system

Abstract

The system according to the embodiment aims to provide optimal recipes based on the contents of the refrigerator and the food preferences of the family. [Solution] A system according to an embodiment includes an input unit, a configuration input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs the contents of the refrigerator. The configuration input unit inputs family composition and food preferences. The analysis unit analyzes the information input by the input unit and the configuration input unit. The generation unit generates a recipe based on the information analyzed by the analysis unit. The provision unit provides the recipe generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, finding the right recipe based on the contents of your refrigerator and your family's food preferences can be tedious and leaves room for improvement.

[0005] The system according to the embodiment aims to provide optimal recipes based on the contents of the refrigerator and the food preferences of the family. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a configuration input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs the contents of the refrigerator. The configuration input unit inputs family composition and food preferences. The analysis unit analyzes the information input by the input unit and the configuration input unit. The generation unit generates a recipe based on the information analyzed by the analysis unit. The provision unit provides the recipe generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal recipes based on the contents of the refrigerator and the family's food preferences. [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 suggestion system according to an embodiment of the present invention uses AI to suggest dinner recipes to be made in the future based on input information such as the contents of the refrigerator, family composition, and food preferences. In this recipe suggestion system, a user inputs the contents of the refrigerator, family composition, and food preferences, and an AI analyzes the input information to generate an optimal dinner recipe. The generated recipe is provided to the user. For example, based on the ingredients in the refrigerator, the system can suggest dishes that children like or that the whole family can enjoy. This system eliminates the need to think about dinner menus every day and allows the whole family to enjoy meals that satisfy them. For example, a user inputs the contents of the refrigerator. An easy input method, such as barcode scanning or voice input, is recommended. Next, the user inputs the family composition and food preferences. The AI ​​analyzes this information and generates an optimal recipe. The generated recipe is provided to the user along with detailed instructions and a list of required ingredients. Furthermore, a user-rated recipe can be added. Based on the user's rating, the AI ​​can improve the next suggestion. This allows for more satisfying recipes. It is also important to consider factors such as nutritional balance and cooking time when suggesting recipes. This allows the recipe suggestion system to suggest the best dinner recipe based on the information input by the user.

[0029] A recipe suggestion system according to an embodiment includes an input unit, a configuration input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs the contents of a refrigerator. The contents of the refrigerator include, for example, but are not limited to, the type, quantity, and expiration date of ingredients. The input unit inputs ingredients using, for example, a barcode scan. The input unit can also input ingredients using voice input. For example, when a user speaks the name of an ingredient, the ingredients are input using voice recognition technology. The input unit can also input ingredients using image recognition technology. For example, the ingredients in the refrigerator are photographed with a camera, and the ingredients are automatically identified and input using image recognition technology. The configuration input unit inputs family composition and food preferences. Family composition includes, for example, but is not limited to, the number of family members, age, and gender. Food preferences include, for example, but are not limited to, favorite ingredients, disliked ingredients, allergy information, and the like. For example, the configuration input unit allows a user to manually input family composition and food preferences. The configuration input unit can also have a function for automatic completion by referencing past data. For example, the system automatically displays family composition and food preferences previously input by the user as candidates. The analysis unit analyzes the information input by the input unit and the configuration input unit. Examples of analysis include, but are not limited to, data analysis techniques and algorithms. The analysis unit analyzes the input information using, for example, data mining technology. The analysis unit can also analyze the input information using a machine learning algorithm. The generation unit generates a recipe based on the information analyzed by the analysis unit. The generated recipe includes, for example, but is not limited to, the type of dish, cooking steps, and required ingredients. The generation unit generates the recipe using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to. For example, the generation AI receives a prompt such as, "Please generate the best dinner recipe based on the contents of my refrigerator, my family composition, and my food preferences," and generates the recipe. The provision unit provides the recipe generated by the generation unit. The provided recipe includes, for example, but is not limited to, detailed steps and a list of required ingredients.The providing unit, for example, displays the generated recipe on the user's device. The providing unit can also print the generated recipe. For example, if the user wishes, the generated recipe is printed using a printer. This allows the recipe suggestion system according to the embodiment to suggest the optimal dinner recipe based on the contents of the refrigerator, family composition, and food preferences.

[0030] The providing unit can provide detailed steps for the generated recipe or a list of required ingredients. For example, the providing unit provides detailed steps for the generated recipe. Detailed steps include, but are not limited to, cooking steps, times, and temperatures. For example, the providing unit provides detailed instructions for each cooking step. The providing unit can also specifically indicate cooking times and temperatures. For example, the providing unit provides detailed steps such as, "Step 1: Cut vegetables. Required time: 10 minutes. Step 2: Stir-fry in a frying pan. Temperature: medium heat. Required time: 5 minutes." The providing unit also provides a list of ingredients required for the generated recipe. The list of required ingredients includes, but is not limited to, the type, quantity, and substitutes of ingredients. For example, the providing unit provides a list for each type of ingredient. The providing unit can also specifically indicate the quantity of ingredients. For example, the providing unit provides a list of required ingredients such as, "Ingredient 1: 200 grams of chicken. Ingredient 2: 1 carrot." This allows the user to check the detailed steps or the list of required ingredients.

[0031] The recipe suggestion system includes a rating collection unit that collects user ratings. The rating collection unit collects user ratings of provided recipes. User ratings include, but are not limited to, satisfaction with the recipe, difficulty of cooking, and taste evaluation. For example, the rating collection unit allows users to assign star ratings to recipes. The rating collection unit also allows users to input comments about recipes. For example, the rating collection unit collects comments such as, "This recipe was very delicious. It was easy to prepare." The rating collection unit can also collect user ratings as numerical data. For example, the rating collection unit allows users to assign scores from 1 to 5 to recipes. This allows the recipe suggestion system to collect user ratings and reflect them in future suggestions.

[0032] The rating collection unit can collect user ratings for the provided recipes. For example, the rating collection unit allows users to assign star ratings to recipes. Star ratings include, but are not limited to, scores from 1 to 5. For example, the rating collection unit can allow users to input comments for recipes. For example, comments include, but are not limited to, satisfaction with the recipe, difficulty of cooking, and taste evaluation. For example, the rating collection unit can allow users to input comments for recipes such as, "This recipe was very delicious. It was easy to prepare." The rating collection unit can also collect user ratings as numerical data. For example, numerical data includes, but are not limited to, scores from 1 to 5. For example, the rating collection unit allows users to assign scores from 1 to 5 to recipes. This allows the recipe suggestion system to collect user ratings and reflect them in future suggestions.

