system

Abstract

The system according to this embodiment aims to efficiently manage the expiration dates of food ingredients and promote their consumption. [Solution] The system according to the embodiment comprises an image recognition unit, an input unit, an alert unit, a recipe presentation unit, and a promotion unit. The image recognition unit takes a photograph of the food ingredients, and the AI ​​performs image recognition. The input unit automatically inputs the recommended consumption date based on the food name recognized by the image recognition unit. The alert unit issues an alert based on the recommended consumption date entered by the input unit. The recipe presentation unit presents a recipe using the food ingredients using a generating AI. The promotion unit encourages the consumption of the food ingredients based on the recipe presented by the recipe presentation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: 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, there is a problem that it is laborious to manage the expiration date of food ingredients and promote consumption, and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently manage the expiration date of food ingredients and promote consumption.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an image recognition unit, an input unit, an alert unit, a recipe presentation unit, and a promotion unit. The image recognition unit takes a photograph of the food ingredients, and the AI ​​performs image recognition. The input unit automatically inputs the recommended consumption date based on the food name recognized by the image recognition unit. The alert unit issues an alert based on the recommended consumption date entered by the input unit. The recipe presentation unit presents a recipe using the food ingredients using a generating AI. The promotion unit encourages the consumption of the food ingredients based on the recipe presented by the recipe presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the expiration dates of food ingredients and promote their consumption. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 application according to an embodiment of the present invention is a system that uses AI to streamline food management. When a user takes a picture of food, the AI ​​performs image recognition and automatically inputs the food name and recommended expiration date (or expiration date if readable) into the app. Furthermore, when the recommended expiration date of the food approaches, an alert is issued, and the generated AI also suggests recipes using that food to encourage consumption. Next, if it is difficult to determine whether the food is spoiled, the AI ​​performs a visual freshness check when the user takes a picture of the food. This makes it easy to check the freshness of the food. Furthermore, if the user wants to freeze the food on the list, the AI ​​suggests a recommended freezing method. This makes it easy to learn how to store food. In addition, the application can be linked with the supermarket's electronic payment system, allowing food items photographed and purchased using the electronic payment system to be automatically reflected in the app's list. This makes it possible to manage food at the same time as shopping. This application streamlines food management in the refrigerator, reduces food waste, and promotes consumption.

[0029] The food ingredient management system according to this embodiment comprises an image recognition unit, an input unit, an alert unit, a recipe presentation unit, and a promotion unit. The image recognition unit takes a photograph of the food ingredient, and the AI ​​performs image recognition. The image recognition unit, for example, takes a photograph of the food ingredient in JPEG format and saves it at a resolution of 1080p. The image recognition unit can perform image recognition using a convolutional neural network (CNN). The image recognition unit also analyzes the photograph of the food ingredient and recognizes the name of the food. The input unit automatically inputs the recommended expiration date based on the name of the food recognized by the image recognition unit. The input unit calculates the recommended expiration date considering, for example, the number of days elapsed since the purchase date and differences depending on the type of food. The alert unit issues an alert based on the recommended expiration date entered by the input unit. The alert unit issues an alert by, for example, a notification sound, a pop-up notification, or an email notification. The recipe presentation unit presents a recipe using the food ingredient using a generating AI. The recipe presentation unit presents a recipe that includes information such as an ingredient list, cooking procedure, and cooking time. The promotion unit encourages the consumption of ingredients based on the recipes presented by the recipe presentation unit. The promotion unit encourages the consumption of ingredients by methods such as providing discount coupons or reminding users of expiration dates. As a result, the ingredient management system according to this embodiment can streamline ingredient management and promote consumption.

[0030] The image recognition unit takes a picture of the food ingredients, and the AI ​​performs image recognition. Specifically, when a user takes a picture of food ingredients using a smartphone or dedicated device, the image recognition unit automatically saves the picture in JPEG format with a resolution of 1080p. This ensures that image details are sufficiently preserved, enabling accurate recognition. The image recognition unit uses a convolutional neural network (CNN) for image recognition. A CNN is an algorithm that extracts features from an image and identifies the type of food ingredient with high accuracy. For example, a CNN analyzes features such as the shape, color, and texture of the food ingredients and recognizes the name of the food based on this information. Furthermore, even if multiple food ingredients are pictured, the image recognition unit can individually recognize each ingredient and list them. This allows the user to manage multiple food ingredients at once. The image recognition unit stores the recognition results in a database and uses them for subsequent processing. For example, the recognized food names are used in the input unit and the recipe presentation unit. In addition, the image recognition unit allows users to manually correct errors, making it easy to correct any errors in the recognition results. This allows the image recognition unit to support the accurate recognition and management of food ingredients, improving the overall accuracy and convenience of the system.

[0031] The input unit automatically enters the recommended expiration date based on the food name recognized by the image recognition unit. Specifically, the input unit searches the database for the recommended expiration date of the corresponding food based on the recognized food name and automatically enters it. For example, it calculates the recommended expiration date considering the number of days elapsed since purchase and differences depending on the type of food. The input unit can also take into account the food's storage method and environmental conditions (temperature, humidity, etc.). This makes it possible to provide a more accurate recommended expiration date. Furthermore, the input unit allows users to manually modify the recommended expiration date, enabling flexible responses to specific conditions and individual needs. For example, it can accommodate situations where a user wants to consume a particular food earlier or extend the recommended expiration date because it is in good storage condition. The input unit saves the recommended expiration dates in the database, which are used by the alert unit and recipe presentation unit. This allows the input unit to streamline food consumption management and reduce waste.

[0032] The alert unit issues alerts based on the recommended consumption date entered in the input unit. Specifically, the alert unit notifies the user when the recommended consumption date approaches. Notification methods include notification sounds, pop-up notifications, and email notifications, which can be selected according to the user's preference. For example, a pop-up notification can be displayed via a smartphone app to inform the user that the recommended consumption date is approaching. By setting up email notifications, users can also check the recommended consumption date even when they are out. The alert unit can also customize the frequency and timing of notifications, allowing for flexible responses to suit the user's lifestyle. For example, notifications can be sent multiple times, such as one week before, three days before, and the day before the recommended consumption date. This helps users not forget to consume ingredients and reduces waste. Furthermore, the alert unit can also notify users if the recommended consumption date has passed, prompting them to discard the ingredients. In this way, the alert unit supports food consumption management and minimizes waste.

[0033] The recipe presentation unit uses a generation AI to present recipes using the specified ingredients. Specifically, the generation AI generates recipes based on the recognized food names. The generation AI considers ingredient combinations, cooking methods, user preferences, and past history to suggest the most suitable recipe. For example, the generation AI can generate easy-to-make dishes or recipes that consider specific nutritional balances based on ingredients found in the refrigerator. The recipe presentation unit presents recipes that include information such as ingredient lists, cooking procedures, and cooking times. Users can check recipes via their smartphones or tablets and easily understand the necessary ingredients and procedures. Furthermore, the recipe presentation unit also provides visual guides using videos and images to clearly explain the cooking procedures. This allows users to cook with confidence, even if it's their first time. The recipe presentation unit collects user feedback, allowing the generation AI to continuously improve the accuracy and variety of recipes. This enables the recipe presentation unit to always provide users with new recipes and promote the consumption of ingredients.

[0034] The promotion department encourages the consumption of ingredients based on recipes presented by the recipe presentation department. Specifically, the promotion department provides means to encourage users to actually try the presented recipes. For example, it encourages ingredient consumption through methods such as providing discount coupons and expiration date reminders. Discount coupons are offered for specific ingredients, related seasonings, and cooking utensils, reducing the cost for users when trying recipes. Expiration date reminders notify users so they don't miss the timing to consume ingredients. Furthermore, the promotion department can collect feedback from users after they have tried a recipe and incorporate it into future recipe suggestions. This allows for recipe suggestions tailored to the user's preferences and needs, further promoting ingredient consumption. The promotion department can also analyze the user's ingredient consumption history and, if certain ingredients tend to be left over, prioritize suggesting recipes that utilize those ingredients. This reduces food waste and supports efficient consumption. The promotion department plays a crucial role in enriching users' diets and streamlining ingredient management and consumption.

[0035] The freshness check unit performs a visual freshness check using AI when it is difficult to determine whether food is spoiled or not, based on a photograph of the food. The freshness check unit checks freshness based on factors such as changes in the food's color, the presence or absence of mold, and the degree of wilting. For example, the freshness check unit takes a photograph of the food and the AI ​​analyzes the changes in color. The freshness check unit can also detect whether mold is growing on the surface of the food. Furthermore, the freshness check unit can analyze the degree of wilting of the food and evaluate its freshness. This makes it easy to check the freshness of food. Some or all of the above processes in the freshness check unit may be performed using AI, or not. For example, the freshness check unit can input a photograph of the food into the AI ​​and have the AI ​​perform the freshness evaluation.

[0036] The storage method suggestion unit, when a user wants to freeze an ingredient from the list, will suggest a recommended freezing method using AI. The storage method suggestion unit suggests a freezing method based on factors such as storage temperature, storage period, and type of storage container. For example, the storage method suggestion unit may suggest the optimal storage temperature depending on the type of ingredient. The storage method suggestion unit can also suggest the storage period for the ingredient. Furthermore, the storage method suggestion unit can suggest the type of storage container and propose an appropriate storage method. This makes it easy to learn how to store ingredients. Some or all of the above processing in the storage method suggestion unit may be performed using AI, or not. For example, the storage method suggestion unit can input ingredient information into the AI ​​and have the AI ​​suggest the optimal storage method.

