system

Abstract

The system according to the embodiment aims to generate appropriate recipes based on information about ingredients in a refrigerator and user information. [Solution] A system according to an embodiment includes an ingredient input unit, a user information input unit, a recipe generation unit, and a recipe providing unit. The ingredient input unit inputs refrigerator ingredient information. The user information input unit inputs user and family information. The recipe generating unit analyzes the information input by the ingredient input unit and the user information input unit and generates a recipe based on specific criteria. The recipe providing unit provides the recipe generated by the recipe generating unit to the user.

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 technology does not adequately generate appropriate recipes based on the ingredients in the refrigerator and family information, and there is room for improvement.

[0005] The system according to the embodiment aims to generate appropriate recipes based on information about ingredients in a refrigerator and user information. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient input unit, a user information input unit, a recipe generation unit, and a recipe providing unit. The ingredient input unit inputs ingredient information for the refrigerator. The user information input unit inputs information about the user and their family. The recipe generating unit analyzes the information input by the ingredient input unit and the user information input unit and generates a recipe based on specific criteria. The recipe providing unit provides the recipe generated by the recipe generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate appropriate recipes based on information about ingredients in the refrigerator and user 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 inputting information about ingredients in a home refrigerator, the user's and family's composition, body size, chronic illnesses, and other factors. In this recipe generation system, a user inputs information about ingredients in the refrigerator, and then inputs other information about the user and family, such as body size and chronic illnesses. This information is input into a generation AI, which analyzes the input information and generates an appropriate recipe. The generated recipe is provided to the user. For example, if the ingredients in the refrigerator are chicken, cabbage, and carrots and the user is on a diet, the generation AI generates a low-calorie, nutritionally balanced recipe. Similarly, if a family member has diabetes, the generation AI generates a low-sugar recipe. In this way, appropriate recipes tailored to the user's needs can be provided. For example, an "ingredient input unit" is provided for inputting information about ingredients in the refrigerator. Multiple input methods, such as barcode scanning and voice input, are supported. Next, a "user information input unit" is provided for inputting information about the user and family, including family composition, body size, chronic illnesses, and other factors. A "recipe generation unit" is provided for analyzing this information and generating an appropriate recipe. The generation AI processes this information and generates the recipe. Finally, we will create a "recipe provider" that provides the generated recipes to the user. We will also add a function for users to rate the recipes, and we are considering a system in which the generation AI continues to learn based on those ratings. This will enable the recipe generation system to generate and provide appropriate recipes based on information about ingredients in the user's refrigerator and family information.

[0029] A recipe generation system according to an embodiment includes an ingredient input unit, a user information input unit, a recipe generation unit, and a recipe provision unit. The ingredient input unit allows a user to input information about ingredients in the refrigerator. The ingredient input unit supports, for example, barcode scanning and voice input. Barcode scanning can read barcodes such as two-dimensional codes (e.g., QR Code (registered trademark)) and UPC codes. Voice input can recognize voice commands and input the names and quantities of ingredients. The user information input unit inputs information about the user and their family. The user information input unit can input information such as family structure, body size, and chronic illnesses. Family structure includes, for example, the number of family members, age, and gender. Body size includes, for example, height, weight, and BMI. Chronic illnesses include, for example, diabetes, high blood pressure, and allergies. The recipe generation unit uses a generation AI to analyze the information input by the ingredient input unit and the user information input unit and generate a recipe based on specific criteria. The generation AI analyzes the input information and generates an appropriate recipe using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Specific criteria include, for example, nutritional balance, cooking time, and ingredient combinations. The recipe providing unit provides the generated recipe to the user. The recipe providing unit displays the recipe through, for example, a web application or a mobile application. The unit also has a function that allows users to rate the recipe, and the generation AI continues to learn based on the user's ratings. This allows the recipe generation system to generate and provide appropriate recipes based on information about ingredients in the user's refrigerator and family information.

[0030] The ingredient input unit can support barcode scanning or voice input. Barcode scanning can read barcodes such as 2D codes and UPC codes. For example, a user can automatically input ingredient information by scanning the barcodes of ingredients in the refrigerator. Voice input can also recognize voice commands and input the names and quantities of ingredients. For example, a user can easily input ingredient information by speaking, for example, "500 grams of chicken." Furthermore, the ingredient input unit can improve user convenience by supporting multiple input methods. For example, both barcode scanning and voice input can be used in combination. This allows a user to easily input ingredient information.

[0031] The user information input unit allows input of information on family composition, body size, and chronic illnesses. Family composition includes, for example, the number of family members, their ages, and their genders. For example, the user can input detailed information on family composition by inputting the number of family members and the ages and genders of each family member. Body size includes, for example, height, weight, and BMI. For example, the user can input the height and weight of themselves or their family members, and the BMI can be calculated automatically. Chronic illnesses include, for example, diabetes, high blood pressure, and allergies. For example, by the user inputting their own or their family members' chronic illnesses, recipes can be generated that match the chronic illnesses. This allows input of detailed information on the user and their family members.

[0032] The recipe generation unit can analyze the input information using a generation AI and generate a recipe based on specific criteria. The generation AI can analyze the input information and generate an appropriate recipe using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Specific criteria include, for example, nutritional balance, cooking time, and ingredient combinations. For example, the generation AI can generate a nutritionally balanced recipe based on the input ingredient information and user information. The generation AI can also generate a recipe that can be cooked in a short time by taking cooking time into account. Furthermore, the generation AI can generate a delicious recipe by taking ingredient combinations into account. This allows the generation AI to generate an appropriate recipe.

