system

Abstract

The system according to this embodiment aims to suggest an optimal meal menu based on the user's conditions and to automatically generate a shopping list of necessary ingredients. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, a discrimination unit, and a list generation unit. The reception unit receives user input conditions. The suggestion unit proposes the optimal menu based on the conditions entered by the reception unit. The discrimination unit performs image discrimination on photos of the contents of the refrigerator and pantry. The list generation unit automatically generates a shopping list of necessary ingredients based on the menu proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=2,1]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to consider conditions such as food ingredient management, budget, and variations when determining daily meal menus, which poses a significant burden on users.

[0005] The system according to the embodiment aims to propose an optimal meal menu based on user conditions and automatically generate a shopping list for necessary food ingredients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, a discrimination unit, and a list generation unit. The reception unit receives user input conditions. The suggestion unit proposes the optimal menu based on the conditions entered by the reception unit. The discrimination unit performs image discrimination on photos of the contents of a refrigerator or pantry. The list generation unit automatically generates a shopping list of necessary ingredients based on the menu proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal meal menu based on the user's conditions and automatically generate a shopping list of necessary ingredients. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 Inspiration Chef, according to an embodiment of the present invention, is a service that uses generative AI to solve the problem of deciding on daily meal menus. The Inspiration Chef significantly reduces the cost (time, effort, and stress) of considering various factors such as ingredient management, budget, and variety when planning meals. Even if you can't think of anything specific you want to eat, the generative AI will ask about the user's food preferences, desired ingredients, and other conditions, and then suggest the most suitable menu. It also features a function where the AI ​​uses image recognition to analyze photos of the refrigerator or pantry and suggests menus that utilize the available ingredients. For example, by suggesting multiple meal menus at once and automatically generating a shopping list of necessary ingredients, it makes daily meal planning easy and efficient, significantly reducing the user's burden. For instance, the user inputs conditions such as food preferences, desired ingredients, budget, and cooking utensils. The generative AI then suggests the most suitable menu based on these conditions. For example, if the user inputs "I want to eat a chicken dish," the generative AI will suggest various chicken recipes. Furthermore, it also has a function where the AI ​​uses image recognition to analyze photos of the refrigerator or pantry and suggests menus that utilize the available ingredients. For example, by suggesting recipes using vegetables and meat already in the refrigerator, it can reduce food waste. It also has a function that suggests multiple meal menus at once and automatically generates a shopping list of necessary ingredients. This allows users to shop efficiently and prepare meals systematically. For instance, when shopping all at once on the weekend, users can create a shopping list based on menus suggested by the generating AI and purchase all necessary ingredients at once. In this way, utilizing the generating AI streamlines meal menu selection, ingredient management, and shopping planning, significantly reducing the user's burden. This service is effective for a variety of target groups, such as busy office workers, housewives overwhelmed with household chores, and students who enjoy cooking at home within a budget. Thus, Inspiration Chef streamlines meal menu selection, ingredient management, and shopping planning, significantly reducing the user's burden.

[0029] The Inspiration Chef according to this embodiment comprises a reception unit, a suggestion unit, a discrimination unit, and a list generation unit. The reception unit accepts user input. User input includes, but is not limited to, examples of food preferences, desired ingredients, budget, and cooking utensils. For example, the reception unit allows the user to input allergy information. The reception unit also allows the user to input their dietary preferences. Furthermore, the reception unit allows the user to input dietary restrictions. The suggestion unit uses a generation AI to propose the optimal menu based on the conditions entered by the reception unit. For example, if the user inputs "I want to eat a dish using chicken," the generation AI will propose various recipes using chicken. The suggestion unit can also use the generation AI to propose nutritionally balanced menus based on the user's conditions. Furthermore, the suggestion unit can use the generation AI to propose menus that take calories into consideration based on the user's conditions. The discrimination unit uses image recognition to analyze photos of the contents of a refrigerator or pantry and proposes menus that make use of the available ingredients. For example, the discrimination unit proposes recipes using vegetables and meat found in the refrigerator. Furthermore, the discrimination unit can use image recognition technology to recognize ingredients in the refrigerator or pantry and suggest menus based on that. In addition, the discrimination unit can use image recognition technology to determine the freshness of ingredients in the refrigerator or pantry and suggest menus based on that. The list generation unit automatically generates a shopping list of necessary ingredients based on the menu suggested by the suggestion unit. For example, the list generation unit automatically generates a list of necessary ingredients based on the suggested menu. The list generation unit can also automatically calculate the quantity of necessary ingredients based on the suggested menu. Furthermore, the list generation unit can also automatically determine the priority of necessary ingredients based on the suggested menu. As a result, the Inspiration Chef according to this embodiment can suggest the optimal menu based on the user's conditions and streamline ingredient management and shopping list generation.

[0030] The reception desk inputs user criteria. These criteria include, but are not limited to, food preferences, desired ingredients, budget, and cooking utensils. For example, the reception desk allows users to input allergy information. It also allows users to input their dietary preferences. Furthermore, it allows users to input dietary restrictions. Specifically, the reception desk is designed to allow users to easily input information through a user interface. For example, using devices such as smartphones, tablets, and PCs, users can input criteria using intuitive forms, checkboxes, and dropdown menus. Regarding allergy information, users can input detailed information about allergies to specific foods or ingredients, and the system will exclude those ingredients from suggested menus based on this information. Regarding dietary preferences, users can select their favorite cuisine genres, specific ingredients, cooking methods, etc., and the system will customize suggested menus based on these selections. Furthermore, regarding dietary restrictions, users can input detailed conditions, such as whether they are on a diet or wish to restrict specific nutrients. This allows the reception desk to collect basic data to respond to the diverse needs of users and suggest menus that are best suited to their individual conditions.

[0031] The suggestion department uses generative AI to propose the most suitable menu based on the conditions entered by the reception department. For example, if a user enters "I want to eat a dish using chicken," the generative AI will suggest various chicken recipes. The suggestion department can also use generative AI to suggest nutritionally balanced menus based on the user's conditions. Furthermore, the suggestion department can use generative AI to suggest menus that take calories into consideration based on the user's conditions. Specifically, the generative AI uses natural language processing technology to analyze the user's input and generate appropriate recipes. For example, if a user enters "low-calorie, high-protein chicken dish," the generative AI searches the database for recipe information and extracts recipes that match the conditions. Furthermore, the generative AI utilizes its knowledge of nutrition to evaluate whether the suggested menu is nutritionally balanced and adjusts the recipe as needed. For example, if there is a deficiency in a particular vitamin or mineral, the generative AI will add ingredients to compensate for it. Also, if there is a calorie restriction, the generative AI will calculate calories and suggest appropriate portions. This allows the proposal department to provide menus that are optimally suited to each user's individual needs, resulting in healthy and satisfying meals.

