System

Abstract

An object of a system according to an embodiment is to automatically generate and provide an optimum recipe based on conditions.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a condition. The analysis unit analyzes the condition input by the reception unit. The generation unit generates a recipe based on the condition analyzed by the analysis unit. The providing unit provides the recipe generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had issues such as the time required to come up with daily lunch box recipes and the difficulty of ensuring nutritional balance.

[0005] The system according to the embodiment aims to automatically generate and provide an optimal recipe based on conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs conditions. The analyzing unit analyzes the conditions input by the receiving unit. The generating unit generates a recipe based on the conditions analyzed by the analyzing unit. The providing unit provides the recipe generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and provide an optimal recipe based on conditions. [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) An automatic recipe generation system according to an embodiment of the present invention uses a generation AI to automatically output daily kindergarten lunch recipes. The automatic recipe generation system inputs information such as nutritional requirements, cooking time, number of children, and budget into the generation AI, which then analyzes these information and creates a one- or two-week supply of recipes. This system is not limited to kindergartens; it can also be used to automatically output recipes for adults' own lunches or their husbands' lunches. For example, in the automatic recipe generation system, a user inputs lunch requirements into the generation AI. For example, if the user wants to consider nutritional balance for children, the user inputs specific nutritional requirements. Other information, such as cooking time, budget, and number of children, can also be input. Furthermore, individual information such as disliked ingredients and allergies can be input. The automatic recipe generation system then analyzes the input information and generates optimal recipes. The generated recipes are provided as one- or two-week menus. For example, for lunches for kindergarteners, nutritionally balanced menus are suggested. For lunches for working adults, quick and easy dishes are suggested. This allows the automatic recipe generation system to eliminate the hassle of having to think up recipes every day. This allows mothers to reduce the time they spend making bento lunches and thinking about menus, and allows children to eat nutritionally balanced bento lunches. It also allows working adults to gain know-how on easy bento recipes. Furthermore, by creating an API for a smartphone app, it has the potential to reach many people across Japan. For example, it is possible to provide recipes for a variety of audiences, including toddlers, elementary school students, junior high school students, high school students, and working adults. This makes it possible to provide a service that caters to a wide range of users.

[0029] An automatic recipe generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives conditions input by a user. For example, conditions such as nutritional information for children, cooking time, the number of children, a budget, food allergies, etc. can be input. The reception unit provides an interface through which the user inputs the conditions using, for example, a smartphone or a PC. The reception unit can also support voice input and image input. For example, when a user inputs conditions by voice, the input is converted into text data using voice recognition technology. The analysis unit analyzes the conditions input by the reception unit. For example, the analysis unit analyzes the input conditions using a generation AI and prepares data for generating an optimal recipe. For example, the analysis unit selects appropriate ingredients and cooking methods based on the input nutritional requirements, cooking time, budget, etc. The generation unit generates recipes based on the data prepared by the analysis unit. For example, the generation unit generates recipes for one or two weeks using a generation AI. The generated recipes are provided as daily menus and ingredient lists. For example, the generation unit suggests nutritionally balanced menus for kindergarteners and quick meals for working adults. The provision unit provides the recipes generated by the generation unit to the user. The provision unit displays the recipes through, for example, a smartphone app or a web app. The provision unit can also print the recipes and provide them on paper. For example, if the user wishes, the recipes can be downloaded in PDF format. This allows the automatic recipe generation system according to the embodiment to provide optimal recipes while saving the user time and effort.

[0030] The reception unit allows users to input conditions such as children's nutrition, cooking time, number of children, budget, disliked ingredients, and allergy information. The reception unit provides an interface through which users can input conditions using, for example, a smartphone or PC. For example, if a user wants to consider the nutritional balance of their children, they can input specific nutrient requirements. The reception unit also allows users to input cooking time, budget, number of children, and other information. For example, if a user wants to cook quickly, they can set the cooking time to 30 minutes or less. Furthermore, the reception unit can also individually input disliked ingredients and allergy information. For example, if a user wants to avoid a specific ingredient, they can add that ingredient to a list. This allows for the input of various conditions to generate more appropriate recipes. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the conditions entered by the user into a generation AI, which then analyzes the conditions.

[0031] The analysis unit can analyze the input conditions and prepare data for generating a recipe. The analysis unit analyzes the input conditions using, for example, a generation AI. For example, the analysis unit selects appropriate ingredients and cooking methods based on the input nutrient requirements, cooking time, budget, etc. The analysis unit can also prepare data for generating a recipe taking into account the input disliked ingredients or allergy information. For example, the analysis unit can suggest alternative ingredients to avoid certain ingredients. Furthermore, the analysis unit can prepare data for generating a nutritionally balanced recipe based on the input conditions. For example, the analysis unit selects ingredients that contain a balanced amount of nutrients such as vitamins, minerals, and protein. This analyzes the conditions and prepares data for generating an optimal recipe, thereby improving the accuracy of the recipe. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit inputs the conditions input by the user into the generation AI, and the generation AI analyzes the conditions.

[0032] The generation unit can generate one-week and two-week worth of recipes based on the analyzed data. The generation unit generates one-week or two-week worth of recipes using, for example, a generation AI. The generated recipes are provided as daily menus and ingredient lists. For example, the generation unit may suggest nutritionally balanced menus for kindergarteners or quick meals for working adults. The generation unit can also generate recipes based on the user's preferences. For example, if the user likes a particular dish, the generation unit may suggest a menu that includes that dish. The generation unit can also generate recipes based on the season or event. For example, cold dishes and light meals may be suggested in the summer, and hot dishes and soups in the winter. This significantly reduces the user's effort by generating one-week or two-week worth of recipes. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit inputs data prepared by the analysis unit into the generation AI, which then generates the recipes.