[0037] The integration unit connects with the supermarket's electronic payment system and automatically reflects the purchased groceries, photographed and processed using the electronic payment system, into the app's list. The integration unit connects with electronic payment systems such as credit card payments and QR code (registered trademark) payments. The integration unit automatically updates the app's list based on purchase history obtained from the electronic payment system. The integration unit can also analyze purchase history and automatically add purchased groceries to the app's list. Furthermore, through its connection with the electronic payment system, the integration unit can reflect purchase history in the app's list in real time. This makes it possible to manage groceries simultaneously with shopping. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input purchase history obtained from the electronic payment system into the AI ​​and have the AI ​​perform the task of reflecting the purchase history in the app's list.

[0038] The image recognition unit uses AI to analyze a photograph of food ingredients and recognize the name of the food. The image recognition unit recognizes the name of the food by, for example, matching it with an image database or performing text analysis. For example, the image recognition unit takes a photograph of food ingredients, and the AI ​​matches it with an image database to recognize the name of the food. The image recognition unit can also recognize the name of the food by performing text analysis. Furthermore, the image recognition unit can analyze the characteristics of the food ingredients to identify the name of the food. This allows the AI ​​to analyze a photograph of food ingredients and obtain the accurate name of the food. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input a photograph of food ingredients into the AI ​​and have the AI ​​perform the recognition of the food name.

[0039] The recipe presentation unit generates a recipe using the given ingredients via a generating AI. The recipe presentation unit generates a recipe using, for example, a generating AI. The recipe presentation unit inputs ingredient information into the generating AI and generates a recipe. The recipe presentation unit can also generate a recipe considering ingredient combinations via the generating AI. Furthermore, the recipe presentation unit can generate a recipe considering the user's preferences and dietary restrictions via the generating AI. This allows the generating AI to generate recipes that are suitable for the user. Some or all of the above-described processes in the recipe presentation unit may be performed using, for example, an AI, or without an AI. For example, the recipe presentation unit can input ingredient information into the generating AI and have the generating AI perform recipe generation.

[0040] The image recognition unit improves the accuracy of image recognition by taking multiple photos from different angles when photographing food items. For example, the image recognition unit takes photos from three angles: the top, side, and bottom of the food item, and the AI ​​integrates and recognizes them. For example, the image recognition unit takes a series of photos from different angles of the food item, and the AI ​​selects and recognizes the clearest image. The image recognition unit can also display a guide to encourage the user to take photos from different angles when photographing food items. This improves the accuracy of image recognition by integrating photos from different angles. Some or all of the above processing in the image recognition unit may be performed using AI, or not. For example, the image recognition unit can input photos taken from different angles into the AI ​​and have the AI ​​perform image integration and recognition.

[0041] The image recognition unit improves the accuracy of image recognition by automatically adjusting the background color and brightness when taking a picture of food. For example, when taking a picture of food, the image recognition unit automatically adjusts the background color to white to improve recognition accuracy. For example, when taking a picture of food, the image recognition unit automatically adjusts the brightness to reduce shadows and improve recognition accuracy. The image recognition unit can also adjust the background color and brightness simultaneously when taking a picture of food to perform recognition under optimal conditions. By adjusting the background color and brightness, the accuracy of image recognition is improved. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without using AI. For example, the image recognition unit can input a picture of food into AI and have the AI ​​perform the adjustment of the background color and brightness.

[0042] The image recognition unit, when taking photos of food ingredients, prioritizes recognizing region-specific ingredients by considering the user's geographical location information. For example, if the user is in a specific region, the image recognition unit prioritizes recognizing ingredients common in that region. The image recognition unit can, for example, create a list of region-specific ingredients based on the user's location information to improve recognition accuracy. Furthermore, if the user is traveling, the image recognition unit can prioritize recognizing ingredients specific to the travel destination. This improves recognition accuracy by prioritizing the recognition of region-specific ingredients. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the recognition of region-specific ingredients.

[0043] The image recognition unit improves recognition accuracy by referring to the user's past shooting history when taking pictures of food ingredients. For example, the image recognition unit improves recognition accuracy based on data of food ingredients that the user has photographed in the past. For example, the image recognition unit prioritizes the recognition of frequently used food ingredients from the user's past shooting history. The image recognition unit can also analyze the user's past shooting history and optimize the recognition algorithm. This improves recognition accuracy by referring to past shooting history. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's past shooting history into AI and have the AI ​​perform the improvement of recognition accuracy.

[0044] The input unit automatically corrects the recommended consumption date by referring to past consumption data based on the food name. For example, the input unit automatically sets an average recommended consumption date for a food name based on past consumption data. For example, the input unit refers to the user's past consumption history and suggests individually customized recommended consumption dates. The input unit can also analyze past consumption data and automatically correct the recommended consumption date according to the season and weather. This improves the accuracy of the recommended consumption date by referring to past consumption data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past consumption data into AI and have AI perform the correction of the recommended consumption date.

[0045] The input unit adjusts the recommended consumption date based on the food name, taking into account seasonal and weather information. For example, the input unit automatically adjusts the recommended consumption date of food according to the season. For example, the input unit corrects the recommended consumption date of food based on weather information. The input unit can also suggest and notify the user of recommended consumption dates according to the season and weather. This improves the accuracy of recommended consumption dates by considering seasonal and weather information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input seasonal and weather information into AI and have the AI ​​perform the adjustment of recommended consumption dates.

[0046] The input unit customizes the recommended consumption date based on the food name and the user's past consumption history. For example, the input unit suggests individually customized recommended consumption dates based on the user's past consumption history. For example, the input unit sets recommended consumption dates for foods that the user frequently consumes by referring to their past consumption history. The input unit can also analyze the user's past consumption history and suggest the optimal recommended consumption date. This allows for individual customization of recommended consumption dates by referring to past consumption history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past consumption history into AI and have AI perform the customization of recommended consumption dates.

[0047] The input unit analyzes the user's eating habits based on the food name and suggests a recommended consumption date. For example, the input unit analyzes the user's eating habits and suggests the optimal recommended consumption date. For example, the input unit customizes the recommended consumption date based on the user's eating habits. The input unit can also automatically set the recommended consumption date considering the user's eating habits. This allows the optimal recommended consumption date to be suggested by analyzing the eating habits. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's eating habits into AI and have AI suggest recommended consumption dates.

[0048] The alert unit adjusts the frequency and intensity of alerts based on the recommended consumption date. For example, the alert unit increases the frequency of alerts as the recommended consumption date approaches. For example, the alert unit adjusts the intensity of alerts based on the recommended consumption date to indicate importance. The alert unit can also change the notification method of alerts depending on the recommended consumption date. This allows the unit to indicate importance by adjusting the frequency and intensity of alerts based on the recommended consumption date. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the recommended consumption date into the AI ​​and have the AI ​​adjust the frequency and intensity of alerts.

[0049] The alert unit customizes the notification method for alerts based on the recommended consumption date. For example, the alert unit selects the most suitable notification method for the user's device based on the recommended consumption date. For example, the alert unit changes the notification sound for alerts depending on the recommended consumption date. The alert unit can also customize how alerts are displayed based on the recommended consumption date. This allows for notifications optimized for the user by customizing the notification method based on the recommended consumption date. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the recommended consumption date into the AI ​​and have the AI ​​perform the customization of the alert notification method.

[0050] The alert unit optimizes the timing of alerts based on the recommended consumption date, taking into account the user's schedule. For example, the alert unit refers to the user's schedule and notifies the user of the alert at the optimal time. For example, the alert unit sets the alert timing to match the user's schedule based on the recommended consumption date. The alert unit can also adjust the timing of alerts based on the user's schedule information. This allows for timely notifications by setting the alert timing to match the user's schedule. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's schedule information into AI and have the AI ​​perform the optimization of the alert timing.

[0051] The alert unit selects a notification method for alerts based on the recommended consumption date and taking into account the user's device information. For example, if the user is using a smartphone, the alert unit provides an alert via push notification. If the user is using a tablet, the alert unit provides an alert optimized for a larger screen. The alert unit can also provide an alert via vibration notification if the user is using a smartwatch. This improves the effectiveness of the alert by selecting the most suitable notification method for the user's device. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's device information into the AI ​​and have the AI ​​select the notification method for alerts.

[0052] The recipe presentation unit adjusts the level of detail in the recipe based on the type and quantity of ingredients. For example, if there are many types of ingredients, the recipe presentation unit provides a recipe with detailed instructions. For example, if there are few ingredients, the recipe presentation unit provides a recipe with concise instructions. The recipe presentation unit can also automatically adjust the level of detail in the recipe according to the type and quantity of ingredients. This allows the user to be provided with the most suitable recipe by adjusting the level of detail according to the type and quantity of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the recipe.

[0053] The recipe presentation unit applies different recipe algorithms based on the types and quantities of ingredients. For example, if there are many types of ingredients, the recipe presentation unit applies a complex recipe algorithm. For example, if there are few ingredients, the recipe presentation unit applies a simple recipe algorithm. The recipe presentation unit can also select the optimal recipe algorithm according to the types and quantities of ingredients. This improves the accuracy of the recipe by selecting the optimal recipe algorithm according to the types and quantities of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the types and quantities of ingredients into the AI ​​and have the AI ​​execute the application of the recipe algorithm.