[0032] The discrimination unit uses image recognition to analyze photos of the inside of refrigerators and pantries and suggests menus that make use of the ingredients in stock. For example, the discrimination unit can suggest recipes using vegetables and meat found in the refrigerator. Furthermore, the discrimination unit can use image recognition technology to recognize ingredients in refrigerators and pantries and suggest menus based on that. In addition, the discrimination unit can use image recognition technology to determine the freshness of ingredients in refrigerators and pantries and suggest menus based on that. Specifically, the discrimination unit analyzes images of the inside of refrigerators and pantries taken using cameras or smartphones. Using image recognition technology, it identifies the type and quantity of ingredients and compares them with a database to understand the inventory status. For example, it can recognize ingredients such as tomatoes, lettuce, and chicken from images of the inside of a refrigerator and suggest recipes using them. It also uses freshness determination technology to analyze the color, shape, and texture of ingredients and evaluate their freshness. For example, if the color of vegetables has changed or discoloration is visible on the surface of meat, it determines that the freshness has decreased and suggests recipes that recommend consuming the ingredients sooner. This allows the discrimination unit to effectively utilize stock ingredients, reduce waste, and provide users with optimal menus.

[0033] The list generation unit automatically generates a shopping list of necessary ingredients based on the menu suggested by the suggestion unit. For example, the list generation unit automatically generates a list of necessary ingredients based on the suggested menu. The list generation unit can also automatically calculate the quantity of necessary ingredients based on the suggested menu. Furthermore, the list generation unit can automatically determine the priority of necessary ingredients based on the suggested menu. Specifically, the list generation unit analyzes the recipe information of the suggested menu and lists the necessary ingredients and their quantities. For example, if the suggested menu is "chicken stewed in tomato sauce," ingredients such as chicken, tomatoes, onions, garlic, and olive oil will be added to the list. The list generation unit also considers the user's inventory information and excludes ingredients already in the refrigerator or pantry to prevent unnecessary purchases. Furthermore, the list generation unit considers freshness and expiration dates when determining the priority of ingredients. For example, ingredients that need to be consumed soon are added to the list first, while ingredients that can be stored for a long time are added later. In this way, the list generation unit can provide an efficient and waste-free shopping list, improving the user's shopping experience.

[0034] The suggestion unit can suggest multiple meal menus at once. For example, it can suggest a week's worth of meal menus at once. It can also suggest three days' worth of meal menus at once. Furthermore, it can suggest a month's worth of meal menus at once. This streamlines meal planning by suggesting multiple meal menus at once. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input user conditions into a generation AI, which can then suggest multiple meal menus.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display ingredients and dishes that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest ingredients and dishes that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which can then suggest the optimal input method.

[0036] The reception desk can customize input fields considering the user's current health status and nutritional balance. For example, if the user is on a diet, the reception desk will prioritize suggesting low-calorie foods and dishes. It can also suggest foods and dishes rich in specific nutrients if the user wishes to consume them. Furthermore, if the user has allergies, the reception desk can suggest foods and dishes that do not contain those allergens. This allows for the suggestion of more appropriate menus by providing input fields tailored to the user's health status and nutritional balance. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's health data into the AI, which can then customize the optimal input fields.

[0037] The reception desk can add input fields that suggest regionally specific ingredients and dishes, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest ingredients that are readily available in that region. If the user is traveling, the reception desk can also suggest local specialty dishes. Furthermore, if the user wants to use local ingredients, the reception desk can suggest ingredients produced in that region. This allows for the provision of more personalized menus by suggesting regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then suggest regionally specific ingredients and dishes.

[0038] The reception desk can analyze the user's social media activity and reflect relevant food preferences and trends in the input fields. For example, the reception desk can suggest relevant recipes based on photos of food the user has shared on social media. It can also analyze posts from cooking accounts the user follows and suggest trending dishes. Furthermore, it can suggest relevant ingredients and dishes based on food posts the user has "liked". In this way, by analyzing social media activity, it is possible to suggest menus based on the user's preferences and trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI, which can then analyze relevant food preferences and trends and reflect them in the input fields.

[0039] The suggestion unit can suggest more personalized menus by referring to the user's past eating history. For example, the suggestion unit can suggest relevant menus based on dishes the user has enjoyed eating in the past. It can also suggest menus considering ingredients the user has avoided in the past. Furthermore, the suggestion unit can suggest dishes the user enjoys eating in a particular season based on their past eating history. In this way, by referring to past eating history, it is possible to suggest more personalized menus. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past eating data into a generative AI, which can then suggest a personalized menu.

[0040] The suggestion unit can customize menus when making suggestions, taking into account the user's health condition and nutritional balance. For example, if the user is on a diet, the suggestion unit will suggest low-calorie menus. Furthermore, if the user wishes to consume a specific nutrient, the suggestion unit can suggest menus that are rich in that nutrient. In addition, if the user has allergies, the suggestion unit can suggest menus that do not contain allergens. This allows for the provision of menus tailored to the user's health condition and nutritional balance, thereby suggesting more appropriate meals. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's health data into a generation AI, which can then suggest menus that take into account the user's health condition and nutritional balance.

[0041] The suggestion unit can suggest regionally specific dishes, taking into account the user's geographical location when making suggestions. For example, if the user is in a specific region, the suggestion unit can suggest local specialty dishes. Furthermore, if the user is traveling, the suggestion unit can suggest traditional dishes of that region. Additionally, if the user wishes to use local ingredients, the suggestion unit can suggest dishes produced in that region. This allows for a more personalized menu by suggesting regionally specific dishes based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input the user's geographical location data into a generative AI, which can then suggest regionally specific dishes.

[0042] The suggestion unit can analyze the user's social media activity and suggest relevant trends and popular menu items when making suggestions. For example, the suggestion unit can suggest relevant recipes based on photos of food shared by the user on social media. It can also analyze posts from cooking accounts followed by the user and suggest trending dishes. Furthermore, the suggestion unit can suggest relevant ingredients and dishes based on food posts that the user has "liked." In this way, by analyzing social media activity, it is possible to suggest menus based on the user's preferences and trends. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's social media data into a generative AI, which can then suggest relevant trends and popular menu items.