[0033] The recipe providing unit can provide the generated recipe to the user. The recipe providing unit displays the recipe through, for example, a web application or a mobile application. For example, the user can view the recipe using a smartphone or tablet. The recipe providing unit also has a function that allows the user to rate the recipe. For example, the user can add star ratings or comments to the recipe. Furthermore, the recipe providing unit has a mechanism that allows the generation AI to continue learning based on the user ratings. For example, the generation AI can collect user rating data and use that data to improve the accuracy of the recipe. This allows the generated recipe to be provided to the user.

[0034] The recipe providing unit can have a function that allows users to rate recipes. For example, the recipe providing unit allows users to give star ratings and comments to recipes. For example, users can try out recipes and rate the results. The recipe providing unit also has a mechanism that allows the generation AI to continue learning based on user ratings. For example, the generation AI can collect user rating data and use that data to improve the accuracy of recipes. This allows users to rate recipes.

[0035] The recipe generation unit allows the generation AI to continue learning based on user ratings. The generation AI can, for example, collect user rating data and use that data to improve the accuracy of the recipe. For example, when a user gives a star rating or comment to a recipe, the generation AI can learn that rating and reflect it in the next recipe generation. This allows the generation AI to continuously learn and improve the accuracy of the recipe.

[0036] The ingredient input unit can acquire freshness information of ingredients and prioritize the input ingredients based on their freshness. For example, when scanning the barcodes of ingredients, the ingredient input unit automatically acquires freshness information and prioritizes displaying ingredients with high freshness. For example, when a user scans the barcodes of ingredients in the refrigerator, the freshness information can be automatically acquired and ingredients with high freshness can be prioritized. The ingredient input unit can also estimate freshness information by inputting the purchase date of ingredients and suggest using less fresh ingredients sooner. Furthermore, the ingredient input unit can analyze freshness information by inputting the storage state of ingredients and determine the optimal order of use. This allows prioritization to be determined based on the freshness of ingredients.

[0037] The ingredient input unit can acquire nutritional value information of ingredients and adjust the importance of the input ingredients based on their nutritional value. For example, when scanning the barcode of an ingredient, the ingredient input unit can automatically acquire nutritional value information and prioritize displaying ingredients with high nutritional value. For example, when a user scans the barcode of an ingredient in the refrigerator, the nutritional value information can be automatically acquired and ingredients with high nutritional value can be prioritized. The ingredient input unit can also analyze the nutritional value information by inputting the nutritional composition table of the ingredient and suggest balanced ingredients. Furthermore, the ingredient input unit can estimate nutritional value information by inputting the type of ingredient and prioritize displaying ingredients that supplement specific nutrients. This allows the importance to be adjusted based on the nutritional value of the ingredient.

[0038] The ingredient input unit can acquire origin information for ingredients and select the input ingredients based on their origin. For example, when scanning the barcode of an ingredient, the ingredient input unit automatically acquires origin information and prioritizes displaying locally produced ingredients. For example, when a user scans the barcode of an ingredient in the refrigerator, the ingredient input unit can automatically acquire origin information and prioritize displaying locally produced ingredients. The ingredient input unit can also prioritize displaying ingredients from a specific region by inputting the origin of the ingredient. Furthermore, the ingredient input unit can analyze the origin information for the ingredient and distinguish between imported ingredients and domestic ingredients. This allows ingredients to be selected based on their origin.

[0039] The ingredient input unit can acquire allergen information for ingredients and filter the input ingredients based on the allergens. For example, when scanning the barcode of an ingredient, the ingredient input unit automatically acquires allergen information and excludes ingredients containing allergens. For example, a user can scan the barcode of an ingredient in a refrigerator to automatically acquire allergen information and exclude ingredients containing allergens. The ingredient input unit can also input allergen information for ingredients and preferentially display ingredients that do not contain allergens. Furthermore, the ingredient input unit can analyze the allergen information for ingredients and display a warning about ingredients that contain allergens. This allows ingredients to be filtered based on the allergen information.

[0040] The user information input unit can acquire the user's past health data and improve the accuracy of the input information based on the health data. The user information input unit can, for example, acquire the user's past health checkup results and improve the accuracy of the input information. For example, a recipe based on a user's health condition can be generated by the user inputting the past health checkup results. The user information input unit can also acquire the user's past food records and improve the relevance of the input information. For example, a recipe that takes into account a user's food preferences and nutritional balance can be generated by the user inputting the past food records. Furthermore, the user information input unit can also acquire the user's past exercise history and improve the accuracy of the input information. For example, a recipe based on the amount of exercise can be generated by the user inputting the past exercise history. This can improve the accuracy of the information based on the past health data.

[0041] The user information input unit acquires the user's dietary history and can increase the relevance of the input information based on the dietary history. The user information input unit, for example, acquires the user's past dietary history and increases the relevance of the input information. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account dietary preferences and nutritional balance. The user information input unit can also analyze the user's past dietary history and reflect preferences for specific ingredients and dishes. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account preferences for specific ingredients and dishes. Furthermore, the user information input unit can also suggest nutritionally balanced recipes based on the user's past dietary history. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account nutritional balance. This increases the relevance of information based on the dietary history.

[0042] The user information input unit can acquire the user's exercise history and customize the input information based on the exercise history. The user information input unit, for example, acquires the user's past exercise history and customizes the input information. For example, a recipe can be generated according to the amount of exercise by the user inputting their past exercise history. The user information input unit can also analyze the user's exercise history and suggest recipes according to the amount of exercise. For example, a nutritionally balanced recipe according to the amount of exercise by the user inputting their past exercise history. Furthermore, the user information input unit can also suggest nutritionally balanced recipes based on the user's exercise history. For example, a recipe that takes nutritional balance into consideration can be generated by the user inputting their past exercise history. This makes it possible to customize information based on the exercise history.

