system

Abstract

The system according to the embodiment aims to generate recipes based on ingredient information and provide information for purchasing ingredients at the optimal price. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a recommendation unit. The collection unit collects ingredient information. The generation unit generates recipes based on the information collected by the collection unit. The provision unit provides the recipes generated by the generation unit to the user. The recommendation unit collects information on nearby supermarkets and recommends the cheapest products.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately generate recipes based on ingredient information, nor do they adequately provide information for purchasing ingredients at the optimal price, so there is room for improvement.

[0005] The system according to the embodiment aims to generate recipes based on ingredient information and provide information for purchasing ingredients at the optimal price. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, and a recommendation unit. The collection unit collects ingredient information. The generation unit generates recipes based on the information collected by the collection unit. The provision unit provides the recipes generated by the generation unit to the user. The recommendation unit collects information on nearby supermarkets and recommends products with the lowest prices. [Effects of the Invention]

[0007] The system according to the embodiment can generate recipes based on ingredient information and provide information for purchasing ingredients at the optimal price. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cooking assistance system according to an embodiment of the present invention collects ingredient information, generates recipes using a generation AI, provides them to the user, and recommends the cheapest items. The cooking assistance system collects ingredient information, generates optimal recipes using a generation AI, and provides them to the user. The cooking assistance system also collects information about nearby supermarkets and recommends the cheapest items. For example, the cooking assistance system collects information about ingredients owned by the user and information about special offers at the supermarket. For example, the cooking assistance system allows the user to input ingredients in the refrigerator or obtain information about special offers at the supermarket. Next, the cooking assistance system generates optimal recipes using the generation AI based on the collected ingredient information. The input to the generation AI is the collected ingredient information itself, and the generation AI generates recipes based on that information. For example, the generation AI receives a prompt such as, "Please suggest a dish that can be made with these ingredients," and generates an optimal recipe. The cooking assistance system then provides the generated recipe to the user. The providing unit displays the generated recipe to the user and provides cooking instructions and necessary ingredients. This allows the user to easily cook. The cooking assistance system also collects price information from nearby supermarkets and recommends the cheapest items. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user, allowing the user to shop economically. This allows the cooking assistance system to collect information on ingredients the user has and supermarket sale items, generate optimal recipes, and provide them to the user. Furthermore, recommending the cheapest products also enables economical shopping. For example, the generation AI can suggest new recipes based on the ingredients the user has. Furthermore, by collecting price information from nearby supermarkets and recommending the cheapest products, the user can decide on a menu using sale items. This allows the user to enjoy new recipes and shop economically.

[0029] A cooking assistance system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a recommendation unit. The collection unit collects information about ingredients owned by a user and supermarket sale items. For example, the collection unit allows a user to input ingredients in a refrigerator. The collection unit can also acquire supermarket sale item information. The collection unit can also automatically collect ingredient information using a sensor. For example, the collection unit detects ingredients in a refrigerator using a sensor and collects the information. The generation unit generates a recipe based on the information collected by the collection unit. The generation unit uses a generation AI to generate an optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also generate a recipe based on ingredient information using a multimodal generation AI. For example, the generation AI receives a prompt such as "Please suggest a dish that can be made with these ingredients" and generates an optimal recipe. The provision unit provides the recipe generated by the generation unit to a user. The provision unit displays the generated recipe to the user and provides cooking instructions and necessary ingredients. For example, the provision unit displays the generated recipe on an application. The providing unit can also send the generated recipe by email. Furthermore, the providing unit can also print and provide the generated recipe. The recommending unit collects information on nearby supermarkets and recommends the cheapest products. The recommending unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommending unit collects online price information from nearby supermarkets. The recommending unit can also collect in-store price information. Furthermore, the recommending unit can collect price information after discounts. In this way, the cooking assistance system according to the embodiment can collect information on ingredients that the user has and special offers from supermarkets, generate optimal recipes, and provide them to the user. Furthermore, recommending the cheapest products enables economical shopping.

[0030] The collection unit can collect information about ingredients that the user owns or supermarket sale items. For example, the collection unit can input ingredients that the user has in the refrigerator. The collection unit can also acquire supermarket sale item information. For example, the collection unit can collect online supermarket price information. The collection unit can also collect in-store price information. Furthermore, the collection unit can also collect discounted price information. In this way, by collecting information about ingredients that the user owns or supermarket sale items, information for generating an optimal recipe can be obtained. Some or all of the above-described 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 online supermarket price information to the generation AI and cause the generation AI to collect price information.