[0043] The discrimination unit can suggest menus while considering the freshness and expiration dates of ingredients in the refrigerator or pantry during the discrimination process. For example, if the expiration date of ingredients in the refrigerator is approaching, the discrimination unit will suggest menus that prioritize the use of those ingredients. The discrimination unit can also suggest menus that make the most of fresh ingredients. Furthermore, if the freshness of the ingredients is low, the discrimination unit can suggest menus that can be enjoyed deliciously by modifying the cooking method. In this way, by considering the freshness and expiration dates of the ingredients, more appropriate menus can be suggested. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input data on the ingredients in the refrigerator into AI, and the AI ​​can suggest menus that take freshness and expiration dates into consideration.

[0044] The discrimination unit can customize menus by considering the nutritional value and calorie information of ingredients during discrimination. For example, the discrimination unit can suggest a balanced menu based on the nutritional value of the ingredients. It can also suggest a low-calorie menu based on the calorie information of the ingredients. Furthermore, the discrimination unit can suggest a menu that makes the most of ingredients that are rich in specific nutrients. In this way, by considering the nutritional value and calorie information of the ingredients, it is possible to suggest a more balanced menu. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input nutritional value data of ingredients into AI, and the AI ​​can suggest a menu that takes nutritional value and calorie information into consideration.

[0045] The discrimination unit can prioritize identifying region-specific ingredients by considering the user's geographical location information during the discrimination process. For example, if the user is in a specific region, the discrimination unit will prioritize identifying ingredients that are readily available in that region. Furthermore, if the user is traveling, the discrimination unit can prioritize identifying local specialties of that region. Additionally, if the user wishes to use local ingredients, the discrimination unit can prioritize identifying ingredients produced in that region. This allows for the provision of more personalized menus by prioritizing region-specific ingredients based on the user's geographical location information. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input the user's geographical location data into an AI, which can then identify region-specific ingredients.

[0046] The discrimination unit can analyze the user's social media activity during discrimination and reflect relevant ingredients and dishes in the discrimination results. For example, the discrimination unit can identify relevant ingredients based on photos of dishes shared by the user on social media. The discrimination unit can also analyze posts from cooking accounts that the user follows and identify trending ingredients. Furthermore, the discrimination unit can identify relevant ingredients based on cooking posts that the user has "liked". In this way, by analyzing social media activity, ingredients and dishes based on the user's preferences and trends can be reflected in the discrimination results. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input the user's social media data into AI, and the AI ​​can reflect relevant ingredients and dishes in the discrimination results.

[0047] The list generation unit can generate a more efficient list by referring to the user's past shopping history during list generation. For example, the list generation unit can automatically add necessary ingredients to the list based on the ingredients the user has purchased in the past. The list generation unit can also prioritize adding frequently purchased ingredients to the list based on the user's past shopping history. Furthermore, the list generation unit can analyze the user's past shopping history and add ingredients purchased during specific seasons to the list. In this way, a more efficient shopping list can be generated by referring to past shopping history. Some or all of the above processes in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's past shopping data into AI, and the AI ​​can generate an efficient list.

[0048] The list generation unit can customize the list during generation, taking into account the user's budget and the price of ingredients. For example, the list generation unit can prioritize adding ingredients that can be purchased within the user's budget to the list. It can also add cost-effective ingredients to the list based on ingredient price information. Furthermore, the list generation unit can add necessary ingredients to the list so as not to exceed the user's budget. In this way, a more appropriate shopping list can be provided by taking into account the user's budget and the price of ingredients. Some or all of the above processing in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's budget data into AI, and the AI ​​can generate a list that takes the budget and prices into consideration.

[0049] The list generation unit can add region-specific ingredients to the list, taking into account the user's geographical location information. For example, if the user is in a specific region, the list generation unit can add ingredients that are readily available in that region. Furthermore, if the user is traveling, the list generation unit can add local specialties to the list. Additionally, if the user wants to use local ingredients, the list generation unit can add ingredients produced in that region. This allows for a more personalized shopping list by adding region-specific ingredients based on the user's geographical location information. Some or all of the above processing in the list generation unit may be performed using AI, or not. For example, the list generation unit can input the user's geographical location data into the AI, which can then add region-specific ingredients to the list.

[0050] The list generation unit can analyze the user's social media activity during list generation and reflect relevant ingredients and dishes in the list. For example, the list generation unit can add relevant ingredients to the list based on photos of dishes the user has shared on social media. The list generation unit can also analyze posts from cooking accounts the user follows and add trending ingredients to the list. Furthermore, the list generation unit can add relevant ingredients to the list based on cooking posts the user has "liked". In this way, by analyzing social media activity, ingredients and dishes based on the user's preferences and trends can be reflected in the list. Some or all of the above processing in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's social media data into AI, which can then reflect relevant ingredients and dishes in the list.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display ingredients and dishes that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest ingredients and dishes that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which can then suggest the optimal input method.

[0053] The reception desk can customize input fields considering the user's current health status and nutritional balance. For example, if the user is on a diet, the reception desk will prioritize suggesting low-calorie foods and dishes. It can also suggest foods and dishes rich in specific nutrients if the user wishes to consume them. Furthermore, if the user has allergies, the reception desk can suggest foods and dishes that do not contain those allergens. This allows for the suggestion of more appropriate menus by providing input fields tailored to the user's health status and nutritional balance. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's health data into the AI, which can then customize the optimal input fields.

[0054] The reception desk can add input fields that suggest regionally specific ingredients and dishes, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest ingredients that are readily available in that region. If the user is traveling, the reception desk can also suggest local specialty dishes. Furthermore, if the user wants to use local ingredients, the reception desk can suggest ingredients produced in that region. This allows for the provision of more personalized menus by suggesting regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then suggest regionally specific ingredients and dishes.

[0055] The suggestion unit can suggest more personalized menus by referring to the user's past eating history. For example, the suggestion unit can suggest relevant menus based on dishes the user has enjoyed eating in the past. It can also suggest menus considering ingredients the user has avoided in the past. Furthermore, the suggestion unit can suggest dishes the user enjoys eating in a particular season based on their past eating history. In this way, by referring to past eating history, it is possible to suggest more personalized menus. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past eating data into a generative AI, which can then suggest a personalized menu.