[0043] The user information input unit can acquire the user's sleep data and adjust the input information based on the sleep data. The user information input unit can, for example, acquire the user's past sleep data and adjust the input information. For example, a recipe based on the quality of sleep can be generated by the user inputting past sleep data. The user information input unit can also analyze the user's sleep data and suggest recipes based on the quality of sleep. For example, a nutritionally balanced recipe based on the quality of sleep can be generated by the user inputting past sleep data. The user information input unit can also suggest nutritionally balanced recipes based on the user's sleep data. For example, a recipe that takes nutritional balance into consideration can be generated by the user inputting past sleep data. This makes it possible to adjust the information based on the sleep data.

[0044] When generating a recipe, the recipe generation unit can determine the priority of recipes taking into account the freshness information of ingredients. The recipe generation unit generates recipes that prioritize using ingredients with high freshness based on, for example, the freshness information of ingredients. For example, a user can input freshness information about ingredients in a refrigerator to generate recipes that prioritize using ingredients with high freshness. The recipe generation unit can also generate recipes that use ingredients with low freshness earlier based on the freshness information of ingredients. Furthermore, the recipe generation unit can analyze the freshness information of ingredients and generate recipes that take into account the optimal order of use. This allows the priority of recipes to be determined based on the freshness information of ingredients.

[0045] When generating a recipe, the recipe generation unit can adjust the nutritional balance of the recipe taking into account the user's health data. The recipe generation unit generates a nutritionally balanced recipe based on, for example, the user's health data. For example, the user can input past health checkup results to generate a recipe that suits the user's health condition. The recipe generation unit can also analyze the user's health data to generate a recipe that supplements a specific nutrient. For example, if the user is deficient in a specific nutrient, the recipe generation unit can generate a recipe that uses ingredients that supplement that nutrient. Furthermore, the recipe generation unit can also generate a recipe that suits the user's health condition based on the user's health data. This makes it possible to generate a nutritionally balanced recipe based on the user's health data.

[0046] The recipe generation unit can select recipes taking into consideration the origin information of ingredients when generating recipes. The recipe generation unit, for example, generates recipes that use locally produced ingredients based on the origin information of ingredients. For example, a user can input origin information of ingredients in the refrigerator to generate recipes that use locally produced ingredients. The recipe generation unit can also generate recipes that use ingredients from a specific region based on the origin information of ingredients. Furthermore, the recipe generation unit can analyze the origin information of ingredients and generate recipes that distinguish between imported ingredients and domestic ingredients. This makes it possible to select recipes based on the origin information of ingredients.

[0047] When generating a recipe, the recipe generation unit can filter the recipe taking into consideration the user's allergen information. The recipe generation unit, for example, generates a recipe that does not contain allergens based on the user's allergen information. For example, a user can input allergen information to generate a recipe that uses ingredients that do not contain allergens. The recipe generation unit can also analyze the user's allergen information and generate a recipe that excludes ingredients that contain allergens. Furthermore, the recipe generation unit can also generate a recipe that uses substitute ingredients that do not contain allergens based on the user's allergen information. This allows recipe filtering based on the user's allergen information.

[0048] When providing a recipe, the recipe providing unit can provide an optimal recipe by referring to the user's past rating data. The recipe providing unit provides an optimal recipe, for example, based on the user's past rating data. For example, it can preferentially display recipes that the user has given high ratings to in the past. The recipe providing unit can also suggest recipes that suit the user's preferences based on the user's past rating data. Furthermore, the recipe providing unit can analyze the user's past rating data and preferentially provide highly rated recipes. This makes it possible to provide an optimal recipe based on the user's past rating data.

[0049] When providing a recipe, the recipe providing unit can select the optimal display method taking into consideration the user's device information. For example, if the user is using a smartphone, the recipe providing unit can provide a display method that matches the screen size. For example, when the user is viewing a recipe using a smartphone, the recipe providing unit can provide a display method that is optimized for the screen size. Furthermore, if the user is using a tablet, the recipe providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the recipe providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information.

[0050] When providing recipes, the recipe providing unit can prioritize providing highly relevant recipes taking into account the user's geographical location information. The recipe providing unit, for example, prioritizes providing recipes using local ingredients based on the user's current location. For example, by the user inputting their current location, recipes using local ingredients can be prioritized. The recipe providing unit can also suggest recipes using local specialties based on the user's geographical location information. Furthermore, the recipe providing unit can analyze the user's geographical location information and provide recipes that are appropriate for the season. This makes it possible to provide highly relevant recipes based on the user's geographical location information.

[0051] When providing a recipe, the recipe providing unit can analyze the user's social media activity and provide related recipes. The recipe providing unit, for example, analyzes the content of the user's social media posts and suggests recipes that the user is interested in. For example, the recipe providing unit can suggest related recipes based on the content of posts that the user has liked or commented on on social media. The recipe providing unit can also suggest related recipes based on the content of posts by the user's followers on social media. Furthermore, the recipe providing unit can analyze the user's social media activity and prioritize providing highly rated recipes. This makes it possible to provide related recipes based on the user's social media activity.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The recipe generation system can also generate recipes based on the storage method of ingredients. For example, it can suggest the optimal cooking method depending on the storage method, such as refrigerated storage, frozen storage, or room temperature storage. For frozen ingredients, it can generate recipes that take into account the thawing method and cooking time. It can also suggest that ingredients stored at room temperature be used sooner. It can also generate recipes that optimize the combination of ingredients based on the storage method and use up all ingredients without waste. This makes it possible to provide recipes that suit the storage method of ingredients.