[0033] The recipe suggestion system includes an improvement unit that improves the next suggestion based on the evaluations collected by the evaluation collection unit. The improvement unit improves the next suggestion based on the evaluations collected by the evaluation collection unit. Examples of improvements include, but are not limited to, evaluation data analysis methods and improvement algorithms. For example, the improvement unit analyzes the evaluation data to optimize the next suggestion. The improvement unit can also improve the next suggestion using a machine learning algorithm. For example, the improvement unit customizes the next suggestion based on the user's evaluation data. This allows the recipe suggestion system to improve the next suggestion based on the user's evaluations.

[0034] The recipe suggestion system includes a balance analysis unit that analyzes nutritional balance. The balance analysis unit analyzes nutritional balance. Nutritional balance includes, but is not limited to, the type of nutrient, recommended intake amounts, and analysis methods. The balance analysis unit performs analysis for each type of nutrient, for example. The balance analysis unit can also perform analysis based on recommended intake amounts. For example, the balance analysis unit analyzes the intake amounts of nutrients such as vitamins, minerals, and proteins. The balance analysis unit can also analyze nutritional balance using a machine learning algorithm. For example, the balance analysis unit analyzes nutritional balance based on the user's dietary history. This allows the recipe suggestion system to suggest recipes that take nutritional balance into consideration.

[0035] The recipe suggestion system includes a time analysis unit that analyzes cooking time. The time analysis unit analyzes cooking time. Cooking time includes, for example, the time for each cooking step, the total cooking time, and an analysis method, but is not limited to these examples. The time analysis unit analyzes the time for each cooking step, for example. The time analysis unit can also analyze the total cooking time. For example, the time analysis unit calculates the total cooking time by adding up the time required for each step. The time analysis unit can also analyze cooking time using a machine learning algorithm. For example, the time analysis unit analyzes cooking time based on past cooking data. This allows the recipe suggestion system to suggest recipes that take cooking time into consideration.

[0036] The input unit automatically inputs the contents of the refrigerator using image recognition technology in addition to barcode scanning and voice input. For example, the input unit may take a photo of ingredients in the refrigerator with a camera and automatically identify and input the ingredients using image recognition technology. The input unit may also scan ingredient packages with a camera and automatically input the ingredients by analyzing the barcodes or labels. For example, the input unit may acquire ingredient information using barcode scanning technology and input the ingredients by comparing it with a database. The input unit may also combine voice input and image recognition to accurately input the ingredient name by simultaneously taking a photo of the ingredient with the camera as the user speaks it. For example, when a user says "tomato" and takes a photo of a tomato with the camera, the input unit combines voice recognition technology and image recognition technology to input the tomato. This allows the refrigerator contents to be automatically input using image recognition technology. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit may input image data acquired by a camera to a generation AI and have the generation AI identify the ingredient from the image data.

[0037] When inputting the contents of the refrigerator, the input unit automatically recognizes expiration dates and prioritizes suggesting ingredients with upcoming expiration dates. For example, the input unit uses image recognition technology to read expiration dates written on ingredient packages and automatically input the information. The input unit can also list ingredients with upcoming expiration dates and suggest recipes that prioritize using them. For example, the input unit notifies the user of ingredients with upcoming expiration dates and encourages the user to use them sooner. The input unit can also suggest optimal recipes based on ingredients with upcoming expiration dates. For example, the input unit inputs recipes that prioritize using ingredients with upcoming expiration dates into the generation AI, and the generation AI generates optimal recipes. This prioritizes suggesting ingredients with upcoming expiration dates, thereby reducing food waste. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without AI. For example, the input unit can input expiration date data into the generation AI and cause the generation AI to suggest ingredients with upcoming expiration dates.

[0038] The configuration input unit adds a function of automatically completing information by referring to past data when inputting family composition and food preferences. For example, the configuration input unit automatically displays family composition and food preferences previously input by the user as candidates. The configuration input unit can also prioritize and suggest frequently used information based on the user's past input history. The configuration input unit can also analyze the user's past input patterns and suggest the optimal input method. For example, the configuration input unit automatically completes family composition and food preferences based on data previously input by the user. This reduces the effort required for input by automatically completing information by referring to past data. Some or all of the above-described processing in the configuration input unit may be performed using, for example, AI, or may be performed without AI. For example, the configuration input unit can input past data into the generation AI and cause the generation AI to perform automatic completion.

[0039] The composition input unit takes into consideration allergy information and health condition when inputting family composition and food preferences. For example, if the user has allergies, the composition input unit inputs that information and suggests ingredients that avoid allergies. The composition input unit can also take the user's health condition into consideration and prioritize suggesting healthy ingredients. The composition input unit can also suggest ingredients suitable for a specific age group based on the user's family composition. For example, when a user inputs allergy information, the composition input unit suggests ingredients that avoid allergies based on that information. This makes it possible to suggest more appropriate recipes by taking allergy information and health condition into consideration. Some or all of the above-mentioned processing in the composition input unit may be performed using, for example, AI, or may be performed without AI. For example, the composition input unit can input allergy information and health condition data into the generation AI and cause the generation AI to suggest appropriate ingredients.

[0040] During analysis, the analysis unit optimizes the analysis algorithm by referring to past analysis data. The analysis unit, for example, selects an optimal analysis algorithm based on the past analysis data. The analysis unit can also analyze the past analysis data to improve the accuracy of the analysis algorithm. The analysis unit can also optimize the efficiency of the analysis by referring to the past analysis data. For example, the analysis unit optimizes the analysis algorithm based on the past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] When generating a recipe, the generation unit optimizes the generation algorithm by referring to past generation data. The generation unit, for example, selects an optimal generation algorithm based on recipe data generated in the past. The generation unit can also analyze past generation data to improve the accuracy of the generation algorithm. The generation unit can also optimize generation efficiency by referring to past generation data. For example, the generation unit optimizes the generation algorithm based on past data. In this way, by optimizing the generation algorithm by referring to past generation data, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation data to the generation AI and cause the generation AI to optimize the generation algorithm.

[0042] When providing a recipe, the providing unit selects the optimal serving method by referring to the user's past ratings. For example, the providing unit preferentially provides recipes that the user has previously rated highly. The providing unit can also select the optimal serving method based on the user's past rating history. The providing unit can also analyze the user's past rating data and optimize the serving method. For example, the providing unit selects the optimal serving method based on the past rating data. In this way, by selecting the optimal serving method by referring to the user's past ratings, it is possible to provide a recipe with higher satisfaction. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past rating data into a generation AI and cause the generation AI to optimize the serving method.

[0043] When collecting reviews, the rating collection unit selects the optimal collection method by referring to the user's past rating history. For example, the rating collection unit prioritizes collecting recipes that the user has previously rated highly. The rating collection unit can also select the optimal collection method based on the user's past rating history. The rating collection unit can also analyze the user's past rating data and optimize the collection method. For example, the rating collection unit selects the optimal collection method based on the past rating data. In this way, by selecting the optimal collection method by referring to the user's past rating history, the efficiency of rating collection can be improved. Some or all of the above-mentioned processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input past rating data into a generation AI and cause the generation AI to optimize the collection method.