[0056] The suggestion unit can customize menus when making suggestions, taking into account the user's health condition and nutritional balance. For example, if the user is on a diet, the suggestion unit will suggest low-calorie menus. Furthermore, if the user wishes to consume a specific nutrient, the suggestion unit can suggest menus that are rich in that nutrient. In addition, if the user has allergies, the suggestion unit can suggest menus that do not contain allergens. This allows for the provision of menus tailored to the user's health condition and nutritional balance, thereby suggesting more appropriate meals. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's health data into a generation AI, which can then suggest menus that take into account the user's health condition and nutritional balance.

[0057] The discrimination unit can suggest menus while considering the freshness and expiration dates of ingredients in the refrigerator or pantry during the discrimination process. For example, if the expiration date of ingredients in the refrigerator is approaching, the discrimination unit will suggest menus that prioritize the use of those ingredients. The discrimination unit can also suggest menus that make the most of fresh ingredients. Furthermore, if the freshness of the ingredients is low, the discrimination unit can suggest menus that can be enjoyed deliciously by modifying the cooking method. In this way, by considering the freshness and expiration dates of the ingredients, more appropriate menus can be suggested. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input data on the ingredients in the refrigerator into AI, and the AI ​​can suggest menus that take freshness and expiration dates into consideration.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk enters the user's criteria. These criteria include food preferences, desired ingredients, budget, cooking utensils, allergy information, dietary preferences, and dietary restrictions. Step 2: The suggestion unit uses a generation AI to propose the optimal menu based on the conditions entered by the reception unit. For example, if the condition is that the user wants to eat a dish using chicken, the generation AI will suggest various recipes using chicken. It can also suggest menus that take nutritional balance and calories into consideration. Step 3: The discrimination unit uses image recognition to analyze photos of the inside of the refrigerator or pantry and suggests menus that make use of the available ingredients. For example, it can suggest recipes using vegetables and meat found in the refrigerator. It can also use image recognition technology to determine the freshness of the ingredients and suggest menus based on that. Step 4: The list generation unit automatically generates a shopping list of necessary ingredients based on the menu proposed by the suggestion unit. For example, it automatically generates a list of necessary ingredients based on the proposed menu, calculates the quantities, and determines the priority.

[0060] (Example of form 2) The Inspiration Chef, according to an embodiment of the present invention, is a service that uses generative AI to solve the problem of deciding on daily meal menus. The Inspiration Chef significantly reduces the cost (time, effort, and stress) of considering various factors such as ingredient management, budget, and variety when planning meals. Even if you can't think of anything specific you want to eat, the generative AI will ask about the user's food preferences, desired ingredients, and other conditions, and then suggest the most suitable menu. It also features a function where the AI ​​uses image recognition to analyze photos of the refrigerator or pantry and suggests menus that utilize the available ingredients. For example, by suggesting multiple meal menus at once and automatically generating a shopping list of necessary ingredients, it makes daily meal planning easy and efficient, significantly reducing the user's burden. For instance, the user inputs conditions such as food preferences, desired ingredients, budget, and cooking utensils. The generative AI then suggests the most suitable menu based on these conditions. For example, if the user inputs "I want to eat a chicken dish," the generative AI will suggest various chicken recipes. Furthermore, it also has a function where the AI ​​uses image recognition to analyze photos of the refrigerator or pantry and suggests menus that utilize the available ingredients. For example, by suggesting recipes using vegetables and meat already in the refrigerator, it can reduce food waste. It also has a function that suggests multiple meal menus at once and automatically generates a shopping list of necessary ingredients. This allows users to shop efficiently and prepare meals systematically. For instance, when shopping all at once on the weekend, users can create a shopping list based on menus suggested by the generating AI and purchase all necessary ingredients at once. In this way, utilizing the generating AI streamlines meal menu selection, ingredient management, and shopping planning, significantly reducing the user's burden. This service is effective for a variety of target groups, such as busy office workers, housewives overwhelmed with household chores, and students who enjoy cooking at home within a budget. Thus, Inspiration Chef streamlines meal menu selection, ingredient management, and shopping planning, significantly reducing the user's burden.

[0061] The Inspiration Chef according to this embodiment comprises a reception unit, a suggestion unit, a discrimination unit, and a list generation unit. The reception unit accepts user input. User input includes, but is not limited to, examples of food preferences, desired ingredients, budget, and cooking utensils. For example, the reception unit allows the user to input allergy information. The reception unit also allows the user to input their dietary preferences. Furthermore, the reception unit allows the user to input dietary restrictions. The suggestion unit uses a generation AI to propose the optimal menu based on the conditions entered by the reception unit. For example, if the user inputs "I want to eat a dish using chicken," the generation AI will propose various recipes using chicken. The suggestion unit can also use the generation AI to propose nutritionally balanced menus based on the user's conditions. Furthermore, the suggestion unit can use the generation AI to propose menus that take calories into consideration based on the user's conditions. The discrimination unit uses image recognition to analyze photos of the contents of a refrigerator or pantry and proposes menus that make use of the available ingredients. For example, the discrimination unit proposes recipes using vegetables and meat found in the refrigerator. Furthermore, the discrimination unit can use image recognition technology to recognize ingredients in the refrigerator or pantry and suggest menus based on that. In addition, the discrimination unit can use image recognition technology to determine the freshness of ingredients in the refrigerator or pantry and suggest menus based on that. The list generation unit automatically generates a shopping list of necessary ingredients based on the menu suggested by the suggestion unit. For example, the list generation unit automatically generates a list of necessary ingredients based on the suggested menu. The list generation unit can also automatically calculate the quantity of necessary ingredients based on the suggested menu. Furthermore, the list generation unit can also automatically determine the priority of necessary ingredients based on the suggested menu. As a result, the Inspiration Chef according to this embodiment can suggest the optimal menu based on the user's conditions and streamline ingredient management and shopping list generation.

[0062] The reception desk inputs user criteria. These criteria include, but are not limited to, food preferences, desired ingredients, budget, and cooking utensils. For example, the reception desk allows users to input allergy information. It also allows users to input their dietary preferences. Furthermore, it allows users to input dietary restrictions. Specifically, the reception desk is designed to allow users to easily input information through a user interface. For example, using devices such as smartphones, tablets, and PCs, users can input criteria using intuitive forms, checkboxes, and dropdown menus. Regarding allergy information, users can input detailed information about allergies to specific foods or ingredients, and the system will exclude those ingredients from suggested menus based on this information. Regarding dietary preferences, users can select their favorite cuisine genres, specific ingredients, cooking methods, etc., and the system will customize suggested menus based on these selections. Furthermore, regarding dietary restrictions, users can input detailed conditions, such as whether they are on a diet or wish to restrict specific nutrients. This allows the reception desk to collect basic data to respond to the diverse needs of users and suggest menus that are best suited to their individual conditions.

