system

Abstract

The system according to this embodiment aims to efficiently manage the expiration dates of food in a refrigerator and reduce food waste. [Solution] The system according to the embodiment comprises a recognition unit, a data conversion unit, an alarm unit, and a suggestion unit. The recognition unit recognizes the type of food and the expiration date indicated on the food. The data conversion unit converts the information recognized by the recognition unit into data. The alarm unit issues an alarm for food items that are nearing their expiration date based on the information converted into data by the data conversion unit. The suggestion unit proposes ways to use the food items before they expire based on the alarm issued by the alarm unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is laborious to manage the expiration dates of foods in a refrigerator, and there is a risk of food waste.

[0005] The system according to the embodiment aims to efficiently manage the expiration dates of foods in a refrigerator and reduce food waste.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a data conversion unit, an alarm unit, and a suggestion unit. The recognition unit recognizes the type of food and the expiration date indicated on the food. The data conversion unit converts the information recognized by the recognition unit into data. The alarm unit issues an alarm for food items that are nearing their expiration date based on the information converted into data by the data conversion unit. The suggestion unit proposes ways to use the food items before they expire based on the alarm issued by the alarm unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the expiration dates of food in a refrigerator and reduce food waste. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The food management system according to an embodiment of the present invention is a system that uses a camera to recognize the type of food and the expiration date displayed on the packaging and digitizes the expiration date of each food item. The food management system issues an alarm in advance for food items that are nearing their expiration date and also analyzes how to use food items before they expire. This prevents food waste in the refrigerator and enables an eco-friendly lifestyle. First, the system uses a camera to recognize the type of food and the expiration date displayed on the packaging. For example, the system takes a picture of the packaging of food items in the refrigerator with the camera, and the AI ​​analyzes the information. The analyzed information is digitized as the type of food and the expiration date. Next, based on the digitized expiration date information, the system issues an alarm in advance for food items that are nearing their expiration date. For example, it sends a notification to the user's smartphone for food items that are within one week of their expiration date to alert the user. Furthermore, it analyzes how to use food items before they expire. The AI ​​suggests recipes and cooking methods based on the food items that are nearing their expiration date. For example, by suggesting recipes for soups and salads using vegetables that are nearing their expiration date, the system can help to use the food items without waste. This system prevents food waste in the refrigerator, enabling an eco-friendly lifestyle. Users can easily manage food expiration dates and utilize ingredients without waste. Reducing food waste also reduces the environmental burden. The food management system automatically manages food expiration dates and reduces waste.

[0029] The food management system according to this embodiment comprises a recognition unit, a data conversion unit, an alarm unit, and a suggestion unit. The recognition unit recognizes the type of food and the expiration date indicated on the food. The recognition unit, for example, takes a picture of the food packaging using a camera, and an AI analyzes the information. The recognition unit can recognize the type of food and the expiration date indicated on the food using image recognition technology. The data conversion unit converts the information recognized by the recognition unit into data. The data conversion unit, for example, registers the recognized information in a database and manages the expiration dates of the food. The data conversion unit can save the recognized information as text data. The alarm unit issues an alarm for food items that are nearing their expiration date based on the information converted into data by the data conversion unit. The alarm unit, for example, sends a notification to the user's smartphone for food items with an expiration date of less than one week, to alert the user. The alarm unit can send notifications using voice alarms or email alarms. The suggestion unit proposes ways to use food items before they expire based on the alarms issued by the alarm unit. The suggestion unit, for example, proposes recipes and cooking methods based on food items that are nearing their expiration date. The proposal department can use AI to analyze how to utilize ingredients and make suggestions to users. As a result, the food management system according to this embodiment can automatically manage the expiration dates of food and reduce waste.

[0030] The recognition unit recognizes the type of food and the expiration date indicated on the packaging. For example, the recognition unit uses a camera to photograph the food packaging, and the AI ​​analyzes that information. Specifically, the camera acquires high-resolution images, and the AI ​​analyzes these images to identify the type of food and the expiration date indicated on the packaging. The AI ​​uses image recognition technology to extract letters and numbers on the packaging and uses OCR (optical character recognition) technology to convert this information into text data. Furthermore, the AI ​​can also identify the food packaging design and logo to identify the brand and type of food. For example, the AI ​​can recognize a specific brand logo and obtain detailed information about the food by matching it with the brand's product list. The AI ​​also identifies the expiration date indicated on the packaging, analyzes the date, and registers it in a database. This allows the recognition unit to accurately recognize the type of food and its expiration date and prepare the data for the data processing unit. In addition, the recognition unit can combine multiple cameras and sensors to accurately acquire information even at different angles and under different lighting conditions. This allows the recognition unit to improve the accuracy of food recognition in various environments.

[0031] The data conversion unit converts the information recognized by the recognition unit into data. For example, the data conversion unit registers the recognized information in a database and manages the expiration dates of food products. Specifically, the data conversion unit receives text data provided by the recognition unit and stores it in the database. The database can centrally manage information such as food type, expiration date, purchase date, and storage location. The data conversion unit efficiently organizes this information and makes it easy to search and update. For example, the data conversion unit automatically lists food products nearing their expiration date and prepares to notify the user. The data conversion unit can also record the consumption history of food products and analyze the user's consumption patterns. This provides the data conversion unit with a foundation for making personalized suggestions based on the user's consumption trends. Furthermore, by utilizing a cloud-based database, the data conversion unit enables access from multiple devices, allowing users to access food management information from anywhere. This enables the data conversion unit to manage food expiration dates efficiently and effectively.

[0032] The alarm unit issues alarms for food items nearing their expiration date based on information digitized by the data digitization unit. For example, the alarm unit sends a notification to the user's smartphone for food items with an expiration date of less than a week, alerting the user. Specifically, the alarm unit retrieves a list of food items nearing their expiration date from a database and sets alarms based on this list. Alarms are notified to the user in multiple ways, including smartphone push notifications, voice alarms, and email alarms. For example, smartphone push notifications display the name of the food item nearing its expiration date and the number of days remaining, allowing the user to check it immediately. Voice alarms provide voice notifications at specific times to draw the user's attention. Email alarms send information about food items nearing their expiration date along with a detailed list. This allows the alarm unit to encourage users to consume food at the appropriate time without missing the expiration date. Furthermore, the alarm unit can customize the frequency of alarms and notification methods according to the user's settings. For example, flexible settings are possible, such as allowing users to receive notifications only during specific time periods or to set alarms only for specific food items. This allows the alarm unit to provide effective notifications tailored to the user's needs, thereby reducing food waste.

