system

Abstract

The system according to the embodiment aims to generate appropriate recipes taking into consideration the health information, allergy information, and refrigerator inventory information of family members. [Solution] A system according to an embodiment includes an input unit, an acquisition unit, a generation unit, and a provision unit. The input unit inputs health information or allergy information of family members and dietary needs. The acquisition unit acquires refrigerator inventory information. The generation unit generates a recipe based on the information obtained by the input unit and the acquisition unit. The provision unit provides the recipe generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide recipes that take into account family health information, allergy information, and refrigerator inventory information, and there is room for improvement.

[0005] The system according to the embodiment aims to generate appropriate recipes taking into consideration the health information, allergy information, and refrigerator inventory information of family members. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an acquisition unit, a generation unit, and a provision unit. The input unit inputs health information or allergy information of family members and dietary needs. The acquisition unit acquires refrigerator inventory information. The generation unit generates a recipe based on the information obtained by the input unit and the acquisition unit. The provision unit provides the recipe generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate appropriate recipes taking into account family health information, allergy information, and refrigerator inventory information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A recipe generation system according to an embodiment of the present invention generates appropriate recipes by taking into account the health status, allergies, and dietary needs of each family member, and even taking into account refrigerator inventory. In this recipe generation system, a user inputs information about the family member's health, allergies, and dietary needs, obtains refrigerator inventory information, and a generation AI generates appropriate recipes based on this information. For example, if a family member has a dairy allergy, the user inputs that information. If a family member is undergoing treatment for diabetes, the user also inputs that information. This allows the generation AI to understand each family member's health status, allergies, and dietary needs. Next, refrigerator inventory information is obtained. For example, the user inputs the types and quantities of ingredients in the refrigerator. This allows the generation AI to understand the refrigerator inventory and obtain basic information for generating appropriate recipes. Based on this information, the generation AI generates appropriate recipes. The generation AI considers each family member's health status, allergies, and dietary needs and generates recipes that make the most of the refrigerator inventory. For example, if a family member has a dairy allergy, the system generates a recipe that does not contain dairy products. Similarly, if a family member is undergoing treatment for diabetes, the system generates a low-carb recipe. This allows the whole family to enjoy meals with peace of mind. This system eliminates the need for users to search for recipes themselves. The generation AI automatically generates appropriate recipes, allowing users to provide meals that are suitable for the whole family without any hassle. It also maximizes refrigerator inventory, reducing food waste. For example, generating recipes using leftover vegetables in the refrigerator reduces food waste and saves money. This allows the recipe generation system to generate appropriate recipes that make the most of refrigerator inventory, taking into account each family member's health condition, allergies, and dietary needs.

[0029] A recipe generation system according to an embodiment includes an input unit, an acquisition unit, a generation unit, and a provision unit. The input unit inputs health information, allergy information, and dietary needs of family members. For example, the input unit can input the health information, allergy information, and dietary needs of family members using a smartphone app or a web interface. The input unit can also allow a user to manually input information. The acquisition unit acquires refrigerator inventory information. For example, the acquisition unit can acquire refrigerator inventory information using a sensor built into the refrigerator. The acquisition unit can also allow a user to manually input the refrigerator inventory information. The generation unit generates recipes that make the most of the refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. For example, the generation unit generates recipes that make the most of the refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member using a generation AI. The generation AI can generate recipes using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the generated recipes. For example, the provision unit can provide the generated recipes using a smartphone app or a web interface. The provision unit can also print the generated recipes on paper. As a result, the recipe generation system according to the embodiment can generate and provide appropriate recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs.

[0030] The input unit can input the family's health information, allergy information, and dietary needs using a smartphone app or web interface. The smartphone app or web interface includes, for example, an input form for inputting the family's health information, allergy information, and dietary needs. The input form includes, for example, items such as medical history, current health condition, and doctor's diagnosis. The allergy information includes, for example, items such as food allergies and drug allergies. The dietary needs include, for example, items such as diabetic diet and low-salt diet. This allows the user to easily input the family's health information, allergy information, and dietary needs. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the information input by the user to a generation AI, which can then analyze the information and extract data necessary for recipe generation.

[0031] The acquisition unit can acquire inventory information of the refrigerator using sensors built into the refrigerator. The sensors built into the refrigerator include, for example, a temperature sensor and a weight sensor. The temperature sensor is used to measure the temperature inside the refrigerator and determine the storage state of ingredients. The weight sensor is used to measure the weight of ingredients inside the refrigerator and determine inventory information. This allows the acquisition unit to automatically acquire inventory information of the refrigerator. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the sensors into a generation AI, which analyzes the data and determines inventory information.