[0054] The recipe presentation unit determines the priority of recipes based on the type and quantity of ingredients. For example, the recipe presentation unit may provide recipes that prioritize the use of ingredients that are nearing their expiration date. For example, if there are many types of ingredients, the recipe presentation unit may provide a balanced recipe. The recipe presentation unit can also provide recipes that use ingredients efficiently if the quantity is small. This enables efficient cooking by determining the priority of recipes according to the type and quantity of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​determine the priority of recipes.

[0055] The recipe presentation unit adjusts the order of the recipes based on the types and quantities of ingredients. For example, the recipe presentation unit may provide a recipe that uses ingredients with the nearest expiration date first. For example, if there are many types of ingredients, the recipe presentation unit may provide a recipe with optimized cooking procedures. The recipe presentation unit can also provide a recipe that allows for efficient cooking if the quantity of ingredients is small. By adjusting the order of the recipes according to the types and quantities of ingredients, efficient cooking becomes possible. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit may input the types and quantities of ingredients into the AI ​​and have the AI ​​perform the adjustment of the recipe order.

[0056] The promotion unit adjusts the frequency and intensity of promotion based on the consumption status of the ingredients. For example, if the consumption status of the ingredients is low, the promotion unit increases the frequency of promotion. For example, if the consumption status of the ingredients is good, the promotion unit decreases the frequency of promotion. The promotion unit can also adjust the intensity of promotion according to the consumption status of the ingredients. In this way, consumption is promoted by adjusting the frequency and intensity of promotion based on the consumption status of the ingredients. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the consumption status of the ingredients into the AI ​​and have the AI ​​perform the adjustment of the frequency and intensity of promotion.

[0057] The promotion unit customizes the notification method for promoting consumption based on the consumption status of the ingredients. For example, if the consumption status of the ingredients is low, the promotion unit will send a push notification. For example, if the consumption status of the ingredients is high, the promotion unit will send an email notification. The promotion unit can also select the most suitable notification method according to the consumption status of the ingredients. In this way, consumption is promoted by customizing the notification method based on the consumption status of the ingredients. Some or all of the above processing in the promotion unit may be performed using AI, for example, or not using AI. For example, the promotion unit can input the consumption status of the ingredients into the AI ​​and have the AI ​​perform the customization of the notification method.

[0058] The promotion unit optimizes the timing of promotions based on the consumption status of ingredients and taking into account the user's schedule. For example, the promotion unit refers to the user's schedule and performs promotions at the optimal time. For example, the promotion unit sets promotion timings that match the user's schedule based on the consumption status of ingredients. The promotion unit can also adjust the timing of promotions based on the user's schedule information. This allows consumption to be promoted at the appropriate time by setting promotion timings that match the user's schedule. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's schedule information into the AI ​​and have the AI ​​perform the optimization of the promotion timing.

[0059] The promotion unit selects a notification method for promoting consumption based on the consumption status of ingredients and taking into account the user's device information. For example, if the user is using a smartphone, the promotion unit will send a push notification. If the user is using a tablet, the promotion unit will send a notification optimized for a larger screen. The promotion unit can also send a vibration notification if the user is using a smartwatch. In this way, consumption is promoted by selecting the most suitable notification method for the user's device. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's device information into the AI ​​and have the AI ​​select the notification method.

[0060] The freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the appearance of the food. For example, the freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the color and shape of the food. For example, the freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the surface condition of the food. The freshness check unit can also apply an algorithm to improve the accuracy of the freshness check based on changes in the appearance of the food. This makes it possible to perform an accurate freshness check by improving the accuracy of the freshness check based on the appearance of the food. Some or all of the above processing in the freshness check unit may be performed using AI, for example, or without using AI. For example, the freshness check unit can input data on the appearance of the food into the AI ​​and have the AI ​​perform the improvement of the freshness check accuracy.

[0061] The freshness check unit improves accuracy by referencing the user's past freshness check history based on the appearance of the ingredients. The freshness check unit improves accuracy based on the user's past freshness check history, for example. The freshness check unit also analyzes the user's past freshness check history and applies the optimal freshness check algorithm. This improves the accuracy of the freshness check by referencing past freshness check history. Some or all of the above processing in the freshness check unit may be performed using AI, for example, or without AI. For example, the freshness check unit can input the user's past freshness check history into the AI ​​and have the AI ​​perform the accuracy improvement.

[0062] The storage method presentation unit adjusts the level of detail in the storage method based on the type and quantity of ingredients. For example, if there are many types of ingredients, the storage method presentation unit provides a detailed storage method. For example, if there are few ingredients, the storage method presentation unit provides a concise storage method. The storage method presentation unit can also automatically adjust the level of detail in the storage method according to the type and quantity of ingredients. This allows the user to be provided with the most suitable storage method by adjusting the level of detail in the storage method presentation unit according to the type and quantity of ingredients. Some or all of the above processing in the storage method presentation unit may be performed using AI, for example, or without AI. For example, the storage method presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the storage method.

[0063] The storage method suggestion unit customizes the storage method based on the type and quantity of ingredients and the user's past storage history. For example, the storage method suggestion unit proposes the optimal storage method based on the user's past storage history. The storage method suggestion unit refers to the user's past storage history based on the type and quantity of ingredients. The storage method suggestion unit can also analyze the user's past storage history and customize the storage method. This allows the storage method to be customized by referring to past storage history. Some or all of the above processing in the storage method suggestion unit may be performed using AI, for example, or without AI. For example, the storage method suggestion unit can input the user's past storage history into AI and have AI perform the customization of the storage method.

[0064] The integration unit automatically analyzes purchase history and reflects it in the app list when integrating with the electronic payment system. For example, the integration unit automatically updates the app list based on the purchase history obtained from the electronic payment system. For example, the integration unit analyzes the purchase history and automatically adds purchased food items to the app list. Furthermore, the integration unit can reflect the purchase history in the app list in real time through integration with the electronic payment system. As a result, the app list is automatically updated by automatically analyzing the purchase history. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the purchase history obtained from the electronic payment system into the AI ​​and have the AI ​​perform the task of reflecting it in the app list.

[0065] The integration unit improves the accuracy of integration when integrating with an electronic payment system by referring to the user's past purchase history. For example, the integration unit improves the accuracy of integration based on the user's past purchase history. For example, the integration unit optimizes the integration content by referring to past purchase history obtained from the electronic payment system. The integration unit can also improve the accuracy of integration by analyzing the user's past purchase history. As a result, the accuracy of integration is improved by referring to past purchase history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past purchase history into AI and have AI perform the integration accuracy improvement.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The food management system can also be equipped with a voice input function. This function allows users to input food information by voice. For example, if a user voice-inputs "I bought tomatoes," the voice input function analyzes the information and adds tomatoes to the food list. The voice input function can also allow users to voice-input the recommended expiration date. This improves convenience by allowing users to input food information without using their hands. Furthermore, the voice input function can search for and present recipes based on the user's voice commands. This allows users to easily obtain recipes by voice.

[0068] The freshness check unit can also be equipped with a temperature sensor. The temperature sensor measures the surface temperature of the food and uses it as part of the freshness check. For example, if the surface temperature of the food is abnormally high, the temperature sensor can determine that the food may be spoiled. The temperature sensor can also monitor temperature fluctuations inside the refrigerator and evaluate whether the food storage environment is appropriate. This enables freshness checks based on temperature information, resulting in a more accurate freshness assessment. Furthermore, the temperature sensor can issue an alert if the food storage temperature is not appropriate.

[0069] The storage method suggestion unit can also be equipped with a humidity sensor. The humidity sensor measures the humidity of the food storage environment and is used to suggest the optimal storage method. For example, if the humidity sensor is too high, it will recommend the use of a desiccant. If the humidity sensor is too low, it can also suggest methods to maintain humidity. This makes it possible to suggest storage methods based on humidity information, thus preserving the quality of the food. Furthermore, the humidity sensor can also issue an alert if the humidity of the storage environment is not appropriate.

[0070] The integration unit can further analyze the user's purchase history and provide personalized food management. For example, the integration unit can automatically add frequently purchased food items to a list. It can also automatically set expiration dates based on the user's purchase history. This enables food management based on the user's purchasing patterns, improving convenience. Furthermore, the integration unit can issue alerts when the expiration date of specific food items is approaching, based on the user's purchase history.

[0071] The image recognition unit can also provide nutritional information about food ingredients. For example, it can analyze a photo of food ingredients and display nutritional information based on the recognized food name. Furthermore, if the user wants to consume a specific nutrient, the image recognition unit can suggest foods that are rich in that nutrient. This allows users to easily obtain nutritional information about food ingredients, supporting a healthy diet. Additionally, the image recognition unit can suggest balanced meals based on the nutritional information of the food ingredients.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The image recognition unit takes a picture of the food item, and the AI ​​performs image recognition. For example, the food item is photographed in JPEG format and saved at a resolution of 1080p. The image recognition unit uses a convolutional neural network (CNN) to perform image recognition, analyzes the food item photograph, and recognizes the name of the food item. Step 2: The input unit automatically enters the recommended expiration date based on the food name recognized by the image recognition unit. For example, it calculates the recommended expiration date considering the number of days elapsed since purchase and differences depending on the type of food. Step 3: The alert unit issues an alert based on the recommended consumption date entered in the input unit. For example, the alert may be issued via a notification sound, a pop-up notification, or an email notification. Step 4: The recipe presentation unit uses a generating AI to present a recipe using the ingredient in question. For example, it presents a recipe that includes information such as an ingredient list, cooking procedure, and cooking time. Step 5: The promotion unit encourages the consumption of ingredients based on the recipes presented by the recipe presentation unit. For example, it encourages the consumption of ingredients by providing discount coupons or reminding users of expiration dates.