[0033] The suggestion unit proposes ways to utilize ingredients before their expiration date based on alarms issued by the alarm unit. For example, the suggestion unit proposes recipes and cooking methods based on ingredients nearing their expiration date. Specifically, the suggestion unit uses AI to analyze a list of ingredients nearing their expiration date and generates optimal recipes and cooking methods based on that analysis. The AI ​​refers to a database of ingredient combinations and cooking methods, and provides personalized suggestions considering the user's preferences and past consumption history. For example, the AI ​​proposes a recipe combining vegetables and meat nearing their expiration date, providing specific cooking steps and a list of necessary seasonings. The suggestion unit also proposes multiple recipes to help the user use up specific ingredients, minimizing food waste. Furthermore, the suggestion unit records whether the user has actually tried the suggested recipes and can continuously improve its suggestions based on that feedback. For example, if a user prefers a particular recipe, it will prioritize suggesting similar recipes. The suggestion unit can also propose special recipes tailored to the season or events. In this way, the suggestion unit enables users to effectively utilize ingredients nearing their expiration date, reducing food waste and increasing the enjoyment of meals.

[0034] The recognition unit can recognize the type of food and the expiration date indicated on the packaging using a camera. For example, the recognition unit can take a picture of the food packaging using a camera, and the AI ​​will analyze that information. The recognition unit can recognize the type of food and the expiration date indicated on the packaging using image recognition technology. The recognition unit can acquire information about food using a smartphone camera or a dedicated scanner camera. This allows for accurate recognition of the type of food and the expiration date indicated on the packaging using a camera. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input image data captured by a camera into a generating AI, and have the generating AI perform the process of extracting information about the type of food and the expiration date from the image data.

[0035] The data conversion unit can convert recognized information into data. For example, the data conversion unit can register the recognized information in a database to manage the expiration dates of food products. The data conversion unit can save the recognized information as text data. The data conversion unit can set the registration format and data type for the database, enabling efficient management of food product expiration date information. This makes it easier to manage food product expiration dates by converting recognized information into data. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information acquired from the recognition unit into a generation AI and have the generation AI perform the process of converting it into a database registration format.

[0036] The alarm unit can issue an alarm for food items that are nearing their expiration date. For example, the alarm unit can alert the user by sending a notification to their smartphone for food items that are within one week of their expiration date. The alarm unit can use voice alarms or email alarms to send notifications. The alarm unit can also issue notification alarms for food items that are nearing their expiration date. This prevents food waste by issuing alarms for food items that are nearing their expiration date. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input expiration date information obtained from the data conversion unit into a generating AI and have the generating AI perform a process to calculate the timing of alarm generation.

[0037] The suggestion unit can propose ways to use ingredients before they expire. For example, the suggestion unit can propose recipes and cooking methods based on ingredients that are nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose them to the user. The suggestion unit can also propose storage and consumption methods based on ingredients that are nearing their expiration date. This reduces food waste by proposing ways to use ingredients before they expire. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input expiration date information obtained from the alarm unit into a generation AI and have the generation AI execute the process of generating recipes and cooking methods.

[0038] The suggestion unit can propose recipes and cooking methods based on ingredients nearing their expiration date. For example, the suggestion unit can propose recipes for soups and salads using vegetables nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose them to the user. The suggestion unit can also propose storage and consumption methods based on ingredients nearing their expiration date. This reduces food waste by suggesting recipes and cooking methods based on ingredients nearing their expiration date. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input expiration date information obtained from the alarm unit into a generation AI and have the generation AI execute the process of generating recipes and cooking methods.

[0039] The recognition unit can improve recognition accuracy by considering the food packaging design and color scheme during recognition. For example, the recognition unit can analyze the color scheme of the packaging and adjust the recognition accuracy if a particular color is prevalent. The recognition unit can evaluate the complexity of the design and improve recognition accuracy in the case of a simple design. The recognition unit can improve readability by considering the font and font size of the packaging. In this way, recognition accuracy is improved by considering the food packaging design and color scheme. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on the packaging design and color scheme into a generating AI and have the generating AI perform the adjustment of recognition accuracy.

[0040] The recognition unit can automatically adjust the placement and angle of food during recognition to obtain the optimal recognition result. For example, the recognition unit can automatically adjust the camera angle to perform recognition from the optimal viewpoint. The recognition unit can analyze the placement of food and automatically adjust it to the optimal placement. If the food being recognized is moving, the recognition unit can track its movement to maintain recognition accuracy. By automatically adjusting the placement and angle of food, the optimal recognition result can be obtained. Some or all of the above processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input camera angle and food placement data into a generating AI and have the generating AI perform adjustments to obtain the optimal recognition result.

[0041] The recognition unit can read barcodes and QR codes (registered trademarks) on food products during recognition to obtain additional information. For example, the recognition unit can read barcodes to obtain detailed product information. The recognition unit can scan QR codes to obtain information about the product's manufacturer and country of origin. The recognition unit can use barcodes and QR codes to obtain nutritional information about the product. In this way, additional information can be obtained by reading barcodes and QR codes on food products. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without AI. For example, the recognition unit can input barcode or QR code data into a generating AI and have the generating AI perform the acquisition of additional information.

[0042] The recognition unit can correct the recognition result by taking into account the temperature and humidity information inside the refrigerator during recognition. For example, if the temperature inside the refrigerator is high, the recognition unit can correct the recognition result and display a shorter expiration date. If the humidity inside the refrigerator is high, the recognition unit can correct the recognition result and evaluate the storage condition. The recognition unit can dynamically correct the recognition result by taking into account fluctuations in temperature and humidity. This allows the recognition result to be corrected by taking into account the temperature and humidity information inside the refrigerator. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input temperature and humidity data inside the refrigerator into a generating AI and have the generating AI perform the correction of the recognition result.

[0043] The data conversion unit can add information about the storage condition and quality of food during the data conversion process. For example, the data conversion unit can evaluate the storage condition of food and add this storage condition information during data conversion. The data conversion unit can also evaluate the quality of food and include this quality information in the data conversion. Based on the storage condition and quality information, the data conversion unit can dynamically adjust the expiration date during data conversion. This improves the accuracy of data conversion by adding information about the storage condition and quality of food. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the storage condition and quality of food into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0044] The data conversion unit can automatically extract and convert nutritional information from food during the data conversion process. For example, the data conversion unit can automatically extract nutritional information from food packaging and convert it into data. The data conversion unit can analyze the nutritional information and add detailed nutritional information during the data conversion process. Based on the nutritional information, the data conversion unit can generate data useful for health management. This improves the accuracy of data conversion by automatically extracting nutritional information from food. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input nutritional information from food into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0045] The data conversion unit can add information about the food manufacturer and place of origin during the data conversion process. For example, the data conversion unit can add information about the food manufacturer during the data conversion process. The data conversion unit can include information about the food's place of origin in the data conversion. The data conversion unit can ensure the traceability of the food based on the manufacturer and place of origin information. As a result, adding information about the food manufacturer and place of origin improves the accuracy of the data conversion. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the food manufacturer and place of origin into a generating AI and have the generating AI perform processes to improve the accuracy of the data conversion.