[0054] The recipe generation system can also customize recipes based on the user's dietary preferences. For example, if the user likes spicy food, it can suggest recipes that use a lot of spices. If the user likes sweet food, it can suggest desserts or sweet dishes. Furthermore, if the user likes a specific cuisine genre (e.g., Italian, Chinese, Japanese, etc.), it can generate recipes specialized for that genre. This makes it possible to provide recipes that match the user's dietary preferences.

[0055] The recipe generation system can also suggest recipes based on the timing of the user's meals. For example, it can generate appropriate recipes according to meal timings such as breakfast, lunch, dinner, and late-night snacks. For breakfast, it can suggest a nutritionally balanced recipe that can be prepared in a short time, and for lunch, it can suggest a recipe that is suitable for replenishing energy. For dinner, it can suggest a relaxing recipe, and for late-night snacks, it can suggest a light and easy-to-digest recipe. This makes it possible to provide recipes according to the timing of meals.

[0056] The recipe generation system can also generate recipes based on the user's state after exercise. For example, it can suggest recipes that include nutrients to promote recovery from fatigue after exercise. After exercise, it can generate recipes that use ingredients that are rich in protein, vitamins, and minerals. It can also suggest recipes that take into account post-exercise hydration. It can also generate recipes that take into account the optimal nutritional balance depending on the type and intensity of exercise. This makes it possible to provide recipes that suit the user's state after exercise.

[0057] The recipe generation system can also generate recipes based on the user's allergy information. For example, if the user is allergic to a particular ingredient, it can suggest recipes that do not include that ingredient. It can also generate recipes that use substitutes for ingredients that the user is allergic to. Furthermore, it can take allergy information into consideration and suggest recipes that use safe ingredients that do not contain allergens. This makes it possible to provide safe recipes based on the user's allergy information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The ingredient input unit allows the user to input information about ingredients in the refrigerator. The ingredient input unit supports barcode scanning and voice input, and can read barcodes such as 2D codes and UPC codes. Voice input also recognizes voice commands, allowing the user to input the names and quantities of ingredients. Step 2: The user information input unit inputs information about the user and their family. The user information input unit can input information such as family structure, body size, and chronic illnesses. Family structure includes the number of family members, age, and gender, while body size includes height, weight, and BMI. Chronic illnesses include diabetes, high blood pressure, and allergies. Step 3: The recipe generation unit uses a generation AI to analyze the information entered by the ingredient input unit and the user information input unit, and generates a recipe based on specific criteria. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the entered information and generate an appropriate recipe. Specific criteria include nutritional balance, cooking time, and ingredient combinations. Step 4: The recipe provider provides the generated recipe to the user. The recipe provider displays the recipe through a web application or mobile application. The recipe provider also has a function that allows users to rate the recipe, and the generation AI continues to learn based on user ratings.

[0060] (Example 2) A recipe generation system according to an embodiment of the present invention generates appropriate recipes by inputting information about ingredients in a home refrigerator, the user's and family's composition, body size, chronic illnesses, and other factors. In this recipe generation system, a user inputs information about ingredients in the refrigerator, and then inputs other information about the user and family, such as body size and chronic illnesses. This information is input into a generation AI, which analyzes the input information and generates an appropriate recipe. The generated recipe is provided to the user. For example, if the ingredients in the refrigerator are chicken, cabbage, and carrots and the user is on a diet, the generation AI generates a low-calorie, nutritionally balanced recipe. Similarly, if a family member has diabetes, the generation AI generates a low-sugar recipe. In this way, appropriate recipes tailored to the user's needs can be provided. For example, an "ingredient input unit" is provided for inputting information about ingredients in the refrigerator. Multiple input methods, such as barcode scanning and voice input, are supported. Next, a "user information input unit" is provided for inputting information about the user and family, including family composition, body size, chronic illnesses, and other factors. A "recipe generation unit" is provided for analyzing this information and generating an appropriate recipe. The generation AI processes this information and generates the recipe. Finally, we will create a "recipe provider" that provides the generated recipes to the user. We will also add a function for users to rate the recipes, and we are considering a system in which the generation AI continues to learn based on those ratings. This will enable the recipe generation system to generate and provide appropriate recipes based on information about ingredients in the user's refrigerator and family information.

[0061] A recipe generation system according to an embodiment includes an ingredient input unit, a user information input unit, a recipe generation unit, and a recipe provision unit. The ingredient input unit allows a user to input information about ingredients in the refrigerator. The ingredient input unit supports, for example, barcode scanning and voice input. Barcode scanning can read barcodes such as two-dimensional codes (e.g., QR codes) and UPC codes. Voice input can recognize voice commands and input the names and quantities of ingredients. The user information input unit inputs information about the user and their family. The user information input unit can input information such as family structure, body size, and chronic illnesses. Family structure includes, for example, the number of family members, age, and gender. Body size includes, for example, height, weight, and BMI. Chronic illnesses include, for example, diabetes, high blood pressure, and allergies. The recipe generation unit uses a generation AI to analyze the information input by the ingredient input unit and the user information input unit and generate a recipe based on specific criteria. The generation AI analyzes the input information and generates an appropriate recipe using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Specific criteria include, for example, nutritional balance, cooking time, and ingredient combinations. The recipe providing unit provides the generated recipe to the user. The recipe providing unit displays the recipe through, for example, a web application or a mobile application. The unit also has a function that allows users to rate the recipe, and the generation AI continues to learn based on the user's ratings. This allows the recipe generation system to generate and provide appropriate recipes based on information about ingredients in the user's refrigerator and family information.

[0062] The ingredient input unit can support barcode scanning or voice input. Barcode scanning can read barcodes such as 2D codes and UPC codes. For example, a user can automatically input ingredient information by scanning the barcodes of ingredients in the refrigerator. Voice input can also recognize voice commands and input the names and quantities of ingredients. For example, a user can easily input ingredient information by speaking, for example, "500 grams of chicken." Furthermore, the ingredient input unit can improve user convenience by supporting multiple input methods. For example, both barcode scanning and voice input can be used in combination. This allows a user to easily input ingredient information.

