System

Abstract

To provide a meal balanced in nutrition to the elderly living in a district where there is difficulty accessing a meal.SOLUTION: A system according to an embodiment of the present invention comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives information on a user's cookware holding state and cooking know-how skills. The collection unit collects food information from at least one of a nearby convenience store, a usable mail-order firm, and a supermarket. The generation unit generates a recipe on the basis of the information collected by the reception unit and the collection unit. The provision unit provides the recipe generated by the generation 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that it was difficult for elderly people living in areas with limited access to food to easily obtain nutritionally balanced meals.

[0005] The system according to the embodiment aims to provide nutritionally balanced meals to elderly people living in areas where access to food is difficult. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's cooking utensil ownership status and cooking know-how skills. The collection unit collects ingredient information from at least one of nearby convenience stores, available mail order sites, and supermarkets. The generation unit generates a recipe based on the information collected by the reception unit and the collection unit. The provision unit provides the recipe generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide nutritionally balanced meals to elderly people living in areas where food access is difficult. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The nutritional balance proposal system according to the embodiment of the present invention is a system that proposes nutritionally balanced meals for elderly people living in food desert areas such as mountainous areas. This nutritional balance proposal system inputs the user's cooking utensil possession status and cooking know-how skills, collects food ingredient information from nearby convenience stores, available mail order services, supermarkets, etc., and the generation AI proposes recipes suitable for the user's cooking skills and cooking utensils owned based on the collected food ingredient information. This proposal allows even elderly people living alone to be provided with healthy meals, and prepares for the declining birthrate and aging population. For example, the user inputs the user's cooking utensil possession status and cooking know-how skills. For example, the user inputs the presence or absence of cooking utensils such as frying pans, pots, and microwaves, and cooking skills such as stir-frying and stewing. This information is input to the generation AI. Next, the generation AI collects food ingredient information from nearby convenience stores, available mail order services, supermarkets, etc. For example, information such as vegetables and meat sold at convenience stores and seasonings that can be purchased by mail order is collected. This creates a list of ingredients available to the user. Based on the collected food ingredient information, the generation AI proposes recipes suitable for the user's cooking skills and cooking utensils owned. For example, for a user who only has a frying pan, a recipe for a stir-fry that can be easily made with the frying pan is proposed. Also, for a user who is good at simmering, a recipe for simmering is proposed. In this way, recipes tailored to the user's situation are provided. This makes it possible to provide healthy meals even for elderly people living alone. Since the user can receive recipes tailored to their own cooking skills and cooking utensils, they can cook without difficulty. In addition, by purchasing ingredients from nearby convenience stores, mail order sites, supermarkets, etc., it becomes easy to procure ingredients. This makes it possible to provide nutritionally balanced meals and prepare for the declining birthrate and aging population. This allows the nutritional balance proposal system to provide appropriate recipes based on the user's ownership of cooking utensils and cooking know-how skills.

[0029] The nutritional balance proposal system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's ownership of cooking utensils and cooking know-how skills. The user's ownership of cooking utensils includes, for example, a frying pan, a pot, an oven, and the like, but is not limited to such examples. The cooking know-how skills include, for example, levels such as beginner, intermediate, and advanced, but is not limited to such examples. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. The collection unit collects food ingredient information from nearby convenience stores, available mail order stores, supermarkets, and the like. The food ingredient information includes, for example, the type, price, and inventory status of food ingredients, but is not limited to such examples. The collection unit obtains food ingredient information of each store through, for example, an online API. The collection unit can also collect food ingredient information from the nearest store based on the user's location information. The generation unit generates a recipe based on the information collected by the reception unit and the collection unit using a generation AI. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). The generation unit generates recipes suitable for the cooking skills and cooking utensils owned by the user. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. The provision unit provides the recipe generated by the generation unit to the user. The provision unit displays the recipe through, for example, a web application or a mobile application. The provision unit can also print the recipe and provide it on paper. Thereby, the nutritional balance proposal system according to the embodiment can provide an appropriate recipe based on the user's ownership of cooking utensils and cooking know-how skills. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's cooking skills and information on cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

[0030] The reception unit receives the user's cooking utensil possession status and cooking know-how skills. The user's cooking utensil possession status includes, but is not limited to, a frying pan, a pot, an oven, etc., for example. Specifically, an interface is provided for inputting a list of cooking utensils owned by the user, and the user can register the cooking utensils owned by the user in detail. For example, detailed information such as the size and material of a frying pan, the type and capacity of a pot, and the function and maximum temperature of an oven can be input. In addition, the cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced, for example. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. For example, the reception unit asks the user questions such as "Are you confident in using a knife?" and "Can you cook by looking at a complicated recipe?", and automatically determines the skill level based on the answers. In addition, a more accurate skill evaluation can be performed by uploading photos and recipes of dishes that the user has made in the past. In this way, the reception unit can grasp the user's cooking utensil possession status and cooking skills in detail, and collect basic information for suggesting optimal recipes to each individual user. In addition, the reception unit can also receive information about the user's preferences and allergies, making it possible to suggest recipes based on the user's health condition and preferences. For example, if a user is allergic to a particular ingredient, recipes that do not contain that ingredient can be suggested.

[0031] The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, etc. The food information includes, for example, the type of food, price, and stock status, but is not limited to such examples. Specifically, the collection unit acquires food information from each store through an online API. For example, the database of each store can be accessed to acquire the latest food information in real time. The collection unit can also collect food information from the nearest store based on the user's location information. This allows the user to preferentially provide information on stores that are easily accessible to the user. Furthermore, the collection unit can also collect nutritional information and production area information on the food, which allows the user to be suggested healthier and safer food. For example, food information suitable for health-conscious users, such as organically grown vegetables and low-calorie food, can be provided. The collection unit can also collect seasonal food information and suggest seasonal recipes based on this. For example, seasonal suggestions are possible, such as recipes using fresh spring vegetables in spring and cold dishes and barbecue recipes in summer. This allows the collection unit to collect a variety of food information according to the user's needs, improving the accuracy and convenience of the entire system.

[0032] The generation unit uses the generation AI to generate recipes based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). Specifically, the generation AI receives the user's cooking utensil possession status, cooking skills, and collected ingredient information as input data, and generates optimal recipes based on this. For example, for a user who only has a frying pan, it generates a stir-fry recipe that can be easily made in a frying pan. It can also generate a stew recipe for a user who is good at simmering. The generation AI learns past recipe data and cooking knowledge, and can create new recipes based on this. For example, it learns combinations and cooking methods of specific ingredients and applies them to propose new recipes. The generation AI can also generate recipes taking into account the user's preferences and allergy information. For example, if a user is allergic to a specific ingredient, it can generate a recipe that does not include that ingredient. In addition, the generation AI can also generate recipes that take nutritional balance into consideration. For example, it can propose recipes that optimize the balance of calories and nutrients according to the user's health condition and goals. This enables the generation unit to generate a variety of recipes that meet the needs of users, thereby improving the convenience and satisfaction of the entire system.