[0074] (Example of form 2) The food management application according to an embodiment of the present invention is a system that uses AI to streamline food management. When a user takes a picture of food, the AI ​​performs image recognition and automatically inputs the food name and recommended expiration date (or expiration date if readable) into the app. Furthermore, when the recommended expiration date of the food approaches, an alert is issued, and the generated AI also suggests recipes using that food to encourage consumption. Next, if it is difficult to determine whether the food is spoiled, the AI ​​performs a visual freshness check when the user takes a picture of the food. This makes it easy to check the freshness of the food. Furthermore, if the user wants to freeze the food on the list, the AI ​​suggests a recommended freezing method. This makes it easy to learn how to store food. In addition, the application can be linked with the supermarket's electronic payment system, allowing food items photographed and purchased using the electronic payment system to be automatically reflected in the app's list. This makes it possible to manage food at the same time as shopping. This application streamlines food management in the refrigerator, reduces food waste, and promotes consumption.

[0075] The food ingredient management system according to this embodiment comprises an image recognition unit, an input unit, an alert unit, a recipe presentation unit, and a promotion unit. The image recognition unit takes a photograph of the food ingredient, and the AI ​​performs image recognition. The image recognition unit, for example, takes a photograph of the food ingredient in JPEG format and saves it at a resolution of 1080p. The image recognition unit can perform image recognition using a convolutional neural network (CNN). The image recognition unit also analyzes the photograph of the food ingredient and recognizes the name of the food. The input unit automatically inputs the recommended expiration date based on the name of the food recognized by the image recognition unit. The input unit calculates the recommended expiration date considering, for example, the number of days elapsed since the purchase date and differences depending on the type of food. The alert unit issues an alert based on the recommended expiration date entered by the input unit. The alert unit issues an alert by, for example, a notification sound, a pop-up notification, or an email notification. The recipe presentation unit presents a recipe using the food ingredient using a generating AI. The recipe presentation unit presents a recipe that includes information such as an ingredient list, cooking procedure, and cooking time. The promotion unit encourages the consumption of ingredients based on the recipes presented by the recipe presentation unit. The promotion unit encourages the consumption of ingredients by methods such as providing discount coupons or reminding users of expiration dates. As a result, the ingredient management system according to this embodiment can streamline ingredient management and promote consumption.

[0076] The image recognition unit takes a picture of the food ingredients, and the AI ​​performs image recognition. Specifically, when a user takes a picture of food ingredients using a smartphone or dedicated device, the image recognition unit automatically saves the picture in JPEG format with a resolution of 1080p. This ensures that image details are sufficiently preserved, enabling accurate recognition. The image recognition unit uses a convolutional neural network (CNN) for image recognition. A CNN is an algorithm that extracts features from an image and identifies the type of food ingredient with high accuracy. For example, a CNN analyzes features such as the shape, color, and texture of the food ingredients and recognizes the name of the food based on this information. Furthermore, even if multiple food ingredients are pictured, the image recognition unit can individually recognize each ingredient and list them. This allows the user to manage multiple food ingredients at once. The image recognition unit stores the recognition results in a database and uses them for subsequent processing. For example, the recognized food names are used in the input unit and the recipe presentation unit. In addition, the image recognition unit allows users to manually correct errors, making it easy to correct any errors in the recognition results. This allows the image recognition unit to support the accurate recognition and management of food ingredients, improving the overall accuracy and convenience of the system.

[0077] The input unit automatically enters the recommended expiration date based on the food name recognized by the image recognition unit. Specifically, the input unit searches the database for the recommended expiration date of the corresponding food based on the recognized food name and automatically enters it. For example, it calculates the recommended expiration date considering the number of days elapsed since purchase and differences depending on the type of food. The input unit can also take into account the food's storage method and environmental conditions (temperature, humidity, etc.). This makes it possible to provide a more accurate recommended expiration date. Furthermore, the input unit allows users to manually modify the recommended expiration date, enabling flexible responses to specific conditions and individual needs. For example, it can accommodate situations where a user wants to consume a particular food earlier or extend the recommended expiration date because it is in good storage condition. The input unit saves the recommended expiration dates in the database, which are used by the alert unit and recipe presentation unit. This allows the input unit to streamline food consumption management and reduce waste.

[0078] The alert unit issues alerts based on the recommended consumption date entered in the input unit. Specifically, the alert unit notifies the user when the recommended consumption date approaches. Notification methods include notification sounds, pop-up notifications, and email notifications, which can be selected according to the user's preference. For example, a pop-up notification can be displayed via a smartphone app to inform the user that the recommended consumption date is approaching. By setting up email notifications, users can also check the recommended consumption date even when they are out. The alert unit can also customize the frequency and timing of notifications, allowing for flexible responses to suit the user's lifestyle. For example, notifications can be sent multiple times, such as one week before, three days before, and the day before the recommended consumption date. This helps users not forget to consume ingredients and reduces waste. Furthermore, the alert unit can also notify users if the recommended consumption date has passed, prompting them to discard the ingredients. In this way, the alert unit supports food consumption management and minimizes waste.

[0079] The recipe presentation unit uses a generation AI to present recipes using the specified ingredients. Specifically, the generation AI generates recipes based on the recognized food names. The generation AI considers ingredient combinations, cooking methods, user preferences, and past history to suggest the most suitable recipe. For example, the generation AI can generate easy-to-make dishes or recipes that consider specific nutritional balances based on ingredients found in the refrigerator. The recipe presentation unit presents recipes that include information such as ingredient lists, cooking procedures, and cooking times. Users can check recipes via their smartphones or tablets and easily understand the necessary ingredients and procedures. Furthermore, the recipe presentation unit also provides visual guides using videos and images to clearly explain the cooking procedures. This allows users to cook with confidence, even if it's their first time. The recipe presentation unit collects user feedback, allowing the generation AI to continuously improve the accuracy and variety of recipes. This enables the recipe presentation unit to always provide users with new recipes and promote the consumption of ingredients.

[0080] The promotion department encourages the consumption of ingredients based on recipes presented by the recipe presentation department. Specifically, the promotion department provides means to encourage users to actually try the presented recipes. For example, it encourages ingredient consumption through methods such as providing discount coupons and expiration date reminders. Discount coupons are offered for specific ingredients, related seasonings, and cooking utensils, reducing the cost for users when trying recipes. Expiration date reminders notify users so they don't miss the timing to consume ingredients. Furthermore, the promotion department can collect feedback from users after they have tried a recipe and incorporate it into future recipe suggestions. This allows for recipe suggestions tailored to the user's preferences and needs, further promoting ingredient consumption. The promotion department can also analyze the user's ingredient consumption history and, if certain ingredients tend to be left over, prioritize suggesting recipes that utilize those ingredients. This reduces food waste and supports efficient consumption. The promotion department plays a crucial role in enriching users' diets and streamlining ingredient management and consumption.

[0081] The freshness check unit performs a visual freshness check using AI when it is difficult to determine whether food is spoiled or not, based on a photograph of the food. The freshness check unit checks freshness based on factors such as changes in the food's color, the presence or absence of mold, and the degree of wilting. For example, the freshness check unit takes a photograph of the food and the AI ​​analyzes the changes in color. The freshness check unit can also detect whether mold is growing on the surface of the food. Furthermore, the freshness check unit can analyze the degree of wilting of the food and evaluate its freshness. This makes it easy to check the freshness of food. Some or all of the above processes in the freshness check unit may be performed using AI, or not. For example, the freshness check unit can input a photograph of the food into the AI ​​and have the AI ​​perform the freshness evaluation.

[0082] The storage method suggestion unit, when a user wants to freeze an ingredient from the list, will suggest a recommended freezing method using AI. The storage method suggestion unit suggests a freezing method based on factors such as storage temperature, storage period, and type of storage container. For example, the storage method suggestion unit may suggest the optimal storage temperature depending on the type of ingredient. The storage method suggestion unit can also suggest the storage period for the ingredient. Furthermore, the storage method suggestion unit can suggest the type of storage container and propose an appropriate storage method. This makes it easy to learn how to store ingredients. Some or all of the above processing in the storage method suggestion unit may be performed using AI, or not. For example, the storage method suggestion unit can input ingredient information into the AI ​​and have the AI ​​suggest the optimal storage method.

[0083] The integration unit connects with the supermarket's electronic payment system and automatically reflects the purchased groceries, photographed and processed using the electronic payment system, into the app's list. The integration unit connects with electronic payment systems such as credit card payments and QR code payments. The integration unit automatically updates the app's list based on purchase history obtained from the electronic payment system. The integration unit can also analyze purchase history and automatically add purchased groceries to the app's list. Furthermore, by connecting with the electronic payment system, the integration unit can reflect purchase history in the app's list in real time. This makes it possible to manage groceries simultaneously with shopping. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input purchase history obtained from the electronic payment system into the AI ​​and have the AI ​​perform the task of reflecting the purchase history in the app's list.