[0046] The data conversion unit can convert food data while considering its usage frequency and consumption history. For example, the data conversion unit can evaluate the usage frequency of food and add usage frequency information during data conversion. The data conversion unit can analyze the consumption history of food and reflect it in the data conversion. The data conversion unit can predict the expiration date based on the usage frequency and consumption history. This improves the accuracy of data conversion by considering the usage frequency and consumption history of food. Some or all of the above processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input food usage frequency and consumption history data into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0047] The alarm unit can select the optimal notification timing when an alarm sounds, taking into account the user's schedule information. For example, the alarm unit can analyze the user's schedule and set an alarm during a free time. If the user has an important appointment, the alarm unit can set an alarm before or after that appointment. The alarm unit can dynamically adjust the optimal alarm timing based on the user's schedule information. This allows the alarm unit to select the optimal notification timing by considering the user's schedule information. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's schedule information into a generating AI and have the generating AI perform the process of calculating the optimal notification timing.

[0048] The alarm unit can apply different alarm sounds and notification methods depending on the expiration date of the food when an alarm is triggered. For example, if the expiration date is approaching, the alarm unit can set a highly urgent alarm sound. If there is still plenty of time before the expiration date, the alarm unit can set a milder alarm sound. The alarm unit can also apply different notification methods (voice, vibration, etc.) depending on the expiration date. This allows for optimal notification for the user by applying different alarm sounds and notification methods depending on the expiration date of the food. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the expiration date information of the food into a generating AI and have the generating AI perform the process of selecting an appropriate alarm sound and notification method.

[0049] The alarm unit can select the optimal notification method when an alarm sounds, taking into account the user's device information. For example, if the user is using a smartphone, the alarm unit can prioritize push notifications. If the user is using a tablet, the alarm unit can provide a notification method optimized for a larger screen. If the user is using a smartwatch, the alarm unit can prioritize vibration notifications. This allows the alarm unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0050] The alarm unit can combine multiple notification methods based on the food's expiration date when an alarm sounds. For example, if the expiration date is approaching, the alarm unit can combine voice notification and push notification. If there is still plenty of time before the expiration date, the alarm unit can combine vibration notification and push notification. The alarm unit can dynamically combine voice notification, vibration notification, and push notification depending on the expiration date. This allows for optimal notification for the user by combining multiple notification methods based on the food's expiration date. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input food expiration date information into a generating AI and have the generating AI perform the process of selecting an appropriate notification method.

[0051] The suggestion unit can suggest the most suitable recipe by referring to the user's past cooking history. For example, the suggestion unit can suggest a similar recipe based on a recipe the user has made in the past. The suggestion unit can suggest a recipe using the user's preferred ingredients based on the user's past cooking history. The suggestion unit can analyze the user's past cooking history and suggest a wide variety of recipes. In this way, the optimal recipe can be suggested by referring to the user's past cooking history. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past cooking history data into a generating AI and have the generating AI execute the process of generating the optimal recipe.

[0052] The suggestion unit can propose healthy recipes while considering the nutritional balance of the food. For example, the suggestion unit can analyze the nutritional components of food and propose a balanced recipe. The suggestion unit can consider the user's health condition and propose recipes that are fortified with specific nutrients. The suggestion unit can propose a healthy meal plan based on the nutritional balance of the food. In this way, healthy recipes can be proposed by considering the nutritional balance of food. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input nutritional component data of food into a generating AI and have the generating AI execute the process of generating healthy recipes.

[0053] The suggestion unit can propose the optimal recipe by considering the user's ingredient inventory information. For example, the suggestion unit can propose a recipe that includes available ingredients based on the user's refrigerator inventory information. The suggestion unit can analyze ingredient inventory information and propose a recipe that uses ingredients without waste. The suggestion unit can propose a recipe that prioritizes the use of specific ingredients based on inventory information. In this way, the optimal recipe can be proposed by considering the user's ingredient inventory information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's ingredient inventory information into a generating AI and have the generating AI execute the process of generating the optimal recipe.

[0054] The suggestion unit can propose customized recipes that take into account the user's dietary preferences and allergy information. For example, the suggestion unit can propose customized recipes based on the user's preferred ingredients. The suggestion unit can propose safe recipes that take into account the user's allergy information. The suggestion unit can propose individually optimized recipes based on the user's dietary preferences and allergy information. This allows for the proposal of customized recipes that take into account the user's dietary preferences and allergy information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's dietary preferences and allergy information into a generating AI and have the generating AI perform the process of generating customized recipes.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The recognition unit can improve recognition accuracy by considering the food packaging design and color scheme during recognition. For example, it can analyze the color scheme of the packaging and adjust the recognition accuracy if a particular color is prevalent. It can also evaluate the complexity of the design and improve recognition accuracy for simpler designs. It can consider the font and font size of the packaging to improve readability. In this way, recognition accuracy can be improved by considering the food packaging design and color scheme. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on the packaging design and color scheme into a generating AI and have the generating AI perform the adjustment of the recognition accuracy.

[0057] The recognition unit can read barcodes or QR codes on food products to obtain additional information during recognition. For example, it can read barcodes to obtain detailed product information. It can scan QR codes to obtain information about the product's manufacturer and country of origin. It can also use barcodes or QR codes to obtain nutritional information about the product. In this way, additional information can be obtained by reading barcodes or QR codes on food products. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without AI. For example, the recognition unit can input barcode or QR code data into a generating AI and have the generating AI perform the acquisition of additional information.

[0058] The recognition unit can correct the recognition result by taking into account the temperature and humidity information inside the refrigerator during recognition. For example, if the temperature inside the refrigerator is high, the recognition result can be corrected to display a shorter expiration date. If the humidity inside the refrigerator is high, the recognition result can be corrected to evaluate the storage condition. The recognition result can be dynamically corrected by taking into account fluctuations in temperature and humidity. This allows the recognition result to be corrected by taking into account the temperature and humidity information inside the refrigerator. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input temperature and humidity data inside the refrigerator into a generating AI and have the generating AI perform the correction of the recognition result.