[0033] The providing unit provides the recipe generated by the generating unit to the user. Specifically, the recipe is displayed through a web application or a mobile application. For example, when the user accesses the application, the generated recipes are displayed in a list, and the user can check detailed cooking procedures and a list of necessary ingredients. 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 and printed on a home printer. Furthermore, the providing unit can also provide a recipe sharing function. For example, the user can share the generated recipe with friends and family via SNS or email. The providing unit can also collect user feedback and use it to improve the recipe. For example, the user can input ratings and comments after trying the recipe, and the generation AI can improve the recipe based on that feedback. This allows the providing unit to provide recipes to the user quickly and appropriately, and improve the satisfaction of the entire system. Furthermore, the providing unit can also make individually customized recipe suggestions based on the user's past usage history. For example, new recipes can be suggested based on recipes that were well-received in the past and ingredients that are frequently used. This allows the providing unit to provide personalized services according to the user's preferences and needs, and further improve the convenience and satisfaction of the entire system.

[0034] The collection unit can collect food ingredient information from at least one of convenience stores, mail order, and supermarkets. The collection unit collects, for example, information on vegetables and meat sold at convenience stores and seasonings available for mail order. The food ingredient information includes, for example, the type of food ingredient, the price, and the stock status, but is not limited to such examples. The collection unit acquires food ingredient information for each store through, for example, an online API. The collection unit can also collect food ingredient information from the nearest store based on the user's location information. This allows the collection unit to create a list of food ingredients available to the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the food ingredient information acquired through the online API to AI, and have the AI ​​organize and classify the food ingredient information.

[0035] The generation unit can generate a recipe suitable for the cooking skill of the user and the cooking utensils owned by the user. The generation unit generates a recipe based on the cooking skill of the user and the cooking utensils owned by the user, for example. The cooking skill includes, for example, beginner, intermediate, and advanced levels, but is not limited to such examples. The cooking utensils owned include, for example, a frying pan, a pot, an oven, and the like, but is not limited to such examples. For example, the generation unit generates a stir-fry recipe that can be easily made with a frying pan for a user who only has a frying pan. In addition, the generation unit can also generate a stew recipe for a user who is good at simmering. In this way, the generation unit can generate a recipe suitable for the cooking skill of the user and the cooking utensils owned by the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information on the cooking skill of the user and the cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

[0036] The providing unit can provide the generated recipe to the user. The providing unit, for example, provides the recipe generated by the generating unit to the user. The recipe includes, for example, an ingredient list, cooking steps, cooking time, etc., but is not limited to such examples. The providing unit, for example, displays the recipe through a web application or a mobile application. The providing unit can also print the recipe and provide it on paper. This allows the providing unit to allow the user to receive an appropriate recipe. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the recipe generated by the generating unit to AI and cause the AI ​​to execute a method of providing the recipe.

[0037] The reception unit can analyze the user's past cooking history and suggest the optimal input method. The reception unit, for example, analyzes the user's past cooking history and suggests the optimal input method. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. The reception unit, for example, automatically displays cooking utensils and cooking skills that the user has frequently used 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 cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past cooking history data to AI and cause the AI ​​to execute the suggestion of the optimal input method.

[0038] The reception unit can perform filtering based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills. The reception unit performs filtering based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills, for example. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs the health condition, the reception unit can suggest appropriate cooking utensils and cooking skills based on the information. In addition, when the user inputs the dietary preferences, the reception unit can also suggest appropriate cooking utensils and cooking skills based on the information. Furthermore, when the user inputs allergy information, the reception unit can also suggest safe cooking utensils and cooking skills based on the information. As a result, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data about the user's health condition and dietary preferences into the AI ​​and have the AI ​​perform filtering.

[0039] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. The reception unit, for example, preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the reception unit can suggest cooking utensils and skills that are easy to obtain in that area. In addition, if the user lives in an urban area, the reception unit can also suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, the reception unit can also suggest cooking utensils and skills that use local specialty products. As a result, the reception unit can preferentially input highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize highly relevant information.

[0040] The reception unit can analyze the social media activity of the user when inputting cooking utensils and cooking skills, and input related information. The reception unit can analyze the social media activity of the user when inputting cooking utensils and cooking skills, and input related information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The related information includes, for example, the user's interests and trend information, and the like, but is not limited to such examples. The reception unit can analyze, for example, photos of dishes shared by the user on social media, and suggest the cooking utensils and skills used. The reception unit can also suggest related cooking utensils and skills based on information on cooking accounts followed by the user. Furthermore, the reception unit can also suggest related cooking utensils and skills based on information on dishes that the user has "liked" on social media. In this way, the reception unit can input related information by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to AI, and cause the AI ​​to perform analysis of the related information.

[0041] The collection unit can select the optimal collection method when collecting food ingredient information by analyzing the user's past purchase history. For example, when collecting food ingredient information, the collection unit analyzes the user's past purchase history to select the optimal collection method. The past purchase history includes, for example, the type of food ingredient purchased, the purchase frequency, the purchase location, etc., but is not limited to such examples. For example, the collection unit collects related food ingredient information based on food ingredients that the user frequently purchased in the past. In addition, the collection unit can also collect food ingredient information purchased in a specific season from the user's past purchase history. Furthermore, the collection unit can analyze the user's past purchase history and collect nutritionally balanced food ingredient information. In this way, the collection unit can select the optimal collection method by analyzing the user's past purchase history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past purchase history data into AI and cause the AI ​​to select the optimal collection method.

[0042] The collection unit can perform filtering based on the user's health condition and dietary preferences when collecting food ingredient information. The collection unit performs filtering based on the user's health condition and dietary preferences when collecting food ingredient information, for example. The health condition includes, but is not limited to, allergy information and nutritional status. The dietary preferences include, but are not limited to, favorite ingredients, disliked ingredients, and dietary style. For example, when the user inputs a health condition, the collection unit collects appropriate food ingredient information based on the information. In addition, when the user inputs dietary preferences, the collection unit can also collect appropriate food ingredient information based on the information. Furthermore, when the user inputs allergy information, the collection unit can also collect safe food ingredient information based on the information. In this way, the collection unit can collect appropriate food ingredient information by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and dietary preferences to AI and have the AI ​​perform filtering.

[0043] The collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the user when collecting food ingredient information. For example, the collection unit prioritizes collecting highly relevant information in consideration of the geographical location information of the user when collecting food ingredient information. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the collection unit collects food ingredient information that is easily available in the area. In addition, if the user lives in an urban area, the collection unit can also collect food ingredient information that is common in the urban area. Furthermore, if the user is traveling to a specific area, the collection unit can also collect food ingredient information of local specialty products of the area. In this way, the collection unit can prioritize collecting highly relevant information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute the priority order of highly relevant information.

[0044] The collection unit can analyze the social media activity of the user when collecting the ingredient information and collect related information. For example, the collection unit can analyze the social media activity of the user when collecting the ingredient information and collect related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related information includes, for example, the user's interests and trend information, etc., but is not limited to such examples. For example, the collection unit can analyze photos of dishes shared by the user on social media and collect related ingredient information. The collection unit can also collect related ingredient information based on information of cooking accounts followed by the user. Furthermore, the collection unit can collect related ingredient information based on information of dishes that the user "liked" on social media. In this way, the collection unit can collect related information by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to execute analysis of the related information.