[0084] The image recognition unit uses AI to analyze a photograph of food ingredients and recognize the name of the food. The image recognition unit recognizes the name of the food by, for example, matching it with an image database or performing text analysis. For example, the image recognition unit takes a photograph of food ingredients, and the AI ​​matches it with an image database to recognize the name of the food. The image recognition unit can also recognize the name of the food by performing text analysis. Furthermore, the image recognition unit can analyze the characteristics of the food ingredients to identify the name of the food. This allows the AI ​​to analyze a photograph of food ingredients and obtain the accurate name of the food. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input a photograph of food ingredients into the AI ​​and have the AI ​​perform the recognition of the food name.

[0085] The recipe presentation unit generates a recipe using the given ingredients via a generating AI. The recipe presentation unit generates a recipe using, for example, a generating AI. The recipe presentation unit inputs ingredient information into the generating AI and generates a recipe. The recipe presentation unit can also generate a recipe considering ingredient combinations via the generating AI. Furthermore, the recipe presentation unit can generate a recipe considering the user's preferences and dietary restrictions via the generating AI. This allows the generating AI to generate recipes that are suitable for the user. Some or all of the above-described processes in the recipe presentation unit may be performed using, for example, an AI, or without an AI. For example, the recipe presentation unit can input ingredient information into the generating AI and have the generating AI perform recipe generation.

[0086] The image recognition unit estimates the user's emotions and adjusts the accuracy of image recognition based on the estimated emotions. The image recognition unit estimates the user's emotions using methods such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the image recognition unit increases the accuracy of image recognition to reduce misrecognition. The image recognition unit can also maintain normal accuracy when the user is relaxed. Furthermore, if the user is in a hurry, the image recognition unit can prioritize the speed of image recognition and slightly reduce accuracy. This reduces misrecognition by adjusting the accuracy of image recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 image recognition unit may be performed using AI, or not using AI. For example, the image recognition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The image recognition unit improves the accuracy of image recognition by taking multiple photos from different angles when photographing food items. For example, the image recognition unit takes photos from three angles: the top, side, and bottom of the food item, and the AI ​​integrates and recognizes them. For example, the image recognition unit takes a series of photos from different angles of the food item, and the AI ​​selects and recognizes the clearest image. The image recognition unit can also display a guide to encourage the user to take photos from different angles when photographing food items. This improves the accuracy of image recognition by integrating photos from different angles. Some or all of the above processing in the image recognition unit may be performed using AI, or not. For example, the image recognition unit can input photos taken from different angles into the AI ​​and have the AI ​​perform image integration and recognition.

[0088] The image recognition unit improves the accuracy of image recognition by automatically adjusting the background color and brightness when taking a picture of food. For example, when taking a picture of food, the image recognition unit automatically adjusts the background color to white to improve recognition accuracy. For example, when taking a picture of food, the image recognition unit automatically adjusts the brightness to reduce shadows and improve recognition accuracy. The image recognition unit can also adjust the background color and brightness simultaneously when taking a picture of food to perform recognition under optimal conditions. By adjusting the background color and brightness, the accuracy of image recognition is improved. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without using AI. For example, the image recognition unit can input a picture of food into AI and have the AI ​​perform the adjustment of the background color and brightness.

[0089] The image recognition unit estimates the user's emotions and adjusts the order in which the image recognition results are displayed based on the estimated emotions. The image recognition unit estimates the user's emotions using methods such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the image recognition unit will display the most reliable recognition result first. The image recognition unit can also display recognition results randomly if the user is relaxed. Furthermore, if the user is in a hurry, the image recognition unit can display recognition results quickly. This improves user convenience by adjusting the order in which the image recognition results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The image recognition unit, when taking photos of food ingredients, prioritizes recognizing region-specific ingredients by considering the user's geographical location information. For example, if the user is in a specific region, the image recognition unit prioritizes recognizing ingredients common in that region. The image recognition unit can, for example, create a list of region-specific ingredients based on the user's location information to improve recognition accuracy. Furthermore, if the user is traveling, the image recognition unit can prioritize recognizing ingredients specific to the travel destination. This improves recognition accuracy by prioritizing the recognition of region-specific ingredients. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the recognition of region-specific ingredients.

[0091] The image recognition unit improves recognition accuracy by referring to the user's past shooting history when taking pictures of food ingredients. For example, the image recognition unit improves recognition accuracy based on data of food ingredients that the user has photographed in the past. For example, the image recognition unit prioritizes the recognition of frequently used food ingredients from the user's past shooting history. The image recognition unit can also analyze the user's past shooting history and optimize the recognition algorithm. This improves recognition accuracy by referring to past shooting history. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can input the user's past shooting history into AI and have the AI ​​perform the improvement of recognition accuracy.

[0092] The input unit estimates the user's emotions and adjusts the input method for the estimated consumption date based on the estimated emotions. The input unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the input unit provides a simple interface and minimizes the input steps. If the user is relaxed, the input unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of the estimated consumption date. This improves the convenience of input by adjusting the input method according to 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 input unit may be performed using AI or not. For example, the input unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The input unit automatically corrects the recommended consumption date by referring to past consumption data based on the food name. For example, the input unit automatically sets an average recommended consumption date for a food name based on past consumption data. For example, the input unit refers to the user's past consumption history and suggests individually customized recommended consumption dates. The input unit can also analyze past consumption data and automatically correct the recommended consumption date according to the season and weather. This improves the accuracy of the recommended consumption date by referring to past consumption data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past consumption data into AI and have AI perform the correction of the recommended consumption date.

[0094] The input unit adjusts the recommended consumption date based on the food name, taking into account seasonal and weather information. For example, the input unit automatically adjusts the recommended consumption date of food according to the season. For example, the input unit corrects the recommended consumption date of food based on weather information. The input unit can also suggest and notify the user of recommended consumption dates according to the season and weather. This improves the accuracy of recommended consumption dates by considering seasonal and weather information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input seasonal and weather information into AI and have the AI ​​perform the adjustment of recommended consumption dates.

[0095] The input unit estimates the user's emotions and adjusts the display method for the estimated consumption date based on the estimated emotions. The input unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the input unit provides a simple and highly visible display method. The input unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the input unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The input unit customizes the recommended consumption date based on the food name and the user's past consumption history. For example, the input unit suggests individually customized recommended consumption dates based on the user's past consumption history. For example, the input unit sets recommended consumption dates for foods that the user frequently consumes by referring to their past consumption history. The input unit can also analyze the user's past consumption history and suggest the optimal recommended consumption date. This allows for individual customization of recommended consumption dates by referring to past consumption history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past consumption history into AI and have AI perform the customization of recommended consumption dates.

[0097] The input unit analyzes the user's eating habits based on the food name and suggests a recommended consumption date. For example, the input unit analyzes the user's eating habits and suggests the optimal recommended consumption date. For example, the input unit customizes the recommended consumption date based on the user's eating habits. The input unit can also automatically set the recommended consumption date considering the user's eating habits. This allows the optimal recommended consumption date to be suggested by analyzing the eating habits. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's eating habits into AI and have AI suggest recommended consumption dates.

[0098] The alert unit estimates the user's emotions and adjusts the timing of alerts based on the estimated emotions. The alert unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the alert unit reduces the frequency of alerts and notifies only at important times. The alert unit can also notify at the normal frequency if the user is relaxed. Furthermore, if the user is in a hurry, the alert unit can speed up the timing of alerts and notify immediately. This allows for timely notifications by adjusting the timing of alerts according to 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 alert unit may be performed using AI or not using AI. For example, the alert unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The alert unit adjusts the frequency and intensity of alerts based on the recommended consumption date. For example, the alert unit increases the frequency of alerts as the recommended consumption date approaches. For example, the alert unit adjusts the intensity of alerts based on the recommended consumption date to indicate importance. The alert unit can also change the notification method of alerts depending on the recommended consumption date. This allows the unit to indicate importance by adjusting the frequency and intensity of alerts based on the recommended consumption date. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the recommended consumption date into the AI ​​and have the AI ​​adjust the frequency and intensity of alerts.

[0100] The alert unit customizes the notification method for alerts based on the recommended consumption date. For example, the alert unit selects the most suitable notification method for the user's device based on the recommended consumption date. For example, the alert unit changes the notification sound for alerts depending on the recommended consumption date. The alert unit can also customize how alerts are displayed based on the recommended consumption date. This allows for notifications optimized for the user by customizing the notification method based on the recommended consumption date. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the recommended consumption date into the AI ​​and have the AI ​​perform the customization of the alert notification method.

[0101] The alert unit estimates the user's emotions and adjusts the alert content based on the estimated emotions. The alert unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the alert unit provides a simple and highly visible alert. If the user is relaxed, the alert unit can also provide an alert with more detailed information. Furthermore, if the user is in a hurry, the alert unit can provide a concise alert. By adjusting the alert content according to the user's emotions, visibility is improved. 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 alert unit may be performed using AI or not. For example, the alert unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The alert unit optimizes the timing of alerts based on the recommended consumption date, taking into account the user's schedule. For example, the alert unit refers to the user's schedule and notifies the user of the alert at the optimal time. For example, the alert unit sets the alert timing to match the user's schedule based on the recommended consumption date. The alert unit can also adjust the timing of alerts based on the user's schedule information. This allows for timely notifications by setting the alert timing to match the user's schedule. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's schedule information into AI and have the AI ​​perform the optimization of the alert timing.