[0044] When collecting ratings, the rating collection unit selects the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the rating collection unit provides a rating method that matches the screen size. Furthermore, if the user is using a tablet, the rating collection unit can also provide a rating method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the rating collection unit can also provide a simple and highly visible rating method. This makes it possible to select the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the rating collection unit may be performed using AI, for example, or may be performed without using AI. For example, the rating collection unit may input the user's device information into the generation AI and cause the generation AI to optimize the collection method.

[0045] During improvement, the improvement unit optimizes the improvement algorithm by referring to past evaluation data. The improvement unit, for example, selects an optimal improvement algorithm based on past evaluation data. The improvement unit can also analyze past evaluation data to improve the accuracy of the improvement algorithm. The improvement unit can also optimize the efficiency of the improvement by referring to past evaluation data. For example, the improvement unit optimizes the improvement algorithm based on past data. In this way, the accuracy of the improvement can be improved by optimizing the improvement algorithm by referring to past evaluation data. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input past evaluation data into a generation AI and cause the generation AI to optimize the improvement algorithm.

[0046] During improvement, the improvement unit selects the optimal improvement method taking into account the user's geographical location information. The improvement unit selects the optimal improvement method, for example, based on the user's geographical location information. The improvement unit can also improve the accuracy of the improvement by taking into account the user's geographical location information. The improvement unit can also optimize the efficiency of the improvement by referring to the user's geographical location information. For example, the improvement unit selects the optimal improvement method based on the user's geographical location information. In this way, the accuracy of the improvement can be improved by selecting the optimal improvement method taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed, for example, using AI or without using AI. For example, the improvement unit can input the user's geographical location information into the generation AI and cause the generation AI to optimize the improvement method.

[0047] The balance analysis unit optimizes the analysis algorithm by referring to past data during nutritional balance analysis. The balance analysis unit, for example, selects an optimal analysis algorithm based on past nutritional balance data. The balance analysis unit can also analyze past nutritional balance data to improve the accuracy of the analysis algorithm. The balance analysis unit can also optimize the efficiency of the analysis by referring to past nutritional balance data. For example, the balance analysis unit optimizes the analysis algorithm based on past data. By optimizing the analysis algorithm by referring to past data, the accuracy of the nutritional balance analysis can be improved. Some or all of the above-mentioned processing in the balance analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the balance analysis unit can input past nutritional balance data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0048] The balance analysis unit selects the optimal analysis method during nutritional balance analysis, taking into account the user's health condition. The balance analysis unit selects the optimal nutritional balance analysis method, for example, based on the user's health condition. The balance analysis unit can also improve the accuracy of the analysis by taking into account the user's health condition. The balance analysis unit can also optimize the efficiency of the analysis by referring to the user's health condition. For example, the balance analysis unit selects the optimal nutritional balance analysis method based on the user's health condition. By selecting the optimal analysis method by taking into account the user's health condition, the accuracy of the nutritional balance analysis can be improved. Some or all of the above-described processing in the balance analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the balance analysis unit can input data on the user's health condition into the generation AI and cause the generation AI to optimize the analysis method.

[0049] The time analysis unit optimizes the analysis algorithm by referring to past data when analyzing cooking times. The time analysis unit, for example, selects an optimal analysis algorithm based on past cooking time data. The time analysis unit can also analyze past cooking time data to improve the accuracy of the analysis algorithm. The time analysis unit can also optimize the efficiency of the analysis by referring to past cooking time data. For example, the time analysis unit optimizes the analysis algorithm based on past data. In this way, by optimizing the analysis algorithm by referring to past data, the accuracy of cooking time analysis can be improved. Some or all of the above-mentioned processing in the time analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the time analysis unit can input past cooking time data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0050] The time analysis unit selects the optimal analysis method taking into account the user's schedule when analyzing cooking time. The time analysis unit selects the optimal cooking time analysis method based on, for example, the user's schedule. The time analysis unit can also improve the accuracy of the analysis by taking the user's schedule into account. The time analysis unit can also optimize the efficiency of the analysis by referring to the user's schedule. For example, the time analysis unit selects the optimal cooking time analysis method based on the user's schedule. In this way, by selecting the optimal analysis method taking the user's schedule into account, the accuracy of the cooking time analysis can be improved. Some or all of the above-described processing in the time analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the time analysis unit can input the user's schedule data into the generation AI and cause the generation AI to optimize the analysis method.

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

[0052] The recipe suggestion system can further include a storage suggestion unit that suggests methods for storing ingredients. The storage suggestion unit suggests, for example, how to store ingredients in a refrigerator. For example, it suggests wrapping vegetables in wet paper towels to maintain humidity. The storage suggestion unit can also suggest freezing methods for ingredients that can be frozen. For example, it suggests dividing meat into small portions, wrapping them in plastic wrap, and freezing them. Furthermore, the storage suggestion unit can notify the user of the shelf life of ingredients and encourage them to use them at the appropriate time. This reduces waste and allows ingredients to be stored fresh for a long period of time.

[0053] The providing unit can provide a customized recipe for the generated recipe based on the user's past ratings. For example, the providing unit can suggest a new recipe that incorporates elements of a recipe that the user has previously given a high rating. The providing unit can also analyze the user's past rating data and provide recipes tailored to the user's preferences. For example, if the user likes spicy food, the providing unit can suggest a recipe that emphasizes spiciness. Furthermore, the providing unit can adjust the difficulty level and cooking time of the recipe based on the user's rating history. This makes it possible to provide a customized recipe tailored to the user's preferences.

[0054] The evaluation collection unit can select the optimal collection method in consideration of the user's device information. For example, if the user is using a smartphone, the evaluation collection unit can provide an evaluation method that matches the screen size. Furthermore, if the user is using a tablet, the evaluation collection unit can also provide an evaluation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the evaluation collection unit can also provide an evaluation method that is simple and highly visible. This makes it possible to select the optimal collection method in consideration of the user's device information.

[0055] The recipe suggestion system may further include a shopping list generation unit that generates a shopping list of ingredients. The shopping list generation unit generates a list of necessary ingredients based on the generated recipe, for example. For example, when a user selects a recipe, the list of necessary ingredients is automatically generated. The shopping list generation unit may also reference the user's past purchase history and add frequently purchased ingredients to the list with priority. Furthermore, the shopping list generation unit may take into account ingredient inventory and add only necessary ingredients to the list. This allows the user to purchase ingredients efficiently.

[0056] The recipe suggestion system can further include an allergy management unit that manages allergy information for ingredients. For example, if a user has allergies, the allergy management unit inputs that information and suggests ingredients that avoid allergies. The allergy management unit can also suggest ingredients suitable for a specific age group based on the user's family structure. For example, when a user inputs allergy information, the allergy management unit suggests ingredients that avoid allergies based on that information. This makes it possible to suggest appropriate recipes that take allergy information into consideration.