[0045] The generation unit can generate an optimal recipe by analyzing the user's past cooking history when generating a recipe. For example, the generation unit can generate an optimal recipe by analyzing the user's past cooking history when generating a recipe. The past cooking history includes, for example, the type of cooked food, the frequency, and the success rate, but is not limited to such examples. For example, the generation unit generates a related recipe based on the food cooked by the user in the past. The generation unit can also generate a nutritionally balanced recipe from the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and generate the most efficient recipe. In this way, the generation unit can generate an optimal recipe by analyzing the user's past cooking history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past cooking history data to the generation AI and cause the generation AI to generate an optimal recipe.

[0046] The generation unit can customize the recipe based on the user's health condition and dietary preferences when generating the recipe. The generation unit customizes the recipe based on the user's health condition and dietary preferences when generating the recipe, for example. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs the health condition, the generation unit generates an appropriate recipe based on the information. In addition, when the user inputs the dietary preferences, the generation unit can also generate an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the generation unit can generate a safe recipe based on the information. In this way, the generation unit can generate an appropriate recipe by customizing the recipe based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's health condition and dietary preferences to the generation AI and cause the generation AI to customize the recipe.

[0047] The generation unit can generate an optimal recipe by considering the geographical location information of the user when generating a recipe. The generation unit generates an optimal recipe by considering the geographical location information of the user when generating a recipe, for example. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The optimal recipe includes, for example, the user's preferences, health condition, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the generation unit generates a recipe using ingredients that are easily available in the area. In addition, if the user lives in an urban area, the generation unit can also generate a recipe using ingredients that are common in the urban area. Furthermore, if the user is traveling to a specific area, the generation unit can generate a recipe using a specialty product of the area. In this way, the generation unit can generate an optimal recipe by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate an optimal recipe.

[0048] The generation unit can analyze the social media activity of the user at the time of recipe generation and generate a related recipe. The generation unit can analyze the social media activity of the user at the time of recipe generation, for example, and generate a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, etc., but is not limited to such examples. The generation unit can analyze, for example, photos of dishes shared by the user on social media and generate a related recipe. The generation unit can also generate a related recipe based on information of a cooking account that the user follows. Furthermore, the generation unit can generate a related recipe based on information of a dish that the user "liked" on social media. In this way, the generation unit can generate a related recipe by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to generate a related recipe.

[0049] The providing unit can select the optimal providing method by referring to the user's past cooking history when providing a recipe. For example, the providing unit selects the optimal providing method by referring to the user's past cooking history when providing a recipe. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. For example, the providing unit provides a related recipe based on the food cooked by the user in the past. The providing unit can also provide a nutritionally balanced recipe from the user's past cooking history. Furthermore, the providing unit can analyze the user's past cooking history and provide the most efficient recipe. Thereby, the providing unit can select the optimal providing method by referring to the user's past cooking history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past cooking history data to AI and cause the AI ​​to select the optimal providing method.

[0050] The providing unit can customize the providing method based on the user's health condition and dietary preferences when providing a recipe. For example, the providing unit customizes the providing method based on the user's health condition and dietary preferences when providing a recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs a health condition, the providing unit provides an appropriate recipe based on the information. In addition, when the user inputs dietary preferences, the providing unit can also provide an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the providing unit can also provide a safe recipe based on the information. Thereby, the providing unit can provide an appropriate recipe by customizing the providing method based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's health condition and dietary preferences into AI and cause the AI ​​to customize the providing method.

[0051] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing a recipe. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing a recipe. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user lives in a mountainous area, the providing unit provides a recipe using ingredients that are easily available in the area. In addition, when the user lives in an urban area, the providing unit can also provide a recipe using ingredients that are common in the urban area. Furthermore, when the user is traveling to a specific area, the providing unit can also provide a recipe using a specialty product of the area. Thereby, the providing unit can select the optimal providing method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal providing method.

[0052] The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. The providing unit can, for example, analyze the social media activity of the user when providing a recipe and provide a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, but is not limited to such examples. The providing unit can, for example, analyze photos of dishes shared by the user on social media and provide a related recipe. The providing unit can also provide a related recipe based on information of a cooking account that the user follows. Furthermore, the providing unit can also provide a related recipe based on information of a dish that the user "liked" on social media. In this way, the providing unit can provide a related recipe by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI ​​to provide a related recipe.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can receive the user's dietary preferences and allergy information in addition to the user's possession of cooking utensils and cooking know-how skills. For example, if the user has an allergy to a particular ingredient, the generation unit can generate a recipe that avoids the allergy by inputting that information. Also, if the user likes a particular ingredient, the generation unit can preferentially generate recipes that use that ingredient by inputting that information. Furthermore, if the user likes a particular dietary style (e.g., vegetarian, gluten-free, etc.), the generation unit can generate a recipe suitable for that style by inputting that information. In this way, the reception unit can provide more personalized recipes by receiving input that takes into account the user's dietary preferences and allergy information.

[0055] The collection unit can analyze the user's purchasing history and collect ingredient information based on information about ingredients purchased in the past. For example, by preferentially collecting ingredients that the user frequently purchased in the past, ingredient information that matches the user's preferences can be provided. In addition, ingredients purchased by the user in a particular season can be analyzed to collect ingredient information suitable for that season. Furthermore, the nutritional value of ingredients purchased by the user in the past can be analyzed to collect ingredient information that is nutritionally balanced. In this way, the collection unit can provide more personalized ingredient information by collecting ingredient information based on the user's purchasing history.

[0056] The generation unit can generate recipes based on the user's health condition and nutritional goals, in addition to the user's cooking skills and cooking utensils. For example, if the user has a specific health condition (e.g., high blood pressure, diabetes, etc.), a recipe that takes health into consideration can be generated based on that information. Also, if the user has a specific nutritional goal (e.g., increasing protein intake, restricting calories, etc.), a nutritionally balanced recipe can be generated based on that information. Furthermore, if the user prefers a specific eating style (e.g., low carbohydrate, high protein, etc.), a recipe suitable for that style can be generated based on that information. In this way, the generation unit can provide recipes that take into consideration the user's health condition and nutritional goals.

[0057] When providing the generated recipe to the user, the providing unit can provide it in an optimal format depending on the type of device and usage status of the user. For example, if the user is using a smartphone, the recipe can be displayed in a mobile-friendly format. Also, if the user is using a tablet, the recipe can be displayed in a format suitable for a large screen. Furthermore, if the user has a printer, the recipe can be printed and provided on paper. This allows the providing unit to provide the recipe in an optimal format depending on the type of device and usage status of the user.

[0058] The reception unit can analyze the user's past cooking history and suggest the optimal input method. For example, it can automatically display as candidates cooking utensils and cooking skills that the user has frequently used in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history.

[0059] The reception unit can filter the cooking utensil ownership status and cooking skills based on the user's health condition and dietary preferences when inputting the cooking utensil ownership status and cooking skills. For example, if the user inputs allergy information, safe cooking utensils and cooking skills can be suggested based on that information. In addition, if the user prefers a specific food ingredient, appropriate cooking utensils and cooking skills can be suggested based on that information. Furthermore, if the user prefers a specific dietary style (e.g., vegetarian, gluten-free, etc.), appropriate cooking utensils and cooking skills can be suggested based on that information. In this way, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences.