[0103] The alert unit selects a notification method for alerts based on the recommended consumption date and taking into account the user's device information. For example, if the user is using a smartphone, the alert unit provides an alert via push notification. If the user is using a tablet, the alert unit provides an alert optimized for a larger screen. The alert unit can also provide an alert via vibration notification if the user is using a smartwatch. This improves the effectiveness of the alert by selecting the most suitable notification method for the user's device. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's device information into the AI ​​and have the AI ​​select the notification method for alerts.

[0104] The recipe presentation unit estimates the user's emotions and adjusts the way the recipe is presented based on the estimated emotions. The recipe presentation unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the recipe presentation unit provides a simple and easy-to-understand recipe. If the user is relaxed, the recipe presentation unit can also provide a recipe with more detailed information. Furthermore, if the user is in a hurry, the recipe presentation unit can provide a concise recipe. By adjusting the way the recipe is presented according to the user's emotions, readability is improved. 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 recipe presentation unit may be performed using AI, or not using AI. For example, the recipe presentation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The recipe presentation unit adjusts the level of detail in the recipe based on the type and quantity of ingredients. For example, if there are many types of ingredients, the recipe presentation unit provides a recipe with detailed instructions. For example, if there are few ingredients, the recipe presentation unit provides a recipe with concise instructions. The recipe presentation unit can also automatically adjust the level of detail in the recipe according to the type and quantity of ingredients. This allows the user to be provided with the most suitable recipe by adjusting the level of detail according to the type and quantity of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the recipe.

[0106] The recipe presentation unit applies different recipe algorithms based on the types and quantities of ingredients. For example, if there are many types of ingredients, the recipe presentation unit applies a complex recipe algorithm. For example, if there are few ingredients, the recipe presentation unit applies a simple recipe algorithm. The recipe presentation unit can also select the optimal recipe algorithm according to the types and quantities of ingredients. This improves the accuracy of the recipe by selecting the optimal recipe algorithm according to the types and quantities of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the types and quantities of ingredients into the AI ​​and have the AI ​​execute the application of the recipe algorithm.

[0107] The recipe presentation unit estimates the user's emotions and adjusts the length of the recipe based on the estimated emotions. The recipe presentation unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the recipe presentation unit provides a short, concise recipe. If the user is relaxed, the recipe presentation unit can also provide a longer recipe with detailed explanations. Furthermore, if the user is in a hurry, the recipe presentation unit can provide a short recipe that can be cooked quickly. By adjusting the length of the recipe according to the user's emotions, readability is improved. 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 recipe presentation unit may be performed using AI or not. For example, the recipe presentation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0108] The recipe presentation unit determines the priority of recipes based on the type and quantity of ingredients. For example, the recipe presentation unit may provide recipes that prioritize the use of ingredients that are nearing their expiration date. For example, if there are many types of ingredients, the recipe presentation unit may provide a balanced recipe. The recipe presentation unit can also provide recipes that use ingredients efficiently if the quantity is small. This enables efficient cooking by determining the priority of recipes according to the type and quantity of ingredients. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​determine the priority of recipes.

[0109] The recipe presentation unit adjusts the order of the recipes based on the types and quantities of ingredients. For example, the recipe presentation unit may provide a recipe that uses ingredients with the nearest expiration date first. For example, if there are many types of ingredients, the recipe presentation unit may provide a recipe with optimized cooking procedures. The recipe presentation unit can also provide a recipe that allows for efficient cooking if the quantity of ingredients is small. By adjusting the order of the recipes according to the types and quantities of ingredients, efficient cooking becomes possible. Some or all of the above processing in the recipe presentation unit may be performed using AI, for example, or without AI. For example, the recipe presentation unit may input the types and quantities of ingredients into the AI ​​and have the AI ​​perform the adjustment of the recipe order.

[0110] The promotion unit estimates the user's emotions and adjusts the method of promoting food consumption based on the estimated user emotions. The promotion unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the promotion unit provides a simple and highly visible promotion method. The promotion unit can also provide a promotion method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the promotion unit can provide a concise promotion method. By adjusting the promotion method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0111] The promotion unit adjusts the frequency and intensity of promotion based on the consumption status of the ingredients. For example, if the consumption status of the ingredients is low, the promotion unit increases the frequency of promotion. For example, if the consumption status of the ingredients is good, the promotion unit decreases the frequency of promotion. The promotion unit can also adjust the intensity of promotion according to the consumption status of the ingredients. In this way, consumption is promoted by adjusting the frequency and intensity of promotion based on the consumption status of the ingredients. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the consumption status of the ingredients into the AI ​​and have the AI ​​perform the adjustment of the frequency and intensity of promotion.

[0112] The promotion unit customizes the notification method for promoting consumption based on the consumption status of the ingredients. For example, if the consumption status of the ingredients is low, the promotion unit will send a push notification. For example, if the consumption status of the ingredients is high, the promotion unit will send an email notification. The promotion unit can also select the most suitable notification method according to the consumption status of the ingredients. In this way, consumption is promoted by customizing the notification method based on the consumption status of the ingredients. Some or all of the above processing in the promotion unit may be performed using AI, for example, or not using AI. For example, the promotion unit can input the consumption status of the ingredients into the AI ​​and have the AI ​​perform the customization of the notification method.

[0113] The facilitator estimates the user's emotions and determines the priority of food consumption based on the estimated emotions. The facilitator estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the facilitator prioritizes ingredients that are easy to consume. If the user is relaxed, the facilitator may also prioritize ingredients that are fun to cook. Furthermore, if the user is in a hurry, the facilitator may prioritize ingredients that can be consumed quickly. This enables efficient consumption by determining the priority of consumption according to the user's emotions. 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 facilitator may be performed using AI, or not using AI. For example, the facilitator can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0114] The promotion unit optimizes the timing of promotions based on the consumption status of ingredients and taking into account the user's schedule. For example, the promotion unit refers to the user's schedule and performs promotions at the optimal time. For example, the promotion unit sets promotion timings that match the user's schedule based on the consumption status of ingredients. The promotion unit can also adjust the timing of promotions based on the user's schedule information. This allows consumption to be promoted at the appropriate time by setting promotion timings that match the user's schedule. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's schedule information into the AI ​​and have the AI ​​perform the optimization of the promotion timing.

[0115] The promotion unit selects a notification method for promoting consumption based on the consumption status of ingredients and taking into account the user's device information. For example, if the user is using a smartphone, the promotion unit will send a push notification. If the user is using a tablet, the promotion unit will send a notification optimized for a larger screen. The promotion unit can also send a vibration notification if the user is using a smartwatch. In this way, consumption is promoted by selecting the most suitable notification method for the user's device. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's device information into the AI ​​and have the AI ​​select the notification method.

[0116] The freshness check unit estimates the user's emotions and adjusts the freshness check criteria based on the estimated emotions. The freshness check unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the freshness check unit performs a strict freshness check. Conversely, if the user is relaxed, the freshness check unit can perform a freshness check using normal criteria. Furthermore, if the user is in a hurry, the freshness check unit can perform a rapid freshness check. By adjusting the freshness check criteria according to the user's emotions, visibility is improved. 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-described processes in the freshness check unit may be performed using AI, or not using AI. For example, the freshness check unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0117] The freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the appearance of the food. For example, the freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the color and shape of the food. For example, the freshness check unit applies an algorithm to improve the accuracy of the freshness check based on the surface condition of the food. The freshness check unit can also apply an algorithm to improve the accuracy of the freshness check based on changes in the appearance of the food. This makes it possible to perform an accurate freshness check by improving the accuracy of the freshness check based on the appearance of the food. Some or all of the above processing in the freshness check unit may be performed using AI, for example, or without using AI. For example, the freshness check unit can input data on the appearance of the food into the AI ​​and have the AI ​​perform the improvement of the freshness check accuracy.

[0118] The freshness check unit estimates the user's emotions and adjusts the order in which the freshness check results are displayed based on the estimated emotions. The freshness check unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the freshness check unit will display the freshest ingredients first. If the user is relaxed, the freshness check unit can display the ingredients regardless of freshness order. Furthermore, if the user is in a hurry, the freshness check unit can display the least fresh ingredients first. By adjusting the order in which the freshness check results are displayed according to the user's emotions, visibility is improved. 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 freshness check unit may be performed using AI, or not using AI. For example, the freshness check unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0119] The freshness check unit improves accuracy by referencing the user's past freshness check history based on the appearance of the ingredients. The freshness check unit improves accuracy based on the user's past freshness check history, for example. The freshness check unit also analyzes the user's past freshness check history and applies the optimal freshness check algorithm. This improves the accuracy of the freshness check by referencing past freshness check history. Some or all of the above processing in the freshness check unit may be performed using AI, for example, or without AI. For example, the freshness check unit can input the user's past freshness check history into the AI ​​and have the AI ​​perform the accuracy improvement.