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

[0058] Step 1: The input unit inputs the contents of the refrigerator. The contents of the refrigerator include the type, quantity, and expiration date of ingredients. The input unit can input ingredients using barcode scanning, voice input, or image recognition technology. For example, when a user speaks the name of an ingredient, the ingredient is input using voice recognition technology. Alternatively, the ingredients in the refrigerator can be photographed with a camera, and the ingredients can be automatically identified and input using image recognition technology. Step 2: The composition input unit inputs the family composition and food preferences. The family composition includes the number of family members, age, gender, etc., while the food preferences include favorite ingredients, disliked ingredients, allergy information, etc. The composition input unit can be manually entered by the user or can have a function to automatically complete the information by referencing past data. For example, the family composition and food preferences previously entered by the user can be automatically displayed as candidates. Step 3: The analysis unit analyzes the information input by the input unit and the configuration input unit. The analysis includes the data analysis method and algorithm to be used. The analysis unit analyzes the input information using data mining techniques and machine learning algorithms. Step 4: The generation unit generates a recipe based on the information analyzed by the analysis unit. The generated recipe includes the type of dish, cooking steps, necessary ingredients, etc. The generation unit generates the recipe using a generation AI. For example, the generation AI receives a prompt such as, "Generate the best dinner recipe based on the contents of the refrigerator, family composition, and food preferences," and generates a recipe. Step 5: The providing unit provides the recipe generated by the generating unit. The provided recipe includes detailed steps and a list of necessary ingredients. The providing unit displays the generated recipe on the user's device and can also print the generated recipe on a printer if the user wishes.

[0059] (Example 2) A recipe suggestion system according to an embodiment of the present invention uses AI to suggest dinner recipes to be made in the future based on input information such as the contents of the refrigerator, family composition, and food preferences. In this recipe suggestion system, a user inputs the contents of the refrigerator, family composition, and food preferences, and an AI analyzes the input information to generate an optimal dinner recipe. The generated recipe is provided to the user. For example, based on the ingredients in the refrigerator, the system can suggest dishes that children like or that the whole family can enjoy. This system eliminates the need to think about dinner menus every day and allows the whole family to enjoy meals that satisfy them. For example, a user inputs the contents of the refrigerator. An easy input method, such as barcode scanning or voice input, is recommended. Next, the user inputs the family composition and food preferences. The AI ​​analyzes this information and generates an optimal recipe. The generated recipe is provided to the user along with detailed instructions and a list of required ingredients. Furthermore, a user-rated recipe can be added. Based on the user's rating, the AI ​​can improve the next suggestion. This allows for more satisfying recipes. It is also important to consider factors such as nutritional balance and cooking time when suggesting recipes. This allows the recipe suggestion system to suggest the best dinner recipe based on the information input by the user.

[0060] A recipe suggestion system according to an embodiment includes an input unit, a configuration input unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs the contents of a refrigerator. The contents of the refrigerator include, for example, but are not limited to, the type, quantity, and expiration date of ingredients. The input unit inputs ingredients using, for example, a barcode scan. The input unit can also input ingredients using voice input. For example, when a user speaks the name of an ingredient, the ingredients are input using voice recognition technology. The input unit can also input ingredients using image recognition technology. For example, the ingredients in the refrigerator are photographed with a camera, and the ingredients are automatically identified and input using image recognition technology. The configuration input unit inputs family composition and food preferences. Family composition includes, for example, but is not limited to, the number of family members, age, and gender. Food preferences include, for example, but are not limited to, favorite ingredients, disliked ingredients, allergy information, and the like. For example, the configuration input unit allows a user to manually input family composition and food preferences. The configuration input unit can also have a function for automatic completion by referencing past data. For example, the system automatically displays family composition and food preferences previously input by the user as candidates. The analysis unit analyzes the information input by the input unit and the configuration input unit. Examples of analysis include, but are not limited to, data analysis techniques and algorithms. The analysis unit analyzes the input information using, for example, data mining technology. The analysis unit can also analyze the input information using a machine learning algorithm. The generation unit generates a recipe based on the information analyzed by the analysis unit. The generated recipe includes, for example, but is not limited to, the type of dish, cooking steps, and required ingredients. The generation unit generates the recipe using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to. For example, the generation AI receives a prompt such as, "Please generate the best dinner recipe based on the contents of my refrigerator, my family composition, and my food preferences," and generates the recipe. The provision unit provides the recipe generated by the generation unit. The provided recipe includes, for example, but is not limited to, detailed steps and a list of required ingredients.The providing unit, for example, displays the generated recipe on the user's device. The providing unit can also print the generated recipe. For example, if the user wishes, the generated recipe is printed using a printer. This allows the recipe suggestion system according to the embodiment to suggest the optimal dinner recipe based on the contents of the refrigerator, family composition, and food preferences.

[0061] The providing unit can provide detailed steps for the generated recipe or a list of required ingredients. For example, the providing unit provides detailed steps for the generated recipe. Detailed steps include, but are not limited to, cooking steps, times, and temperatures. For example, the providing unit provides detailed instructions for each cooking step. The providing unit can also specifically indicate cooking times and temperatures. For example, the providing unit provides detailed steps such as, "Step 1: Cut vegetables. Required time: 10 minutes. Step 2: Stir-fry in a frying pan. Temperature: medium heat. Required time: 5 minutes." The providing unit also provides a list of ingredients required for the generated recipe. The list of required ingredients includes, but is not limited to, the type, quantity, and substitutes of ingredients. For example, the providing unit provides a list for each type of ingredient. The providing unit can also specifically indicate the quantity of ingredients. For example, the providing unit provides a list of required ingredients such as, "Ingredient 1: 200 grams of chicken. Ingredient 2: 1 carrot." This allows the user to check the detailed steps or the list of required ingredients.

[0062] The recipe suggestion system includes a rating collection unit that collects user ratings. The rating collection unit collects user ratings of provided recipes. User ratings include, but are not limited to, satisfaction with the recipe, difficulty of cooking, and taste evaluation. For example, the rating collection unit allows users to assign star ratings to recipes. The rating collection unit also allows users to input comments about recipes. For example, the rating collection unit collects comments such as, "This recipe was very delicious. It was easy to prepare." The rating collection unit can also collect user ratings as numerical data. For example, the rating collection unit allows users to assign scores from 1 to 5 to recipes. This allows the recipe suggestion system to collect user ratings and reflect them in future suggestions.