[0060] When inputting cooking utensils and cooking skills, the reception unit can preferentially input highly relevant information taking into account the geographical location information of the user. For example, if the user lives in a mountainous area, cooking utensils and skills that are easily available in that area can be suggested. Also, if the user lives in an urban area, cooking utensils and skills that are common in urban areas can be suggested. Furthermore, if the user is traveling to a specific area, cooking utensils and skills that use local specialties of that area can be suggested. In this way, the reception unit can preferentially input highly relevant information by taking into account the geographical location information of the user.

[0061] When inputting cooking utensils and cooking skills, the reception unit can analyze the user's social media activity and input related information. For example, it can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. It can also suggest related cooking utensils and skills based on information about cooking accounts the user follows. It can also suggest related cooking utensils and skills based on information about dishes that the user "liked" on social media. In this way, the reception unit can input related information by analyzing the user's social media activity.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit receives the user's ownership of cooking utensils and cooking know-how skills. The user's ownership of cooking utensils includes, but is not limited to, frying pans, pots, ovens, etc. The cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. Step 2: The collection unit collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The ingredient information includes, for example, the type of ingredient, price, stock status, etc., but is not limited to these examples. The collection unit obtains ingredient information from each store, for example, through an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information. Step 3: The generation unit uses a generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates a recipe using, for example, a text generation AI (e.g., LLM). The generation unit also generates a recipe suitable for the user's cooking skills and cooking utensils. For example, for a user who only has a frying pan, the generation unit generates a recipe for a stir-fry that can be easily made in the frying pan. The generation unit can also generate a recipe for a stew for a user who is good at stews. Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through, for example, a web application or a mobile application. The providing unit can also print the recipe and provide it on paper.

[0064] (Example 2) The nutritional balance proposal system according to the embodiment of the present invention is a system that proposes nutritionally balanced meals for elderly people living in food desert areas such as mountainous areas. This nutritional balance proposal system inputs the user's cooking utensil possession status and cooking know-how skills, collects food ingredient information from nearby convenience stores, available mail order services, supermarkets, etc., and the generation AI proposes recipes suitable for the user's cooking skills and cooking utensils owned based on the collected food ingredient information. This proposal allows even elderly people living alone to be provided with healthy meals, and prepares for the declining birthrate and aging population. For example, the user inputs the user's cooking utensil possession status and cooking know-how skills. For example, the user inputs the presence or absence of cooking utensils such as frying pans, pots, and microwaves, and cooking skills such as stir-frying and stewing. This information is input to the generation AI. Next, the generation AI collects food ingredient information from nearby convenience stores, available mail order services, supermarkets, etc. For example, information such as vegetables and meat sold at convenience stores and seasonings that can be purchased by mail order is collected. This creates a list of ingredients available to the user. Based on the collected food ingredient information, the generation AI proposes recipes suitable for the user's cooking skills and cooking utensils owned. For example, for a user who only has a frying pan, a recipe for a stir-fry that can be easily made with the frying pan is proposed. Also, for a user who is good at simmering, a recipe for simmering is proposed. In this way, recipes tailored to the user's situation are provided. This makes it possible to provide healthy meals even for elderly people living alone. Since the user can receive recipes tailored to their own cooking skills and cooking utensils, they can cook without difficulty. In addition, by purchasing ingredients from nearby convenience stores, mail order sites, supermarkets, etc., it becomes easy to procure ingredients. This makes it possible to provide nutritionally balanced meals and prepare for the declining birthrate and aging population. This allows the nutritional balance proposal system to provide appropriate recipes based on the user's ownership of cooking utensils and cooking know-how skills.

[0065] The nutritional balance proposal system according to the embodiment includes a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives the user's ownership of cooking utensils and cooking know-how skills. The user's ownership of cooking utensils includes, for example, a frying pan, a pot, an oven, and the like, but is not limited to such examples. The cooking know-how skills include, for example, levels such as beginner, intermediate, and advanced, but is not limited to such examples. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. The collection unit collects food ingredient information from nearby convenience stores, available mail order stores, supermarkets, and the like. The food ingredient information includes, for example, the type, price, and inventory status of food ingredients, but is not limited to such examples. The collection unit obtains food ingredient information of each store through, for example, an online API. The collection unit can also collect food ingredient information from the nearest store based on the user's location information. The generation unit generates a recipe based on the information collected by the reception unit and the collection unit using a generation AI. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). The generation unit generates recipes suitable for the cooking skills and cooking utensils owned by the user. For example, the generation unit generates a stir-fry recipe that can be easily made in a frying pan for a user who only has a frying pan. The generation unit can also generate a stew recipe for a user who is good at simmering. The provision unit provides the recipe generated by the generation unit to the user. The provision unit displays the recipe through, for example, a web application or a mobile application. The provision unit can also print the recipe and provide it on paper. Thereby, the nutritional balance proposal system according to the embodiment can provide an appropriate recipe based on the user's ownership of cooking utensils and cooking know-how skills. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's cooking skills and information on cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

[0066] The reception unit receives the user's cooking utensil possession status and cooking know-how skills. The user's cooking utensil possession status includes, but is not limited to, a frying pan, a pot, an oven, etc., for example. Specifically, an interface is provided for inputting a list of cooking utensils owned by the user, and the user can register the cooking utensils owned by the user in detail. For example, detailed information such as the size and material of a frying pan, the type and capacity of a pot, and the function and maximum temperature of an oven can be input. In addition, the cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced, for example. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. For example, the reception unit asks the user questions such as "Are you confident in using a knife?" and "Can you cook by looking at a complicated recipe?", and automatically determines the skill level based on the answers. In addition, a more accurate skill evaluation can be performed by uploading photos and recipes of dishes that the user has made in the past. In this way, the reception unit can grasp the user's cooking utensil possession status and cooking skills in detail, and collect basic information for suggesting optimal recipes to each individual user. In addition, the reception unit can also receive information about the user's preferences and allergies, making it possible to suggest recipes based on the user's health condition and preferences. For example, if a user is allergic to a particular ingredient, recipes that do not contain that ingredient can be suggested.

[0067] The collection unit collects food information from nearby convenience stores, available mail order stores, supermarkets, etc. The food information includes, for example, the type of food, price, and stock status, but is not limited to such examples. Specifically, the collection unit acquires food information from each store through an online API. For example, the database of each store can be accessed to acquire the latest food information in real time. The collection unit can also collect food information from the nearest store based on the user's location information. This allows the user to preferentially provide information on stores that are easily accessible to the user. Furthermore, the collection unit can also collect nutritional information and production area information on the food, which allows the user to be suggested healthier and safer food. For example, food information suitable for health-conscious users, such as organically grown vegetables and low-calorie food, can be provided. The collection unit can also collect seasonal food information and suggest seasonal recipes based on this. For example, seasonal suggestions are possible, such as recipes using fresh spring vegetables in spring and cold dishes and barbecue recipes in summer. This allows the collection unit to collect a variety of food information according to the user's needs, improving the accuracy and convenience of the entire system.