[0120] The storage method presentation unit estimates the user's emotions and adjusts the presentation method of the storage method based on the estimated user emotions. The storage method presentation unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the storage method presentation unit provides a simple and highly visible storage method. If the user is relaxed, the storage method presentation unit can also provide a storage method that includes detailed information. Furthermore, if the user is in a hurry, the storage method presentation unit can provide a concise storage method. By adjusting the presentation method of the storage method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 storage method presentation unit may be performed using AI, for example, or without AI. For example, the storage method presentation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0121] The storage method presentation unit adjusts the level of detail in the storage method based on the type and quantity of ingredients. For example, if there are many types of ingredients, the storage method presentation unit provides a detailed storage method. For example, if there are few ingredients, the storage method presentation unit provides a concise storage method. The storage method presentation unit can also automatically adjust the level of detail in the storage method according to the type and quantity of ingredients. This allows the user to be provided with the most suitable storage method by adjusting the level of detail in the storage method presentation unit according to the type and quantity of ingredients. Some or all of the above processing in the storage method presentation unit may be performed using AI, for example, or without AI. For example, the storage method presentation unit can input the type and quantity of ingredients into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the storage method.

[0122] The storage method suggestion unit estimates the user's emotions and determines the priority of storage methods based on the estimated emotions. The storage method suggestion unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the storage method suggestion unit prioritizes easy-to-execute storage methods. It can also prioritize more detailed storage methods if the user is relaxed. Furthermore, if the user is in a hurry, the storage method suggestion unit can prioritize storage methods that can be executed quickly. This enables efficient storage by determining the priority of storage methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 storage method suggestion unit may be performed using AI or not. For example, the storage method suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0123] The storage method suggestion unit customizes the storage method based on the type and quantity of ingredients and the user's past storage history. For example, the storage method suggestion unit proposes the optimal storage method based on the user's past storage history. The storage method suggestion unit refers to the user's past storage history based on the type and quantity of ingredients. The storage method suggestion unit can also analyze the user's past storage history and customize the storage method. This allows the storage method to be customized by referring to past storage history. Some or all of the above processing in the storage method suggestion unit may be performed using AI, for example, or without AI. For example, the storage method suggestion unit can input the user's past storage history into AI and have AI perform the customization of the storage method.

[0124] The interaction unit estimates the user's emotions and adjusts the timing of interaction based on the estimated emotions. The interaction unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the interaction unit reduces the frequency of interaction and only interacts at important times. The interaction unit can also interact at the normal frequency if the user is relaxed. Furthermore, if the user is in a hurry, the interaction unit can speed up the timing of interaction and interact immediately. This improves visibility by adjusting the timing of interaction according to 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 interaction unit may be performed using AI, or not using AI. For example, the interaction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0125] The integration unit automatically analyzes purchase history and reflects it in the app list when integrating with the electronic payment system. For example, the integration unit automatically updates the app list based on the purchase history obtained from the electronic payment system. For example, the integration unit analyzes the purchase history and automatically adds purchased food items to the app list. Furthermore, the integration unit can reflect the purchase history in the app list in real time through integration with the electronic payment system. As a result, the app list is automatically updated by automatically analyzing the purchase history. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the purchase history obtained from the electronic payment system into the AI ​​and have the AI ​​perform the task of reflecting it in the app list.

[0126] The interaction unit estimates the user's emotions and adjusts the content of the interaction based on the estimated emotions. The interaction unit estimates the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is stressed, the interaction unit provides simple and highly visible interaction content. If the user is relaxed, the interaction unit can also provide interaction content that includes detailed information. Furthermore, if the user is in a hurry, the interaction unit can provide interaction content that gets straight to the point. By adjusting the content of the interaction according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0127] The integration unit improves the accuracy of integration when integrating with an electronic payment system by referring to the user's past purchase history. For example, the integration unit improves the accuracy of integration based on the user's past purchase history. For example, the integration unit optimizes the integration content by referring to past purchase history obtained from the electronic payment system. The integration unit can also improve the accuracy of integration by analyzing the user's past purchase history. As a result, the accuracy of integration is improved by referring to past purchase history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past purchase history into AI and have AI perform the integration accuracy improvement.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The food management system can also be equipped with a voice input function. This function allows users to input food information by voice. For example, if a user voice-inputs "I bought tomatoes," the voice input function analyzes the information and adds tomatoes to the food list. The voice input function can also allow users to voice-input the recommended expiration date. This improves convenience by allowing users to input food information without using their hands. Furthermore, the voice input function can search for and present recipes based on the user's voice commands. This allows users to easily obtain recipes by voice.

[0130] The freshness check unit can also be equipped with a temperature sensor. The temperature sensor measures the surface temperature of the food and uses it as part of the freshness check. For example, if the surface temperature of the food is abnormally high, the temperature sensor can determine that the food may be spoiled. The temperature sensor can also monitor temperature fluctuations inside the refrigerator and evaluate whether the food storage environment is appropriate. This enables freshness checks based on temperature information, resulting in a more accurate freshness assessment. Furthermore, the temperature sensor can issue an alert if the food storage temperature is not appropriate.

[0131] The storage method suggestion unit can also be equipped with a humidity sensor. The humidity sensor measures the humidity of the food storage environment and is used to suggest the optimal storage method. For example, if the humidity sensor is too high, it will recommend the use of a desiccant. If the humidity sensor is too low, it can also suggest methods to maintain humidity. This makes it possible to suggest storage methods based on humidity information, thus preserving the quality of the food. Furthermore, the humidity sensor can also issue an alert if the humidity of the storage environment is not appropriate.

[0132] The integration unit can further analyze the user's purchase history and provide personalized food management. For example, the integration unit can automatically add frequently purchased food items to a list. It can also automatically set expiration dates based on the user's purchase history. This enables food management based on the user's purchasing patterns, improving convenience. Furthermore, the integration unit can issue alerts when the expiration date of specific food items is approaching, based on the user's purchase history.

[0133] The image recognition unit can also provide nutritional information about food ingredients. For example, it can analyze a photo of food ingredients and display nutritional information based on the recognized food name. Furthermore, if the user wants to consume a specific nutrient, the image recognition unit can suggest foods that are rich in that nutrient. This allows users to easily obtain nutritional information about food ingredients, supporting a healthy diet. Additionally, the image recognition unit can suggest balanced meals based on the nutritional information of the food ingredients.

[0134] The recipe presentation unit can estimate the user's emotions and adjust the difficulty level of the recipe based on those emotions. For example, if the user is stressed, it can provide an easy, simple recipe. If the user is relaxed, it can provide a challenging, elaborate recipe. Furthermore, if the user is in a hurry, it can provide a recipe that can be cooked in a short time. In this way, the burden of cooking can be reduced by adjusting the difficulty level of the recipe according to the user's emotions.

[0135] The alert function can estimate the user's emotions and adjust the alert content based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible alert. If the user is relaxed, it can provide an alert with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point alert. By adjusting the alert content according to the user's emotions, visibility is improved.

[0136] The promotion unit can estimate the user's emotions and adjust the method of promoting food consumption based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible promotion method. If the user is relaxed, it can provide a promotion method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a promotion method that gets straight to the point. By adjusting the promotion method according to the user's emotions, visibility is improved.

[0137] The freshness check unit can estimate the user's emotions and adjust the freshness check criteria based on those emotions. For example, if the user is stressed, it can perform a strict freshness check. If the user is relaxed, it can perform a freshness check using normal criteria. Furthermore, if the user is in a hurry, it can perform a freshness check quickly. By adjusting the freshness check criteria according to the user's emotions, visibility is improved.