[0063] The suggestion department uses generative AI to propose the most suitable menu based on the conditions entered by the reception department. For example, if a user enters "I want to eat a dish using chicken," the generative AI will suggest various chicken recipes. The suggestion department can also use generative AI to suggest nutritionally balanced menus based on the user's conditions. Furthermore, the suggestion department can use generative AI to suggest menus that take calories into consideration based on the user's conditions. Specifically, the generative AI uses natural language processing technology to analyze the user's input and generate appropriate recipes. For example, if a user enters "low-calorie, high-protein chicken dish," the generative AI searches the database for recipe information and extracts recipes that match the conditions. Furthermore, the generative AI utilizes its knowledge of nutrition to evaluate whether the suggested menu is nutritionally balanced and adjusts the recipe as needed. For example, if there is a deficiency in a particular vitamin or mineral, the generative AI will add ingredients to compensate for it. Also, if there is a calorie restriction, the generative AI will calculate calories and suggest appropriate portions. This allows the proposal department to provide menus that are optimally suited to each user's individual needs, resulting in healthy and satisfying meals.

[0064] The discrimination unit uses image recognition to analyze photos of the inside of refrigerators and pantries and suggests menus that make use of the ingredients in stock. For example, the discrimination unit can suggest recipes using vegetables and meat found in the refrigerator. Furthermore, the discrimination unit can use image recognition technology to recognize ingredients in refrigerators and pantries and suggest menus based on that. In addition, the discrimination unit can use image recognition technology to determine the freshness of ingredients in refrigerators and pantries and suggest menus based on that. Specifically, the discrimination unit analyzes images of the inside of refrigerators and pantries taken using cameras or smartphones. Using image recognition technology, it identifies the type and quantity of ingredients and compares them with a database to understand the inventory status. For example, it can recognize ingredients such as tomatoes, lettuce, and chicken from images of the inside of a refrigerator and suggest recipes using them. It also uses freshness determination technology to analyze the color, shape, and texture of ingredients and evaluate their freshness. For example, if the color of vegetables has changed or discoloration is visible on the surface of meat, it determines that the freshness has decreased and suggests recipes that recommend consuming the ingredients sooner. This allows the discrimination unit to effectively utilize stock ingredients, reduce waste, and provide users with optimal menus.

[0065] The list generation unit automatically generates a shopping list of necessary ingredients based on the menu suggested by the suggestion unit. For example, the list generation unit automatically generates a list of necessary ingredients based on the suggested menu. The list generation unit can also automatically calculate the quantity of necessary ingredients based on the suggested menu. Furthermore, the list generation unit can automatically determine the priority of necessary ingredients based on the suggested menu. Specifically, the list generation unit analyzes the recipe information of the suggested menu and lists the necessary ingredients and their quantities. For example, if the suggested menu is "chicken stewed in tomato sauce," ingredients such as chicken, tomatoes, onions, garlic, and olive oil will be added to the list. The list generation unit also considers the user's inventory information and excludes ingredients already in the refrigerator or pantry to prevent unnecessary purchases. Furthermore, the list generation unit considers freshness and expiration dates when determining the priority of ingredients. For example, ingredients that need to be consumed soon are added to the list first, while ingredients that can be stored for a long time are added later. In this way, the list generation unit can provide an efficient and waste-free shopping list, improving the user's shopping experience.

[0066] The suggestion unit can suggest multiple meal menus at once. For example, it can suggest a week's worth of meal menus at once. It can also suggest three days' worth of meal menus at once. Furthermore, it can suggest a month's worth of meal menus at once. This streamlines meal planning by suggesting multiple meal menus at once. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input user conditions into a generation AI, which can then suggest multiple meal menus.

[0067] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of conditions. This allows for a more user-friendly interface by adjusting the display of the input interface 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and adjust the interface based on the result.

[0068] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display ingredients and dishes that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest ingredients and dishes that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which can then suggest the optimal input method.

[0069] The reception desk can customize input fields considering the user's current health status and nutritional balance. For example, if the user is on a diet, the reception desk will prioritize suggesting low-calorie foods and dishes. It can also suggest foods and dishes rich in specific nutrients if the user wishes to consume them. Furthermore, if the user has allergies, the reception desk can suggest foods and dishes that do not contain those allergens. This allows for the suggestion of more appropriate menus by providing input fields tailored to the user's health status and nutritional balance. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's health data into the AI, which can then customize the optimal input fields.

[0070] The reception desk can estimate the user's emotions and prioritize input fields based on the estimated emotions. For example, if the user is tired, the reception desk may prioritize displaying the easiest input fields. It may also prioritize displaying more detailed input fields if the user is excited. Furthermore, if the user is relaxed, it may prioritize displaying customizable input fields. This allows for a more user-friendly interface by prioritizing input fields 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and prioritize input fields based on the result.

[0071] The reception desk can add input fields that suggest regionally specific ingredients and dishes, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest ingredients that are readily available in that region. If the user is traveling, the reception desk can also suggest local specialty dishes. Furthermore, if the user wants to use local ingredients, the reception desk can suggest ingredients produced in that region. This allows for the provision of more personalized menus by suggesting regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then suggest regionally specific ingredients and dishes.

[0072] The reception desk can analyze the user's social media activity and reflect relevant food preferences and trends in the input fields. For example, the reception desk can suggest relevant recipes based on photos of food the user has shared on social media. It can also analyze posts from cooking accounts the user follows and suggest trending dishes. Furthermore, it can suggest relevant ingredients and dishes based on food posts the user has "liked". In this way, by analyzing social media activity, it is possible to suggest menus based on the user's preferences and trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI, which can then analyze relevant food preferences and trends and reflect them in the input fields.

[0073] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy-to-prepare menu. If the user is relaxed, the suggestion unit can also suggest a menu that can be prepared over a longer period of time. Furthermore, if the user is in a hurry, the suggestion unit can suggest a menu that can be prepared quickly. In this way, by adjusting the menu suggestion method according to the user's emotions, a more appropriate menu can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's facial expression data into a generative AI, the generative AI can estimate the emotions, and the menu suggestion method can be adjusted based on the result.