[0033] The providing unit can provide the generated recipe to the user. The providing unit displays the recipe through, for example, a smartphone app or a web app. The providing unit can also print the recipe and provide it on paper. For example, if the user wishes, the recipe can be downloaded in PDF format. The providing unit can also send the recipe by email. For example, the recipe can be sent to an email address registered by the user. In this way, by providing the generated recipe to the user, the user can easily use the recipe. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the recipe generated by the generation unit into the generation AI, and the generation AI provides the recipe.

[0034] The generation unit can generate recipes tailored to a target audience, such as kindergarteners or working adults. The generation unit generates recipes tailored to a target audience, such as kindergarteners or working adults, using, for example, a generation AI. For example, the generation unit suggests nutritionally balanced menus for kindergarteners or quick meals for working adults. The generation unit can also generate recipes tailored to a user's preferences. For example, if a user prefers a particular dish, the generation unit suggests a menu that includes that dish. Furthermore, the generation unit can generate recipes tailored to a season or event. For example, cold dishes and light meals are suggested in the summer, and hot dishes and soups are suggested in the winter. This allows for the generation of recipes tailored to a wide range of users. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs data prepared by the analysis unit into the generation AI, which then generates a recipe.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can analyze the user's past input history using, for example, a generation AI. For example, the reception unit can automatically display conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's past input data into a generation AI, which then suggests the optimal input method.

[0036] The reception unit can perform filtering based on the user's current health condition and dietary history at the time of input. For example, if the user inputs the results of a recent health check, the reception unit can suggest recipes containing appropriate nutrients based on the results. Furthermore, if the user inputs their past dietary history, the reception unit can suggest recipes containing balanced nutrients. Furthermore, if the user sets a specific health goal, the reception unit can suggest recipes that match that goal. In this way, filtering based on the user's health condition and dietary history can suggest more appropriate recipes. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's health data and dietary history data into the generation AI, and the generation AI performs filtering.

[0037] The reception unit can select an input means depending on the user's input method (voice, text, image) at the time of input. For example, if the user selects voice input, the reception unit inputs conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can input conditions using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's input data to a generation AI, which selects the optimal input means.

[0038] When inputting, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize suggesting ingredients that are easily available in that area. Furthermore, if the user is traveling, the reception unit can suggest ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the reception unit can prioritize suggesting recipes from that area. This allows more appropriate conditions to be input by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs the user's geographical location data to the generation AI, which then suggests highly relevant conditions.

[0039] The reception unit can analyze the user's social media activity and input related conditions at the time of input. The reception unit can analyze the user's social media activity using, for example, a generation AI. For example, the reception unit can suggest conditions by referring to recipes shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related ingredients and dishes. Furthermore, the reception unit can suggest related conditions by referring to the activity of the user's friends on social media. In this way, related conditions can be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's social media data to the generation AI, and the generation AI suggests related conditions.

[0040] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past. Furthermore, the reception unit can analyze the user's past feedback and improve the input interface. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's feedback data to the generation AI, which then customizes the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input conditions. For example, if nutritional balance is important, the analysis unit performs a detailed nutritional analysis. Furthermore, if cooking time is important, the analysis unit can prioritize analysis of recipes that can be made quickly. Furthermore, if budget is important, the analysis unit can prioritize analysis of recipes with high cost performance. In this way, by adjusting the level of detail of the analysis based on the importance of the conditions, the efficiency of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs conditions entered by the user into a generation AI, and the generation AI adjusts the level of detail of the analysis based on the importance of the conditions.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the condition. For example, the analysis unit can apply a nutrition analysis algorithm to conditions related to nutrition. Furthermore, the analysis unit can apply a cooking time analysis algorithm to conditions related to cooking time. Furthermore, the analysis unit can apply a cost analysis algorithm to conditions related to budget. In this way, by applying different analysis algorithms depending on the category of the condition, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit inputs conditions entered by a user into a generation AI, and the generation AI applies different analysis algorithms depending on the category of the condition.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, performs the analysis by referring to recipes that the user previously liked. The analysis unit can also perform the analysis by excluding recipes that the user previously avoided. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the user's past analysis data into the generation AI, and the generation AI improves the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the conditions were submitted. For example, the analysis unit prioritizes analysis of the most recently submitted conditions. The analysis unit can also prioritize analysis of conditions that have not been submitted for a long period of time. Furthermore, the analysis unit can prioritize analysis of conditions submitted within a specific time period. This improves the efficiency of analysis by determining the priority of analysis based on the time when the conditions were submitted. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit inputs time data of the conditions submitted by the user into the generation AI, and the generation AI determines the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of conditions. For example, the analysis unit prioritizes analysis of highly relevant conditions. The analysis unit can also postpone analysis of less relevant conditions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. This improves analysis efficiency by adjusting the order of analysis based on the relevance of conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs relevance data of conditions entered by a user into the generation AI, and the generation AI adjusts the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Also, if the user does not have technical expertise, the analysis unit can use simple terminology. Furthermore, the analysis unit can adjust the explanation of the analysis results according to the user's level of expertise. This improves understanding of the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's level of expertise data into the generation AI, and the generation AI adjusts the use of technical terminology.