[0068] The generation unit uses the generation AI to generate recipes based on the information collected by the reception unit and the collection unit. The generation AI generates recipes using, for example, a text generation AI (for example, LLM). Specifically, the generation AI receives the user's cooking utensil possession status, cooking skills, and collected ingredient information as input data, and generates optimal recipes based on this. For example, for a user who only has a frying pan, it generates a stir-fry recipe that can be easily made in a frying pan. It can also generate a stew recipe for a user who is good at simmering. The generation AI learns past recipe data and cooking knowledge, and can create new recipes based on this. For example, it learns combinations of specific ingredients and cooking methods, and applies them to propose new recipes. The generation AI can also generate recipes taking into account the user's preferences and allergy information. For example, if a user is allergic to a specific ingredient, it can generate a recipe that does not include that ingredient. In addition, the generation AI can also generate recipes that take nutritional balance into consideration. For example, it can propose recipes that optimize the balance of calories and nutrients according to the user's health condition and goals. This enables the generation unit to generate a variety of recipes that meet the needs of users, thereby improving the convenience and satisfaction of the entire system.

[0069] The providing unit provides the recipe generated by the generating unit to the user. Specifically, the recipe is displayed through a web application or a mobile application. For example, when the user accesses the application, the generated recipes are displayed in a list, and the user can check detailed cooking procedures and a list of necessary ingredients. 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 and printed on a home printer. Furthermore, the providing unit can also provide a recipe sharing function. For example, the user can share the generated recipe with friends and family via SNS or email. The providing unit can also collect user feedback and use it to improve the recipe. For example, the user can input ratings and comments after trying the recipe, and the generation AI can improve the recipe based on that feedback. This allows the providing unit to provide recipes to the user quickly and appropriately, and improve the satisfaction of the entire system. Furthermore, the providing unit can also make individually customized recipe suggestions based on the user's past usage history. For example, new recipes can be suggested based on recipes that were well-received in the past and ingredients that are frequently used. This allows the providing unit to provide personalized services according to the user's preferences and needs, and further improve the convenience and satisfaction of the entire system.

[0070] The collection unit can collect food ingredient information from at least one of convenience stores, mail order, and supermarkets. The collection unit collects, for example, information on vegetables and meat sold at convenience stores and seasonings available for mail order. The food ingredient information includes, for example, the type of food ingredient, the price, and the stock status, but is not limited to such examples. The collection unit acquires food ingredient information for each store through, for example, an online API. The collection unit can also collect food ingredient information from the nearest store based on the user's location information. This allows the collection unit to create a list of food ingredients available to the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the food ingredient information acquired through the online API to AI, and have the AI ​​organize and classify the food ingredient information.

[0071] The generation unit can generate a recipe suitable for the cooking skill of the user and the cooking utensils owned by the user. The generation unit generates a recipe based on the cooking skill of the user and the cooking utensils owned by the user, for example. The cooking skill includes, for example, beginner, intermediate, and advanced levels, but is not limited to such examples. The cooking utensils owned include, for example, a frying pan, a pot, an oven, and the like, but is not limited to such examples. For example, the generation unit generates a stir-fry recipe that can be easily made with a frying pan for a user who only has a frying pan. In addition, the generation unit can also generate a stew recipe for a user who is good at simmering. In this way, the generation unit can generate a recipe suitable for the cooking skill of the user and the cooking utensils owned by the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information on the cooking skill of the user and the cooking utensils owned by the user to the generation AI and cause the generation AI to generate a recipe.

[0072] The providing unit can provide the generated recipe to the user. The providing unit, for example, provides the recipe generated by the generating unit to the user. The recipe includes, for example, an ingredient list, cooking steps, cooking time, etc., but is not limited to such examples. The providing unit, for example, displays the recipe through a web application or a mobile application. The providing unit can also print the recipe and provide it on paper. This allows the providing unit to allow the user to receive an appropriate recipe. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the recipe generated by the generating unit to AI and cause the AI ​​to execute a method of providing the recipe.

[0073] The reception unit can estimate the user's emotion and adjust the input method of the cooking utensils and cooking skills based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input method of the cooking utensils and cooking skills based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input cooking utensils and cooking skills. In this way, the reception unit can adjust the input method according to the user's emotion, allowing the user to input without feeling stressed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0074] The reception unit can analyze the user's past cooking history and suggest the optimal input method. The reception unit, for example, analyzes the user's past cooking history and suggests the optimal input method. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. The reception unit, for example, automatically displays cooking utensils and cooking skills that the user has frequently used 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 cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past cooking history data to AI and cause the AI ​​to execute the suggestion of the optimal input method.

[0075] The reception unit can perform filtering based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills. The reception unit performs filtering based on the user's health condition and dietary preferences when inputting the possession status of cooking utensils and cooking skills, for example. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs the health condition, the reception unit can suggest appropriate cooking utensils and cooking skills based on the information. In addition, when the user inputs the dietary preferences, the reception unit can also suggest appropriate cooking utensils and cooking skills based on the information. Furthermore, when the user inputs allergy information, the reception unit can also suggest safe cooking utensils and cooking skills based on the information. As a result, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data about the user's health condition and dietary preferences into the AI ​​and have the AI ​​perform filtering.

[0076] The reception unit can estimate the user's emotion and determine the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the reception unit preferentially suggests easy-to-use cooking utensils and basic cooking skills. In addition, when the user is relaxed, the reception unit can also suggest complex cooking utensils and advanced cooking skills. Furthermore, when the user is in a hurry, the reception unit can also preferentially suggest cooking utensils and skills that can cook quickly. In this way, the reception unit can determine the priority order of cooking utensils and cooking skills according to the user's emotion, allowing the user to input without feeling stressed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0077] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. The reception unit, for example, preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting cooking utensils and cooking skills. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the reception unit can suggest cooking utensils and skills that are easy to obtain in that area. In addition, if the user lives in an urban area, the reception unit can also suggest cooking utensils and skills that are common in urban areas. Furthermore, if the user is traveling to a specific area, the reception unit can also suggest cooking utensils and skills that use local specialty products. As a result, the reception unit can preferentially input highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the AI ​​and have the AI ​​prioritize highly relevant information.

[0078] The reception unit can analyze the social media activity of the user when inputting cooking utensils and cooking skills, and input related information. The reception unit can analyze the social media activity of the user when inputting cooking utensils and cooking skills, and input related information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The related information includes, for example, the user's interests and trend information, and the like, but is not limited to such examples. The reception unit can analyze, for example, photos of dishes shared by the user on social media, and suggest the cooking utensils and skills used. The reception unit can also suggest related cooking utensils and skills based on information on cooking accounts followed by the user. Furthermore, the reception unit can also suggest related cooking utensils and skills based on information on dishes that the user has "liked" on social media. In this way, the reception unit can input related information by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to AI, and cause the AI ​​to perform analysis of the related information.