[0059] The data conversion unit can add information about the storage condition and quality of food during the data conversion process. For example, it can evaluate the storage condition of food and add this information during data conversion. It can also evaluate the quality of food and include this quality information in the data conversion. Based on the storage condition and quality information, it can dynamically adjust the expiration date during data conversion. By adding information about the storage condition and quality of food, the accuracy of the data conversion can be improved. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the storage condition and quality of food into a generating AI and have the generating AI perform processes to improve the accuracy of the data conversion.

[0060] The alarm unit can select the optimal notification timing when an alarm sounds, taking into account the user's schedule information. For example, it can analyze the user's schedule and set an alarm during a free time. If the user has an important appointment, it can set an alarm before or after that appointment. The optimal alarm timing can be dynamically adjusted based on the user's schedule information. This allows the system to select the optimal notification timing by considering the user's schedule information. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's schedule information into a generating AI and have the generating AI perform the process of calculating the optimal notification timing.

[0061] The suggestion unit can propose the optimal recipe by considering the user's ingredient inventory information. For example, it can propose a recipe that includes available ingredients based on the user's refrigerator inventory information. It can analyze ingredient inventory information and propose a recipe that uses ingredients without waste. It can propose a recipe that prioritizes the use of specific ingredients based on inventory information. In this way, the optimal recipe can be proposed by considering the user's ingredient inventory information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's ingredient inventory information into a generation AI and have the generation AI execute the process of generating the optimal recipe.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The recognition unit recognizes the food type and expiration date. The recognition unit, for example, takes a picture of the food packaging using a camera, and the AI ​​analyzes that information. The recognition unit can recognize the food type and expiration date using image recognition technology. Step 2: The data conversion unit converts the information recognized by the recognition unit into data. For example, the data conversion unit registers the recognized information in a database to manage the expiration dates of food products. The data conversion unit can also save the recognized information as text data. Step 3: The alarm unit issues an alarm for food items nearing their expiration date based on the information digitized by the data digitization unit. For example, the alarm unit sends a notification to the user's smartphone for food items with an expiration date of less than one week, thereby alerting the user. The alarm unit can send notifications using voice alarms or email alarms. Step 4: The suggestion unit proposes ways to use ingredients that are nearing their expiration date based on alarms issued by the alarm unit. For example, the suggestion unit proposes recipes and cooking methods based on ingredients that are nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose solutions to the user.

[0064] (Example of form 2) The food management system according to an embodiment of the present invention is a system that uses a camera to recognize the type of food and the expiration date displayed on the packaging and digitizes the expiration date of each food item. The food management system issues an alarm in advance for food items that are nearing their expiration date and also analyzes how to use food items before they expire. This prevents food waste in the refrigerator and enables an eco-friendly lifestyle. First, the system uses a camera to recognize the type of food and the expiration date displayed on the packaging. For example, the system takes a picture of the packaging of food items in the refrigerator with the camera, and the AI ​​analyzes the information. The analyzed information is digitized as the type of food and the expiration date. Next, based on the digitized expiration date information, the system issues an alarm in advance for food items that are nearing their expiration date. For example, it sends a notification to the user's smartphone for food items that are within one week of their expiration date to alert the user. Furthermore, it analyzes how to use food items before they expire. The AI ​​suggests recipes and cooking methods based on the food items that are nearing their expiration date. For example, by suggesting recipes for soups and salads using vegetables that are nearing their expiration date, the system can help to use the food items without waste. This system prevents food waste in the refrigerator, enabling an eco-friendly lifestyle. Users can easily manage food expiration dates and utilize ingredients without waste. Reducing food waste also reduces the environmental burden. The food management system automatically manages food expiration dates and reduces waste.

[0065] The food management system according to this embodiment comprises a recognition unit, a data conversion unit, an alarm unit, and a suggestion unit. The recognition unit recognizes the type of food and the expiration date indicated on the food. The recognition unit, for example, takes a picture of the food packaging using a camera, and an AI analyzes the information. The recognition unit can recognize the type of food and the expiration date indicated on the food using image recognition technology. The data conversion unit converts the information recognized by the recognition unit into data. The data conversion unit, for example, registers the recognized information in a database and manages the expiration dates of the food. The data conversion unit can save the recognized information as text data. The alarm unit issues an alarm for food items that are nearing their expiration date based on the information converted into data by the data conversion unit. The alarm unit, for example, sends a notification to the user's smartphone for food items with an expiration date of less than one week, to alert the user. The alarm unit can send notifications using voice alarms or email alarms. The suggestion unit proposes ways to use food items before they expire based on the alarms issued by the alarm unit. The suggestion unit, for example, proposes recipes and cooking methods based on food items that are nearing their expiration date. The proposal department can use AI to analyze how to utilize ingredients and make suggestions to users. As a result, the food management system according to this embodiment can automatically manage the expiration dates of food and reduce waste.

[0066] The recognition unit recognizes the type of food and the expiration date indicated on the packaging. For example, the recognition unit uses a camera to photograph the food packaging, and the AI ​​analyzes that information. Specifically, the camera acquires high-resolution images, and the AI ​​analyzes these images to identify the type of food and the expiration date indicated on the packaging. The AI ​​uses image recognition technology to extract letters and numbers on the packaging and uses OCR (optical character recognition) technology to convert this information into text data. Furthermore, the AI ​​can also identify the food packaging design and logo to identify the brand and type of food. For example, the AI ​​can recognize a specific brand logo and obtain detailed information about the food by matching it with the brand's product list. The AI ​​also identifies the expiration date indicated on the packaging, analyzes the date, and registers it in a database. This allows the recognition unit to accurately recognize the type of food and its expiration date and prepare the data for the data processing unit. In addition, the recognition unit can combine multiple cameras and sensors to accurately acquire information even at different angles and under different lighting conditions. This allows the recognition unit to improve the accuracy of food recognition in various environments.

[0067] The data conversion unit converts the information recognized by the recognition unit into data. For example, the data conversion unit registers the recognized information in a database and manages the expiration dates of food products. Specifically, the data conversion unit receives text data provided by the recognition unit and stores it in the database. The database can centrally manage information such as food type, expiration date, purchase date, and storage location. The data conversion unit efficiently organizes this information and makes it easy to search and update. For example, the data conversion unit automatically lists food products nearing their expiration date and prepares to notify the user. The data conversion unit can also record the consumption history of food products and analyze the user's consumption patterns. This provides the data conversion unit with a foundation for making personalized suggestions based on the user's consumption trends. Furthermore, by utilizing a cloud-based database, the data conversion unit enables access from multiple devices, allowing users to access food management information from anywhere. This enables the data conversion unit to manage food expiration dates efficiently and effectively.