[0047] The generation unit can adjust the level of detail of the recipe based on the importance of the conditions when generating a recipe. For example, if nutritional balance is important, the generation unit provides a recipe including detailed nutritional information. Furthermore, if cooking time is important, the generation unit can provide a recipe that can be made quickly. Furthermore, if budget is important, the generation unit can provide a recipe with high cost performance. In this way, by adjusting the level of detail of the recipe based on the importance of the conditions, it is possible to provide a recipe that meets the needs of the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs conditions entered by the user into the generation AI, and the generation AI adjusts the level of detail of the recipe based on the importance of the conditions.

[0048] When generating a recipe, the generation unit can apply different generation algorithms depending on the category of the conditions. For example, the generation unit can apply a nutritional analysis algorithm to conditions related to nutrition. Furthermore, the generation unit can apply a cooking time analysis algorithm to conditions related to cooking time. Furthermore, the generation unit can apply a cost analysis algorithm to conditions related to budget. In this way, by applying different generation algorithms depending on the category of conditions, the accuracy of the recipe is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs conditions entered by a user into the generation AI, and the generation AI applies different generation algorithms depending on the category of the conditions.

[0049] When generating a recipe, the generation unit can improve the accuracy of generation by referring to the user's past recipe results. The generation unit generates a recipe by referring to, for example, recipes that the user has previously preferred. The generation unit can also generate a recipe by excluding recipes that the user has previously avoided. Furthermore, the generation unit can adjust the generation algorithm based on the user's past feedback. This improves the accuracy of generation by referring to the past recipe results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past recipe data into the generation AI, and the generation AI improves the accuracy of generation.

[0050] When generating a recipe, the generation unit can determine the priority of the recipe based on the time when the conditions were submitted. For example, the generation unit can prioritize the most recently submitted conditions in the recipe. The generation unit can also prioritize the conditions that have not been submitted for a long period of time in the recipe. Furthermore, the generation unit can prioritize the conditions submitted within a specific time period in the recipe. This allows recipes that meet the needs of the user to be provided by determining the priority of the recipe based on the time when the conditions were submitted. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the generation unit inputs time data of the conditions submitted by the user into the generation AI, and the generation AI determines the priority of the recipe.

[0051] The generation unit can adjust the order of the recipe based on the relevance of the conditions when generating a recipe. For example, the generation unit preferentially reflects highly relevant conditions in the recipe. The generation unit can also postpone less relevant conditions. Furthermore, the generation unit can dynamically adjust the order of the recipe based on the relevance of the conditions. This makes it possible to provide recipes that meet the needs of the user by adjusting the order of the recipe based on the relevance of the conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit inputs relevance data of conditions entered by the user into the generation AI, and the generation AI adjusts the order of the recipe.

[0052] When generating a recipe, the generation unit can adjust the use of technical terms in the recipe according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can use detailed technical terms. Also, if the user does not have technical expertise, the generation unit can use simple terms. Furthermore, the generation unit can adjust the recipe description according to the user's level of expertise. This improves understanding of the recipe by adjusting the use of technical terms in the recipe according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's level of expertise data into the generation AI, and the generation AI adjusts the use of technical terms.

[0053] When providing a recipe, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit can prioritize providing recipes that the user has previously preferred. The providing unit can also exclude recipes that the user has previously avoided. Furthermore, the providing unit can select the optimal providing method based on the user's past usage history. This makes it possible to select the optimal providing method by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit inputs the user's past usage data into the generation AI, and the generation AI selects the optimal providing method.

[0054] When providing a recipe, the providing unit can customize the provided content based on the user's current living situation. For example, if the user is busy, the providing unit can provide a recipe that can be made quickly. Also, if the user is relaxed, the providing unit can provide a recipe that can be made over time. Furthermore, if the user has specific ingredients, the providing unit can provide a recipe using those ingredients. In this way, by customizing the provided content based on the user's current living situation, recipes that meet the user's needs can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the user's living situation data into the generation AI, and the generation AI customizes the provided content.

[0055] The providing unit can improve the providing method by reflecting user feedback when providing a recipe. For example, if a user provides feedback on a provided recipe, the providing unit improves the providing method based on that feedback. Also, if a user likes a particular recipe, the providing unit can provide a recipe similar to that recipe. Furthermore, the providing unit can analyze the user's feedback and dynamically adjust the providing method. This allows the providing method to be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs user feedback data into the generation AI, which then improves the providing method.

[0056] When providing a recipe, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide recipes using ingredients that are easily available in that area. Furthermore, if the user is traveling, the providing unit can provide recipes using ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the providing unit can provide recipes from that area. This allows the optimal providing method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the user's geographical location data into the generation AI, which then selects the optimal providing method.

[0057] When providing a recipe, the providing unit can analyze the user's social media activity and customize the provided content. The providing unit, for example, uses a generation AI to analyze the user's social media activity. For example, the providing unit customizes the provided content by referring to recipes shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related recipes. Furthermore, the providing unit can provide related recipes by referring to the activity of the user's friends on social media. In this way, the provided content can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's social media data into the generation AI, and the generation AI customizes the provided content.

[0058] When providing a recipe, the providing unit can customize the serving method by reflecting the user's past feedback. The providing unit, for example, uses a generation AI to analyze the user's past feedback. For example, the providing unit suggests an optimal serving method based on feedback provided by the user in the past. The providing unit can also avoid serving methods that the user has been dissatisfied with in the past. Furthermore, the providing unit can analyze the user's past feedback and improve the serving method. This makes it possible to customize the serving method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's feedback data into the generation AI, and the generation AI customizes the serving method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past meal history and suggest recipes based on the user's preferences and eating patterns. For example, it can prioritize suggestions of dishes that the user has frequently eaten in the past or ingredients that the user liked. The reception unit can also suggest recipes that exclude ingredients or dishes that the user has avoided in the past. Furthermore, the reception unit can analyze the nutritional balance from the user's meal history and suggest recipes that supplement missing nutrients. This makes it possible to provide more personalized recipes by taking the user's past meal history into consideration.