[0063] The user information input unit allows input of information on family composition, body size, and chronic illnesses. Family composition includes, for example, the number of family members, their ages, and their genders. For example, the user can input detailed information on family composition by inputting the number of family members and the ages and genders of each family member. Body size includes, for example, height, weight, and BMI. For example, the user can input the height and weight of themselves or their family members, and the BMI can be calculated automatically. Chronic illnesses include, for example, diabetes, high blood pressure, and allergies. For example, by the user inputting their own or their family members' chronic illnesses, recipes can be generated that match the chronic illnesses. This allows input of detailed information on the user and their family members.

[0064] The recipe generation unit can analyze the input information using a generation AI and generate a recipe based on specific criteria. The generation AI can analyze the input information and generate an appropriate recipe using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Specific criteria include, for example, nutritional balance, cooking time, and ingredient combinations. For example, the generation AI can generate a nutritionally balanced recipe based on the input ingredient information and user information. The generation AI can also generate a recipe that can be cooked in a short time by taking cooking time into account. Furthermore, the generation AI can generate a delicious recipe by taking ingredient combinations into account. This allows the generation AI to generate an appropriate recipe.

[0065] The recipe providing unit can provide the generated recipe to the user. The recipe providing unit displays the recipe through, for example, a web application or a mobile application. For example, the user can view the recipe using a smartphone or tablet. The recipe providing unit also has a function that allows the user to rate the recipe. For example, the user can add star ratings or comments to the recipe. Furthermore, the recipe providing unit has a mechanism that allows the generation AI to continue learning based on the user ratings. For example, the generation AI can collect user rating data and use that data to improve the accuracy of the recipe. This allows the generated recipe to be provided to the user.

[0066] The recipe providing unit can have a function that allows users to rate recipes. For example, the recipe providing unit allows users to give star ratings and comments to recipes. For example, users can try out recipes and rate the results. The recipe providing unit also has a mechanism that allows the generation AI to continue learning based on user ratings. For example, the generation AI can collect user rating data and use that data to improve the accuracy of recipes. This allows users to rate recipes.

[0067] The recipe generation unit allows the generation AI to continue learning based on user ratings. The generation AI can, for example, collect user rating data and use that data to improve the accuracy of the recipe. For example, when a user gives a star rating or comment to a recipe, the generation AI can learn that rating and reflect it in the next recipe generation. This allows the generation AI to continuously learn and improve the accuracy of the recipe.

[0068] The ingredient input unit can estimate the user's emotions and adjust the timing of ingredient input based on the estimated user emotions. For example, the ingredient input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, the ingredient input procedure can be simplified to allow input to be completed in a short time. Furthermore, if the user is relaxed, detailed input options can be provided to allow the user to enter detailed information such as the type and amount of ingredients. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter ingredient information. This allows the timing of ingredient input to be adjusted according to the user's emotions. Emotion estimation is achieved 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.

[0069] The ingredient input unit can acquire freshness information of ingredients and prioritize the input ingredients based on their freshness. For example, when scanning the barcodes of ingredients, the ingredient input unit automatically acquires freshness information and prioritizes displaying ingredients with high freshness. For example, when a user scans the barcodes of ingredients in the refrigerator, the freshness information can be automatically acquired and ingredients with high freshness can be prioritized. The ingredient input unit can also estimate freshness information by inputting the purchase date of ingredients and suggest using less fresh ingredients sooner. Furthermore, the ingredient input unit can analyze freshness information by inputting the storage state of ingredients and determine the optimal order of use. This allows prioritization to be determined based on the freshness of ingredients.

[0070] The ingredient input unit can acquire nutritional value information of ingredients and adjust the importance of the input ingredients based on their nutritional value. For example, when scanning the barcode of an ingredient, the ingredient input unit can automatically acquire nutritional value information and prioritize displaying ingredients with high nutritional value. For example, when a user scans the barcode of an ingredient in the refrigerator, the nutritional value information can be automatically acquired and ingredients with high nutritional value can be prioritized. The ingredient input unit can also analyze the nutritional value information by inputting the nutritional composition table of the ingredient and suggest balanced ingredients. Furthermore, the ingredient input unit can estimate nutritional value information by inputting the type of ingredient and prioritize displaying ingredients that supplement specific nutrients. This allows the importance to be adjusted based on the nutritional value of the ingredient.

[0071] The ingredient input unit can estimate the user's emotions and determine the priority of ingredients to be input based on the estimated user emotions. The ingredient input unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, ingredients that are easy to prepare are displayed preferentially. If the user is relaxed, ingredients that take time to prepare but are delicious are displayed preferentially. Furthermore, if the user is in a hurry, ingredients that can be quickly prepared are displayed preferentially. In this way, the priority of ingredients can be determined 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.

[0072] The ingredient input unit can acquire origin information for ingredients and select the input ingredients based on their origin. For example, when scanning the barcode of an ingredient, the ingredient input unit automatically acquires origin information and prioritizes displaying locally produced ingredients. For example, when a user scans the barcode of an ingredient in the refrigerator, the ingredient input unit can automatically acquire origin information and prioritize displaying locally produced ingredients. The ingredient input unit can also prioritize displaying ingredients from a specific region by inputting the origin of the ingredient. Furthermore, the ingredient input unit can analyze the origin information for the ingredient and distinguish between imported ingredients and domestic ingredients. This allows ingredients to be selected based on their origin.