[0068] The alarm unit issues alarms for food items nearing their expiration date based on information digitized by the data digitization unit. For example, the alarm unit sends a notification to the user's smartphone for food items with an expiration date of less than a week, alerting the user. Specifically, the alarm unit retrieves a list of food items nearing their expiration date from a database and sets alarms based on this list. Alarms are notified to the user in multiple ways, including smartphone push notifications, voice alarms, and email alarms. For example, smartphone push notifications display the name of the food item nearing its expiration date and the number of days remaining, allowing the user to check it immediately. Voice alarms provide voice notifications at specific times to draw the user's attention. Email alarms send information about food items nearing their expiration date along with a detailed list. This allows the alarm unit to encourage users to consume food at the appropriate time without missing the expiration date. Furthermore, the alarm unit can customize the frequency of alarms and notification methods according to the user's settings. For example, flexible settings are possible, such as allowing users to receive notifications only during specific time periods or to set alarms only for specific food items. This allows the alarm unit to provide effective notifications tailored to the user's needs, thereby reducing food waste.

[0069] The suggestion unit proposes ways to utilize ingredients before their expiration date based on alarms issued by the alarm unit. For example, the suggestion unit proposes recipes and cooking methods based on ingredients nearing their expiration date. Specifically, the suggestion unit uses AI to analyze a list of ingredients nearing their expiration date and generates optimal recipes and cooking methods based on that analysis. The AI ​​refers to a database of ingredient combinations and cooking methods, and provides personalized suggestions considering the user's preferences and past consumption history. For example, the AI ​​proposes a recipe combining vegetables and meat nearing their expiration date, providing specific cooking steps and a list of necessary seasonings. The suggestion unit also proposes multiple recipes to help the user use up specific ingredients, minimizing food waste. Furthermore, the suggestion unit records whether the user has actually tried the suggested recipes and can continuously improve its suggestions based on that feedback. For example, if a user prefers a particular recipe, it will prioritize suggesting similar recipes. The suggestion unit can also propose special recipes tailored to the season or events. In this way, the suggestion unit enables users to effectively utilize ingredients nearing their expiration date, reducing food waste and increasing the enjoyment of meals.

[0070] The recognition unit can recognize the type of food and the expiration date indicated on the packaging using a camera. For example, the recognition unit can take a picture of the food packaging using a camera, and the AI ​​will analyze that information. The recognition unit can recognize the type of food and the expiration date indicated on the packaging using image recognition technology. The recognition unit can acquire information about food using a smartphone camera or a dedicated scanner camera. This allows for accurate recognition of the type of food and the expiration date indicated on the packaging using a camera. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input image data captured by a camera into a generating AI, and have the generating AI perform the process of extracting information about the type of food and the expiration date from the image data.

[0071] The data conversion unit can convert recognized information into data. For example, the data conversion unit can register the recognized information in a database to manage the expiration dates of food products. The data conversion unit can save the recognized information as text data. The data conversion unit can set the registration format and data type for the database, enabling efficient management of food product expiration date information. This makes it easier to manage food product expiration dates by converting recognized information into data. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information acquired from the recognition unit into a generation AI and have the generation AI perform the process of converting it into a database registration format.

[0072] The alarm unit can issue an alarm for food items that are nearing their expiration date. For example, the alarm unit can alert the user by sending a notification to their smartphone for food items that are within one week of their expiration date. The alarm unit can use voice alarms or email alarms to send notifications. The alarm unit can also issue notification alarms for food items that are nearing their expiration date. This prevents food waste by issuing alarms for food items that are nearing their expiration date. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input expiration date information obtained from the data conversion unit into a generating AI and have the generating AI perform a process to calculate the timing of alarm generation.

[0073] The suggestion unit can propose ways to use ingredients before they expire. For example, the suggestion unit can propose recipes and cooking methods based on ingredients that are nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose them to the user. The suggestion unit can also propose storage and consumption methods based on ingredients that are nearing their expiration date. This reduces food waste by proposing ways to use ingredients before they expire. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input expiration date information obtained from the alarm unit into a generation AI and have the generation AI execute the process of generating recipes and cooking methods.

[0074] The suggestion unit can propose recipes and cooking methods based on ingredients nearing their expiration date. For example, the suggestion unit can propose recipes for soups and salads using vegetables nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose them to the user. The suggestion unit can also propose storage and consumption methods based on ingredients nearing their expiration date. This reduces food waste by suggesting recipes and cooking methods based on ingredients nearing their expiration date. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input expiration date information obtained from the alarm unit into a generation AI and have the generation AI execute the process of generating recipes and cooking methods.

[0075] The recognition unit can estimate the user's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the user is stressed, the recognition unit can increase recognition accuracy to reduce misrecognition. If the user is relaxed, the recognition unit can maintain normal recognition accuracy and prioritize processing speed. If the user is in a hurry, the recognition unit can increase both recognition accuracy and processing speed. This reduces misrecognition by adjusting recognition accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The recognition unit can improve recognition accuracy by considering the food packaging design and color scheme during recognition. For example, the recognition unit can analyze the color scheme of the packaging and adjust the recognition accuracy if a particular color is prevalent. The recognition unit can evaluate the complexity of the design and improve recognition accuracy in the case of a simple design. The recognition unit can improve readability by considering the font and font size of the packaging. In this way, recognition accuracy is improved by considering the food packaging design and color scheme. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on the packaging design and color scheme into a generating AI and have the generating AI perform the adjustment of recognition accuracy.

[0077] The recognition unit can automatically adjust the placement and angle of food during recognition to obtain the optimal recognition result. For example, the recognition unit can automatically adjust the camera angle to perform recognition from the optimal viewpoint. The recognition unit can analyze the placement of food and automatically adjust it to the optimal placement. If the food being recognized is moving, the recognition unit can track its movement to maintain recognition accuracy. By automatically adjusting the placement and angle of food, the optimal recognition result can be obtained. Some or all of the above processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input camera angle and food placement data into a generating AI and have the generating AI perform adjustments to obtain the optimal recognition result.

[0078] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated user emotions. For example, if the user is tense, the recognition unit can provide a simple and highly visible display method. If the user is relaxed, the recognition unit can provide a display method that includes detailed information. If the user is in a hurry, the recognition unit can provide a display method that gets straight to the point. By adjusting the display method of the recognition results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not using AI. For example, the recognition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0079] The recognition unit can read barcodes or QR codes on food products to obtain additional information during recognition. For example, the recognition unit can read barcodes to obtain detailed product information. The recognition unit can scan QR codes to obtain information about the product's manufacturer and country of origin. The recognition unit can use barcodes or QR codes to obtain nutritional information about the product. In this way, additional information can be obtained by reading barcodes or QR codes on food products. Some or all of the above-described processes in the recognition unit may be performed using AI or not. For example, the recognition unit can input barcode or QR code data into a generating AI and have the generating AI perform the acquisition of additional information.