[0032] The acquisition unit allows a user to manually input refrigerator inventory information. An input form for a user to manually input refrigerator inventory information includes, for example, items such as the type of ingredient, quantity, and expiration date. The input form includes, for example, input fields such as text boxes and drop-down lists. This allows a user to manually input refrigerator inventory information. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the inventory information entered by the user to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0033] The generation unit can generate recipes that make the most of refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. The generation unit, for example, uses a generation AI to generate recipes that make the most of refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. The generation AI can generate recipes using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates appropriate recipes based on the family member's health information, allergy information, and dietary needs. For example, if a member has a dairy allergy, the generation AI generates a recipe that does not contain dairy products. Also, if a member is undergoing treatment for diabetes, the generation AI generates a low-carbohydrate recipe. This allows all family members to enjoy meals with peace of mind. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the recipe generated by the generation AI to a provision unit, which then provides the recipe to the user.

[0034] The providing unit can provide the generated recipe using a smartphone app or a web interface. The smartphone app or web interface includes, for example, an interface for displaying the generated recipe. The interface displays information such as the recipe title, ingredients, and steps. The providing unit can also print the generated recipe on paper. For example, the providing unit can download and print the generated recipe in PDF format. This allows the user to easily check and use the generated recipe. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate an interface for providing the recipe generated by the generation AI to the user.

[0035] The input unit can add a periodic reminder function for automatically updating the family's health information and allergy information upon input. The input unit, for example, displays a reminder at a time set by the user to prompt the user to update the health information and allergy information. For example, the input unit can set the user to receive a reminder after a specific event (e.g., a regular health checkup). The input unit can also prompt the user to customize the reminder and input specific information (e.g., the discovery of a new allergy). This allows the family's health information and allergy information to be updated periodically. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the timing and content of the reminder display to a generation AI, which then generates the content of the reminder.

[0036] The input unit can provide an auto-completion function that reduces the effort of input by referring to past input history during input. The input unit, for example, automatically completes health information or allergy information previously input by the user. For example, the input unit can automatically suggest related information based on dietary therapy information previously input by the user. The input unit can also prioritize displaying frequently used information based on information previously input by the user. This reduces the effort of input by referring to the past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the past input history to a generation AI, which then generates the content for the auto-completion.

[0037] The input unit may add a voice input function to input information by voice, allowing the user to input information by voice. The input unit may, for example, allow the user to input health information or allergy information by voice. For example, the input unit may allow the user to input the need for a dietary therapy by voice. The input unit may also convert the information input by voice into text and allow the user to confirm it. This allows the user to input information by voice. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may convert the user's voice into text using voice recognition technology and input it into the generation AI.

[0038] The input unit can automatically acquire family health information and allergy information in cooperation with other health management apps when inputting the information. The input unit automatically acquires health information, for example, from a health management app used by the user. For example, the input unit can automatically acquire allergy information from an allergy management app used by the user. The input unit can also automatically acquire dietary therapy information from a dietary therapy app used by the user. This allows automatic acquisition of information in cooperation with other health management apps. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information acquired from other health management apps to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0039] The acquisition unit can improve the accuracy of the sensors used to update the refrigerator's inventory information in real time during acquisition. The acquisition unit, for example, changes the sensors in the refrigerator to more accurate ones to accurately acquire inventory information. For example, the acquisition unit can optimize the placement of the sensors to accurately acquire information on all ingredients in the refrigerator. The acquisition unit can also transmit sensor data to the cloud in real time to constantly maintain up-to-date inventory information. This allows the refrigerator's inventory information to be updated in real time. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the sensors into a generation AI, which analyzes the data and updates the inventory information.

[0040] Upon acquisition, the acquisition unit can store the refrigerator inventory information in the cloud and make it accessible from multiple devices. For example, the acquisition unit can store the refrigerator inventory information in the cloud and make it accessible from a smartphone or tablet. For example, the acquisition unit can share the inventory information stored in the cloud with all family members so that anyone can check the latest information. The acquisition unit can also link the inventory information stored in the cloud with other apps and use it for recipe generation. This makes it possible to store the refrigerator inventory information in the cloud and make it accessible from multiple devices. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the inventory information stored in the cloud to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0041] During acquisition, the acquisition unit can simultaneously acquire inventory information for the pantry and freezer in addition to the refrigerator. The acquisition unit, for example, works in conjunction with a sensor in the refrigerator to acquire inventory information for the pantry and freezer. For example, the acquisition unit can install dedicated sensors in the pantry and freezer to acquire inventory information. The acquisition unit can also acquire inventory information for the pantry and freezer manually entered by the user. This makes it possible to simultaneously acquire inventory information for the pantry and freezer in addition to the refrigerator. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input inventory information for the pantry and freezer to the generation AI, which can analyze the information and extract data necessary for recipe generation.

[0042] The acquisition unit can enable easy acquisition of ingredient information using a barcode scanner at the time of acquisition. The acquisition unit, for example, enables a user to easily acquire ingredient information using a barcode scanner. For example, the acquisition unit can automatically reflect the information acquired by the barcode scanner in inventory information. The acquisition unit can also store the information acquired by the barcode scanner in the cloud so that it can be accessed from other devices. This enables easy acquisition of ingredient information using a barcode scanner. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the information acquired by the barcode scanner into a generation AI, which analyzes the information and updates the inventory information.