[0063] The rating collection unit can collect user ratings for the provided recipes. For example, the rating collection unit allows users to assign star ratings to recipes. Star ratings include, but are not limited to, scores from 1 to 5. For example, the rating collection unit can allow users to input comments for recipes. For example, comments include, but are not limited to, satisfaction with the recipe, difficulty of cooking, and taste evaluation. For example, the rating collection unit can allow users to input comments for recipes such as, "This recipe was very delicious. It was easy to prepare." The rating collection unit can also collect user ratings as numerical data. For example, numerical data includes, but are not limited to, scores from 1 to 5. For example, the rating collection unit allows users to assign scores from 1 to 5 to recipes. This allows the recipe suggestion system to collect user ratings and reflect them in future suggestions.

[0064] The recipe suggestion system includes an improvement unit that improves the next suggestion based on the evaluations collected by the evaluation collection unit. The improvement unit improves the next suggestion based on the evaluations collected by the evaluation collection unit. Examples of improvements include, but are not limited to, evaluation data analysis methods and improvement algorithms. For example, the improvement unit analyzes the evaluation data to optimize the next suggestion. The improvement unit can also improve the next suggestion using a machine learning algorithm. For example, the improvement unit customizes the next suggestion based on the user's evaluation data. This allows the recipe suggestion system to improve the next suggestion based on the user's evaluations.

[0065] The recipe suggestion system includes a balance analysis unit that analyzes nutritional balance. The balance analysis unit analyzes nutritional balance. Nutritional balance includes, but is not limited to, the type of nutrient, recommended intake amounts, and analysis methods. The balance analysis unit performs analysis for each type of nutrient, for example. The balance analysis unit can also perform analysis based on recommended intake amounts. For example, the balance analysis unit analyzes the intake amounts of nutrients such as vitamins, minerals, and proteins. The balance analysis unit can also analyze nutritional balance using a machine learning algorithm. For example, the balance analysis unit analyzes nutritional balance based on the user's dietary history. This allows the recipe suggestion system to suggest recipes that take nutritional balance into consideration.

[0066] The recipe suggestion system includes a time analysis unit that analyzes cooking time. The time analysis unit analyzes cooking time. Cooking time includes, for example, the time for each cooking step, the total cooking time, and an analysis method, but is not limited to these examples. The time analysis unit analyzes the time for each cooking step, for example. The time analysis unit can also analyze the total cooking time. For example, the time analysis unit calculates the total cooking time by adding up the time required for each step. The time analysis unit can also analyze cooking time using a machine learning algorithm. For example, the time analysis unit analyzes cooking time based on past cooking data. This allows the recipe suggestion system to suggest recipes that take cooking time into consideration.

[0067] The input unit estimates the user's emotions and adjusts the timing of inputting the refrigerator contents based on the estimated user emotions. For example, if the user is feeling stressed, the input unit postpones inputting the refrigerator contents until a more relaxed time. Furthermore, if the user is relaxed, the input unit can also encourage the user to input the refrigerator contents and suggest that the user input more detailed information. Furthermore, if the user is in a hurry, the input unit can provide a simplified input method, enabling the user to quickly input the refrigerator contents. This allows the timing of inputting the refrigerator contents to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0068] The input unit automatically inputs the contents of the refrigerator using image recognition technology in addition to barcode scanning and voice input. For example, the input unit may take a photo of ingredients in the refrigerator with a camera and automatically identify and input the ingredients using image recognition technology. The input unit may also scan ingredient packages with a camera and automatically input the ingredients by analyzing the barcodes or labels. For example, the input unit may acquire ingredient information using barcode scanning technology and input the ingredients by comparing it with a database. The input unit may also combine voice input and image recognition to accurately input the ingredient name by simultaneously taking a photo of the ingredient with the camera as the user speaks it. For example, when a user says "tomato" and takes a photo of a tomato with the camera, the input unit combines voice recognition technology and image recognition technology to input the tomato. This allows the refrigerator contents to be automatically input using image recognition technology. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit may input image data acquired by a camera to a generation AI and have the generation AI identify the ingredient from the image data.

[0069] When inputting the contents of the refrigerator, the input unit automatically recognizes expiration dates and prioritizes suggesting ingredients with upcoming expiration dates. For example, the input unit uses image recognition technology to read expiration dates written on ingredient packages and automatically input the information. The input unit can also list ingredients with upcoming expiration dates and suggest recipes that prioritize using them. For example, the input unit notifies the user of ingredients with upcoming expiration dates and encourages the user to use them sooner. The input unit can also suggest optimal recipes based on ingredients with upcoming expiration dates. For example, the input unit inputs recipes that prioritize using ingredients with upcoming expiration dates into the generation AI, and the generation AI generates optimal recipes. This prioritizes suggesting ingredients with upcoming expiration dates, thereby reducing food waste. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without AI. For example, the input unit can input expiration date data into the generation AI and cause the generation AI to suggest ingredients with upcoming expiration dates.

[0070] The composition input unit estimates the user's emotions and adjusts the input method for the family composition and food preferences based on the estimated user emotions. For example, when the user is stressed, the composition input unit provides a simplified input method, allowing the user to quickly input the family composition and food preferences. Furthermore, when the user is relaxed, the composition input unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the composition input unit can prioritize voice input, allowing the user to quickly input the family composition and food preferences. This allows the input method for the family composition and food preferences to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the composition input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the composition input unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0071] The configuration input unit adds a function of automatically completing information by referring to past data when inputting family composition and food preferences. For example, the configuration input unit automatically displays family composition and food preferences previously input by the user as candidates. The configuration input unit can also prioritize and suggest frequently used information based on the user's past input history. The configuration input unit can also analyze the user's past input patterns and suggest the optimal input method. For example, the configuration input unit automatically completes family composition and food preferences based on data previously input by the user. This reduces the effort required for input by automatically completing information by referring to past data. Some or all of the above-described processing in the configuration input unit may be performed using, for example, AI, or may be performed without AI. For example, the configuration input unit can input past data into the generation AI and cause the generation AI to perform automatic completion.

[0072] The composition input unit takes into consideration allergy information and health condition when inputting family composition and food preferences. For example, if the user has allergies, the composition input unit inputs that information and suggests ingredients that avoid allergies. The composition input unit can also take the user's health condition into consideration and prioritize suggesting healthy ingredients. The composition input unit can also suggest ingredients suitable for a specific age group based on the user's family composition. For example, when a user inputs allergy information, the composition input unit suggests ingredients that avoid allergies based on that information. This makes it possible to suggest more appropriate recipes by taking allergy information and health condition into consideration. Some or all of the above-mentioned processing in the composition input unit may be performed using, for example, AI, or may be performed without AI. For example, the composition input unit can input allergy information and health condition data into the generation AI and cause the generation AI to suggest appropriate ingredients.