[0061] The generation unit can generate recipes taking into account the user's geographical location information. For example, if the user lives in a specific area, recipes using ingredients that are easily available in that area can be suggested. Also, if the user is traveling, recipes using ingredients that are available at the travel destination can be suggested. Furthermore, if the user prefers cuisine from a specific region, traditional recipes from that region can be suggested. In this way, by taking into account the user's geographical location information, more appropriate recipes can be provided.

[0062] The reception unit can analyze the user's social media activity and suggest related recipes. For example, it can suggest similar recipes based on recipes the user has shared on social media. It can also analyze the content of the user's social media posts to suggest related ingredients and dishes. It can also suggest related recipes based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide more personalized recipes.

[0063] The analysis unit can generate recipes taking into account the user's health data. For example, if the user inputs the results of a recent health check, recipes containing appropriate nutrients can be suggested based on those results. Also, if the user sets a specific health goal, recipes that match that goal can be suggested. Furthermore, if the user inputs their past diet history, recipes containing balanced nutrients can be suggested. This makes it possible to provide more appropriate recipes by taking the user's health data into consideration.

[0064] The providing unit can improve the recipe providing method by reflecting the user's past feedback. For example, if the user provides feedback on a provided recipe, the providing unit can improve the providing method based on that feedback. Also, if the user likes a particular recipe, it can provide recipes similar to that recipe. Furthermore, it can analyze the user's feedback and dynamically adjust the providing method. In this way, the providing method can be improved by reflecting the user's feedback.

[0065] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis can be performed by referring to recipes that the user liked in the past. The analysis can also be performed by excluding recipes that the user avoided in the past. Furthermore, the analysis algorithm can be adjusted based on the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit accepts conditions entered by the user. For example, conditions can be entered such as nutritional information for children, cooking time, number of children, budget, disliked ingredients, and allergy information. The reception unit provides an interface for users to enter conditions using a smartphone or computer. It can also support voice input and image input. For example, if the user enters conditions by voice, it converts them into text data using voice recognition technology. Step 2: The analysis unit analyzes the conditions entered by the reception unit. The analysis unit uses the generation AI to analyze the entered conditions and prepares data to generate the optimal recipe. For example, it selects appropriate ingredients and cooking methods based on the entered nutritional requirements, cooking time, budget, etc. Step 3: The generation unit generates recipes based on the data prepared by the analysis unit. The generation unit uses generative AI to generate recipes for one or two weeks. The generated recipes are provided as daily menus and ingredient lists. For example, it suggests nutritionally balanced menus for kindergarten children and quick and easy meals for working adults. Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through a smartphone app or a web app. The recipe can also be printed and provided in paper form. For example, if the user wishes, the recipe can be downloaded in PDF format.

[0068] (Example 2) An automatic recipe generation system according to an embodiment of the present invention uses a generation AI to automatically output daily kindergarten lunch recipes. The automatic recipe generation system inputs information such as nutritional requirements, cooking time, number of children, and budget into the generation AI, which then analyzes these information and creates a one- or two-week supply of recipes. This system is not limited to kindergartens; it can also be used to automatically output recipes for adults' own lunches or their husbands' lunches. For example, in the automatic recipe generation system, a user inputs lunch requirements into the generation AI. For example, if the user wants to consider nutritional balance for children, the user inputs specific nutritional requirements. Other information, such as cooking time, budget, and number of children, can also be input. Furthermore, individual information such as disliked ingredients and allergies can be input. The automatic recipe generation system then analyzes the input information and generates optimal recipes. The generated recipes are provided as one- or two-week menus. For example, for lunches for kindergarteners, nutritionally balanced menus are suggested. For lunches for working adults, quick and easy dishes are suggested. This allows the automatic recipe generation system to eliminate the hassle of having to think up recipes every day. This allows mothers to reduce the time they spend making bento lunches and thinking about menus, and allows children to eat nutritionally balanced bento lunches. It also allows working adults to gain know-how on easy bento recipes. Furthermore, by creating an API for a smartphone app, it has the potential to reach many people across Japan. For example, it is possible to provide recipes for a variety of audiences, including toddlers, elementary school students, junior high school students, high school students, and working adults. This makes it possible to provide a service that caters to a wide range of users.

[0069] An automatic recipe generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives conditions input by a user. For example, conditions such as nutritional information for children, cooking time, the number of children, a budget, food allergies, etc. can be input. The reception unit provides an interface through which the user inputs the conditions using, for example, a smartphone or a PC. The reception unit can also support voice input and image input. For example, when a user inputs conditions by voice, the input is converted into text data using voice recognition technology. The analysis unit analyzes the conditions input by the reception unit. For example, the analysis unit analyzes the input conditions using a generation AI and prepares data for generating an optimal recipe. For example, the analysis unit selects appropriate ingredients and cooking methods based on the input nutritional requirements, cooking time, budget, etc. The generation unit generates recipes based on the data prepared by the analysis unit. For example, the generation unit generates recipes for one or two weeks using a generation AI. The generated recipes are provided as daily menus and ingredient lists. For example, the generation unit suggests nutritionally balanced menus for kindergarteners and quick meals for working adults. The provision unit provides the recipes generated by the generation unit to the user. The provision unit displays the recipes through, for example, a smartphone app or a web app. The provision unit can also print the recipes and provide them on paper. For example, if the user wishes, the recipes can be downloaded in PDF format. This allows the automatic recipe generation system according to the embodiment to provide optimal recipes while saving the user time and effort.