[0043] During generation, the generation unit can refer to past recipe generation history and preferentially generate recipes that match the user's preferences. The generation unit, for example, generates similar recipes based on recipes that the user has previously liked. For example, the generation unit can take into account ingredients that the user has avoided in the past and generate recipes that do not include those ingredients. The generation unit can also preferentially generate recipes that match the user's preferences based on data on recipes that the user has previously rated. This makes it possible to generate recipes that match the user's preferences by referring to the past recipe generation history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past recipe generation history into a generation AI, which can analyze the information and generate recipes that match the user's preferences.

[0044] The generation unit can add an algorithm for generating a recipe according to the season and weather at the time of generation. The generation unit, for example, generates a recipe using ingredients according to the season. For example, the generation unit can generate a recipe according to the weather (e.g., hot dishes for cold days). The generation unit can also generate a recipe that takes into account how to store ingredients according to the season and weather. This makes it possible to generate recipes according to the season and weather. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the season and weather into the generation AI, which analyzes the information and generates a recipe.

[0045] The generation unit can provide an option for generating a recipe that preferentially uses a specific ingredient at the time of generation. For example, the generation unit generates a recipe that uses a specific ingredient when the user specifies that ingredient. For example, the generation unit can generate a recipe that preferentially uses a specific ingredient based on refrigerator inventory information. The generation unit can also generate a recipe that does not include an ingredient that the user specifies to which they are allergic. This makes it possible to generate a recipe that preferentially uses a specific ingredient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input an option for generating a recipe that preferentially uses a specific ingredient into the generation AI, and the generation AI can analyze the information and generate the recipe.

[0046] The generation unit can suggest a balanced meal by referring to the user's dietary history during generation. The generation unit generates a nutritionally balanced recipe based on, for example, the user's past dietary history. For example, if the generation unit determines from the user's dietary history that a specific nutrient is lacking, the generation unit can generate a recipe that supplements that nutrient. The generation unit can also analyze the user's dietary history and generate a recipe according to their health condition. This makes it possible to suggest a balanced meal by referring to the user's dietary history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's dietary history into the generation AI, which can analyze the information and generate a balanced recipe.

[0047] The providing unit can provide multimedia content including detailed recipe steps and videos at the time of providing. The providing unit, for example, provides a video that explains the recipe steps in detail. For example, the providing unit can explain the recipe steps with photos. The providing unit can also provide the recipe steps with audio guidance. This makes it possible to provide multimedia content including detailed recipe steps and videos. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate the recipe steps as videos, photos, or audio guidance using a generation AI and provide them to the user.

[0048] The providing unit can add a recipe rating function when providing the recipe, allowing users to rate the recipe. The providing unit, for example, provides a function that allows users to rate recipes with stars. For example, the providing unit can provide a function that allows users to add comments to recipes and share them with other users. The providing unit can also provide a function that allows users to recommend recipes to other users based on their recipe ratings. This allows users to rate recipes. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze user ratings and comments using a generation AI and suggest recommended recipes to other users.

[0049] The providing unit can add a recipe sharing function when providing the recipe, allowing the user to share the recipe with other users. The providing unit, for example, provides a function that allows the user to share the recipe through a social networking site (SNS). For example, the providing unit can provide a function that allows the user to share the recipe through email or a messaging app. The providing unit can also provide a function that allows the user to generate a two-dimensional code (for example, a QR code (registered trademark)) so that other users can easily access the recipe. This allows the user to share the recipe with other users. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can optimize a recipe sharing method using a generation AI and provide the recipe to the user.

[0050] The providing unit can add a recipe printing function when providing the recipe, allowing the user to print the recipe on paper. The providing unit can provide, for example, a function that allows the user to download the recipe in PDF format and print it. For example, the providing unit can provide a function that allows the user to directly send the recipe to a printer and print it. The providing unit can also provide a function that allows the user to select and print a portion of the recipe. This allows the user to print the recipe on paper. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can optimize the printing format of the recipe using generation AI and provide it to the user.

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

[0052] The recipe generation system can also generate recipes that take into account nutrient balance. For example, if a user is deficient in a particular nutrient (e.g., vitamin D, calcium, iron, etc.) based on health information input by the user, the generation unit can generate a recipe that supplements that nutrient. The generation unit can also generate an appropriate recipe by taking into account the nutritional balance according to the user's age and gender. Furthermore, the generation unit can generate a recipe that prevents the user from consuming an excessive amount of a particular nutrient. This allows the user to maintain a healthy diet.

[0053] The input unit can be added with a function that allows the user to input their ingredient preferences and dislikes. For example, if the user likes a particular ingredient (e.g., broccoli, tomato, fish, etc.), they can input that information. They can also input ingredients they dislike (e.g., bell peppers, eggplant, shellfish, etc.). This allows the generation unit to generate recipes that take into account the user's preferences and dislikes. Furthermore, the input unit can also input the reason why the user avoids a particular ingredient (e.g., allergy, texture, taste, etc.). This allows the generation unit to grasp the user's ingredient preferences in more detail and generate an appropriate recipe.