[0080] The recognition unit can correct the recognition result by taking into account the temperature and humidity information inside the refrigerator during recognition. For example, if the temperature inside the refrigerator is high, the recognition unit can correct the recognition result and display a shorter expiration date. If the humidity inside the refrigerator is high, the recognition unit can correct the recognition result and evaluate the storage condition. The recognition unit can dynamically correct the recognition result by taking into account fluctuations in temperature and humidity. This allows the recognition result to be corrected by taking into account the temperature and humidity information inside the refrigerator. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input temperature and humidity data inside the refrigerator into a generating AI and have the generating AI perform the correction of the recognition result.

[0081] The data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. For example, if the user is stressed, the data processing unit will prioritize processing important ingredients. If the user is relaxed, the data processing unit can process all ingredients equally. If the user is in a hurry, the data processing unit can prioritize processing ingredients that are nearing their expiration date. In this way, by determining the priority of data processing based on the user's emotions, important ingredients can be processed preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI or not. For example, the data processing unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data conversion unit can add information about the storage condition and quality of food during the data conversion process. For example, the data conversion unit can evaluate the storage condition of food and add this storage condition information during data conversion. The data conversion unit can also evaluate the quality of food and include this quality information in the data conversion. Based on the storage condition and quality information, the data conversion unit can dynamically adjust the expiration date during data conversion. This improves the accuracy of data conversion by adding information about the storage condition and quality of food. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the storage condition and quality of food into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0083] The data conversion unit can automatically extract and convert nutritional information from food during the data conversion process. For example, the data conversion unit can automatically extract nutritional information from food packaging and convert it into data. The data conversion unit can analyze the nutritional information and add detailed nutritional information during the data conversion process. Based on the nutritional information, the data conversion unit can generate data useful for health management. This improves the accuracy of data conversion by automatically extracting nutritional information from food. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input nutritional information from food into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0084] The data processing unit can estimate the user's emotions and adjust the level of detail in the data processing based on the estimated emotions. For example, if the user is stressed, the data processing unit can perform simplified data processing. If the user is relaxed, the data processing unit can perform detailed data processing. If the user is in a hurry, the data processing unit can process only the essential information. This allows for optimal data processing for the user by adjusting the level of detail in the data processing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI or not. For example, the data processing unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data conversion unit can add information about the food manufacturer and place of origin during the data conversion process. For example, the data conversion unit can add information about the food manufacturer during the data conversion process. The data conversion unit can include information about the food's place of origin in the data conversion. The data conversion unit can ensure the traceability of the food based on the manufacturer and place of origin information. As a result, adding information about the food manufacturer and place of origin improves the accuracy of the data conversion. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the food manufacturer and place of origin into a generating AI and have the generating AI perform processes to improve the accuracy of the data conversion.

[0086] The data conversion unit can convert food data while considering its usage frequency and consumption history. For example, the data conversion unit can evaluate the usage frequency of food and add usage frequency information during data conversion. The data conversion unit can analyze the consumption history of food and reflect it in the data conversion. The data conversion unit can predict the expiration date based on the usage frequency and consumption history. This improves the accuracy of data conversion by considering the usage frequency and consumption history of food. Some or all of the above processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input food usage frequency and consumption history data into a generating AI and have the generating AI perform processes to improve the accuracy of data conversion.

[0087] The alarm unit can estimate the user's emotions and adjust the timing of alarms based on those emotions. For example, if the user is stressed, the alarm unit can reduce the frequency of alarms. If the user is relaxed, the alarm unit can maintain normal alarm timing. If the user is in a hurry, the alarm unit can prioritize important alarms. By adjusting the timing of alarms based on the user's emotions, alarms can be issued at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The alarm unit can select the optimal notification timing when an alarm sounds, taking into account the user's schedule information. For example, the alarm unit can analyze the user's schedule and set an alarm during a free time. If the user has an important appointment, the alarm unit can set an alarm before or after that appointment. The alarm unit can dynamically adjust the optimal alarm timing based on the user's schedule information. This allows the alarm unit to select the optimal notification timing by considering the user's schedule information. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's schedule information into a generating AI and have the generating AI perform the process of calculating the optimal notification timing.

[0089] The alarm unit can apply different alarm sounds and notification methods depending on the expiration date of the food when an alarm is triggered. For example, if the expiration date is approaching, the alarm unit can set a highly urgent alarm sound. If there is still plenty of time before the expiration date, the alarm unit can set a milder alarm sound. The alarm unit can also apply different notification methods (voice, vibration, etc.) depending on the expiration date. This allows for optimal notification for the user by applying different alarm sounds and notification methods depending on the expiration date of the food. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the expiration date information of the food into a generating AI and have the generating AI perform the process of selecting an appropriate alarm sound and notification method.

[0090] The alarm unit can estimate the user's emotions and determine the priority of alarms based on the estimated emotions. For example, if the user is stressed, the alarm unit will prioritize notifying only important alarms. If the user is relaxed, the alarm unit can notify all alarms equally. If the user is in a hurry, the alarm unit can prioritize notifying high-urgency alarms. In this way, important alarms can be prioritized by determining the priority of alarms based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The alarm unit can select the optimal notification method when an alarm sounds, taking into account the user's device information. For example, if the user is using a smartphone, the alarm unit can prioritize push notifications. If the user is using a tablet, the alarm unit can provide a notification method optimized for a larger screen. If the user is using a smartwatch, the alarm unit can prioritize vibration notifications. This allows the alarm unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's device information into a generating AI and have the generating AI perform the process of selecting the optimal notification method.

[0092] The alarm unit can combine multiple notification methods based on the food's expiration date when an alarm sounds. For example, if the expiration date is approaching, the alarm unit can combine voice notification and push notification. If there is still plenty of time before the expiration date, the alarm unit can combine vibration notification and push notification. The alarm unit can dynamically combine voice notification, vibration notification, and push notification depending on the expiration date. This allows for optimal notification for the user by combining multiple notification methods based on the food's expiration date. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input food expiration date information into a generating AI and have the generating AI perform the process of selecting an appropriate notification method.