[0073] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit increases the accuracy of the analysis and provides results quickly. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and improve accuracy. Furthermore, if the user is in a hurry, the analysis unit can perform a simplified analysis and provide results quickly. This allows the accuracy of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] During analysis, the analysis unit optimizes the analysis algorithm by referring to past analysis data. The analysis unit, for example, selects an optimal analysis algorithm based on the past analysis data. The analysis unit can also analyze the past analysis data to improve the accuracy of the analysis algorithm. The analysis unit can also optimize the efficiency of the analysis by referring to the past analysis data. For example, the analysis unit optimizes the analysis algorithm based on the past data. In this way, the accuracy of the analysis can be improved by optimizing the analysis algorithm by referring to the past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0075] The generation unit estimates the user's emotions and adjusts the content of the generated recipe based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a simple and hassle-free recipe. The generation unit can also generate a more elaborate recipe if the user is relaxed. The generation unit can also generate a recipe that can be prepared in a short time if the user is in a hurry. This allows the content of the generated recipe to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the recipe.

[0076] When generating a recipe, the generation unit optimizes the generation algorithm by referring to past generation data. The generation unit, for example, selects an optimal generation algorithm based on recipe data generated in the past. The generation unit can also analyze past generation data to improve the accuracy of the generation algorithm. The generation unit can also optimize generation efficiency by referring to past generation data. For example, the generation unit optimizes the generation algorithm based on past data. In this way, by optimizing the generation algorithm by referring to past generation data, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past generation data to the generation AI and cause the generation AI to optimize the generation algorithm.

[0077] The providing unit estimates the user's emotions and adjusts the recipe presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit provides a simple, highly visible recipe. Furthermore, if the user is relaxed, the providing unit can provide a recipe with detailed steps. Furthermore, if the user is in a hurry, the providing unit can provide a recipe that focuses on the main points. This makes it possible to adjust the recipe presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the recipe presentation method.

[0078] When providing a recipe, the providing unit selects the optimal serving method by referring to the user's past ratings. For example, the providing unit preferentially provides recipes that the user has previously rated highly. The providing unit can also select the optimal serving method based on the user's past rating history. The providing unit can also analyze the user's past rating data and optimize the serving method. For example, the providing unit selects the optimal serving method based on the past rating data. In this way, by selecting the optimal serving method by referring to the user's past ratings, it is possible to provide a recipe with higher satisfaction. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past rating data into a generation AI and cause the generation AI to optimize the serving method.

[0079] The evaluation collection unit estimates the user's emotions and adjusts the evaluation collection method based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation collection unit provides a simple evaluation method and quickly collects evaluations. Furthermore, if the user is relaxed, the evaluation collection unit can provide detailed evaluation options and suggest a customizable evaluation method. Furthermore, if the user is in a hurry, the evaluation collection unit can prioritize voice input and quickly collect evaluations. This allows the evaluation collection method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the evaluation collection method.

[0080] When collecting reviews, the rating collection unit selects the optimal collection method by referring to the user's past rating history. For example, the rating collection unit prioritizes collecting recipes that the user has previously rated highly. The rating collection unit can also select the optimal collection method based on the user's past rating history. The rating collection unit can also analyze the user's past rating data and optimize the collection method. For example, the rating collection unit selects the optimal collection method based on the past rating data. In this way, by selecting the optimal collection method by referring to the user's past rating history, the efficiency of rating collection can be improved. Some or all of the above-mentioned processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input past rating data into a generation AI and cause the generation AI to optimize the collection method.

[0081] The evaluation collection unit estimates the user's emotions and determines the priority of the evaluations based on the estimated user emotions. For example, when the user is stressed, the evaluation collection unit prioritizes collecting only important evaluations. Furthermore, when the user is relaxed, the evaluation collection unit can also collect detailed evaluations. Furthermore, when the user is in a hurry, the evaluation collection unit can prioritize collecting minimal evaluations. This allows the evaluation priorities to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation collection unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the evaluations.

[0082] When collecting ratings, the rating collection unit selects the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the rating collection unit provides a rating method that matches the screen size. Furthermore, if the user is using a tablet, the rating collection unit can also provide a rating method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the rating collection unit can also provide a simple and highly visible rating method. This makes it possible to select the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the rating collection unit may be performed using AI, for example, or may be performed without using AI. For example, the rating collection unit may input the user's device information into the generation AI and cause the generation AI to optimize the collection method.

[0083] The improvement unit estimates the user's emotions and adjusts the improvement method based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit provides a simple and highly visible improvement method. Furthermore, if the user is relaxed, the improvement unit can provide an improvement method including detailed steps. Furthermore, if the user is in a hurry, the improvement unit can provide an improvement method that focuses on the main points. This allows the improvement method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the improvement method.

[0084] During improvement, the improvement unit optimizes the improvement algorithm by referring to past evaluation data. The improvement unit, for example, selects an optimal improvement algorithm based on past evaluation data. The improvement unit can also analyze past evaluation data to improve the accuracy of the improvement algorithm. The improvement unit can also optimize the efficiency of the improvement by referring to past evaluation data. For example, the improvement unit optimizes the improvement algorithm based on past data. In this way, the accuracy of the improvement can be improved by optimizing the improvement algorithm by referring to past evaluation data. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input past evaluation data into a generation AI and cause the generation AI to optimize the improvement algorithm.

[0085] The improvement unit estimates the user's emotions and determines the priority of improvements based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit prioritizes only important improvements. Furthermore, if the user is relaxed, the improvement unit can also perform detailed improvements. Furthermore, if the user is in a hurry, the improvement unit can prioritize minimal improvements. This allows the priority of improvements to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of improvements.

[0086] During improvement, the improvement unit selects the optimal improvement method taking into account the user's geographical location information. The improvement unit selects the optimal improvement method, for example, based on the user's geographical location information. The improvement unit can also improve the accuracy of the improvement by taking into account the user's geographical location information. The improvement unit can also optimize the efficiency of the improvement by referring to the user's geographical location information. For example, the improvement unit selects the optimal improvement method based on the user's geographical location information. In this way, the accuracy of the improvement can be improved by selecting the optimal improvement method taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed, for example, using AI or without using AI. For example, the improvement unit can input the user's geographical location information into the generation AI and cause the generation AI to optimize the improvement method.

[0087] The balance analysis unit estimates the user's emotions and adjusts the nutritional balance analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the balance analysis unit provides a simple and highly visible nutritional balance analysis method. Furthermore, if the user is relaxed, the balance analysis unit can provide a nutritional balance analysis method that includes detailed steps. Furthermore, if the user is in a hurry, the balance analysis unit can provide a nutritional balance analysis method that focuses on the main points. This allows the nutritional balance analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the balance analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the balance analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the nutritional balance analysis method.