[0079] The collection unit can estimate the user's emotion and adjust the method of collecting ingredient information based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the method of collecting ingredient information based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the collection unit preferentially collects ingredient information that can be easily cooked. In addition, when the user is relaxed, the collection unit can also collect ingredient information that takes time to cook but is nutritious. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect ingredient information that can be cooked immediately. In this way, the collection unit can adjust the method of collecting ingredient information according to the user's emotion, thereby allowing the user to collect ingredient information without feeling stressed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0080] The collection unit can select the optimal collection method when collecting food ingredient information by analyzing the user's past purchase history. For example, when collecting food ingredient information, the collection unit analyzes the user's past purchase history to select the optimal collection method. The past purchase history includes, for example, the type of food ingredient purchased, the purchase frequency, the purchase location, etc., but is not limited to such examples. For example, the collection unit collects related food ingredient information based on food ingredients that the user frequently purchased in the past. In addition, the collection unit can also collect food ingredient information purchased in a specific season from the user's past purchase history. Furthermore, the collection unit can analyze the user's past purchase history and collect nutritionally balanced food ingredient information. In this way, the collection unit can select the optimal collection method by analyzing the user's past purchase history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past purchase history data into AI and cause the AI ​​to select the optimal collection method.

[0081] The collection unit can perform filtering based on the user's health condition and dietary preferences when collecting food ingredient information. The collection unit performs filtering based on the user's health condition and dietary preferences when collecting food ingredient information, for example. The health condition includes, but is not limited to, allergy information and nutritional status. The dietary preferences include, but are not limited to, favorite ingredients, disliked ingredients, and dietary style. For example, when the user inputs a health condition, the collection unit collects appropriate food ingredient information based on the information. In addition, when the user inputs dietary preferences, the collection unit can also collect appropriate food ingredient information based on the information. Furthermore, when the user inputs allergy information, the collection unit can also collect safe food ingredient information based on the information. In this way, the collection unit can collect appropriate food ingredient information by filtering based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and dietary preferences to AI and have the AI ​​perform filtering.

[0082] The collection unit can estimate the user's emotion and determine the priority of the ingredient information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the ingredient information to be collected based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the collection unit preferentially collects ingredient information that can be easily cooked. In addition, when the user is relaxed, the collection unit can also collect ingredient information that takes time to cook but is nutritious. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect ingredient information that can be cooked immediately. In this way, the collection unit can collect ingredient information without the user feeling stressed by determining the priority of the ingredient information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0083] The collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the user when collecting food ingredient information. For example, the collection unit prioritizes collecting highly relevant information in consideration of the geographical location information of the user when collecting food ingredient information. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The highly relevant information includes, for example, nearby store information, local specialty products, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the collection unit collects food ingredient information that is easily available in the area. In addition, if the user lives in an urban area, the collection unit can also collect food ingredient information that is common in the urban area. Furthermore, if the user is traveling to a specific area, the collection unit can also collect food ingredient information of local specialty products of the area. In this way, the collection unit can prioritize collecting highly relevant information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to execute the priority order of highly relevant information.

[0084] The collection unit can analyze the social media activity of the user when collecting the ingredient information and collect related information. For example, the collection unit can analyze the social media activity of the user when collecting the ingredient information and collect related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related information includes, for example, the user's interests and trend information, etc., but is not limited to such examples. For example, the collection unit can analyze photos of dishes shared by the user on social media and collect related ingredient information. The collection unit can also collect related ingredient information based on information of cooking accounts followed by the user. Furthermore, the collection unit can collect related ingredient information based on information of dishes that the user "liked" on social media. In this way, the collection unit can collect related information by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to execute analysis of the related information.

[0085] The generation unit can estimate the user's emotion and adjust the recipe generation method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the recipe generation method based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is relaxed, the generation AI of the generation unit generates a recipe that proceeds at a leisurely pace. In addition, when the user is in a hurry, the generation AI of the generation unit can also generate a recipe that can be cooked in the shortest time. Furthermore, when the user is excited, the generation AI of the generation unit can also generate a recipe with a visually stimulating effect added. Thereby, the generation unit can generate a recipe without the user feeling stressed by adjusting the recipe generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can generate an optimal recipe by analyzing the user's past cooking history when generating a recipe. For example, the generation unit can generate an optimal recipe by analyzing the user's past cooking history when generating a recipe. The past cooking history includes, for example, the type of cooked food, the frequency, and the success rate, but is not limited to such examples. For example, the generation unit generates a related recipe based on the food cooked by the user in the past. The generation unit can also generate a nutritionally balanced recipe from the user's past cooking history. Furthermore, the generation unit can analyze the user's past cooking history and generate the most efficient recipe. In this way, the generation unit can generate an optimal recipe by analyzing the user's past cooking history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past cooking history data to the generation AI and cause the generation AI to generate an optimal recipe.

[0087] The generation unit can customize the recipe based on the user's health condition and dietary preferences when generating the recipe. The generation unit customizes the recipe based on the user's health condition and dietary preferences when generating the recipe, for example. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs the health condition, the generation unit generates an appropriate recipe based on the information. In addition, when the user inputs the dietary preferences, the generation unit can also generate an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the generation unit can generate a safe recipe based on the information. In this way, the generation unit can generate an appropriate recipe by customizing the recipe based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's health condition and dietary preferences to the generation AI and cause the generation AI to customize the recipe.

[0088] The generation unit can estimate the user's emotion and determine the priority of the recipes to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and determines the priority of the recipes to be generated based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the generation unit generates a recipe that is easy to make preferentially. In addition, when the user is relaxed, the generation unit can also generate a recipe that takes time to cook but is nutritious. Furthermore, when the user is in a hurry, the generation unit can also generate a recipe that can be cooked immediately preferentially. In this way, the generation unit can generate a recipe without the user feeling stressed by determining the priority of the recipes according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can generate an optimal recipe by considering the geographical location information of the user when generating a recipe. The generation unit generates an optimal recipe by considering the geographical location information of the user when generating a recipe, for example. The geographical location information includes, for example, GPS data, address information, etc., but is not limited to such examples. The optimal recipe includes, for example, the user's preferences, health condition, etc., but is not limited to such examples. For example, if the user lives in a mountainous area, the generation unit generates a recipe using ingredients that are easily available in the area. In addition, if the user lives in an urban area, the generation unit can also generate a recipe using ingredients that are common in the urban area. Furthermore, if the user is traveling to a specific area, the generation unit can generate a recipe using a specialty product of the area. In this way, the generation unit can generate an optimal recipe by considering the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the geographical location information of the user to the generation AI and cause the generation AI to generate an optimal recipe.

[0090] The generation unit can analyze the social media activity of the user at the time of recipe generation and generate a related recipe. The generation unit can analyze the social media activity of the user at the time of recipe generation, for example, and generate a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, etc., but is not limited to such examples. The generation unit can analyze, for example, photos of dishes shared by the user on social media and generate a related recipe. The generation unit can also generate a related recipe based on information of a cooking account that the user follows. Furthermore, the generation unit can generate a related recipe based on information of a dish that the user "liked" on social media. In this way, the generation unit can generate a related recipe by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's social media activity data to the generation AI and cause the generation AI to generate a related recipe.