[0054] The acquisition unit can take into account the freshness and expiration date of ingredients when acquiring refrigerator inventory information. For example, the acquisition unit can measure the freshness of ingredients using sensors inside the refrigerator and generate recipes that prioritize using ingredients that have lost their freshness. The acquisition unit can also grasp the expiration date of ingredients and generate recipes that prioritize using ingredients that are close to their expiration date. Furthermore, the acquisition unit can also consider the storage state of ingredients (e.g., frozen, refrigerated, room temperature, etc.) and suggest appropriate storage methods. This reduces food waste and is economical.

[0055] The providing unit can add a function that allows a user to order necessary ingredients online when creating a recipe. For example, the providing unit can generate a list of necessary ingredients based on the created recipe, and the user can send the list to an online shopping site. The providing unit can also automatically add ingredients selected by the user to a cart to simplify the ordering process. Furthermore, the providing unit can suggest alternatives when the user selects a specific ingredient. This allows the user to obtain the necessary ingredients without hassle.

[0056] When a user generates a recipe using specific ingredients, the generation unit can display the nutritional information and health benefits of the ingredients. For example, the generation unit can display the nutritional components (e.g., vitamins, minerals, protein, etc.) of the ingredients selected by the user. The generation unit can also display the health benefits of the ingredients (e.g., immune system improvement, fatigue recovery, diet effects, etc.). Furthermore, the generation unit can suggest ingredients that are suitable for the user to achieve specific health goals (e.g., weight loss, muscle building, blood pressure management, etc.). This allows the user to select healthy meals.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input unit inputs the family's health information, allergy information, and dietary needs. For example, the input unit can input the family's health information, allergy information, and dietary needs using a smartphone app or a web interface. The user can also input the information manually. Step 2: The acquisition unit acquires the refrigerator inventory information. For example, the acquisition unit can acquire the refrigerator inventory information using a sensor built into the refrigerator. Alternatively, the user can manually input the refrigerator inventory information. Step 3: The generator generates recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs. For example, the generator uses a generation AI to generate recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs. The generation AI can generate recipes using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the generated recipe. For example, the providing unit can provide the generated recipe using a smartphone app or a web interface. The generated recipe can also be printed on paper.

[0059] (Example 2) A recipe generation system according to an embodiment of the present invention generates appropriate recipes by taking into account the health status, allergies, and dietary needs of each family member, and even taking into account refrigerator inventory. In this recipe generation system, a user inputs information about the family member's health, allergies, and dietary needs, obtains refrigerator inventory information, and a generation AI generates appropriate recipes based on this information. For example, if a family member has a dairy allergy, the user inputs that information. If a family member is undergoing treatment for diabetes, the user also inputs that information. This allows the generation AI to understand each family member's health status, allergies, and dietary needs. Next, refrigerator inventory information is obtained. For example, the user inputs the types and quantities of ingredients in the refrigerator. This allows the generation AI to understand the refrigerator inventory and obtain basic information for generating appropriate recipes. Based on this information, the generation AI generates appropriate recipes. The generation AI considers each family member's health status, allergies, and dietary needs and generates recipes that make the most of the refrigerator inventory. For example, if a family member has a dairy allergy, the system generates a recipe that does not contain dairy products. Similarly, if a family member is undergoing treatment for diabetes, the system generates a low-carb recipe. This allows the whole family to enjoy meals with peace of mind. This system eliminates the need for users to search for recipes themselves. The generation AI automatically generates appropriate recipes, allowing users to provide meals that are suitable for the whole family without any hassle. It also maximizes refrigerator inventory, reducing food waste. For example, generating recipes using leftover vegetables in the refrigerator reduces food waste and saves money. This allows the recipe generation system to generate appropriate recipes that make the most of refrigerator inventory, taking into account each family member's health condition, allergies, and dietary needs.

[0060] A recipe generation system according to an embodiment includes an input unit, an acquisition unit, a generation unit, and a provision unit. The input unit inputs health information, allergy information, and dietary needs of family members. For example, the input unit can input the health information, allergy information, and dietary needs of family members using a smartphone app or a web interface. The input unit can also allow a user to manually input information. The acquisition unit acquires refrigerator inventory information. For example, the acquisition unit can acquire refrigerator inventory information using a sensor built into the refrigerator. The acquisition unit can also allow a user to manually input the refrigerator inventory information. The generation unit generates recipes that make the most of the refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. For example, the generation unit generates recipes that make the most of the refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member using a generation AI. The generation AI can generate recipes using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the generated recipes. For example, the provision unit can provide the generated recipes using a smartphone app or a web interface. The provision unit can also print the generated recipes on paper. As a result, the recipe generation system according to the embodiment can generate and provide appropriate recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs.