[0088] The balance analysis unit optimizes the analysis algorithm by referring to past data during nutritional balance analysis. The balance analysis unit, for example, selects an optimal analysis algorithm based on past nutritional balance data. The balance analysis unit can also analyze past nutritional balance data to improve the accuracy of the analysis algorithm. The balance analysis unit can also optimize the efficiency of the analysis by referring to past nutritional balance data. For example, the balance analysis unit optimizes the analysis algorithm based on past data. By optimizing the analysis algorithm by referring to past data, the accuracy of the nutritional balance analysis can be improved. Some or all of the above-mentioned processing in the balance analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the balance analysis unit can input past nutritional balance data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0089] The balance analysis unit estimates the user's emotions and determines the priority of nutritional balance based on the estimated user emotions. For example, if the user is feeling stressed, the balance analysis unit prioritizes analysis of only important nutrients. Furthermore, if the user is relaxed, the balance analysis unit can also analyze detailed nutrients. Furthermore, if the user is in a hurry, the balance analysis unit can prioritize analysis of the minimum number of nutrients. This allows the priority of nutritional balance to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the balance analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the balance analysis unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of nutritional balance.

[0090] The balance analysis unit selects the optimal analysis method during nutritional balance analysis, taking into account the user's health condition. The balance analysis unit selects the optimal nutritional balance analysis method, for example, based on the user's health condition. The balance analysis unit can also improve the accuracy of the analysis by taking into account the user's health condition. The balance analysis unit can also optimize the efficiency of the analysis by referring to the user's health condition. For example, the balance analysis unit selects the optimal nutritional balance analysis method based on the user's health condition. By selecting the optimal analysis method by taking into account the user's health condition, the accuracy of the nutritional balance analysis can be improved. Some or all of the above-described processing in the balance analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the balance analysis unit can input data on the user's health condition into the generation AI and cause the generation AI to optimize the analysis method.

[0091] The time analysis unit estimates the user's emotions and adjusts the cooking time analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the time analysis unit provides a simple and highly visible cooking time analysis method. Furthermore, if the user is relaxed, the time analysis unit can provide a cooking time analysis method that includes detailed steps. Furthermore, if the user is in a hurry, the time analysis unit can provide a cooking time analysis method that focuses on the main points. This allows the cooking time analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the time analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the time analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the cooking time analysis method.

[0092] The time analysis unit optimizes the analysis algorithm by referring to past data when analyzing cooking times. The time analysis unit, for example, selects an optimal analysis algorithm based on past cooking time data. The time analysis unit can also analyze past cooking time data to improve the accuracy of the analysis algorithm. The time analysis unit can also optimize the efficiency of the analysis by referring to past cooking time data. For example, the time analysis unit optimizes the analysis algorithm based on past data. In this way, by optimizing the analysis algorithm by referring to past data, the accuracy of cooking time analysis can be improved. Some or all of the above-mentioned processing in the time analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the time analysis unit can input past cooking time data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0093] The time analysis unit estimates the user's emotions and prioritizes cooking times based on the estimated user emotions. For example, if the user is feeling stressed, the time analysis unit prioritizes analysis of only important cooking times. Furthermore, if the user is relaxed, the time analysis unit can analyze detailed cooking times as well. Furthermore, if the user is in a hurry, the time analysis unit can prioritize analysis of the minimum cooking time. This allows prioritization of cooking times according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the time analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the time analysis unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of cooking times.

[0094] The time analysis unit selects the optimal analysis method taking into account the user's schedule when analyzing cooking time. The time analysis unit selects the optimal cooking time analysis method based on, for example, the user's schedule. The time analysis unit can also improve the accuracy of the analysis by taking the user's schedule into account. The time analysis unit can also optimize the efficiency of the analysis by referring to the user's schedule. For example, the time analysis unit selects the optimal cooking time analysis method based on the user's schedule. In this way, by selecting the optimal analysis method taking the user's schedule into account, the accuracy of the cooking time analysis can be improved. Some or all of the above-described processing in the time analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the time analysis unit can input the user's schedule data into the generation AI and cause the generation AI to optimize the analysis method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, configuration input unit, analysis unit, generation unit, provision unit, evaluation collection unit, improvement unit, balance analysis unit, and time analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs the contents of the refrigerator using the camera 42 or microphone 38B of the smart device 14. The configuration input unit inputs family composition and food preferences using the touch panel 38A of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit generates a recipe using the specific processing unit 290 of the data processing device 12. The provision unit displays the generated recipe using the display 40A of the smart device 14. The evaluation collection unit collects user evaluations using the touch panel 38A of the smart device 14. The improvement unit improves the next proposal using the specific processing unit 290 of the data processing device 12. The balance analysis unit analyzes nutritional balance using the specific processing unit 290 of the data processing device 12. The time analysis unit analyzes the cooking time using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, configuration input unit, analysis unit, generation unit, provision unit, evaluation collection unit, improvement unit, balance analysis unit, and time analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs the contents of the refrigerator using the camera 42 or microphone 238 of the smart glasses 214. The configuration input unit inputs family composition and food preferences using the touch panel of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit generates a recipe using the specific processing unit 290 of the data processing device 12. The provision unit displays the generated recipe using the display of the smart glasses 214. The evaluation collection unit collects user evaluations using the touch panel of the smart glasses 214. The improvement unit improves the next suggestion using the specific processing unit 290 of the data processing device 12. The balance analysis unit analyzes nutritional balance using the specific processing unit 290 of the data processing device 12. The time analysis unit analyzes the cooking time using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, configuration input unit, analysis unit, generation unit, provision unit, evaluation collection unit, improvement unit, balance analysis unit, and time analysis unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit inputs the contents of the refrigerator using the camera 42 or microphone 238 of the headset terminal 314. The configuration input unit inputs family composition and food preferences using the touch panel of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit generates recipes using the specific processing unit 290 of the data processing device 12. The provision unit displays the generated recipes using the display 343 of the headset terminal 314. The evaluation collection unit collects user evaluations using the touch panel of the headset terminal 314. The improvement unit improves the next proposal using the specific processing unit 290 of the data processing device 12. The balance analysis unit analyzes nutritional balance using the specific processing unit 290 of the data processing device 12. The time analysis unit analyzes the cooking time using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, configuration input unit, analysis unit, generation unit, provision unit, evaluation collection unit, improvement unit, balance analysis unit, and time analysis unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs the contents of the refrigerator using the camera 42 or microphone 238 of the robot 414. The configuration input unit inputs family composition and food preferences using the touch panel of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit generates a recipe using the specific processing unit 290 of the data processing device 12. The provision unit displays the generated recipe using the display of the robot 414. The evaluation collection unit collects user evaluations using the touch panel of the robot 414. The improvement unit improves the next proposal using the specific processing unit 290 of the data processing device 12. The balance analysis unit analyzes nutritional balance using the specific processing unit 290 of the data processing device 12. The time analysis unit analyzes the cooking time using the specific processing unit 290 of the data processing device 12.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The recipe suggestion system can further include a storage suggestion unit that suggests methods for storing ingredients. The storage suggestion unit suggests, for example, how to store ingredients in a refrigerator. For example, it suggests wrapping vegetables in wet paper towels to maintain humidity. The storage suggestion unit can also suggest freezing methods for ingredients that can be frozen. For example, it suggests dividing meat into small portions, wrapping them in plastic wrap, and freezing them. Furthermore, the storage suggestion unit can notify the user of the shelf life of ingredients and encourage them to use them at the appropriate time. This reduces waste and allows ingredients to be stored fresh for a long period of time.

