most recent recipe selection history. The learning unit can also weight the learning data based on past recipe selection history. Furthermore, the learning unit can adjust the weighting of the learning data taking into account the time of submission of the recipe selection history. In this way, more appropriate learning can be performed by weighting the learning data based on the time of submission of the recipe selection history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input recipe selection history data to a generation AI and cause the generation AI to weight the learning data.

[0116] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit can adjust the learning algorithm based on, for example, user feedback. The learning unit can also correct errors and improve accuracy based on user feedback. Furthermore, the learning unit can analyze user feedback and select an optimal learning method. In this way, the learning algorithm can be adjusted and accuracy can be improved by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, suggestion unit, and learning unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information about ingredients in the refrigerator using the camera 42 or sensors of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to determine the status of the ingredients. The notification unit, realized, for example, by the control unit 46A of the smart device 14, notifies the user of ingredients whose expiration date is approaching. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests recipes that take into account the user's preferences and constraints. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns the user's past selection history and suggests optimal recipes. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, suggestion unit, and learning 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 collection unit can collect information about ingredients in the refrigerator using the camera 42 or sensors of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to determine the status of the ingredients. The notification unit, realized, for example, by the control unit 46A of the smart glasses 214, notifies the user of ingredients whose expiration date is approaching. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests recipes that take into account the user's preferences and constraints. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, learns the user's past selection history and suggests optimal recipes. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, suggestion unit, and learning unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect information about ingredients in the refrigerator using the camera 42 or sensors of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to determine the status of the ingredients. The notification unit is realized, for example, by the control unit 46A of the headset terminal 314 and notifies the user of ingredients whose expiration date is approaching. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests recipes that take the user's preferences and constraints into consideration. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past selection history to suggest optimal recipes. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, suggestion unit, and learning unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information about ingredients in the refrigerator using the camera 42 or sensors of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to determine the status of the ingredients. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of ingredients whose expiration date is approaching. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests recipes that take into account the user's preferences and constraints. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the user's past selection history to suggest optimal recipes.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The collection unit can analyze the user's food purchase history and prioritize collection of frequently purchased food ingredients. For example, the collection unit can identify food ingredients that the user frequently purchases and prioritize monitoring the inventory status of those food ingredients. The collection unit can also track the expiration dates of food ingredients previously purchased by the user and prioritize collection of food ingredients that are approaching their expiration date. Furthermore, the collection unit can adjust the timing of seasonal food ingredient collection based on the user's purchase history. This allows for more efficient food ingredient collection by utilizing the user's purchase history.

[0119] The analysis unit can analyze the nutritional value of ingredients and suggest ingredients based on the user's health condition. For example, the analysis unit can suggest ingredients to supplement vitamins or minerals based on the user's health data. In addition, if the user is on a diet, the analysis unit can analyze and suggest low-calorie ingredients preferentially. Furthermore, the analysis unit can take into account the user's allergy information and exclude ingredients that may cause allergies. This makes it possible to suggest ingredients based on the user's health condition.

[0120] The notification unit can adjust the timing of notifications taking into account the user's schedule. For example, the notification unit can work with the user's calendar app to send notifications that avoid busy times. The notification unit can also send notifications during times when the user is relaxing. Furthermore, if the user is traveling, the notification unit can also adjust notifications to match the time zone at the user's destination. This allows notifications to be sent at appropriate times that fit the user's schedule.

[0121] The suggestion unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and easy recipe. If the user is relaxed, the suggestion unit can suggest a slightly more time-consuming recipe. Furthermore, if the user is excited, the suggestion unit can suggest a more challenging recipe. In this way, by adjusting the difficulty of the recipe according to the user's emotions, more appropriate recipes can be suggested.

[0122] The learning unit can estimate the user's emotions and adjust the update frequency of the learning data based on the estimated user's emotions. For example, if the user is relaxed, the update frequency of the learning data can be increased. Also, if the user is in a hurry, the update frequency of the learning data can be decreased. Furthermore, if the user is excited, the update frequency of the learning data can be adjusted. In this way, more appropriate learning can be performed by adjusting the update frequency of the learning data according to the user's emotions.

[0123] The collection unit can collect information for optimizing the arrangement of ingredients in the refrigerator. For example, the collection unit can analyze the arrangement of ingredients and suggest an arrangement method for efficiently using the space in the refrigerator. The collection unit can also optimize the temperature distribution in the refrigerator based on the arrangement of ingredients. Furthermore, the collection unit can change the arrangement of ingredients to place ingredients with an approaching expiration date in a prominent position. In this way, by optimizing the arrangement of ingredients in the refrigerator, food waste can be reduced.