[0070] The reception unit allows users to input conditions such as children's nutrition, cooking time, number of children, budget, disliked ingredients, and allergy information. The reception unit provides an interface through which users can input conditions using, for example, a smartphone or PC. For example, if a user wants to consider the nutritional balance of their children, they can input specific nutrient requirements. The reception unit also allows users to input cooking time, budget, number of children, and other information. For example, if a user wants to cook quickly, they can set the cooking time to 30 minutes or less. Furthermore, the reception unit can also individually input disliked ingredients and allergy information. For example, if a user wants to avoid a specific ingredient, they can add that ingredient to a list. This allows for the input of various conditions to generate more appropriate recipes. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the conditions entered by the user into a generation AI, which then analyzes the conditions.

[0071] The analysis unit can analyze the input conditions and prepare data for generating a recipe. The analysis unit analyzes the input conditions using, for example, a generation AI. For example, the analysis unit selects appropriate ingredients and cooking methods based on the input nutrient requirements, cooking time, budget, etc. The analysis unit can also prepare data for generating a recipe taking into account the input disliked ingredients or allergy information. For example, the analysis unit can suggest alternative ingredients to avoid certain ingredients. Furthermore, the analysis unit can prepare data for generating a nutritionally balanced recipe based on the input conditions. For example, the analysis unit selects ingredients that contain a balanced amount of nutrients such as vitamins, minerals, and protein. This analyzes the conditions and prepares data for generating an optimal recipe, thereby improving the accuracy of the recipe. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit inputs the conditions input by the user into the generation AI, and the generation AI analyzes the conditions.

[0072] The generation unit can generate one-week and two-week worth of recipes based on the analyzed data. The generation unit generates one-week or two-week worth of recipes using, for example, a generation AI. The generated recipes are provided as daily menus and ingredient lists. For example, the generation unit may suggest nutritionally balanced menus for kindergarteners or quick meals for working adults. The generation unit can also generate recipes based on the user's preferences. For example, if the user likes a particular dish, the generation unit may suggest a menu that includes that dish. The generation unit can also generate recipes based on the season or event. For example, cold dishes and light meals may be suggested in the summer, and hot dishes and soups in the winter. This significantly reduces the user's effort by generating one-week or two-week worth of recipes. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit inputs data prepared by the analysis unit into the generation AI, which then generates the recipes.

[0073] The providing unit can provide the generated recipe to the user. The providing unit displays the recipe through, for example, a smartphone app or a web app. The providing unit can also print the recipe and provide it on paper. For example, if the user wishes, the recipe can be downloaded in PDF format. The providing unit can also send the recipe by email. For example, the recipe can be sent to an email address registered by the user. In this way, by providing the generated recipe to the user, the user can easily use the recipe. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the recipe generated by the generation unit into the generation AI, and the generation AI provides the recipe.

[0074] The generation unit can generate recipes tailored to a target audience, such as kindergarteners or working adults. The generation unit generates recipes tailored to a target audience, such as kindergarteners or working adults, using, for example, a generation AI. For example, the generation unit suggests nutritionally balanced menus for kindergarteners or quick meals for working adults. The generation unit can also generate recipes tailored to a user's preferences. For example, if a user prefers a particular dish, the generation unit suggests a menu that includes that dish. Furthermore, the generation unit can generate recipes tailored to a season or event. For example, cold dishes and light meals are suggested in the summer, and hot dishes and soups are suggested in the winter. This allows for the generation of recipes tailored to a wide range of users. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs data prepared by the analysis unit into the generation AI, which then generates a recipe.

[0075] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly enter conditions. This improves user convenience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit inputs the user's facial expression data and voice data into the generation AI, which then estimates the emotion.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit can analyze the user's past input history using, for example, a generation AI. For example, the reception unit can automatically display conditions that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest conditions to be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's past input data into a generation AI, which then suggests the optimal input method.

[0077] The reception unit can perform filtering based on the user's current health condition and dietary history at the time of input. For example, if the user inputs the results of a recent health check, the reception unit can suggest recipes containing appropriate nutrients based on the results. Furthermore, if the user inputs their past dietary history, the reception unit can suggest recipes containing balanced nutrients. Furthermore, if the user sets a specific health goal, the reception unit can suggest recipes that match that goal. In this way, filtering based on the user's health condition and dietary history can suggest more appropriate recipes. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's health data and dietary history data into the generation AI, and the generation AI performs filtering.

[0078] The reception unit can select an input means depending on the user's input method (voice, text, image) at the time of input. For example, if the user selects voice input, the reception unit inputs conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can input conditions using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's input data to a generation AI, which selects the optimal input means.

[0079] The reception unit can estimate the user's emotions and determine the priority of the conditions to be input based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is feeling stressed, the reception unit prioritizes input of the most important conditions. Furthermore, if the user is relaxed, the reception unit can provide the user with time to input detailed conditions. Furthermore, if the user is in a hurry, the reception unit allows the user to input only the minimum number of conditions. This improves input efficiency by determining the priority of the conditions to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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-mentioned processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit inputs the user's facial expression data and voice data into the generation AI, and the generation AI estimates the emotion.