[0031] The generation unit can generate the most suitable recipe using the AI ​​based on the collected ingredient information. The generation unit uses the generation AI to generate the optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also use a multimodal generation AI to generate a recipe based on the ingredient information. For example, the generation AI receives a prompt such as, "Please suggest a dish that can be made with these ingredients," and generates an optimal recipe. The generation unit can also use the generation AI to generate a recipe that takes into account ingredient combinations. For example, the generation AI generates a recipe taking into account the nutritional value and flavor compatibility of the ingredients. This allows the AI ​​to generate the optimal recipe based on the collected ingredient information, thereby enabling it to suggest new dishes to the user. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input the collected ingredient information into the generation AI and cause the generation AI to generate a recipe.

[0032] The providing unit can display the generated recipe to the user and provide cooking instructions and necessary ingredients. The providing unit can display the generated recipe to the user and provide cooking instructions and necessary ingredients. For example, the providing unit can display the generated recipe on an application. The providing unit can also send the generated recipe by email. The providing unit can also print and provide the generated recipe. For example, the providing unit can display the generated recipe as step-by-step instructions. The providing unit can also display the generated recipe using videos or images. This allows the user to easily cook a dish by displaying the generated recipe to the user and providing cooking instructions and necessary ingredients. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated recipe to the generation AI and cause the generation AI to execute a recipe display method.

[0033] The recommendation unit can collect price information from nearby supermarkets and recommend the lowest-priced products to the user. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommendation unit collects online price information from nearby supermarkets. The recommendation unit can also collect in-store price information. The recommendation unit can also collect discounted price information. For example, the recommendation unit collects price information from nearby supermarkets and lists the cheapest products. The recommendation unit can also recommend optimal products based on the user's preferences. For example, the recommendation unit recommends optimal products based on information about products the user has previously purchased. This allows the user to shop economically by collecting price information from nearby supermarkets and recommending the cheapest products to the user. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without AI. For example, the recommendation unit can input price information from nearby supermarkets into the generation AI and have the generation AI recommend the cheapest products.

[0034] The collection unit can analyze the user's past ingredient purchase history and select the most appropriate collection method. For example, the collection unit prioritizes collection of ingredients that the user has frequently purchased in the past. The collection unit can also predict and collect ingredients that the user will purchase in a particular season based on the user's past purchase history. Furthermore, the collection unit can prioritize collection of ingredients needed for a particular dish based on the user's past purchase history. This makes it possible to select a more appropriate collection method and efficiently collect ingredient information by analyzing the user's past ingredient purchase history. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past ingredient purchase history into the generation AI and have the generation AI select the optimal collection method.

[0035] When collecting ingredient information, the collection unit can filter the information based on the user's current meal plan and health condition. For example, if the user is on a diet, the collection unit can prioritize collecting low-calorie ingredient information. Furthermore, if the user has a specific allergy, the collection unit can also collect ingredient information that does not contain that allergen. Furthermore, if the user wants to consume a specific nutrient, the collection unit can collect ingredient information that contains a large amount of that nutrient. By filtering based on the user's current meal plan and health condition, more appropriate ingredient information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's health condition into the generation AI and have the generation AI perform the filtering.

[0036] When collecting ingredient information, the collection unit can select the most appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect ingredient information using voice recognition technology. If the user uses text input, the collection unit can also collect ingredient information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect ingredient information using image recognition technology. This allows ingredient information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0037] When collecting ingredient information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting sale information from supermarkets in that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting ingredient information from travel destinations. Furthermore, if the user is at home, the collection unit can prioritize collecting ingredient information from nearby supermarkets. This allows for the provision of more appropriate ingredient information by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0038] When collecting ingredient information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect necessary ingredient information based on photos of dishes shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related ingredient information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related ingredient information. In this way, by analyzing the user's social media activities, related ingredient information can be collected efficiently. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0039] When collecting ingredient information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes the collection of ingredient information that the user has previously rated highly. The collection unit can also exclude ingredient information that the user has previously rated poorly. Furthermore, the collection unit can improve the collection method based on the user's past feedback and collect more appropriate ingredient information. This makes it possible to collect more appropriate ingredient information by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] When generating a recipe, the generation unit can adjust the level of detail of the recipe based on the importance of the ingredients. For example, the generation unit generates a recipe that includes detailed descriptions of the main ingredients. The generation unit can also generate a recipe that includes concise descriptions of the supplementary ingredients. Furthermore, the generation unit can also provide detailed descriptions of the recipe steps according to the importance of the ingredients. In this way, by adjusting the level of detail of the recipe based on the importance of the ingredients, it is possible to provide an optimal recipe for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the ingredients into the generation AI and cause the generation AI to adjust the level of detail of the recipe.