[0074] The suggestion unit can suggest more personalized menus by referring to the user's past eating history. For example, the suggestion unit can suggest relevant menus based on dishes the user has enjoyed eating in the past. It can also suggest menus considering ingredients the user has avoided in the past. Furthermore, the suggestion unit can suggest dishes the user enjoys eating in a particular season based on their past eating history. In this way, by referring to past eating history, it is possible to suggest more personalized menus. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past eating data into a generative AI, which can then suggest a personalized menu.

[0075] The suggestion unit can customize menus when making suggestions, taking into account the user's health condition and nutritional balance. For example, if the user is on a diet, the suggestion unit will suggest low-calorie menus. Furthermore, if the user wishes to consume a specific nutrient, the suggestion unit can suggest menus that are rich in that nutrient. In addition, if the user has allergies, the suggestion unit can suggest menus that do not contain allergens. This allows for the provision of menus tailored to the user's health condition and nutritional balance, thereby suggesting more appropriate meals. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's health data into a generation AI, which can then suggest menus that take into account the user's health condition and nutritional balance.

[0076] The suggestion unit can estimate the user's emotions and determine menu priorities based on those emotions. For example, if the user is tired, the suggestion unit will prioritize suggesting the easiest menu items to prepare. If the user is excited, the suggestion unit can also prioritize suggesting challenging menu items. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggesting menu items that can be prepared over time. This allows for the provision of more appropriate menu items by prioritizing them 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 processing described above in the suggestion unit may be performed using or without generative AI. For example, the suggestion unit can input user facial expression data into a generative AI, which will estimate the emotions and determine menu priorities based on the results.

[0077] The suggestion unit can suggest regionally specific dishes, taking into account the user's geographical location when making suggestions. For example, if the user is in a specific region, the suggestion unit can suggest local specialty dishes. Furthermore, if the user is traveling, the suggestion unit can suggest traditional dishes of that region. Additionally, if the user wishes to use local ingredients, the suggestion unit can suggest dishes produced in that region. This allows for a more personalized menu by suggesting regionally specific dishes based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input the user's geographical location data into a generative AI, which can then suggest regionally specific dishes.

[0078] The suggestion unit can analyze the user's social media activity and suggest relevant trends and popular menu items when making suggestions. For example, the suggestion unit can suggest relevant recipes based on photos of food shared by the user on social media. It can also analyze posts from cooking accounts followed by the user and suggest trending dishes. Furthermore, the suggestion unit can suggest relevant ingredients and dishes based on food posts that the user has "liked." In this way, by analyzing social media activity, it is possible to suggest menus based on the user's preferences and trends. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's social media data into a generative AI, which can then suggest relevant trends and popular menu items.

[0079] The discrimination unit can estimate the user's emotions and adjust the accuracy of image recognition based on the estimated emotions. For example, if the user is stressed, the discrimination unit can perform a simple image recognition and provide results quickly. If the user is relaxed, the discrimination unit can perform a detailed image recognition and provide highly accurate results. Furthermore, if the user is in a hurry, the discrimination unit can identify only the most important ingredients and provide results quickly. In this way, by adjusting the accuracy of image recognition according to the user's emotions, more appropriate recognition results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input the user's facial expression data into a generative AI, the generative AI can estimate emotions, and the accuracy of image recognition can be adjusted based on the result.

[0080] The discrimination unit can suggest menus while considering the freshness and expiration dates of ingredients in the refrigerator or pantry during the discrimination process. For example, if the expiration date of ingredients in the refrigerator is approaching, the discrimination unit will suggest menus that prioritize the use of those ingredients. The discrimination unit can also suggest menus that make the most of fresh ingredients. Furthermore, if the freshness of the ingredients is low, the discrimination unit can suggest menus that can be enjoyed deliciously by modifying the cooking method. In this way, by considering the freshness and expiration dates of the ingredients, more appropriate menus can be suggested. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input data on the ingredients in the refrigerator into AI, and the AI ​​can suggest menus that take freshness and expiration dates into consideration.

[0081] The discrimination unit can customize menus by considering the nutritional value and calorie information of ingredients during discrimination. For example, the discrimination unit can suggest a balanced menu based on the nutritional value of the ingredients. It can also suggest a low-calorie menu based on the calorie information of the ingredients. Furthermore, the discrimination unit can suggest a menu that makes the most of ingredients that are rich in specific nutrients. In this way, by considering the nutritional value and calorie information of the ingredients, it is possible to suggest a more balanced menu. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input nutritional value data of ingredients into AI, and the AI ​​can suggest a menu that takes nutritional value and calorie information into consideration.

[0082] The discrimination unit can estimate the user's emotions and adjust the display method of the discrimination result based on the estimated user emotions. For example, if the user is tense, the discrimination unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise display method. By adjusting the display method of the discrimination result according to the user's emotions, a more user-friendly interface can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input user facial expression data into a generative AI, the generative AI can estimate emotions, and the display method of the discrimination result can be adjusted based on the result.

[0083] The discrimination unit can prioritize identifying region-specific ingredients by considering the user's geographical location information during the discrimination process. For example, if the user is in a specific region, the discrimination unit will prioritize identifying ingredients that are readily available in that region. Furthermore, if the user is traveling, the discrimination unit can prioritize identifying local specialties of that region. Additionally, if the user wishes to use local ingredients, the discrimination unit can prioritize identifying ingredients produced in that region. This allows for the provision of more personalized menus by prioritizing region-specific ingredients based on the user's geographical location information. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input the user's geographical location data into an AI, which can then identify region-specific ingredients.

[0084] The discrimination unit can analyze the user's social media activity during discrimination and reflect relevant ingredients and dishes in the discrimination results. For example, the discrimination unit can identify relevant ingredients based on photos of dishes shared by the user on social media. The discrimination unit can also analyze posts from cooking accounts that the user follows and identify trending ingredients. Furthermore, the discrimination unit can identify relevant ingredients based on cooking posts that the user has "liked". In this way, by analyzing social media activity, ingredients and dishes based on the user's preferences and trends can be reflected in the discrimination results. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input the user's social media data into AI, and the AI ​​can reflect relevant ingredients and dishes in the discrimination results.

[0085] The list generation unit can estimate the user's emotions and adjust how the shopping list is displayed based on the estimated emotions. For example, if the user is stressed, the list generation unit can provide a simple and highly visible list. If the user is relaxed, the list generation unit can also provide a list with more detailed information. Furthermore, if the user is in a hurry, the list generation unit can provide a concise list. In this way, by adjusting how the shopping list is displayed according to the user's emotions, a more user-friendly list can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotions, and the shopping list display method can be adjusted based on the result.