[0080] When inputting, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize suggesting ingredients that are easily available in that area. Furthermore, if the user is traveling, the reception unit can suggest ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the reception unit can prioritize suggesting recipes from that area. This allows more appropriate conditions to be input by taking the user's geographical location information into consideration. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs the user's geographical location data to the generation AI, which then suggests highly relevant conditions.

[0081] The reception unit can analyze the user's social media activity and input related conditions at the time of input. The reception unit can analyze the user's social media activity using, for example, a generation AI. For example, the reception unit can suggest conditions by referring to recipes shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related ingredients and dishes. Furthermore, the reception unit can suggest related conditions by referring to the activity of the user's friends on social media. In this way, related conditions can be input by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the user's social media data to the generation AI, and the generation AI suggests related conditions.

[0082] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also avoid input methods that the user has been dissatisfied with in the past. Furthermore, the reception unit can analyze the user's past feedback and improve the input interface. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's feedback data to the generation AI, which then customizes the input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit uses, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and suggests an optimal recipe. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and suggest a simple recipe. Furthermore, if the user is feeling stressed, the analysis unit can perform a simple analysis and suggest an easy-to-make recipe. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit inputs the user's facial expression data and voice data into the generation AI, which then estimates the emotion.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input conditions. For example, if nutritional balance is important, the analysis unit performs a detailed nutritional analysis. Furthermore, if cooking time is important, the analysis unit can prioritize analysis of recipes that can be made quickly. Furthermore, if budget is important, the analysis unit can prioritize analysis of recipes with high cost performance. In this way, by adjusting the level of detail of the analysis based on the importance of the conditions, the efficiency of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs conditions entered by the user into a generation AI, and the generation AI adjusts the level of detail of the analysis based on the importance of the conditions.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the condition. For example, the analysis unit can apply a nutrition analysis algorithm to conditions related to nutrition. Furthermore, the analysis unit can apply a cooking time analysis algorithm to conditions related to cooking time. Furthermore, the analysis unit can apply a cost analysis algorithm to conditions related to budget. In this way, by applying different analysis algorithms depending on the category of the condition, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit inputs conditions entered by a user into a generation AI, and the generation AI applies different analysis algorithms depending on the category of the condition.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, performs the analysis by referring to recipes that the user previously liked. The analysis unit can also perform the analysis by excluding recipes that the user previously avoided. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's past feedback. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the user's past analysis data into the generation AI, and the generation AI improves the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the analysis unit performs a quick analysis when the user is in a hurry. The analysis unit can perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a simple analysis when the user is stressed. This improves the efficiency of analysis by determining the analysis priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit inputs the user's facial expression data and voice data into the generation AI, and the generation AI estimates the emotion.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time when the conditions were submitted. For example, the analysis unit prioritizes analysis of the most recently submitted conditions. The analysis unit can also prioritize analysis of conditions that have not been submitted for a long period of time. Furthermore, the analysis unit can prioritize analysis of conditions submitted within a specific time period. This improves the efficiency of analysis by determining the priority of analysis based on the time when the conditions were submitted. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit inputs time data of the conditions submitted by the user into the generation AI, and the generation AI determines the priority of analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of conditions. For example, the analysis unit prioritizes analysis of highly relevant conditions. The analysis unit can also postpone analysis of less relevant conditions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of conditions. This improves analysis efficiency by adjusting the order of analysis based on the relevance of conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs relevance data of conditions entered by a user into the generation AI, and the generation AI adjusts the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Also, if the user does not have technical expertise, the analysis unit can use simple terminology. Furthermore, the analysis unit can adjust the explanation of the analysis results according to the user's level of expertise. This improves understanding of the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs the user's level of expertise data into the generation AI, and the generation AI adjusts the use of technical terminology.

[0091] The generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the generation unit can provide a detailed recipe when the user is relaxed. The generation unit can also provide a concise recipe when the user is in a hurry. Furthermore, the generation unit can provide a simple recipe when the user is stressed. This improves user convenience by adjusting the way the recipe is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit inputs the user's facial expression data and voice data into the generation AI, and the generation AI estimates the emotion.

[0092] The generation unit can adjust the level of detail of the recipe based on the importance of the conditions when generating a recipe. For example, if nutritional balance is important, the generation unit provides a recipe including detailed nutritional information. Furthermore, if cooking time is important, the generation unit can provide a recipe that can be made quickly. Furthermore, if budget is important, the generation unit can provide a recipe with high cost performance. In this way, by adjusting the level of detail of the recipe based on the importance of the conditions, it is possible to provide a recipe that meets the needs of the user. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs conditions entered by the user into the generation AI, and the generation AI adjusts the level of detail of the recipe based on the importance of the conditions.

[0093] When generating a recipe, the generation unit can apply different generation algorithms depending on the category of the conditions. For example, the generation unit can apply a nutritional analysis algorithm to conditions related to nutrition. Furthermore, the generation unit can apply a cooking time analysis algorithm to conditions related to cooking time. Furthermore, the generation unit can apply a cost analysis algorithm to conditions related to budget. In this way, by applying different generation algorithms depending on the category of conditions, the accuracy of the recipe is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs conditions entered by a user into the generation AI, and the generation AI applies different generation algorithms depending on the category of the conditions.

[0094] When generating a recipe, the generation unit can improve the accuracy of generation by referring to the user's past recipe results. The generation unit generates a recipe by referring to, for example, recipes that the user has previously preferred. The generation unit can also generate a recipe by excluding recipes that the user has previously avoided. Furthermore, the generation unit can adjust the generation algorithm based on the user's past feedback. This improves the accuracy of generation by referring to the past recipe results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's past recipe data into the generation AI, and the generation AI improves the accuracy of generation.