[0041] When generating a recipe, the generation unit can apply different generation algorithms depending on the category of ingredients. For example, the generation unit can apply a health-oriented algorithm to a recipe whose main ingredient is vegetables. The generation unit can also apply a hearty algorithm to a recipe whose main ingredient is meat. Furthermore, the generation unit can apply an algorithm that emphasizes sweetness to a recipe whose main ingredient is dessert. In this way, by applying different generation algorithms depending on the category of ingredients, more appropriate recipes can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of ingredients into the generation AI and cause the generation AI to apply different generation algorithms.

[0042] When generating a recipe, the generation unit can improve the accuracy of generation by referring to the user's past recipe results. For example, the generation unit generates a new recipe by incorporating features of recipes that the user has previously rated highly. The generation unit can also generate a new recipe by avoiding features of recipes that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past recipe results and generate an optimal recipe. In this way, by referring to the user's past recipe results, the accuracy of generation can be improved and more appropriate recipes can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input the user's past recipe results into the generation AI and cause the generation AI to improve the accuracy of recipe generation.

[0043] When generating a recipe, the generation unit can determine the priority of recipes based on the freshness of ingredients. For example, the generation unit generates recipes that prioritize the use of ingredients with high freshness. The generation unit can also generate recipes that use ingredients with low freshness earlier. Furthermore, the generation unit can generate recipes that suggest the optimal timing to use ingredients depending on the freshness of the ingredients. In this way, by determining the priority of recipes based on the freshness of ingredients, it is possible to provide recipes that prioritize the use of ingredients with high freshness. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the freshness of ingredients into the generation AI and have the generation AI determine the priority of recipes.

[0044] When generating a recipe, the generation unit can adjust the order of the recipe based on the relevance of ingredients. For example, the generation unit generates a recipe that uses a main ingredient first. The generation unit can also generate a recipe that uses a supplementary ingredient later. Furthermore, the generation unit can generate a recipe that suggests an optimal cooking order based on the relevance of ingredients. In this way, by adjusting the order of the recipe based on the relevance of ingredients, more efficient cooking procedures can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of ingredients into the generation AI and cause the generation AI to adjust the order of the recipe.

[0045] When generating a recipe, the generation unit can adjust the difficulty of the recipe according to the user's cooking skill level. The generation unit, for example, generates an easy recipe for beginners. The generation unit can also generate a slightly more difficult recipe for intermediate cooks. Furthermore, the generation unit can generate an advanced recipe for advanced cooks. This makes it possible to provide the optimal recipe for the user by adjusting the difficulty of the recipe according to the user's cooking skill level. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's cooking skill level into the generation AI and have the generation AI adjust the difficulty of the recipe.

[0046] When providing a recipe, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the providing unit can customize the display method based on the user's past operation history. In this way, by referring to the user's past operation history, a more appropriate recipe display method can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history into the generation AI and cause the generation AI to select the optimal display method.

[0047] When providing a recipe, the providing unit can customize the display content according to the user's current task. For example, when the user is cooking, the providing unit can prioritize displaying cooking steps. Furthermore, when the user is shopping, the providing unit can also prioritize displaying a list of necessary ingredients. Furthermore, when the user is checking a recipe, the providing unit can also prioritize displaying detailed instructions. In this way, by customizing the display content according to the user's current task, a more appropriate recipe display can be provided. Some 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 current task into the generation AI and cause the generation AI to customize the display content.

[0048] The providing unit can improve the display method by reflecting user feedback when providing a recipe. For example, the providing unit can prioritize providing display methods that users have previously rated highly. The providing unit can also exclude display methods that users have previously rated poorly. Furthermore, the providing unit can improve the display method based on user feedback and provide a more appropriate recipe display. In this way, a more appropriate recipe display method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0049] When providing a recipe, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide a more appropriate recipe display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0050] When providing a recipe, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the recipe based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the recipe in that language. This makes it possible to provide a more appropriate recipe display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language setting into a generation AI and cause the generation AI to execute multilingual display content.

[0051] When providing a recipe, the providing unit can customize the display method according to the user's visual and auditory characteristics. For example, if the user is visually impaired, the providing unit can provide audio guidance preferentially. Furthermore, if the user is hearing impaired, the providing unit can also provide a visually easy-to-understand display method. Furthermore, the providing unit can also provide an optimal display method according to the user's visual and auditory characteristics. This allows for customizing the display method according to the user's visual and auditory characteristics to provide a more appropriate recipe display. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's visual and auditory characteristics into the generation AI and cause the generation AI to customize the display method.