[0097] The providing unit can provide a customized recipe for the generated recipe based on the user's past ratings. For example, the providing unit can suggest a new recipe that incorporates elements of a recipe that the user has previously given a high rating. The providing unit can also analyze the user's past rating data and provide recipes tailored to the user's preferences. For example, if the user likes spicy food, the providing unit can suggest a recipe that emphasizes spiciness. Furthermore, the providing unit can adjust the difficulty level and cooking time of the recipe based on the user's rating history. This makes it possible to provide a customized recipe tailored to the user's preferences.

[0098] The evaluation collection unit can estimate the user's emotions and adjust the evaluation collection method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple evaluation method can be provided to quickly collect evaluations. Alternatively, if the user is relaxed, the evaluation collection unit can provide detailed evaluation options and suggest a customizable evaluation method. Alternatively, if the user is in a hurry, the evaluation collection unit can prioritize voice input to quickly collect evaluations. This makes it possible to adjust the evaluation collection method according to the user's emotions.

[0099] The evaluation collection unit can select the optimal collection method in consideration of the user's device information. For example, if the user is using a smartphone, the evaluation collection unit can provide an evaluation method that matches the screen size. Furthermore, if the user is using a tablet, the evaluation collection unit can also provide an evaluation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the evaluation collection unit can also provide an evaluation method that is simple and highly visible. This makes it possible to select the optimal collection method in consideration of the user's device information.

[0100] The recipe suggestion system may further include a shopping list generation unit that generates a shopping list of ingredients. The shopping list generation unit generates a list of necessary ingredients based on the generated recipe, for example. For example, when a user selects a recipe, the list of necessary ingredients is automatically generated. The shopping list generation unit may also reference the user's past purchase history and add frequently purchased ingredients to the list with priority. Furthermore, the shopping list generation unit may take into account ingredient inventory and add only necessary ingredients to the list. This allows the user to purchase ingredients efficiently.

[0101] The balance analysis unit can estimate the user's emotions and adjust the nutritional balance analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible nutritional balance analysis method is provided. Furthermore, if the user is relaxed, the balance analysis unit can provide a nutritional balance analysis method that includes detailed steps. Furthermore, if the user is in a hurry, the balance analysis unit can provide a nutritional balance analysis method that focuses on the main points. This makes it possible to adjust the nutritional balance analysis method according to the user's emotions.

[0102] The time analysis unit can estimate the user's emotions and adjust the cooking time analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible cooking time analysis method can be provided. Furthermore, if the user is relaxed, the time analysis unit can provide a cooking time analysis method that includes detailed steps. Furthermore, if the user is in a hurry, the time analysis unit can provide a cooking time analysis method that focuses on the main points. This makes it possible to adjust the cooking time analysis method according to the user's emotions.

[0103] The recipe suggestion system can further include an allergy management unit that manages allergy information for ingredients. For example, if a user has allergies, the allergy management unit inputs that information and suggests ingredients that avoid allergies. The allergy management unit can also suggest ingredients suitable for a specific age group based on the user's family structure. For example, when a user inputs allergy information, the allergy management unit suggests ingredients that avoid allergies based on that information. This makes it possible to suggest appropriate recipes that take allergy information into consideration.

[0104] The generation unit can estimate the user's emotions and adjust the content of the generated recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a simple and hassle-free recipe. If the user is relaxed, the generation unit can also generate an elaborate recipe. If the user is in a hurry, the generation unit can also generate a recipe that can be cooked in a short time. In this way, the content of the generated recipe can be adjusted according to the user's emotions.

[0105] The providing unit can estimate the user's emotions and adjust the recipe providing method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible recipe. If the user is relaxed, the providing unit can also provide a recipe with detailed steps. If the user is in a hurry, the providing unit can also provide a recipe that focuses on the main points. This makes it possible to adjust the recipe providing method according to the user's emotions.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The input unit inputs the contents of the refrigerator. The contents of the refrigerator include the type, quantity, and expiration date of ingredients. The input unit can input ingredients using barcode scanning, voice input, or image recognition technology. For example, when a user speaks the name of an ingredient, the ingredient is input using voice recognition technology. Alternatively, the ingredients in the refrigerator can be photographed with a camera, and the ingredients can be automatically identified and input using image recognition technology. Step 2: The composition input unit inputs the family composition and food preferences. The family composition includes the number of family members, age, gender, etc., while the food preferences include favorite ingredients, disliked ingredients, allergy information, etc. The composition input unit can be manually entered by the user or can have a function to automatically complete the information by referencing past data. For example, the family composition and food preferences previously entered by the user can be automatically displayed as candidates. Step 3: The analysis unit analyzes the information input by the input unit and the configuration input unit. The analysis includes the data analysis method and algorithm to be used. The analysis unit analyzes the input information using data mining techniques and machine learning algorithms. Step 4: The generation unit generates a recipe based on the information analyzed by the analysis unit. The generated recipe includes the type of dish, cooking steps, necessary ingredients, etc. The generation unit generates the recipe using a generation AI. For example, the generation AI receives a prompt such as, "Generate the best dinner recipe based on the contents of the refrigerator, family composition, and food preferences," and generates a recipe. Step 5: The providing unit provides the recipe generated by the generating unit. The provided recipe includes detailed steps and a list of necessary ingredients. The providing unit displays the generated recipe on the user's device and can also print the generated recipe on a printer if the user wishes.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the contents of the refrigerator; A configuration input section for inputting family composition and food preferences; an analysis unit that analyzes the information input by the input unit and the configuration input unit; a generation unit that generates a recipe based on the information analyzed by the analysis unit; a providing unit that provides the recipe generated by the generating unit; Equipped with A system characterized by:

2. The providing unit Providing detailed instructions or a list of required ingredients for the generated recipe 2. The system of claim 1.

3. Equipped with a rating collection unit that collects user ratings 2. The system of claim 1.

4. The evaluation collection unit Collect user ratings for provided recipes 4. The system of claim 3.

5. An improvement unit that improves the next proposal based on the evaluations collected by the evaluation collection unit.

4. The system of claim 3.

6. Equipped with a balance analysis unit that analyzes nutritional balance 2. The system of claim 1.

7. Equipped with a time analysis unit that analyzes cooking time 2. The system of claim 1.

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

2. The system of claim 1.

9. The input unit When entering the contents of the refrigerator, in addition to barcode scanning and voice input, image recognition technology is used to automatically enter the information.

2. The system of claim 1.

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