[0061] The input unit can input the family's health information, allergy information, and dietary needs using a smartphone app or web interface. The smartphone app or web interface includes, for example, an input form for inputting the family's health information, allergy information, and dietary needs. The input form includes, for example, items such as medical history, current health condition, and doctor's diagnosis. The allergy information includes, for example, items such as food allergies and drug allergies. The dietary needs include, for example, items such as diabetic diet and low-salt diet. This allows the user to easily input the family's health information, allergy information, and dietary needs. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the information input by the user to a generation AI, which can then analyze the information and extract data necessary for recipe generation.

[0062] The acquisition unit can acquire inventory information of the refrigerator using sensors built into the refrigerator. The sensors built into the refrigerator include, for example, a temperature sensor and a weight sensor. The temperature sensor is used to measure the temperature inside the refrigerator and determine the storage state of ingredients. The weight sensor is used to measure the weight of ingredients inside the refrigerator and determine inventory information. This allows the acquisition unit to automatically acquire inventory information of the refrigerator. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the sensors into a generation AI, which analyzes the data and determines inventory information.

[0063] The acquisition unit allows a user to manually input refrigerator inventory information. An input form for a user to manually input refrigerator inventory information includes, for example, items such as the type of ingredient, quantity, and expiration date. The input form includes, for example, input fields such as text boxes and drop-down lists. This allows a user to manually input refrigerator inventory information. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the inventory information entered by the user to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0064] The generation unit can generate recipes that make the most of refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. The generation unit, for example, uses a generation AI to generate recipes that make the most of refrigerator inventory, taking into account the health conditions, allergies, and dietary needs of each family member. The generation AI can generate recipes using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates appropriate recipes based on the family member's health information, allergy information, and dietary needs. For example, if a member has a dairy allergy, the generation AI generates a recipe that does not contain dairy products. Also, if a member is undergoing treatment for diabetes, the generation AI generates a low-carbohydrate recipe. This allows all family members to enjoy meals with peace of mind. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the recipe generated by the generation AI to a provision unit, which then provides the recipe to the user.

[0065] The providing unit can provide the generated recipe using a smartphone app or a web interface. The smartphone app or web interface includes, for example, an interface for displaying the generated recipe. The interface displays information such as the recipe title, ingredients, and steps. The providing unit can also print the generated recipe on paper. For example, the providing unit can download and print the generated recipe in PDF format. This allows the user to easily check and use the generated recipe. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate an interface for providing the recipe generated by the generation AI to the user.

[0066] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, the input unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expressions and adjusts the design of the input interface. The input unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the design of the input interface. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations and adjusts the design of the input interface. This makes it possible to adjust the design of the input interface according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0067] The input unit can add a periodic reminder function for automatically updating the family's health information and allergy information upon input. The input unit, for example, displays a reminder at a time set by the user to prompt the user to update the health information and allergy information. For example, the input unit can set the user to receive a reminder after a specific event (e.g., a regular health checkup). The input unit can also prompt the user to customize the reminder and input specific information (e.g., the discovery of a new allergy). This allows the family's health information and allergy information to be updated periodically. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the timing and content of the reminder display to a generation AI, which then generates the content of the reminder.

[0068] The input unit can provide an auto-completion function that reduces the effort of input by referring to past input history during input. The input unit, for example, automatically completes health information or allergy information previously input by the user. For example, the input unit can automatically suggest related information based on dietary therapy information previously input by the user. The input unit can also prioritize displaying frequently used information based on information previously input by the user. This reduces the effort of input by referring to the past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the past input history to a generation AI, which then generates the content for the auto-completion.

[0069] The input unit can estimate the user's emotions and determine the priority of inputs based on the estimated user emotions. For example, the input unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expressions and determines the priority of inputs. The input unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of inputs. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations and determines the priority of inputs. This makes it possible to determine the priority of inputs according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0070] The input unit may add a voice input function to input information by voice, allowing the user to input information by voice. The input unit may, for example, allow the user to input health information or allergy information by voice. For example, the input unit may allow the user to input the need for a dietary therapy by voice. The input unit may also convert the information input by voice into text and allow the user to confirm it. This allows the user to input information by voice. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may convert the user's voice into text using voice recognition technology and input it into the generation AI.

[0071] The input unit can automatically acquire family health information and allergy information in cooperation with other health management apps when inputting the information. The input unit automatically acquires health information, for example, from a health management app used by the user. For example, the input unit can automatically acquire allergy information from an allergy management app used by the user. The input unit can also automatically acquire dietary therapy information from a dietary therapy app used by the user. This allows automatic acquisition of information in cooperation with other health management apps. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input information acquired from other health management apps to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0072] The acquisition unit can estimate the user's emotions and adjust the frequency of inventory information acquisition based on the estimated user emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression and adjusts the frequency of inventory information acquisition. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the frequency of inventory information acquisition. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on heart rate fluctuations and adjusts the frequency of inventory information acquisition. This makes it possible to adjust the frequency of inventory information acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The acquisition unit can improve the accuracy of the sensors used to update the refrigerator's inventory information in real time during acquisition. The acquisition unit, for example, changes the sensors in the refrigerator to more accurate ones to accurately acquire inventory information. For example, the acquisition unit can optimize the placement of the sensors to accurately acquire information on all ingredients in the refrigerator. The acquisition unit can also transmit sensor data to the cloud in real time to constantly maintain up-to-date inventory information. This allows the refrigerator's inventory information to be updated in real time. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired from the sensors into a generation AI, which analyzes the data and updates the inventory information.