[0093] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy recipe. If the user is relaxed, the suggestion unit can suggest a detailed recipe or cooking method. If the user is in a hurry, the suggestion unit can suggest a recipe that can be prepared in a short time. By adjusting the suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The suggestion unit can suggest the most suitable recipe by referring to the user's past cooking history. For example, the suggestion unit can suggest a similar recipe based on a recipe the user has made in the past. The suggestion unit can suggest a recipe using the user's preferred ingredients based on the user's past cooking history. The suggestion unit can analyze the user's past cooking history and suggest a wide variety of recipes. In this way, the optimal recipe can be suggested by referring to the user's past cooking history. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past cooking history data into a generating AI and have the generating AI execute the process of generating the optimal recipe.

[0095] The suggestion unit can propose healthy recipes while considering the nutritional balance of the food. For example, the suggestion unit can analyze the nutritional components of food and propose a balanced recipe. The suggestion unit can consider the user's health condition and propose recipes that are fortified with specific nutrients. The suggestion unit can propose a healthy meal plan based on the nutritional balance of the food. In this way, healthy recipes can be proposed by considering the nutritional balance of food. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input nutritional component data of food into a generating AI and have the generating AI execute the process of generating healthy recipes.

[0096] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting simple and easy recipes. If the user is relaxed, the suggestion unit can prioritize suggesting detailed recipes and cooking methods. If the user is in a hurry, the suggestion unit can prioritize suggesting recipes that can be prepared in a short time. In this way, by determining the priority of suggestions based on the user's emotions, it is possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The suggestion unit can propose the optimal recipe by considering the user's ingredient inventory information. For example, the suggestion unit can propose a recipe that includes available ingredients based on the user's refrigerator inventory information. The suggestion unit can analyze ingredient inventory information and propose a recipe that uses ingredients without waste. The suggestion unit can propose a recipe that prioritizes the use of specific ingredients based on inventory information. In this way, the optimal recipe can be proposed by considering the user's ingredient inventory information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's ingredient inventory information into a generating AI and have the generating AI execute the process of generating the optimal recipe.

[0098] The suggestion unit can propose customized recipes that take into account the user's dietary preferences and allergy information. For example, the suggestion unit can propose customized recipes based on the user's preferred ingredients. The suggestion unit can propose safe recipes that take into account the user's allergy information. The suggestion unit can propose individually optimized recipes based on the user's dietary preferences and allergy information. This allows for the proposal of customized recipes that take into account the user's dietary preferences and allergy information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's dietary preferences and allergy information into a generating AI and have the generating AI perform the process of generating customized recipes.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The recognition unit can estimate the user's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the user is stressed, the recognition accuracy can be increased to reduce misrecognition. If the user is relaxed, the recognition accuracy can be kept normal and processing speed can be prioritized. If the user is in a hurry, both recognition accuracy and processing speed can be improved. In this way, misrecognition can be reduced by adjusting the recognition accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The recognition unit can improve recognition accuracy by considering the food packaging design and color scheme during recognition. For example, it can analyze the color scheme of the packaging and adjust the recognition accuracy if a particular color is prevalent. It can also evaluate the complexity of the design and improve recognition accuracy for simpler designs. It can consider the font and font size of the packaging to improve readability. In this way, recognition accuracy can be improved by considering the food packaging design and color scheme. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input data on the packaging design and color scheme into a generating AI and have the generating AI perform the adjustment of the recognition accuracy.

[0102] The data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. For example, if the user is stressed, the data processing of important ingredients can be prioritized. If the user is relaxed, all ingredients can be processed equally. If the user is in a hurry, the data processing of ingredients nearing their expiration date can be prioritized. In this way, by determining the priority of data processing based on the user's emotions, important ingredients can be processed preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI or not. For example, the data processing unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The alarm unit can estimate the user's emotions and adjust the timing of alarms based on those emotions. For example, if the user is stressed, the frequency of alarms can be reduced. If the user is relaxed, the normal alarm timing can be maintained. If the user is in a hurry, important alarms can be prioritized. By adjusting the timing of alarms based on the user's emotions, alarms can be issued at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is stressed, it can suggest a simple and easy recipe. If the user is relaxed, it can suggest a detailed recipe or cooking method. If the user is in a hurry, it can suggest a recipe that can be cooked in a short time. By adjusting the suggestions based on the user's emotions, it becomes possible to provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The recognition unit can read barcodes or QR codes on food products to obtain additional information during recognition. For example, it can read barcodes to obtain detailed product information. It can scan QR codes to obtain information about the product's manufacturer and country of origin. It can also use barcodes or QR codes to obtain nutritional information about the product. In this way, additional information can be obtained by reading barcodes or QR codes on food products. Some or all of the above processing in the recognition unit may be performed using AI, or it may be performed without AI. For example, the recognition unit can input barcode or QR code data into a generating AI and have the generating AI perform the acquisition of additional information.

[0106] The recognition unit can correct the recognition result by taking into account the temperature and humidity information inside the refrigerator during recognition. For example, if the temperature inside the refrigerator is high, the recognition result can be corrected to display a shorter expiration date. If the humidity inside the refrigerator is high, the recognition result can be corrected to evaluate the storage condition. The recognition result can be dynamically corrected by taking into account fluctuations in temperature and humidity. This allows the recognition result to be corrected by taking into account the temperature and humidity information inside the refrigerator. Some or all of the above processing in the recognition unit may be performed using AI or not. For example, the recognition unit can input temperature and humidity data inside the refrigerator into a generating AI and have the generating AI perform the correction of the recognition result.

[0107] The data conversion unit can add information about the storage condition and quality of food during the data conversion process. For example, it can evaluate the storage condition of food and add this information during data conversion. It can also evaluate the quality of food and include this quality information in the data conversion. Based on the storage condition and quality information, it can dynamically adjust the expiration date during data conversion. By adding information about the storage condition and quality of food, the accuracy of the data conversion can be improved. Some or all of the above-described processes in the data conversion unit may be performed using AI or not. For example, the data conversion unit can input information about the storage condition and quality of food into a generating AI and have the generating AI perform processes to improve the accuracy of the data conversion.

[0108] The alarm unit can select the optimal notification timing when an alarm sounds, taking into account the user's schedule information. For example, it can analyze the user's schedule and set an alarm during a free time. If the user has an important appointment, it can set an alarm before or after that appointment. The optimal alarm timing can be dynamically adjusted based on the user's schedule information. This allows the system to select the optimal notification timing by considering the user's schedule information. Some or all of the above-described processes in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's schedule information into a generating AI and have the generating AI perform the process of calculating the optimal notification timing.