[0124] The analysis unit can analyze the user's eating patterns and predict food ingredient consumption. For example, the analysis unit can predict future food ingredient consumption based on the user's past eating history. The analysis unit can also suggest the timing of food ingredient purchases based on the user's eating patterns. Furthermore, the analysis unit can analyze the user's eating patterns and predict the expiration date of food ingredients. This makes it possible to predict food ingredient consumption based on the user's eating patterns.

[0125] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, a concise and to-the-point notification can be provided. If the user is feeling relaxed, a notification including detailed information can be provided. Furthermore, if the user is excited, a visually stimulating notification can be provided. In this way, by adjusting the content of the notification according to the user's emotions, more appropriate notifications can be provided.

[0126] The suggestion unit can estimate the user's emotions and adjust the frequency of recipe suggestions based on the estimated user's emotions. For example, if the user is relaxed, the frequency of recipe suggestions can be increased. Also, if the user is in a hurry, the frequency of recipe suggestions can be decreased. Furthermore, if the user is excited, the frequency of recipe suggestions can be adjusted. In this way, by adjusting the frequency of recipe suggestions according to the user's emotions, more appropriate recipes can be suggested.

[0127] The learning unit can analyze the user's ingredient use history and learn the ingredient use patterns. For example, the learning unit can identify ingredients that the user uses frequently and learn the ingredient use patterns. The learning unit can also learn the ingredients that the user uses in a particular season. Furthermore, the learning unit can predict the frequency of ingredient use based on the user's ingredient use history. In this way, by learning the user's ingredient use patterns, it becomes possible to suggest more appropriate ingredients.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects information about ingredients in the refrigerator. The collection unit can use a camera or sensor to collect the type, quantity, and expiration date of ingredients in the refrigerator. For example, the collection unit can take images of ingredients with a camera installed in the refrigerator and identify the type and quantity of ingredients using image analysis technology. The collection unit can also detect the expiration date of ingredients using a sensor. For example, the expiration date of ingredients can be read using an RFID tag. Step 2: The analysis unit analyzes the condition of the ingredients based on the information collected by the collection unit. The analysis unit analyzes the collected image data to determine the freshness and degree of spoilage of the ingredients. It also analyzes the collected expiration date information to identify ingredients that are approaching their expiration date. For example, image analysis technology can be used to evaluate the freshness of ingredients and detect signs of spoilage. Step 3: The notification unit notifies the user of ingredients that are approaching their expiration date based on the information analyzed by the analysis unit. If there are ingredients whose expiration date is within three days, the notification unit displays a notification on the smartphone or refrigerator display. For example, the notification is sent to the user's smartphone using a push notification. It can also display a pop-up message on the refrigerator display. Step 4: The suggestion unit suggests recipes that are suitable for the ingredients notified by the notification unit. If there are vegetables that are close to their expiration date, the suggestion unit suggests recipes that use those vegetables. The suggestion unit selects recipes taking into consideration the user's preferences, allergies, dietary restrictions, etc. For example, it analyzes the user's past recipe selection history and suggests recipes that suit the user's preferences. Step 5: The learning unit learns the user's preferences and constraints based on the recipes proposed by the suggestion unit. The learning unit analyzes the user's past recipes and ingredient usage history to learn the user's preferences, allergies, and dietary constraints. For example, it uses a machine learning algorithm to model the user's preferences and suggest optimal recipes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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 collection unit that collects information about ingredients in the refrigerator; an analysis unit that analyzes the state of ingredients based on the information collected by the collection unit; a notification unit that notifies the user of food ingredients whose expiration date is approaching based on the information analyzed by the analysis unit; a suggestion unit that suggests recipes suitable for the ingredients notified by the notification unit; A system comprising: a learning unit that learns user preferences and constraints based on the recipes proposed by the suggestion unit.

2. The collecting unit Using a camera or sensor, collect information on the type and quantity of food in the refrigerator, as well as expiration dates.

2. The system of claim 1.

3. The analysis unit Analyzing the collected information and understanding the condition of ingredients 2. The system of claim 1.

4. The notification unit Notify your smartphone or refrigerator display when food is nearing its expiration date 2. The system of claim 1.

5. The proposal unit Suggest recipes that prioritize the use of ingredients with close expiration dates 2. The system of claim 1.

6. The learning unit Learns the user's past recipe selection history and allergy information to understand the user's preferences and restrictions 2. The system of claim 1.

7. The collecting unit Estimates the user's emotions and adjusts the timing of food collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting ingredients, analyze the user's past ingredient usage history and select the collection method.

2. The system of claim 1.

Images