[0091] The providing unit can estimate the user's emotion and adjust the recipe providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the recipe providing method based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is stressed, the providing unit provides a simple and highly visible recipe. In addition, when the user is relaxed, the providing unit can also provide a recipe including detailed explanations. Furthermore, when the user is in a hurry, the providing unit can also provide a concise recipe with the main points. Thereby, the providing unit can adjust the recipe providing method according to the user's emotion, so that the user can receive the recipe without feeling stressed. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0092] The providing unit can select the optimal providing method by referring to the user's past cooking history when providing a recipe. For example, the providing unit selects the optimal providing method by referring to the user's past cooking history when providing a recipe. The past cooking history includes, for example, the type of food cooked, the frequency, the success rate, etc., but is not limited to such examples. For example, the providing unit provides a related recipe based on the food cooked by the user in the past. The providing unit can also provide a nutritionally balanced recipe from the user's past cooking history. Furthermore, the providing unit can analyze the user's past cooking history and provide the most efficient recipe. Thereby, the providing unit can select the optimal providing method by referring to the user's past cooking history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past cooking history data to AI and cause the AI ​​to select the optimal providing method.

[0093] The providing unit can customize the providing method based on the user's health condition and dietary preferences when providing a recipe. For example, the providing unit customizes the providing method based on the user's health condition and dietary preferences when providing a recipe. The health condition includes, for example, allergy information, nutritional status, etc., but is not limited to such examples. The dietary preferences include, for example, favorite ingredients, disliked ingredients, dietary style, etc., but is not limited to such examples. For example, when the user inputs a health condition, the providing unit provides an appropriate recipe based on the information. In addition, when the user inputs dietary preferences, the providing unit can also provide an appropriate recipe based on the information. Furthermore, when the user inputs allergy information, the providing unit can also provide a safe recipe based on the information. Thereby, the providing unit can provide an appropriate recipe by customizing the providing method based on the user's health condition and dietary preferences. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the user's health condition and dietary preferences into AI and cause the AI ​​to customize the providing method.

[0094] The providing unit can estimate the user's emotion and determine the order of providing the recipes based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the order of providing the recipes based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is feeling stressed, the providing unit provides recipes that are easy to make preferentially. In addition, when the user is relaxed, the providing unit can also provide recipes that take time to cook but are nutritious. Furthermore, when the user is in a hurry, the providing unit can also provide recipes that can be cooked immediately preferentially. In this way, the providing unit can determine the order of providing the recipes according to the user's emotion, so that the user can receive the recipes without feeling stressed. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0095] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing a recipe. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing a recipe. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user lives in a mountainous area, the providing unit provides a recipe using ingredients that are easily available in the area. In addition, when the user lives in an urban area, the providing unit can also provide a recipe using ingredients that are common in the urban area. Furthermore, when the user is traveling to a specific area, the providing unit can also provide a recipe using a specialty product of the area. Thereby, the providing unit can select the optimal providing method by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to select the optimal providing method.

[0096] The providing unit can analyze the social media activity of the user when providing a recipe and provide a related recipe. The providing unit can, for example, analyze the social media activity of the user when providing a recipe and provide a related recipe. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The related recipe includes, for example, the user's interests and trend information, but is not limited to such examples. The providing unit can, for example, analyze photos of dishes shared by the user on social media and provide a related recipe. The providing unit can also provide a related recipe based on information of a cooking account that the user follows. Furthermore, the providing unit can also provide a related recipe based on information of a dish that the user "liked" on social media. In this way, the providing unit can provide a related recipe by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and cause the AI ​​to provide a related recipe.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can receive the user's dietary preferences and allergy information in addition to the user's possession of cooking utensils and cooking know-how skills. For example, if the user has an allergy to a particular ingredient, the generation unit can generate a recipe that avoids the allergy by inputting that information. Also, if the user likes a particular ingredient, the generation unit can preferentially generate recipes that use that ingredient by inputting that information. Furthermore, if the user likes a particular dietary style (e.g., vegetarian, gluten-free, etc.), the generation unit can generate a recipe suitable for that style by inputting that information. In this way, the reception unit can provide more personalized recipes by receiving input that takes into account the user's dietary preferences and allergy information.

[0099] The collection unit can analyze the user's purchasing history and collect ingredient information based on information about ingredients purchased in the past. For example, by preferentially collecting ingredients that the user frequently purchased in the past, ingredient information that matches the user's preferences can be provided. In addition, ingredients purchased by the user in a particular season can be analyzed to collect ingredient information suitable for that season. Furthermore, the nutritional value of ingredients purchased by the user in the past can be analyzed to collect ingredient information that is nutritionally balanced. In this way, the collection unit can provide more personalized ingredient information by collecting ingredient information based on the user's purchasing history.

[0100] The generation unit can generate recipes based on the user's health condition and nutritional goals, in addition to the user's cooking skills and cooking utensils. For example, if the user has a specific health condition (e.g., high blood pressure, diabetes, etc.), a recipe that takes health into consideration can be generated based on that information. Also, if the user has a specific nutritional goal (e.g., increasing protein intake, restricting calories, etc.), a nutritionally balanced recipe can be generated based on that information. Furthermore, if the user prefers a specific eating style (e.g., low carbohydrate, high protein, etc.), a recipe suitable for that style can be generated based on that information. In this way, the generation unit can provide recipes that take into consideration the user's health condition and nutritional goals.

[0101] When providing the generated recipe to the user, the providing unit can provide it in an optimal format depending on the type of device and usage status of the user. For example, if the user is using a smartphone, the recipe can be displayed in a mobile-friendly format. Also, if the user is using a tablet, the recipe can be displayed in a format suitable for a large screen. Furthermore, if the user has a printer, the recipe can be printed and provided on paper. This allows the providing unit to provide the recipe in an optimal format depending on the type of device and usage status of the user.

[0102] The reception unit can estimate the user's emotions and adjust the input method of cooking utensils and cooking skills based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Also, 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 the user to quickly input cooking utensils and cooking skills. In this way, the reception unit can adjust the input method according to the user's emotions, allowing the user to input without feeling stressed.

[0103] The reception unit can analyze the user's past cooking history and suggest the optimal input method. For example, it can automatically display as candidates cooking utensils and cooking skills that the user has frequently used in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest cooking utensils and cooking skills to be used in a specific time period from the user's past cooking history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past cooking history.

[0104] The reception unit can filter the cooking utensil ownership status and cooking skills based on the user's health condition and dietary preferences when inputting the cooking utensil ownership status and cooking skills. For example, if the user inputs allergy information, safe cooking utensils and cooking skills can be suggested based on that information. In addition, if the user prefers a specific food ingredient, appropriate cooking utensils and cooking skills can be suggested based on that information. Furthermore, if the user prefers a specific dietary style (e.g., vegetarian, gluten-free, etc.), appropriate cooking utensils and cooking skills can be suggested based on that information. In this way, the reception unit can suggest appropriate cooking utensils and cooking skills by filtering based on the user's health condition and dietary preferences.