[0095] The generation unit can estimate the user's emotions and adjust the length of the recipe based on the estimated user emotions. The generation unit uses, for example, facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the generation unit can provide a short, concise recipe. Furthermore, if the user is relaxed, the generation unit can provide a longer recipe with detailed instructions. Furthermore, if the user is stressed, the generation unit can provide a simple, easy-to-understand recipe. This improves user convenience by adjusting the length of the recipe 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. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit inputs the user's facial expression data and voice data into the generation AI, which then estimates the emotion.

[0096] When generating a recipe, the generation unit can determine the priority of the recipe based on the time when the conditions were submitted. For example, the generation unit can prioritize the most recently submitted conditions in the recipe. The generation unit can also prioritize the conditions that have not been submitted for a long period of time in the recipe. Furthermore, the generation unit can prioritize the conditions submitted within a specific time period in the recipe. This allows recipes that meet the needs of the user to be provided by determining the priority of the recipe based on the time when the conditions were submitted. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the generation unit inputs time data of the conditions submitted by the user into the generation AI, and the generation AI determines the priority of the recipe.

[0097] The generation unit can adjust the order of the recipe based on the relevance of the conditions when generating a recipe. For example, the generation unit preferentially reflects highly relevant conditions in the recipe. The generation unit can also postpone less relevant conditions. Furthermore, the generation unit can dynamically adjust the order of the recipe based on the relevance of the conditions. This makes it possible to provide recipes that meet the needs of the user by adjusting the order of the recipe based on the relevance of the conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit inputs relevance data of conditions entered by the user into the generation AI, and the generation AI adjusts the order of the recipe.

[0098] When generating a recipe, the generation unit can adjust the use of technical terms in the recipe according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can use detailed technical terms. Also, if the user does not have technical expertise, the generation unit can use simple terms. Furthermore, the generation unit can adjust the recipe description according to the user's level of expertise. This improves understanding of the recipe by adjusting the use of technical terms in the recipe according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's level of expertise data into the generation AI, and the generation AI adjusts the use of technical terms.

[0099] The providing unit can estimate the user's emotions and adjust the recipe provision method based on the estimated user emotions. The providing unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the providing unit can provide a detailed recipe when the user is relaxed. Furthermore, the providing unit can provide a concise recipe when the user is in a hurry. Furthermore, the providing unit can provide a simple recipe when the user is stressed. This improves user convenience by adjusting the recipe provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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-mentioned processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit inputs the user's facial expression data and voice data into the generation AI, and the generation AI estimates the emotion.

[0100] When providing a recipe, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit can prioritize providing recipes that the user has previously preferred. The providing unit can also exclude recipes that the user has previously avoided. Furthermore, the providing unit can select the optimal providing method based on the user's past usage history. This makes it possible to select the optimal providing method by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit inputs the user's past usage data into the generation AI, and the generation AI selects the optimal providing method.

[0101] When providing a recipe, the providing unit can customize the provided content based on the user's current living situation. For example, if the user is busy, the providing unit can provide a recipe that can be made quickly. Also, if the user is relaxed, the providing unit can provide a recipe that can be made over time. Furthermore, if the user has specific ingredients, the providing unit can provide a recipe using those ingredients. In this way, by customizing the provided content based on the user's current living situation, recipes that meet the user's needs can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the user's living situation data into the generation AI, and the generation AI customizes the provided content.

[0102] The providing unit can improve the providing method by reflecting user feedback when providing a recipe. For example, if a user provides feedback on a provided recipe, the providing unit improves the providing method based on that feedback. Also, if a user likes a particular recipe, the providing unit can provide a recipe similar to that recipe. Furthermore, the providing unit can analyze the user's feedback and dynamically adjust the providing method. This allows the providing method to be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs user feedback data into the generation AI, which then improves the providing method.

[0103] The providing unit can estimate the user's emotions and adjust the order in which recipes are provided based on the estimated user emotions. The providing unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is in a hurry, the providing unit prioritizes providing recipes that can be provided quickly. Furthermore, if the user is relaxed, the providing unit can provide detailed recipes. Furthermore, if the user is feeling stressed, the providing unit can provide simple recipes. This improves user convenience by adjusting the order in which recipes are provided 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. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's facial expression data and voice data into the generation AI, which then estimates the emotion.

[0104] When providing a recipe, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the providing unit can provide recipes using ingredients that are easily available in that area. Furthermore, if the user is traveling, the providing unit can provide recipes using ingredients that are available at the user's travel destination. Furthermore, if the user prefers cuisine from a specific area, the providing unit can provide recipes from that area. This allows the optimal providing method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the user's geographical location data into the generation AI, which then selects the optimal providing method.

[0105] When providing a recipe, the providing unit can analyze the user's social media activity and customize the provided content. The providing unit, for example, uses a generation AI to analyze the user's social media activity. For example, the providing unit customizes the provided content by referring to recipes shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related recipes. Furthermore, the providing unit can provide related recipes by referring to the activity of the user's friends on social media. In this way, the provided content can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's social media data into the generation AI, and the generation AI customizes the provided content.