[0073] The ingredient input unit can acquire allergen information for ingredients and filter the input ingredients based on the allergens. For example, when scanning the barcode of an ingredient, the ingredient input unit automatically acquires allergen information and excludes ingredients containing allergens. For example, a user can scan the barcode of an ingredient in a refrigerator to automatically acquire allergen information and exclude ingredients containing allergens. The ingredient input unit can also input allergen information for ingredients and preferentially display ingredients that do not contain allergens. Furthermore, the ingredient input unit can analyze the allergen information for ingredients and display a warning about ingredients that contain allergens. This allows ingredients to be filtered based on the allergen information.

[0074] The user information input unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. The user information input unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the information input procedure can be simplified to allow input in a short time. Also, if the user is relaxed, detailed input options can be provided to allow detailed input of family composition, body size, etc. Furthermore, if the user is in a hurry, voice input can be prioritized to allow information to be input quickly. This makes it possible to adjust the timing of information input 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.

[0075] The user information input unit can acquire the user's past health data and improve the accuracy of the input information based on the health data. The user information input unit can, for example, acquire the user's past health checkup results and improve the accuracy of the input information. For example, a recipe based on a user's health condition can be generated by the user inputting the past health checkup results. The user information input unit can also acquire the user's past food records and improve the relevance of the input information. For example, a recipe that takes into account a user's food preferences and nutritional balance can be generated by the user inputting the past food records. Furthermore, the user information input unit can also acquire the user's past exercise history and improve the accuracy of the input information. For example, a recipe based on the amount of exercise can be generated by the user inputting the past exercise history. This can improve the accuracy of the information based on the past health data.

[0076] The user information input unit acquires the user's dietary history and can increase the relevance of the input information based on the dietary history. The user information input unit, for example, acquires the user's past dietary history and increases the relevance of the input information. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account dietary preferences and nutritional balance. The user information input unit can also analyze the user's past dietary history and reflect preferences for specific ingredients and dishes. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account preferences for specific ingredients and dishes. Furthermore, the user information input unit can also suggest nutritionally balanced recipes based on the user's past dietary history. For example, by the user inputting his or her past dietary history, it is possible to generate recipes that take into account nutritional balance. This increases the relevance of information based on the dietary history.

[0077] The user information input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. The user information input unit, for example, captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the unit prioritizes inputting only important information. Also, if the user is relaxed, the unit prioritizes inputting detailed information. Furthermore, if the user is in a hurry, the unit prioritizes inputting the minimum necessary information. This allows the priority of information to be determined 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The user information input unit can acquire the user's exercise history and customize the input information based on the exercise history. The user information input unit, for example, acquires the user's past exercise history and customizes the input information. For example, a recipe can be generated according to the amount of exercise by the user inputting their past exercise history. The user information input unit can also analyze the user's exercise history and suggest recipes according to the amount of exercise. For example, a nutritionally balanced recipe according to the amount of exercise by the user inputting their past exercise history. Furthermore, the user information input unit can also suggest nutritionally balanced recipes based on the user's exercise history. For example, a recipe that takes nutritional balance into consideration can be generated by the user inputting their past exercise history. This makes it possible to customize information based on the exercise history.

[0079] The user information input unit can acquire the user's sleep data and adjust the input information based on the sleep data. The user information input unit can, for example, acquire the user's past sleep data and adjust the input information. For example, a recipe based on the quality of sleep can be generated by the user inputting past sleep data. The user information input unit can also analyze the user's sleep data and suggest recipes based on the quality of sleep. For example, a nutritionally balanced recipe based on the quality of sleep can be generated by the user inputting past sleep data. The user information input unit can also suggest nutritionally balanced recipes based on the user's sleep data. For example, a recipe that takes nutritional balance into consideration can be generated by the user inputting past sleep data. This makes it possible to adjust the information based on the sleep data.

[0080] The recipe generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated user emotions. The recipe generation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, a simple and easy-to-understand recipe is generated. If the user is relaxed, a recipe with detailed instructions is generated. Furthermore, if the user is in a hurry, a recipe that can be prepared in a short time is generated. This makes it possible to adjust the way the recipe is presented 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] When generating a recipe, the recipe generation unit can determine the priority of recipes taking into account the freshness information of ingredients. The recipe generation unit generates recipes that prioritize using ingredients with high freshness based on, for example, the freshness information of ingredients. For example, a user can input freshness information about ingredients in a refrigerator to generate recipes that prioritize using ingredients with high freshness. The recipe generation unit can also generate recipes that use ingredients with low freshness earlier based on the freshness information of ingredients. Furthermore, the recipe generation unit can analyze the freshness information of ingredients and generate recipes that take into account the optimal order of use. This allows the priority of recipes to be determined based on the freshness information of ingredients.

[0082] When generating a recipe, the recipe generation unit can adjust the nutritional balance of the recipe taking into account the user's health data. The recipe generation unit generates a nutritionally balanced recipe based on, for example, the user's health data. For example, the user can input past health checkup results to generate a recipe that suits the user's health condition. The recipe generation unit can also analyze the user's health data to generate a recipe that supplements a specific nutrient. For example, if the user is deficient in a specific nutrient, the recipe generation unit can generate a recipe that uses ingredients that supplement that nutrient. Furthermore, the recipe generation unit can also generate a recipe that suits the user's health condition based on the user's health data. This makes it possible to generate a nutritionally balanced recipe based on the user's health data.

[0083] The recipe generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. The recipe generation unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, a short and to-the-point recipe is generated. If the user is relaxed, a longer recipe with detailed instructions is generated. Furthermore, if the user is in a hurry, a recipe that can be cooked in a short time is generated. This allows the length of the recipe to be adjusted 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The recipe generation unit can select recipes taking into consideration the origin information of ingredients when generating recipes. The recipe generation unit, for example, generates recipes that use locally produced ingredients based on the origin information of ingredients. For example, a user can input origin information of ingredients in the refrigerator to generate recipes that use locally produced ingredients. The recipe generation unit can also generate recipes that use ingredients from a specific region based on the origin information of ingredients. Furthermore, the recipe generation unit can analyze the origin information of ingredients and generate recipes that distinguish between imported ingredients and domestic ingredients. This makes it possible to select recipes based on the origin information of ingredients.