[0052] The recommendation unit can improve the accuracy of recommendations by taking into account the interrelationships between products when making recommendations. For example, the recommendation unit makes recommendations by taking into account combinations of main ingredients and supplementary ingredients. The recommendation unit can also make recommendations based on combinations of products previously purchased by the user. Furthermore, the recommendation unit can analyze the interrelationships between products and recommend optimal combinations. In this way, by taking the interrelationships between products into consideration, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the interrelationships between products into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0053] When making a recommendation, the recommendation unit can take into consideration the attribute information of the product provider. For example, the recommendation unit can preferentially recommend products provided by local producers. The recommendation unit can also preferentially recommend providers of organically grown products. Furthermore, the recommendation unit can recommend products from specific brands or providers. In this way, by taking into consideration the attribute information of the product provider, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the attribute information of the product provider into the generation AI and have the generation AI execute the recommendation.

[0054] When making a recommendation, the recommendation unit can weight the recommendation based on the frequency of product provision. For example, the recommendation unit prioritizes recommending products that the user frequently purchases. The recommendation unit can also recommend products that the user has never purchased in the past. Furthermore, the recommendation unit can adjust the weighting of the recommendation based on the frequency of product provision. In this way, by weighting the recommendation based on the frequency of product provision, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit can be performed using a generation AI. For example, the recommendation unit can input the frequency of product provision into the generation AI and have the generation AI perform weighting of the recommendations.

[0055] The recommendation unit can make recommendations taking into account the geographical distribution of products. For example, if the user is in a specific region, the recommendation unit can prioritize recommending products in that region. Also, if the user is traveling, the recommendation unit can prioritize recommending products in the travel destination. Furthermore, if the user is at home, the recommendation unit can prioritize recommending products from nearby supermarkets. In this way, by taking the geographical distribution of products into consideration, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit can be performed using a generation AI. For example, the recommendation unit can input the geographical distribution of products into the generation AI and have the generation AI execute recommendations.

[0056] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to literature related to the product. The recommendation unit makes recommendations based on, for example, reviews and ratings about the product. The recommendation unit can also make recommendations by referring to research papers and articles about the product. Furthermore, the recommendation unit can analyze literature related to the product and make optimal recommendations. This makes it possible to recommend more appropriate products by referring to literature related to the product. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input literature related to the product into the generation AI and have the generation AI improve the accuracy of the recommendations.

[0057] The recommendation unit can make recommendations taking into account the market value of the product. For example, the recommendation unit preferentially recommends products with high market value. The recommendation unit can also recommend products that exclude products with low market value. Furthermore, the recommendation unit can analyze the market value of the product and make optimal recommendations. This makes it possible to recommend more appropriate products by taking the market value of the product into consideration. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the market value of the product into the generation AI and have the generation AI execute the recommendation.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can also analyze the user's ingredient consumption patterns and prioritize the ingredient information to be collected based on the ingredient's expiration date. For example, the collection unit can prioritize the collection of ingredients with an approaching expiration date, thereby reducing waste. The collection unit can also achieve efficient ingredient management by postponing ingredients with a long expiration date. Furthermore, the collection unit can predict the expiration date of specific ingredients based on the user's past consumption patterns and adjust the information to be collected. This makes the user's ingredient management more efficient and reduces ingredient waste.

[0060] The generation unit can also generate recipes taking into account the user's ingredient allergy information. For example, the generation unit can prioritize generating recipes that do not include ingredients to which the user has allergies. The generation unit can also suggest substitutes for ingredients to which the user has allergies. Furthermore, the generation unit can generate recipes that do not cause allergic reactions based on the user's allergy information. This allows the user to enjoy cooking with peace of mind while protecting their health.

[0061] The providing unit can also adjust the difficulty of the recipes according to the user's cooking skill. For example, the providing unit can prioritize displaying easy recipes for beginners. The providing unit can also display slightly more difficult recipes for intermediate cooks. Furthermore, the providing unit can display more advanced recipes for advanced cooks. This allows the user to be provided with optimal recipes according to their cooking skill, increasing the enjoyment of cooking.

[0062] The recommendation unit can also analyze the user's past purchase history and prioritize recommend products that the user likes. For example, the recommendation unit can analyze the trends of products that the user has purchased in the past and recommend similar products. The recommendation unit can also prioritize recommending products that the user has given high ratings to in the past. Furthermore, the recommendation unit can also suggest new products based on the user's purchase history. This makes it possible to recommend products that match the user's preferences, thereby increasing shopping satisfaction.