[0106] When providing a recipe, the providing unit can customize the serving method by reflecting the user's past feedback. The providing unit, for example, uses a generation AI to analyze the user's past feedback. For example, the providing unit suggests an optimal serving method based on feedback provided by the user in the past. The providing unit can also avoid serving methods that the user has been dissatisfied with in the past. Furthermore, the providing unit can analyze the user's past feedback and improve the serving method. This makes it possible to customize the serving method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs the user's feedback data into the generation AI, and the generation AI customizes the serving method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive conditions input by a user using the reception device 38 of the smart device 14. For example, the analysis unit can analyze the conditions input by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a recipe by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated recipe to a user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive conditions input by a user using the microphone 238 of the smart glasses 214. For example, the analysis unit can analyze the conditions input by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a recipe by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated recipe to a user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive conditions input by a user using the microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze the conditions input by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a recipe by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated recipe to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive conditions input by a user using the microphone 238 of the robot 414. For example, the analysis unit can analyze the conditions input by the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a recipe by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide the generated recipe to the user using the speaker 240 of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past meal history and suggest recipes based on the user's preferences and eating patterns. For example, it can prioritize suggestions of dishes that the user has frequently eaten in the past or ingredients that the user liked. The reception unit can also suggest recipes that exclude ingredients or dishes that the user has avoided in the past. Furthermore, the reception unit can analyze the nutritional balance from the user's meal history and suggest recipes that supplement missing nutrients. This makes it possible to provide more personalized recipes by taking the user's past meal history into consideration.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis of recipes that are simple and easy to make. Also, if the user is relaxed, it can prioritize analysis of recipes that can be made over time. Furthermore, if the user is in a hurry, it can prioritize analysis of recipes that can be made quickly. In this way, by adjusting the analysis priority according to the user's emotions, it is possible to provide recipes that meet the user's needs.

[0110] The generation unit can generate recipes taking into account the user's geographical location information. For example, if the user lives in a specific area, recipes using ingredients that are easily available in that area can be suggested. Also, if the user is traveling, recipes using ingredients that are available at the travel destination can be suggested. Furthermore, if the user prefers cuisine from a specific region, traditional recipes from that region can be suggested. In this way, by taking into account the user's geographical location information, more appropriate recipes can be provided.

[0111] The providing unit can estimate the user's emotions and adjust the recipe providing method based on the estimated user emotions. For example, if the user is relaxed, a detailed recipe can be provided. If the user is in a hurry, a concise recipe can be provided. Furthermore, if the user is feeling stressed, a simple recipe can be provided. In this way, by adjusting the recipe providing method according to the user's emotions, convenience for the user is improved.

[0112] The reception unit can analyze the user's social media activity and suggest related recipes. For example, it can suggest similar recipes based on recipes the user has shared on social media. It can also analyze the content of the user's social media posts to suggest related ingredients and dishes. It can also suggest related recipes based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide more personalized recipes.

[0113] The analysis unit can generate recipes taking into account the user's health data. For example, if the user inputs the results of a recent health check, recipes containing appropriate nutrients can be suggested based on those results. Also, if the user sets a specific health goal, recipes that match that goal can be suggested. Furthermore, if the user inputs their past diet history, recipes containing balanced nutrients can be suggested. This makes it possible to provide more appropriate recipes by taking the user's health data into consideration.

[0114] The generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed recipe can be provided. If the user is in a hurry, a concise recipe can be provided. Furthermore, if the user is stressed, a simple recipe can be provided. This improves user convenience by adjusting the way the recipe is presented according to the user's emotions.

[0115] The providing unit can improve the recipe providing method by reflecting the user's past feedback. For example, if the user provides feedback on a provided recipe, the providing unit can improve the providing method based on that feedback. Also, if the user likes a particular recipe, it can provide recipes similar to that recipe. Furthermore, it can analyze the user's feedback and dynamically adjust the providing method. In this way, the providing method can be improved by reflecting the user's feedback.

[0116] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow conditions to be entered quickly. In this way, user convenience is improved by adjusting the input interface according to the user's emotions.

[0117] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis can be performed by referring to recipes that the user liked in the past. The analysis can also be performed by excluding recipes that the user avoided in the past. Furthermore, the analysis algorithm can be adjusted based on the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit accepts conditions entered by the user. For example, conditions can be entered such as nutritional information for children, cooking time, number of children, budget, disliked ingredients, and allergy information. The reception unit provides an interface for users to enter conditions using a smartphone or computer. It can also support voice input and image input. For example, if the user enters conditions by voice, it converts them into text data using voice recognition technology. Step 2: The analysis unit analyzes the conditions entered by the reception unit. The analysis unit uses the generation AI to analyze the entered conditions and prepares data to generate the optimal recipe. For example, it selects appropriate ingredients and cooking methods based on the entered nutritional requirements, cooking time, budget, etc. Step 3: The generation unit generates recipes based on the data prepared by the analysis unit. The generation unit uses generative AI to generate recipes for one or two weeks. The generated recipes are provided as daily menus and ingredient lists. For example, it suggests nutritionally balanced menus for kindergarten children and quick and easy meals for working adults. Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through a smartphone app or a web app. The recipe can also be printed and provided in paper form. For example, if the user wishes, the recipe can be downloaded in PDF format.

[0120] 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

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

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

[0150] In the 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.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

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

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. a reception section for inputting conditions; an analysis unit that analyzes the conditions input by the reception unit; a generation unit that generates a recipe based on the conditions 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 reception unit Enter the conditions for children's nutrition, cooking time, number of children, budget, disliked ingredients, and allergy information.

2. The system of claim 1.

3. The analysis unit Analyzes the input conditions and prepares the data to generate a recipe 2. The system of claim 1.

4. The generation unit Generate one-week and two-week recipes based on the analyzed data 2. The system of claim 1.

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

6. The generation unit Generate recipes for kindergarteners, adults, and other target audiences 2. The system of claim 1.

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

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

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