[0105] The reception unit can estimate the user's emotions and determine the priority order of cooking utensils and cooking skills to be input based on the estimated user's emotions. For example, if the user is stressed, easy-to-use cooking utensils and basic cooking skills can be preferentially suggested. Also, if the user is relaxed, complex cooking utensils and advanced cooking skills can be suggested. Furthermore, if the user is in a hurry, cooking utensils and skills that allow quick cooking can be preferentially suggested. In this way, the reception unit can determine the priority order of cooking utensils and cooking skills according to the user's emotions, allowing the user to input without feeling stressed.

[0106] When inputting cooking utensils and cooking skills, the reception unit can preferentially input highly relevant information taking into account the geographical location information of the user. For example, if the user lives in a mountainous area, cooking utensils and skills that are easily available in that area can be suggested. Also, if the user lives in an urban area, cooking utensils and skills that are common in urban areas can be suggested. Furthermore, if the user is traveling to a specific area, cooking utensils and skills that use local specialties of that area can be suggested. In this way, the reception unit can preferentially input highly relevant information by taking into account the geographical location information of the user.

[0107] When inputting cooking utensils and cooking skills, the reception unit can analyze the user's social media activity and input related information. For example, it can analyze photos of dishes shared by the user on social media and suggest the cooking utensils and skills used. It can also suggest related cooking utensils and skills based on information about cooking accounts the user follows. It can also suggest related cooking utensils and skills based on information about dishes that the user "liked" on social media. In this way, the reception unit can input related information by analyzing the user's social media activity.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit receives the user's ownership of cooking utensils and cooking know-how skills. The user's ownership of cooking utensils includes, but is not limited to, frying pans, pots, ovens, etc. The cooking know-how skills include, but are not limited to, levels such as beginner, intermediate, and advanced. The reception unit provides, for example, an interface for inputting a list of cooking utensils owned by the user. The reception unit can also present questions to evaluate the user's cooking skills and determine the skill level based on the answers. Step 2: The collection unit collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The ingredient information includes, for example, the type of ingredient, price, stock status, etc., but is not limited to these examples. The collection unit obtains ingredient information from each store, for example, through an online API. The collection unit can also collect ingredient information from the nearest store based on the user's location information. Step 3: The generation unit uses a generation AI to generate a recipe based on the information collected by the reception unit and the collection unit. The generation AI generates a recipe using, for example, a text generation AI (e.g., LLM). The generation unit also generates a recipe suitable for the user's cooking skills and cooking utensils. For example, for a user who only has a frying pan, the generation unit generates a recipe for a stir-fry that can be easily made in the frying pan. The generation unit can also generate a recipe for a stew for a user who is good at stews. Step 4: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the recipe through, for example, a web application or a mobile application. The providing unit can also print the recipe and provide it on paper.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned reception unit, collection 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 is realized by the control unit 46A of the smart device 14, and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned reception unit, collection 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 is realized by the control unit 46A of the smart glasses 214, and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned reception unit, collection 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 is realized by the control unit 46A of the headset type terminal 314, and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] Each of the multiple elements including the above-mentioned reception unit, collection 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 is realized by the control unit 46A of the robot 414, and receives the user's cooking utensil possession status and cooking know-how skills. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects ingredient information from nearby convenience stores, available mail order sites, supermarkets, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a recipe based on the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated recipe to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A reception unit that receives the user's cooking utensil possession status and cooking know-how skills; A collection unit that collects food information from at least one of nearby convenience stores, available mail-order stores, and supermarkets; a generation unit that generates a recipe based on the information collected by the reception unit and the collection unit; A providing unit that provides the recipe generated by the generating unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect information about ingredients from at least one of the following: convenience stores, online stores, or supermarkets 2. The system of claim 1, (Appendix 3) The generation unit is Generate recipes suited to the user's cooking skills and cooking utensils 2. The system of claim 1, (Appendix 4) The providing unit is Providing the generated recipe to the user 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the user's emotions and adjust the input method of cooking utensils and cooking skills based on the estimated user's emotions. 2. The system of claim 1, (Appendix 6) The reception unit is Analyzes the user's cooking history and suggests the best input method 2. The system of claim 1, (Appendix 7) The reception unit is When inputting cooking utensil ownership and cooking skills, filtering is performed based on the user's health condition and dietary preferences. 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and prioritize the cooking utensils and cooking skills to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 9) The reception unit is When inputting cooking utensils and cooking skills, the system takes into account the user's geographic location information and prioritizes inputting the most relevant information. 2. The system of claim 1, (Appendix 10) The reception unit is When inputting cooking utensils and cooking skills, the system analyzes the user's social media activity and inputs related information. 2. The system of claim 1, (Appendix 11) The collecting unit includes: The method estimates the user's emotions and adjusts the method of collecting ingredient information based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting food information, the optimal collection method is selected by analyzing the user's past purchase history. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting food information, filtering is performed based on the user's health condition and dietary preferences. 2. The system of claim 1, (Appendix 14) The collecting unit includes: The system estimates the user's emotions and prioritizes the food information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting food information, the system prioritizes collecting information that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting food information, the social media activity of users is analyzed to collect related information. 2. The system of claim 1, (Appendix 17) The generation unit is Infer user emotions and adjust recipe generation methods based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The generation unit is When generating recipes, the system analyzes the user's past cooking history to generate optimal recipes. 2. The system of claim 1, (Appendix 19) The generation unit is Customize recipes based on the user's health status and dietary preferences when generating recipes 2. The system of claim 1, (Appendix 20) The generation unit is Infer user emotions and prioritize recipes based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The generation unit is When generating recipes, the optimal recipe is generated taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 22) The generation unit is When generating recipes, the system analyzes the user's social media activity and generates relevant recipes. 2. The system of claim 1, (Appendix 23) The providing unit is Infer user emotions and adjust recipe serving methods based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The providing unit is When providing a recipe, the system selects the optimal way to provide the recipe by referring to the user's past cooking history. 2. The system of claim 1, (Appendix 25) The providing unit is Customize recipes based on the user's health status and dietary preferences 2. The system of claim 1, (Appendix 26) The providing unit is Infer user emotions and determine the order in which recipes are served based on the inferred user emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing recipes, the optimal method of providing them is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 28) The providing unit is When providing recipes, analyze the user's social media activity and provide related recipes 2. The system of claim 1, [Explanation of symbols]

[0182] 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 unit that receives the user's cooking utensil possession status and cooking know-how skills; A collection unit that collects food information from at least one of nearby convenience stores, available mail-order stores, and supermarkets; a generation unit that generates a recipe based on the information collected by the reception unit and the collection unit; A providing unit that provides the recipe generated by the generating unit. A system characterized by:

2. The generation unit is Generate recipes suited to the user's cooking skills and cooking utensils 2. The system of claim 1 .

3. The reception unit is Estimate the user's emotions and adjust the input method of cooking utensils and cooking skills based on the estimated user's emotions.

2. The system of claim 1 .

4. The reception unit is Analyzes the user's cooking history and suggests the best input method 2. The system of claim 1 .

5. The reception unit is When inputting cooking utensil ownership and cooking skills, filtering is performed based on the user's health condition and dietary preferences.

2. The system of claim 1 .

6. The reception unit is Estimate the user's emotions and prioritize the cooking utensils and cooking skills to be input based on the estimated user's emotions.

2. The system of claim 1 .

Images