[0138] The save method presentation unit can estimate the user's emotions and adjust the presentation method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible save method. If the user is relaxed, it can provide a save method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a save method that gets straight to the point. By adjusting the save method presentation method according to the user's emotions, visibility is improved.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The image recognition unit takes a picture of the food item, and the AI ​​performs image recognition. For example, the food item is photographed in JPEG format and saved at a resolution of 1080p. The image recognition unit uses a convolutional neural network (CNN) to perform image recognition, analyzes the food item photograph, and recognizes the name of the food item. Step 2: The input unit automatically enters the recommended expiration date based on the food name recognized by the image recognition unit. For example, it calculates the recommended expiration date considering the number of days elapsed since purchase and differences depending on the type of food. Step 3: The alert unit issues an alert based on the recommended consumption date entered in the input unit. For example, the alert may be issued via a notification sound, a pop-up notification, or an email notification. Step 4: The recipe presentation unit uses a generating AI to present a recipe using the ingredient in question. For example, it presents a recipe that includes information such as an ingredient list, cooking procedure, and cooking time. Step 5: The promotion unit encourages the consumption of ingredients based on the recipes presented by the recipe presentation unit. For example, it encourages the consumption of ingredients by providing discount coupons or reminding users of expiration dates.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the image recognition unit, input unit, alert unit, recipe presentation unit, promotion unit, freshness check unit, storage method presentation unit, and linkage unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the image recognition unit takes a picture of the food using the camera 42 of the smart device 14 and performs image recognition using the identification processing unit 290 of the data processing unit 12. The input unit automatically inputs the food name and recommended consumption date using the identification processing unit 290 of the data processing unit 12. The alert unit issues a notification using the output device 40 of the smart device 14. The recipe presentation unit presents a recipe using AI generated by the identification processing unit 290 of the data processing unit 12. The promotion unit encourages the consumption of the food using the output device 40 of the smart device 14. The freshness check unit checks the freshness of the food using the camera 42 of the smart device 14 and evaluates it using the identification processing unit 290 of the data processing unit 12. The storage method presentation unit presents a freezing storage method using the identification processing unit 290 of the data processing unit 12. The integration unit uses the communication I / F 26 of the data processing device 12 to connect with the electronic payment system and reflect the purchase history in the app's list. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the image recognition unit, input unit, alert unit, recipe presentation unit, promotion unit, freshness check unit, storage method presentation unit, and linkage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the image recognition unit takes a picture of the food using the camera 42 of the smart glasses 214 and performs image recognition using the identification processing unit 290 of the data processing unit 12. The input unit automatically inputs the food name and recommended consumption date using the identification processing unit 290 of the data processing unit 12. The alert unit provides notifications using the speaker 240 of the smart glasses 214. The recipe presentation unit presents recipes using AI generated by the identification processing unit 290 of the data processing unit 12. The promotion unit encourages the consumption of food using the speaker 240 of the smart glasses 214. The freshness check unit checks the freshness of the food using the camera 42 of the smart glasses 214 and evaluates it using the identification processing unit 290 of the data processing unit 12. The storage method presentation unit presents a freezing storage method using the specific processing unit 290 of the data processing device 12. The linking unit links with the electronic payment system using the communication I / F 26 of the data processing device 12 and reflects the purchase history in the app's list. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the image recognition unit, input unit, alert unit, recipe presentation unit, promotion unit, freshness check unit, storage method presentation unit, and linkage unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the image recognition unit takes a picture of the food using the camera 42 of the headset terminal 314 and performs image recognition using the identification processing unit 290 of the data processing unit 12. The input unit automatically inputs the food name and recommended consumption date using the identification processing unit 290 of the data processing unit 12. The alert unit provides notifications using the speaker 240 of the headset terminal 314. The recipe presentation unit presents recipes using AI generated by the identification processing unit 290 of the data processing unit 12. The promotion unit encourages the consumption of food using the speaker 240 of the headset terminal 314. The freshness check unit checks the freshness of food using the camera 42 of the headset terminal 314 and evaluates it using the identification processing unit 290 of the data processing unit 12. The storage method presentation unit presents a freezing storage method using the specific processing unit 290 of the data processing device 12. The linking unit links with the electronic payment system using the communication I / F 26 of the data processing device 12 and reflects the purchase history in the app's list. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements described above, including the image recognition unit, input unit, alert unit, recipe presentation unit, promotion unit, freshness check unit, storage method presentation unit, and coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the image recognition unit takes a picture of the food using the camera 42 of the robot 414 and performs image recognition using the identification processing unit 290 of the data processing unit 12. The input unit automatically inputs the food name and recommended consumption date using the identification processing unit 290 of the data processing unit 12. The alert unit makes a notification using the speaker 240 of the robot 414. The recipe presentation unit presents a recipe using AI generated by the identification processing unit 290 of the data processing unit 12. The promotion unit uses the speaker 240 of the robot 414 to encourage the consumption of the food. The freshness check unit checks the freshness of the food using the camera 42 of the robot 414 and evaluates it using the identification processing unit 290 of the data processing unit 12. The storage method presentation unit presents a freezing storage method using the identification processing unit 290 of the data processing unit 12. The integration unit uses the communication I / F 26 of the data processing device 12 to connect with the electronic payment system and reflect the purchase history in the app's list. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) Take a picture of the ingredients, The image recognition unit performs AI image recognition, An input unit that automatically inputs the recommended expiration date based on the food name recognized by the image recognition unit, An alert unit that issues an alert based on the recommended consumption date entered by the input unit, A recipe presentation unit that uses a generating AI to present recipes using the ingredients in question, The system includes a promotion unit that encourages the consumption of ingredients based on the recipe presented by the recipe presentation unit. A system characterized by the following features. (Note 2) When it's difficult to determine if food is spoiled, take a picture of it. It features a freshness check unit that uses AI to perform a visual freshness check. The system described in Appendix 1, characterized by the features described herein. (Note 3) If you want to freeze the ingredients on the list It features a storage method suggestion section that uses AI to recommend the best freezing method for food. The system described in Appendix 1, characterized by the features described herein. (Note 4) In conjunction with the supermarket's electronic payment system, It features a built-in integration that automatically updates the list of food items purchased and photographed using an electronic payment system within the app. The system described in Appendix 1, characterized by the features described herein. (Note 5) The image recognition unit, AI analyzes photos of food items and recognizes their names. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recipe display unit is, The AI ​​generates recipes using the ingredients in question. The system described in Appendix 1, characterized by the features described herein. (Note 7) The image recognition unit, It estimates the user's emotions and adjusts the accuracy of image recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The image recognition unit, When taking photos of food ingredients, multiple photos are taken from different angles to improve the accuracy of image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The image recognition unit, When taking photos of food ingredients, the system automatically adjusts the background color and brightness to improve the accuracy of image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 10) The image recognition unit, It estimates the user's emotions and adjusts the order in which image recognition results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The image recognition unit, When taking photos of food ingredients, the system prioritizes recognizing regionally specific ingredients by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The image recognition unit, When taking photos of food ingredients, the system improves recognition accuracy by referencing the user's past photo history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned input unit is The system estimates the user's emotions and adjusts the input method for the estimated consumption date based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned input unit is Based on the food name, the system automatically adjusts the recommended consumption date by referencing past consumption data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned input unit is Based on the food name, the recommended consumption date is adjusted taking into account seasonal and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned input unit is The system estimates the user's emotions and adjusts how the recommended consumption date is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned input unit is Based on the food name, the recommended consumption date is customized by referencing the user's past consumption history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is Based on the food names, the system analyzes the user's eating habits and suggests recommended consumption dates. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, It estimates the user's emotions and adjusts the timing of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, Adjust the frequency and intensity of alerts based on the recommended consumption date. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, Customize how alerts are sent based on the recommended consumption date. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, It estimates the user's emotions and adjusts the content of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, Based on the recommended consumption date, the timing of alerts is optimized to take the user's schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, Based on the recommended consumption date, the system selects the notification method for alerts, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recipe display unit is, The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recipe display unit is, Adjust the level of detail in the recipe based on the type and quantity of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recipe display unit is, Apply different recipe algorithms based on the type and quantity of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recipe display unit is, It estimates the user's emotions and adjusts the recipe length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recipe display unit is, Prioritize recipes based on the type and quantity of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recipe display unit is, Adjust the order of the recipe based on the type and quantity of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned promotion unit is The system estimates user emotions and adjusts methods for promoting food consumption based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned promotion unit is The frequency and intensity of promotions are adjusted based on the consumption of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned promotion unit is Customize how promotional notifications are sent based on food consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned promotion unit is The system estimates the user's emotions and determines the priority of food consumption based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned promotion unit is Based on food consumption patterns, the timing of promotions is optimized considering the user's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned promotion unit is Based on the consumption patterns of food ingredients, the notification method for promotion will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The freshness checking unit is, The system estimates the user's emotions and adjusts the freshness check criteria based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The freshness checking unit is, An algorithm is applied to improve the accuracy of freshness checks based on the appearance of the ingredients. The system described in Appendix 2, characterized by the features described herein. (Note 39) The freshness checking unit is, The system estimates the user's emotions and adjusts the order in which the freshness check results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The freshness checking unit is, Based on the appearance of the ingredients, the system improves accuracy by referencing the user's past freshness check history. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned storage method display unit is: It estimates the user's emotions and adjusts the way it presents storage options based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned storage method display unit is: Adjust the level of detail in the storage instructions based on the type and quantity of ingredients. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned storage method display unit is: The system estimates the user's emotions and prioritizes storage methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned storage method display unit is: Based on the type and quantity of ingredients, the system customizes the storage method by referring to the user's past saving history. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the timing of collaboration based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned linkage unit is, When integrating with an electronic payment system, purchase history is automatically analyzed and reflected in the app's list. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the content of the interaction based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned linkage unit is, When integrating with electronic payment systems, we improve the accuracy of the integration by referring to the user's past purchase history. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0213] 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. Take a picture of the ingredients, The AI ​​performs image recognition in the image recognition unit, An input unit that automatically inputs the recommended expiration date based on the food name recognized by the image recognition unit, An alert unit that issues an alert based on the recommended consumption date entered by the input unit, A recipe presentation unit that uses a generating AI to present recipes using the ingredients in question, The system includes a promotion unit that encourages the consumption of ingredients based on the recipe presented by the recipe presentation unit. A system characterized by the following features.

2. When it's difficult to determine if food is spoiled, take a picture of it. It features a freshness check unit that uses AI to perform a visual freshness check. The system according to feature 1.

3. If you want to freeze the ingredients on the list It features a storage method suggestion section where AI recommends the best freezing method for preserving food. The system according to feature 1.

4. In conjunction with the supermarket's electronic payment system, It features a built-in integration that automatically updates the list of food items purchased and photographed using an electronic payment system within the app. The system according to feature 1.

5. The image recognition unit, AI analyzes photos of food ingredients and recognizes their names. The system according to feature 1.

6. The aforementioned recipe display unit is, The AI ​​generates recipes using the ingredients in question. The system according to feature 1.

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

8. The image recognition unit, When taking photos of food ingredients, multiple photos are taken from different angles to improve the accuracy of image recognition. The system according to feature 1.

Images