[0086] The list generation unit can generate a more efficient list by referring to the user's past shopping history during list generation. For example, the list generation unit can automatically add necessary ingredients to the list based on the ingredients the user has purchased in the past. The list generation unit can also prioritize adding frequently purchased ingredients to the list based on the user's past shopping history. Furthermore, the list generation unit can analyze the user's past shopping history and add ingredients purchased during specific seasons to the list. In this way, a more efficient shopping list can be generated by referring to past shopping history. Some or all of the above processes in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's past shopping data into AI, and the AI ​​can generate an efficient list.

[0087] The list generation unit can customize the list during generation, taking into account the user's budget and the price of ingredients. For example, the list generation unit can prioritize adding ingredients that can be purchased within the user's budget to the list. It can also add cost-effective ingredients to the list based on ingredient price information. Furthermore, the list generation unit can add necessary ingredients to the list so as not to exceed the user's budget. In this way, a more appropriate shopping list can be provided by taking into account the user's budget and the price of ingredients. Some or all of the above processing in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's budget data into AI, and the AI ​​can generate a list that takes the budget and prices into consideration.

[0088] The list generation unit can estimate the user's emotions and determine the priority of the shopping list based on the estimated emotions. For example, if the user is tired, the list generation unit will prioritize displaying the most important ingredients on the list. It can also prioritize displaying lists containing detailed information if the user is excited. Furthermore, if the user is relaxed, it can prioritize displaying customizable lists. This allows for more efficient shopping by prioritizing the shopping list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the list generation unit may be performed using AI or not. For example, the list generation unit can input user facial expression data into a generative AI, which will estimate the emotions and determine the priority of the shopping list based on the result.

[0089] The list generation unit can add region-specific ingredients to the list, taking into account the user's geographical location information. For example, if the user is in a specific region, the list generation unit can add ingredients that are readily available in that region. Furthermore, if the user is traveling, the list generation unit can add local specialties to the list. Additionally, if the user wants to use local ingredients, the list generation unit can add ingredients produced in that region. This allows for a more personalized shopping list by adding region-specific ingredients based on the user's geographical location information. Some or all of the above processing in the list generation unit may be performed using AI, or not. For example, the list generation unit can input the user's geographical location data into the AI, which can then add region-specific ingredients to the list.

[0090] The list generation unit can analyze the user's social media activity during list generation and reflect relevant ingredients and dishes in the list. For example, the list generation unit can add relevant ingredients to the list based on photos of dishes the user has shared on social media. The list generation unit can also analyze posts from cooking accounts the user follows and add trending ingredients to the list. Furthermore, the list generation unit can add relevant ingredients to the list based on cooking posts the user has "liked". In this way, by analyzing social media activity, ingredients and dishes based on the user's preferences and trends can be reflected in the list. Some or all of the above processing in the list generation unit may be performed using AI or not. For example, the list generation unit can input the user's social media data into AI, which can then reflect relevant ingredients and dishes in the list.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple and easy-to-prepare menu. If the user is relaxed, the suggestion unit can also suggest a menu that can be prepared over a longer period of time. Furthermore, if the user is in a hurry, the suggestion unit can suggest a menu that can be prepared quickly. In this way, by adjusting the menu suggestion method according to the user's emotions, a more appropriate menu can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's facial expression data into a generative AI, the generative AI can estimate the emotions, and the menu suggestion method can be adjusted based on the result.

[0093] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display ingredients and dishes that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest ingredients and dishes that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which can then suggest the optimal input method.

[0094] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of conditions. This allows for a more user-friendly interface by adjusting the display of the input interface 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and adjust the interface based on the result.

[0095] The reception desk can customize input fields considering the user's current health status and nutritional balance. For example, if the user is on a diet, the reception desk will prioritize suggesting low-calorie foods and dishes. It can also suggest foods and dishes rich in specific nutrients if the user wishes to consume them. Furthermore, if the user has allergies, the reception desk can suggest foods and dishes that do not contain those allergens. This allows for the suggestion of more appropriate menus by providing input fields tailored to the user's health status and nutritional balance. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's health data into the AI, which can then customize the optimal input fields.

[0096] The reception desk can estimate the user's emotions and prioritize input fields based on the estimated emotions. For example, if the user is tired, the reception desk may prioritize displaying the easiest input fields. It may also prioritize displaying more detailed input fields if the user is excited. Furthermore, if the user is relaxed, it may prioritize displaying customizable input fields. This allows for a more user-friendly interface by prioritizing input fields 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and prioritize input fields based on the result.

[0097] The reception desk can add input fields that suggest regionally specific ingredients and dishes, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest ingredients that are readily available in that region. If the user is traveling, the reception desk can also suggest local specialty dishes. Furthermore, if the user wants to use local ingredients, the reception desk can suggest ingredients produced in that region. This allows for the provision of more personalized menus by suggesting regionally specific ingredients and dishes based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then suggest regionally specific ingredients and dishes.

[0098] The suggestion unit can suggest more personalized menus by referring to the user's past eating history. For example, the suggestion unit can suggest relevant menus based on dishes the user has enjoyed eating in the past. It can also suggest menus considering ingredients the user has avoided in the past. Furthermore, the suggestion unit can suggest dishes the user enjoys eating in a particular season based on their past eating history. In this way, by referring to past eating history, it is possible to suggest more personalized menus. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's past eating data into a generative AI, which can then suggest a personalized menu.

[0099] The suggestion unit can customize menus when making suggestions, taking into account the user's health condition and nutritional balance. For example, if the user is on a diet, the suggestion unit will suggest low-calorie menus. Furthermore, if the user wishes to consume a specific nutrient, the suggestion unit can suggest menus that are rich in that nutrient. In addition, if the user has allergies, the suggestion unit can suggest menus that do not contain allergens. This allows for the provision of menus tailored to the user's health condition and nutritional balance, thereby suggesting more appropriate meals. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's health data into a generation AI, which can then suggest menus that take into account the user's health condition and nutritional balance.

[0100] The discrimination unit can estimate the user's emotions and adjust the accuracy of image recognition based on the estimated emotions. For example, if the user is stressed, the discrimination unit can perform a simple image recognition and provide results quickly. If the user is relaxed, the discrimination unit can perform a detailed image recognition and provide highly accurate results. Furthermore, if the user is in a hurry, the discrimination unit can identify only the most important ingredients and provide results quickly. In this way, by adjusting the accuracy of image recognition according to the user's emotions, more appropriate recognition results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI or not. For example, the discrimination unit can input the user's facial expression data into a generative AI, the generative AI can estimate emotions, and the accuracy of image recognition can be adjusted based on the result.