[0074] Upon acquisition, the acquisition unit can store the refrigerator inventory information in the cloud and make it accessible from multiple devices. For example, the acquisition unit can store the refrigerator inventory information in the cloud and make it accessible from a smartphone or tablet. For example, the acquisition unit can share the inventory information stored in the cloud with all family members so that anyone can check the latest information. The acquisition unit can also link the inventory information stored in the cloud with other apps and use it for recipe generation. This makes it possible to store the refrigerator inventory information in the cloud and make it accessible from multiple devices. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the inventory information stored in the cloud to a generation AI, which can analyze the information and extract data necessary for recipe generation.

[0075] The acquisition unit can estimate the user's emotions and adjust the display method of the inventory information based on the estimated user emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression and adjusts the display method of the inventory information. The acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the inventory information. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the inventory information. This makes it possible to adjust the display method of the inventory information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] During acquisition, the acquisition unit can simultaneously acquire inventory information for the pantry and freezer in addition to the refrigerator. The acquisition unit, for example, works in conjunction with a sensor in the refrigerator to acquire inventory information for the pantry and freezer. For example, the acquisition unit can install dedicated sensors in the pantry and freezer to acquire inventory information. The acquisition unit can also acquire inventory information for the pantry and freezer manually entered by the user. This makes it possible to simultaneously acquire inventory information for the pantry and freezer in addition to the refrigerator. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input inventory information for the pantry and freezer to the generation AI, which can analyze the information and extract data necessary for recipe generation.

[0077] The acquisition unit can enable easy acquisition of ingredient information using a barcode scanner at the time of acquisition. The acquisition unit, for example, enables a user to easily acquire ingredient information using a barcode scanner. For example, the acquisition unit can automatically reflect the information acquired by the barcode scanner in inventory information. The acquisition unit can also store the information acquired by the barcode scanner in the cloud so that it can be accessed from other devices. This enables easy acquisition of ingredient information using a barcode scanner. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the information acquired by the barcode scanner into a generation AI, which analyzes the information and updates the inventory information.

[0078] The generation unit can estimate the user's emotions and adjust the difficulty level of the recipe based on the estimated user emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the difficulty level of the recipe. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the difficulty level of the recipe. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the difficulty level of the recipe. This makes it possible to adjust the difficulty level of the recipe according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0079] During generation, the generation unit can refer to past recipe generation history and preferentially generate recipes that match the user's preferences. The generation unit, for example, generates similar recipes based on recipes that the user has previously liked. For example, the generation unit can take into account ingredients that the user has avoided in the past and generate recipes that do not include those ingredients. The generation unit can also preferentially generate recipes that match the user's preferences based on data on recipes that the user has previously rated. This makes it possible to generate recipes that match the user's preferences by referring to the past recipe generation history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past recipe generation history into a generation AI, which can analyze the information and generate recipes that match the user's preferences.

[0080] The generation unit can add an algorithm for generating a recipe according to the season and weather at the time of generation. The generation unit, for example, generates a recipe using ingredients according to the season. For example, the generation unit can generate a recipe according to the weather (e.g., hot dishes for cold days). The generation unit can also generate a recipe that takes into account how to store ingredients according to the season and weather. This makes it possible to generate recipes according to the season and weather. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the season and weather into the generation AI, which analyzes the information and generates a recipe.

[0081] The generation unit can estimate the user's emotions and adjust the display order of recipes based on the estimated user emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the display order of recipes. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display order of recipes. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the display order of recipes. This makes it possible to adjust the display order of recipes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The generation unit can provide an option for generating a recipe that preferentially uses a specific ingredient at the time of generation. For example, the generation unit generates a recipe that uses a specific ingredient when the user specifies that ingredient. For example, the generation unit can generate a recipe that preferentially uses a specific ingredient based on refrigerator inventory information. The generation unit can also generate a recipe that does not include an ingredient that the user specifies to which they are allergic. This makes it possible to generate a recipe that preferentially uses a specific ingredient. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input an option for generating a recipe that preferentially uses a specific ingredient into the generation AI, and the generation AI can analyze the information and generate the recipe.

[0083] The generation unit can suggest a balanced meal by referring to the user's dietary history during generation. The generation unit generates a nutritionally balanced recipe based on, for example, the user's past dietary history. For example, if the generation unit determines from the user's dietary history that a specific nutrient is lacking, the generation unit can generate a recipe that supplements that nutrient. The generation unit can also analyze the user's dietary history and generate a recipe according to their health condition. This makes it possible to suggest a balanced meal by referring to the user's dietary history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's dietary history into the generation AI, which can analyze the information and generate a balanced recipe.