[0085] When generating a recipe, the recipe generation unit can filter the recipe taking into consideration the user's allergen information. The recipe generation unit, for example, generates a recipe that does not contain allergens based on the user's allergen information. For example, a user can input allergen information to generate a recipe that uses ingredients that do not contain allergens. The recipe generation unit can also analyze the user's allergen information and generate a recipe that excludes ingredients that contain allergens. Furthermore, the recipe generation unit can also generate a recipe that uses substitute ingredients that do not contain allergens based on the user's allergen information. This allows recipe filtering based on the user's allergen information.

[0086] The recipe providing unit can estimate the user's emotions and adjust the recipe display method based on the estimated user emotions. The recipe providing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is feeling stressed, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points is provided. This makes it possible to adjust the recipe display method 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] When providing a recipe, the recipe providing unit can provide an optimal recipe by referring to the user's past rating data. The recipe providing unit provides an optimal recipe, for example, based on the user's past rating data. For example, it can preferentially display recipes that the user has given high ratings to in the past. The recipe providing unit can also suggest recipes that suit the user's preferences based on the user's past rating data. Furthermore, the recipe providing unit can analyze the user's past rating data and preferentially provide highly rated recipes. This makes it possible to provide an optimal recipe based on the user's past rating data.

[0088] When providing a recipe, the recipe providing unit can select the optimal display method taking into consideration the user's device information. For example, if the user is using a smartphone, the recipe providing unit can provide a display method that matches the screen size. For example, when the user is viewing a recipe using a smartphone, the recipe providing unit can provide a display method that is optimized for the screen size. Furthermore, if the user is using a tablet, the recipe providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the recipe providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information.

[0089] The recipe providing unit can estimate the user's emotions and adjust the display order of recipes based on the estimated user emotions. The recipe providing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is stressed, recipes that are easy to prepare are displayed preferentially. If the user is relaxed, recipes that take a long time to prepare but are delicious are displayed preferentially. Furthermore, if the user is in a hurry, recipes that can be prepared in a short time are displayed preferentially. This allows the display order of recipes to be adjusted 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] When providing recipes, the recipe providing unit can prioritize providing highly relevant recipes taking into account the user's geographical location information. The recipe providing unit, for example, prioritizes providing recipes using local ingredients based on the user's current location. For example, by the user inputting their current location, recipes using local ingredients can be prioritized. The recipe providing unit can also suggest recipes using local specialties based on the user's geographical location information. Furthermore, the recipe providing unit can analyze the user's geographical location information and provide recipes that are appropriate for the season. This makes it possible to provide highly relevant recipes based on the user's geographical location information.

[0091] When providing a recipe, the recipe providing unit can analyze the user's social media activity and provide related recipes. The recipe providing unit, for example, analyzes the content of the user's social media posts and suggests recipes that the user is interested in. For example, the recipe providing unit can suggest related recipes based on the content of posts that the user has liked or commented on on social media. The recipe providing unit can also suggest related recipes based on the content of posts by the user's followers on social media. Furthermore, the recipe providing unit can analyze the user's social media activity and prioritize providing highly rated recipes. This makes it possible to provide related recipes based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned ingredient input unit, user information input unit, recipe generation unit, and recipe providing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the ingredient input unit is realized using the barcode scanning or voice input function of the smart device 14. The user information input unit inputs information such as family composition, body size, and chronic illnesses using the input interface of the smart device 14. The recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate recipe using a generation AI. The recipe providing unit provides the generated recipe to the user via the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned ingredient input unit, user information input unit, recipe generation unit, and recipe providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the ingredient input unit is realized using the camera 42 or voice input function of the smart glasses 214. The user information input unit inputs information such as family composition, body size, and chronic illnesses using the input interface of the smart glasses 214. The recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate recipe using a generation AI. The recipe providing unit provides the generated recipe to the user through the display and speakers of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned ingredient input unit, user information input unit, recipe generation unit, and recipe provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the ingredient input unit is realized using the camera 42 or voice input function of the headset terminal 314. The user information input unit inputs information such as family composition, body size, and chronic illnesses using the input interface of the headset terminal 314. The recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate recipe using a generation AI. The recipe provision unit provides the generated recipe to the user via the display 343 or speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned ingredient input unit, user information input unit, recipe generation unit, and recipe provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the ingredient input unit is realized using the camera 42 or voice input function of the robot 414. The user information input unit inputs information such as family composition, body size, and chronic illnesses using the input interface of the robot 414. The recipe generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate recipe using a generation AI. The recipe provision unit provides the generated recipe to the user via the display and speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The recipe generation system can also generate recipes based on the storage method of ingredients. For example, it can suggest the optimal cooking method depending on the storage method, such as refrigerated storage, frozen storage, or room temperature storage. For frozen ingredients, it can generate recipes that take into account the thawing method and cooking time. It can also suggest that ingredients stored at room temperature be used sooner. It can also generate recipes that optimize the combination of ingredients based on the storage method and use up all ingredients without waste. This makes it possible to provide recipes that suit the storage method of ingredients.

[0094] The recipe generation system can also customize recipes based on the user's dietary preferences. For example, if the user likes spicy food, it can suggest recipes that use a lot of spices. If the user likes sweet food, it can suggest desserts or sweet dishes. Furthermore, if the user likes a specific cuisine genre (e.g., Italian, Chinese, Japanese, etc.), it can generate recipes specialized for that genre. This makes it possible to provide recipes that match the user's dietary preferences.