[0109] The suggestion unit can propose the optimal recipe by considering the user's ingredient inventory information. For example, it can propose a recipe that includes available ingredients based on the user's refrigerator inventory information. It can analyze ingredient inventory information and propose a recipe that uses ingredients without waste. It can propose a recipe that prioritizes the use of specific ingredients based on inventory information. In this way, the optimal recipe can be proposed by considering the user's ingredient inventory information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's ingredient inventory information into a generation AI and have the generation AI execute the process of generating the optimal recipe.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The recognition unit recognizes the food type and expiration date. The recognition unit, for example, takes a picture of the food packaging using a camera, and the AI ​​analyzes that information. The recognition unit can recognize the food type and expiration date using image recognition technology. Step 2: The data conversion unit converts the information recognized by the recognition unit into data. For example, the data conversion unit registers the recognized information in a database to manage the expiration dates of food products. The data conversion unit can also save the recognized information as text data. Step 3: The alarm unit issues an alarm for food items nearing their expiration date based on the information digitized by the data digitization unit. For example, the alarm unit sends a notification to the user's smartphone for food items with an expiration date of less than one week, thereby alerting the user. The alarm unit can send notifications using voice alarms or email alarms. Step 4: The suggestion unit proposes ways to use ingredients that are nearing their expiration date based on alarms issued by the alarm unit. For example, the suggestion unit proposes recipes and cooking methods based on ingredients that are nearing their expiration date. The suggestion unit can use AI to analyze how to use ingredients and propose solutions to the user.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the recognition unit, data processing unit, alarm unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart device 14 to photograph the food packaging and the control unit 46A analyzes the information. The data processing unit is implemented in the specific processing unit 290 of the data processing unit 12 and registers the recognized information in the database 24. The alarm unit is implemented in the specific processing unit 46A of the smart device 14 and sends a notification to the smartphone when the expiration date of the food is approaching. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses AI to analyze how to use the food and makes suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the recognition unit, data processing unit, alarm unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit uses the camera 42 of the smart glasses 214 to photograph the food packaging and the control unit 46A analyzes the information. The data processing unit is implemented in the identification processing unit 290 of the data processing device 12 and registers the recognized information in the database 24. The alarm unit is implemented in the control unit 46A of the smart glasses 214 and sends a notification to the smartphone when the expiration date of the food is approaching. The suggestion unit is implemented in the identification processing unit 290 of the data processing device 12 and uses AI to analyze how to use the food and makes suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the recognition unit, data processing unit, alarm unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the headset terminal 314 to photograph the food packaging and the control unit 46A analyzes the information. The data processing unit is implemented in the identification processing unit 290 of the data processing unit 12 and registers the recognized information in the database 24. The alarm unit is implemented in the identification processing unit 46A of the headset terminal 314 and sends a notification to the smartphone when the expiration date of the food is approaching. The suggestion unit is implemented in the identification processing unit 290 of the data processing unit 12 and uses AI to analyze how to use the food and makes suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recognition unit, data processing unit, alarm unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the robot 414 to photograph the food packaging and the control unit 46A analyzes the information. The data processing unit is implemented in the specific processing unit 290 of the data processing unit 12 and registers the recognized information in the database 24. The alarm unit is implemented in the specific processing unit 46A of the robot 414 and sends a notification to a smartphone when food is nearing its expiration date. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses AI to analyze how to use the food and makes suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A recognition unit that recognizes the food type and expiration date label, A data conversion unit that converts the information recognized by the recognition unit into data, An alarm unit that issues an alarm for food items nearing their expiration date based on the information digitized by the aforementioned data digitization unit, The system includes a suggestion unit that proposes ways to utilize ingredients before their expiration date based on an alarm issued by the aforementioned alarm unit. A system characterized by the following features. (Note 2) The recognition unit, The camera is used to recognize the type of food and the expiration date indicated on the label. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data conversion unit, The recognized information is converted into data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alarm unit is, It will sound an alarm for food items that are nearing their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose ways to use ingredients before they expire. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest recipes and cooking methods based on ingredients that are nearing their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recognition unit, It estimates the user's emotions and adjusts the recognition accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recognition unit, During recognition, the system takes into account the food packaging design and color scheme to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recognition unit, During recognition, the system automatically adjusts the placement and angle of the food items to obtain the optimal recognition result. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, During recognition, the barcode or QR code on the food item is read to obtain additional information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, During recognition, the recognition result is corrected by taking into account the temperature and humidity information inside the refrigerator. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned data conversion unit, We estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned data conversion unit, When digitizing the data, we add information about the storage conditions and quality of the food. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned data conversion unit, During data conversion, nutritional information of food products is automatically extracted and converted into data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data conversion unit, The system estimates the user's emotions and adjusts the level of detail in the data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned data conversion unit, When digitizing the data, add information about the food manufacturer and place of origin. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data conversion unit, When digitizing the data, the frequency of food use and consumption history will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alarm unit is, It estimates the user's emotions and adjusts the alarm timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alarm unit is, When an alarm sounds, the system selects the optimal notification timing, taking into account the user's schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alarm unit is, When an alarm sounds, different alarm sounds and notification methods are applied depending on the expiration date of the food item. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alarm unit is, It estimates the user's emotions and determines the priority of alarms based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alarm unit is, When an alarm sounds, the system selects the optimal notification method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alarm unit is, When an alarm sounds, combine multiple notification methods based on the expiration date of the food. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, the system will refer to the user's past cooking history to propose the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, we suggest healthy recipes that take into account the nutritional balance of the foods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a suggestion, we take into account the user's ingredient inventory information to propose the most suitable recipe. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, the system will propose customized recipes that take into account the user's dietary preferences and allergy information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes the food type and expiration date label, A data conversion unit that converts the information recognized by the recognition unit into data, An alarm unit that issues an alarm for food items nearing their expiration date based on the information digitized by the aforementioned data digitization unit, The system includes a suggestion unit that proposes ways to utilize ingredients before their expiration date based on an alarm issued by the aforementioned alarm unit. A system characterized by the following features.

2. The recognition unit, The camera is used to recognize the type of food and the expiration date indicated on the label. The system according to feature 1.

3. The aforementioned data conversion unit, The recognized information is converted into data. The system according to feature 1.

4. The alarm unit is, It will sound an alarm for food items that are nearing their expiration date. The system according to feature 1.

5. The aforementioned proposal section is, We propose ways to use ingredients before they expire. The system according to feature 1.

6. The aforementioned proposal section is, We suggest recipes and cooking methods based on ingredients that are nearing their expiration date. The system according to feature 1.

7. The recognition unit, It estimates the user's emotions and adjusts the recognition accuracy based on the estimated emotions. The system according to feature 1.

8. The recognition unit, During recognition, the system takes into account the food packaging design and color scheme to improve recognition accuracy. The system according to feature 1.

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