[0101] The discrimination unit can suggest menus while considering the freshness and expiration dates of ingredients in the refrigerator or pantry during the discrimination process. For example, if the expiration date of ingredients in the refrigerator is approaching, the discrimination unit will suggest menus that prioritize the use of those ingredients. The discrimination unit can also suggest menus that make the most of fresh ingredients. Furthermore, if the freshness of the ingredients is low, the discrimination unit can suggest menus that can be enjoyed deliciously by modifying the cooking method. In this way, by considering the freshness and expiration dates of the ingredients, more appropriate menus can be suggested. Some or all of the above processing in the discrimination unit may be performed using AI, or it may be performed without AI. For example, the discrimination unit can input data on the ingredients in the refrigerator into AI, and the AI ​​can suggest menus that take freshness and expiration dates into consideration.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk enters the user's criteria. These criteria include food preferences, desired ingredients, budget, cooking utensils, allergy information, dietary preferences, and dietary restrictions. Step 2: The suggestion unit uses a generation AI to propose the optimal menu based on the conditions entered by the reception unit. For example, if the condition is that the user wants to eat a dish using chicken, the generation AI will suggest various recipes using chicken. It can also suggest menus that take nutritional balance and calories into consideration. Step 3: The discrimination unit uses image recognition to analyze photos of the inside of the refrigerator or pantry and suggests menus that make use of the available ingredients. For example, it can suggest recipes using vegetables and meat found in the refrigerator. It can also use image recognition technology to determine the freshness of the ingredients and suggest menus based on that. Step 4: The list generation unit automatically generates a shopping list of necessary ingredients based on the menu proposed by the suggestion unit. For example, it automatically generates a list of necessary ingredients based on the proposed menu, calculates the quantities, and determines the priority.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the reception unit, proposal unit, discrimination unit, and list generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which takes user conditions as input. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which proposes the optimal menu using generated AI. The discrimination unit uses, for example, the camera 42 of the smart device 14 to perform image discrimination on photos of the inside of a refrigerator or pantry, and proposes a menu that makes use of the available ingredients. The list generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which automatically generates a shopping list of necessary ingredients based on the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, suggestion unit, discrimination unit, and list generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which takes user conditions as input. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12, which uses generated AI to suggest the optimal menu. The discrimination unit uses the camera 42 of the smart glasses 214 to perform image discrimination on photos of the inside of a refrigerator or pantry and suggests a menu that makes use of the available ingredients. The list generation unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically generates a shopping list of necessary ingredients based on the suggested menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, suggestion unit, discrimination unit, and list generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which takes user conditions as input. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses generated AI to suggest the optimal menu. The discrimination unit uses the camera 42 of the headset terminal 314 to perform image discrimination on photos of the inside of a refrigerator or pantry and suggests a menu that makes use of the available ingredients. The list generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates a shopping list of necessary ingredients based on the suggested menu. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, proposal unit, discrimination unit, and list generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes user conditions as input. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the optimal menu using generated AI. The discrimination unit uses, for example, the camera 42 of the robot 414 to perform image discrimination on photos of the inside of a refrigerator or pantry and proposes a menu that makes use of the available ingredients. The list generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically generates a shopping list of necessary ingredients based on the proposed menu. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) A reception area where users enter their conditions, A proposal unit that proposes the optimal menu based on the conditions entered by the reception unit, A discrimination unit that uses image recognition to identify photos inside refrigerators and pantries, The system includes a list generation unit that automatically generates a shopping list of necessary ingredients based on the menu proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Suggest multiple meal menus at once. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Customize input fields to take into account the user's current health status and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Add an input field that suggests local ingredients and dishes, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze users' social media activity and reflect related food preferences and trends in the input fields. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, It estimates the user's emotions and adjusts the menu suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making suggestions, the system will refer to the user's past meal history to suggest more personalized menus. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, customize the menu considering the user's health condition and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and determines menu priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration and propose regionally specific dishes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and propose relevant trends and popular menu items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of image recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned discrimination unit is When making a decision, we will suggest a menu that takes into account the freshness and expiration date of the ingredients in the refrigerator and pantry. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned discrimination unit is When selecting ingredients, the menu is customized by considering the nutritional value and calorie information of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts how the results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned discrimination unit is During the identification process, the system takes the user's geographical location into consideration and prioritizes identifying regionally specific ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned discrimination unit is During the identification process, the system analyzes the user's social media activity and reflects related ingredients and dishes in the identification result. The system described in Appendix 1, characterized by the features described herein. (Note 21) The list generation unit, It estimates the user's emotions and adjusts how the shopping list is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The list generation unit, When generating lists, the system references the user's past purchase history to create more efficient lists. The system described in Appendix 1, characterized by the features described herein. (Note 23) The list generation unit, When generating a list, customize it to take into account the user's budget and the price of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 24) The list generation unit, It estimates the user's emotions and prioritizes items on the shopping list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The list generation unit, When generating the list, the user's geographical location is taken into consideration, and regionally specific ingredients are added to the list. The system described in Appendix 1, characterized by the features described herein. (Note 26) The list generation unit, When generating lists, the system analyzes users' social media activity and reflects relevant ingredients and dishes in the lists. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their conditions, A proposal unit that proposes the optimal menu based on the conditions entered by the reception unit, A discrimination unit that uses image recognition to identify photos of the inside of refrigerators and pantries, The system includes a list generation unit that automatically generates a shopping list of necessary ingredients based on the menu proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Suggest multiple meal menus at once. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

5. The aforementioned reception unit is Customize input fields to take into account the user's current health status and nutritional balance. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is Add an input field that suggests local ingredients and dishes, taking the user's geographical location into account. The system according to feature 1.

8. The aforementioned reception unit is Analyze users' social media activity and reflect related food preferences and trends in the input fields. The system according to feature 1.

9. The aforementioned proposal section is, It estimates the user's emotions and adjusts the menu suggestion method based on the estimated user emotions. The system according to feature 1.

10. The aforementioned proposal section is, When making suggestions, the system will refer to the user's past meal history to suggest more personalized menus. The system according to feature 1.

Images