[0084] The providing unit can estimate the user's emotions and adjust the recipe presentation method based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the recipe presentation method. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the recipe presentation method. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and adjusts the recipe presentation method. This makes it possible to adjust the recipe presentation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0085] The providing unit can provide multimedia content including detailed recipe steps and videos at the time of providing. The providing unit, for example, provides a video that explains the recipe steps in detail. For example, the providing unit can explain the recipe steps with photos. The providing unit can also provide the recipe steps with audio guidance. This makes it possible to provide multimedia content including detailed recipe steps and videos. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate the recipe steps as videos, photos, or audio guidance using a generation AI and provide them to the user.

[0086] The providing unit can add a recipe rating function when providing the recipe, allowing users to rate the recipe. The providing unit, for example, provides a function that allows users to rate recipes with stars. For example, the providing unit can provide a function that allows users to add comments to recipes and share them with other users. The providing unit can also provide a function that allows users to recommend recipes to other users based on their recipe ratings. This allows users to rate recipes. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze user ratings and comments using a generation AI and suggest recommended recipes to other users.

[0087] The providing unit can estimate the user's emotions and adjust the timing of providing a recipe based on the estimated user's emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the timing of providing the recipe. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of providing the recipe. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the timing of providing the recipe. This makes it possible to adjust the timing of providing the recipe according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0088] The providing unit can add a recipe sharing function when providing the recipe, allowing the user to share the recipe with other users. The providing unit, for example, provides a function that allows the user to share the recipe through a social networking site (SNS). For example, the providing unit can provide a function that allows the user to share the recipe through email or a messaging app. The providing unit can also provide a function that allows the user to generate a two-dimensional code (for example, a QR code) so that other users can easily access the recipe. This allows the user to share the recipe with other users. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can optimize the recipe sharing method using a generation AI and provide the recipe to the user.

[0089] The providing unit can add a recipe printing function when providing the recipe, allowing the user to print the recipe on paper. The providing unit can provide, for example, a function that allows the user to download the recipe in PDF format and print it. For example, the providing unit can provide a function that allows the user to directly send the recipe to a printer and print it. The providing unit can also provide a function that allows the user to select and print a portion of the recipe. This allows the user to print the recipe on paper. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can optimize the printing format of the recipe using generation AI and provide it to the user. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, acquisition unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and allows a user to input health information, allergy information, and dietary needs of their family members. The acquisition unit can acquire refrigerator inventory information using, for example, the camera 42 or a sensor of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate recipe using a generation AI. The provision unit can provide the generated recipe using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, acquisition unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and allows a user to input family health information, allergy information, and dietary needs. The acquisition unit can acquire refrigerator inventory information using, for example, the camera 42 or a sensor of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate recipe using a generation AI. The provision unit can provide the generated recipe using, for example, an output device of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset type terminal 314, and allows the user to input health information, allergy information, and dietary needs of their family. The acquisition unit can acquire refrigerator inventory information using, for example, the camera 42 or a sensor of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate recipe using a generation AI. The provision unit can provide the generated recipe using, for example, an output device of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, acquisition unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414, and allows the user to input health information, allergy information, and dietary needs of the family. The acquisition unit can acquire refrigerator inventory information using, for example, the camera 42 or a sensor of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate recipe using a generation AI. The provision unit can provide the generated recipe using, for example, an output device of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The recipe generation system can also generate recipes that take into account nutrient balance. For example, if a user is deficient in a particular nutrient (e.g., vitamin D, calcium, iron, etc.) based on health information input by the user, the generation unit can generate a recipe that supplements that nutrient. The generation unit can also generate an appropriate recipe by taking into account the nutritional balance according to the user's age and gender. Furthermore, the generation unit can generate a recipe that prevents the user from consuming an excessive amount of a particular nutrient. This allows the user to maintain a healthy diet.

[0092] The input unit can be added with a function that allows the user to input their ingredient preferences and dislikes. For example, if the user likes a particular ingredient (e.g., broccoli, tomato, fish, etc.), they can input that information. They can also input ingredients they dislike (e.g., bell peppers, eggplant, shellfish, etc.). This allows the generation unit to generate recipes that take into account the user's preferences and dislikes. Furthermore, the input unit can also input the reason why the user avoids a particular ingredient (e.g., allergy, texture, taste, etc.). This allows the generation unit to grasp the user's ingredient preferences in more detail and generate an appropriate recipe.

[0093] The acquisition unit can take into account the freshness and expiration date of ingredients when acquiring refrigerator inventory information. For example, the acquisition unit can measure the freshness of ingredients using sensors inside the refrigerator and generate recipes that prioritize using ingredients that have lost their freshness. The acquisition unit can also grasp the expiration date of ingredients and generate recipes that prioritize using ingredients that are close to their expiration date. Furthermore, the acquisition unit can also consider the storage state of ingredients (e.g., frozen, refrigerated, room temperature, etc.) and suggest appropriate storage methods. This reduces food waste and is economical.