[0095] The recipe generation system can also suggest recipes based on the timing of the user's meals. For example, it can generate appropriate recipes according to meal timings such as breakfast, lunch, dinner, and late-night snacks. For breakfast, it can suggest a nutritionally balanced recipe that can be prepared in a short time, and for lunch, it can suggest a recipe that is suitable for replenishing energy. For dinner, it can suggest a relaxing recipe, and for late-night snacks, it can suggest a light and easy-to-digest recipe. This makes it possible to provide recipes according to the timing of meals.

[0096] The recipe generation system can also generate recipes based on the user's state after exercise. For example, it can suggest recipes that include nutrients to promote recovery from fatigue after exercise. After exercise, it can generate recipes that use ingredients that are rich in protein, vitamins, and minerals. It can also suggest recipes that take into account post-exercise hydration. It can also generate recipes that take into account the optimal nutritional balance depending on the type and intensity of exercise. This makes it possible to provide recipes that suit the user's state after exercise.

[0097] The recipe generation system can also generate recipes based on the user's allergy information. For example, if the user is allergic to a particular ingredient, it can suggest recipes that do not include that ingredient. It can also generate recipes that use substitutes for ingredients that the user is allergic to. Furthermore, it can take allergy information into consideration and suggest recipes that use safe ingredients that do not contain allergens. This makes it possible to provide safe recipes based on the user's allergy information.

[0098] The recipe generation system can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated emotions. For example, if the user is feeling stressed, a simple and hassle-free recipe can be suggested. If the user is relaxed, a slightly more elaborate recipe can be suggested. Furthermore, if the user is in a hurry, a recipe that can be cooked in a short time can be suggested. In this way, the difficulty of the recipe can be adjusted according to the user's emotions.

[0099] The recipe generation system can estimate the user's emotions and adjust recipe portion sizes based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes with small portions that are satisfying. If the user is relaxed, it can suggest recipes with normal portion sizes. Furthermore, if the user is in a hurry, it can suggest recipes with small portions that can be cooked in a short time. In this way, it is possible to adjust recipe portion sizes according to the user's emotions.

[0100] The recipe generation system can estimate the user's emotions and adjust the recipe cooking steps based on the estimated emotions. For example, if the user is feeling stressed, it can suggest simple and easy-to-understand cooking steps. If the user is relaxed, it can suggest detailed cooking steps. Furthermore, if the user is in a hurry, it can suggest steps that allow cooking in a short time. In this way, it is possible to adjust the cooking steps according to the user's emotions.

[0101] The recipe generation system can estimate the user's emotions and adjust the seasoning of a recipe based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes that use herbs and spices that have a relaxing effect. If the user is relaxed, it can suggest recipes with normal seasonings. Furthermore, if the user is in a hurry, it can suggest recipes with simple and easy seasonings. In this way, it is possible to adjust the seasoning according to the user's emotions.

[0102] The recipe generation system can estimate the user's emotions and adjust the presentation of the recipe based on the estimated emotions. For example, if the user is feeling stressed, a simple and beautiful presentation can be suggested. If the user is relaxed, an elaborate presentation can be suggested. Furthermore, if the user is in a hurry, a quick presentation can be suggested. In this way, the presentation can be adjusted according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The ingredient input unit allows the user to input information about ingredients in the refrigerator. The ingredient input unit supports barcode scanning and voice input, and can read barcodes such as 2D codes and UPC codes. Voice input also recognizes voice commands, allowing the user to input the names and quantities of ingredients. Step 2: The user information input unit inputs information about the user and their family. The user information input unit can input information such as family structure, body size, and chronic illnesses. Family structure includes the number of family members, age, and gender, while body size includes height, weight, and BMI. Chronic illnesses include diabetes, high blood pressure, and allergies. Step 3: The recipe generation unit uses a generation AI to analyze the information entered by the ingredient input unit and the user information input unit, and generates a recipe based on specific criteria. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the entered information and generate an appropriate recipe. Specific criteria include nutritional balance, cooking time, and ingredient combinations. Step 4: The recipe provider provides the generated recipe to the user. The recipe provider displays the recipe through a web application or mobile application. The recipe provider also has a function that allows users to rate the recipe, and the generation AI continues to learn based on user ratings.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 ingredient input unit for inputting ingredient information for the refrigerator; a user information input section for inputting information about the user and his / her family; a recipe generation unit that analyzes the information input by the ingredient input unit and the user information input unit and generates a recipe based on specific criteria; a recipe providing unit that provides a user with the recipe generated by the recipe generating unit; Equipped with A system characterized by:

2. The ingredient input unit Supports barcode scanning or voice input 2. The system of claim 1.

3. The user information input unit Enter information about your family, body size, and chronic illnesses 2. The system of claim 1.

4. The recipe generation unit Generative AI analyzes input information and generates recipes based on specific criteria 2. The system of claim 1.

5. The recipe providing unit Providing the generated recipe to the user 2. The system of claim 1.

6. The recipe providing unit Users can rate recipes 2. The system of claim 1.

7. The recipe generation unit Generative AI continues to learn based on user evaluations 2. The system of claim 1.

8. The ingredient input unit Estimate the user's emotions and adjust the timing of ingredient input based on the estimated user emotions.

2. The system of claim 1.

9. The ingredient input unit Obtaining freshness information of ingredients and prioritizing the input ingredients based on freshness 2. The system of claim 1.

10. The ingredient input unit Obtaining nutritional information of ingredients and adjusting the importance of the entered ingredients based on their nutritional value 2. The system of claim 1.

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