[0094] The generation unit can estimate the user's emotions and suggest recipe variations based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can suggest recipes that use ingredients with a relaxing effect. Also, if the user is tired, the generation unit can suggest recipes that are suitable for replenishing energy. Furthermore, if the user is feeling a particular emotion (e.g., joy, sadness, anger, etc.), the generation unit can suggest recipes that correspond to that emotion. This allows the user to enjoy meals that correspond to their emotions.

[0095] The providing unit can estimate the user's emotions and customize the recipe providing method based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide the recipe at a slow pace. If the user is in a hurry, the providing unit can provide the recipe briefly. Furthermore, if the user is feeling a particular emotion (e.g., joy, sadness, anger, etc.), the providing unit can select a recipe providing method that corresponds to that emotion. This makes it possible to provide the optimal recipe according to the user's emotions.

[0096] The generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user emotions. For example, if the user is tired, the generation unit can suggest a simple and easy recipe. Alternatively, if the user is relaxed, the generation unit can suggest a recipe that requires a little more effort. Furthermore, if the user is feeling a particular emotion (e.g., joy, sadness, anger, etc.), the generation unit can adjust the difficulty of the recipe according to that emotion. This makes it possible to provide the optimal recipe according to the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the timing of providing the recipe based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide the recipe at a slow pace. If the user is in a hurry, the providing unit can also provide the recipe quickly. Furthermore, if the user is feeling a particular emotion (e.g., joy, sadness, anger, etc.), the providing unit can select the timing of providing the recipe according to that emotion. This makes it possible to provide the optimal recipe according to the user's emotions.

[0098] The generation unit can estimate the user's emotions and adjust the display order of recipes based on the estimated user emotions. For example, if the user is relaxed, the generation unit can display recipes at a slow pace. If the user is in a hurry, the generation unit can display recipes quickly. Furthermore, if the user is feeling a particular emotion (e.g., joy, sadness, anger, etc.), the generation unit can select the display order of recipes according to that emotion. This makes it possible to display recipes optimally according to the user's emotions.

[0099] The providing unit can add a function that allows a user to order necessary ingredients online when creating a recipe. For example, the providing unit can generate a list of necessary ingredients based on the created recipe, and the user can send the list to an online shopping site. The providing unit can also automatically add ingredients selected by the user to a cart to simplify the ordering process. Furthermore, the providing unit can suggest alternatives when the user selects a specific ingredient. This allows the user to obtain the necessary ingredients without hassle.

[0100] When a user generates a recipe using specific ingredients, the generation unit can display the nutritional information and health benefits of the ingredients. For example, the generation unit can display the nutritional components (e.g., vitamins, minerals, protein, etc.) of the ingredients selected by the user. The generation unit can also display the health benefits of the ingredients (e.g., immune system improvement, fatigue recovery, diet effects, etc.). Furthermore, the generation unit can suggest ingredients that are suitable for the user to achieve specific health goals (e.g., weight loss, muscle building, blood pressure management, etc.). This allows the user to select healthy meals.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs the family's health information, allergy information, and dietary needs. For example, the input unit can input the family's health information, allergy information, and dietary needs using a smartphone app or a web interface. The user can also input the information manually. Step 2: The acquisition unit acquires the refrigerator inventory information. For example, the acquisition unit can acquire the refrigerator inventory information using a sensor built into the refrigerator. Alternatively, the user can manually input the refrigerator inventory information. Step 3: The generator generates recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs. For example, the generator uses a generation AI to generate recipes that make the most of the refrigerator's inventory, taking into account each family member's health condition, allergies, and dietary needs. The generation AI can generate recipes using text generation AI (e.g., LLM) or multimodal generation AI. Step 4: The providing unit provides the generated recipe. For example, the providing unit can provide the generated recipe using a smartphone app or a web interface. The generated recipe can also be printed on paper.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting family health information, allergy information, and dietary needs; an acquisition unit that acquires refrigerator inventory information; a generation unit that generates a recipe based on the information obtained by the input unit and the acquisition unit; a providing unit that provides the recipe generated by the generating unit; Equipped with A system characterized by:

2. The input unit Enter family health or allergy information, as well as dietary needs, using a smartphone app or web interface 2. The system of claim 1.

3. The acquisition unit Obtaining refrigerator inventory information using sensors built into the refrigerator 2. The system of claim 1.

4. The acquisition unit The user manually enters the refrigerator inventory information 2. The system of claim 1.

5. The generation unit Generate recipes that take into account each family member's health conditions, allergies, and dietary needs and make the most of what's in your refrigerator 2. The system of claim 1.

6. The providing unit Serve recipes generated using a smartphone app or web interface 2. The system of claim 1.

7. The input unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Add regular reminders to automatically update your family's health and allergy information as you enter it 2. The system of claim 1.

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