[0063] The collection unit can also adjust the ingredient information to be collected taking into account the user's ingredient storage method. For example, the collection unit can prioritize collection of ingredients that require refrigeration. The collection unit can also postpone collection of ingredients that can be stored at room temperature. Furthermore, the collection unit can adjust the ingredient information to be collected depending on the user's storage space. This makes it possible to provide optimal ingredient information according to the user's ingredient storage method.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects information about ingredients that the user has and supermarket sale items. For example, the collection unit allows the user to input ingredients that are in the refrigerator. The collection unit can also obtain supermarket sale item information. Furthermore, the collection unit can automatically collect ingredient information using a sensor. For example, the collection unit detects ingredients in the refrigerator with a sensor and collects that information. Step 2: The generation unit generates a recipe based on the information collected by the collection unit. The generation unit uses a generation AI to generate an optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also generate a recipe based on ingredient information using a multimodal generation AI. For example, the generation AI receives a prompt such as "Please suggest a dish that can be made with these ingredients" and generates an optimal recipe. Step 3: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the generated recipe to the user and provides cooking steps and necessary ingredients. For example, the providing unit displays the generated recipe on an application. The providing unit can also send the generated recipe by email. Furthermore, the providing unit can also print and provide the generated recipe. Step 4: The recommendation unit collects information about nearby supermarkets and recommends the cheapest products. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommendation unit collects online price information from nearby supermarkets. The recommendation unit can also collect in-store price information. Furthermore, the recommendation unit can collect price information after discounts.

[0066] (Example 2) A cooking assistance system according to an embodiment of the present invention collects ingredient information, generates recipes using a generation AI, provides them to the user, and recommends the cheapest items. The cooking assistance system collects ingredient information, generates optimal recipes using a generation AI, and provides them to the user. The cooking assistance system also collects information about nearby supermarkets and recommends the cheapest items. For example, the cooking assistance system collects information about ingredients owned by the user and information about special offers at the supermarket. For example, the cooking assistance system allows the user to input ingredients in the refrigerator or obtain information about special offers at the supermarket. Next, the cooking assistance system generates optimal recipes using the generation AI based on the collected ingredient information. The input to the generation AI is the collected ingredient information itself, and the generation AI generates recipes based on that information. For example, the generation AI receives a prompt such as, "Please suggest a dish that can be made with these ingredients," and generates an optimal recipe. The cooking assistance system then provides the generated recipe to the user. The providing unit displays the generated recipe to the user and provides cooking instructions and necessary ingredients. This allows the user to easily cook. The cooking assistance system also collects price information from nearby supermarkets and recommends the cheapest items. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user, allowing the user to shop economically. This allows the cooking assistance system to collect information on ingredients the user has and supermarket sale items, generate optimal recipes, and provide them to the user. Furthermore, recommending the cheapest products also enables economical shopping. For example, the generation AI can suggest new recipes based on the ingredients the user has. Furthermore, by collecting price information from nearby supermarkets and recommending the cheapest products, the user can decide on a menu using sale items. This allows the user to enjoy new recipes and shop economically.

[0067] A cooking assistance system according to an embodiment includes a collection unit, a generation unit, a provision unit, and a recommendation unit. The collection unit collects information about ingredients owned by a user and supermarket sale items. For example, the collection unit allows a user to input ingredients in a refrigerator. The collection unit can also acquire supermarket sale item information. The collection unit can also automatically collect ingredient information using a sensor. For example, the collection unit detects ingredients in a refrigerator using a sensor and collects the information. The generation unit generates a recipe based on the information collected by the collection unit. The generation unit uses a generation AI to generate an optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also generate a recipe based on ingredient information using a multimodal generation AI. For example, the generation AI receives a prompt such as "Please suggest a dish that can be made with these ingredients" and generates an optimal recipe. The provision unit provides the recipe generated by the generation unit to a user. The provision unit displays the generated recipe to the user and provides cooking instructions and necessary ingredients. For example, the provision unit displays the generated recipe on an application. The providing unit can also send the generated recipe by email. Furthermore, the providing unit can also print and provide the generated recipe. The recommending unit collects information on nearby supermarkets and recommends the cheapest products. The recommending unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommending unit collects online price information from nearby supermarkets. The recommending unit can also collect in-store price information. Furthermore, the recommending unit can collect price information after discounts. In this way, the cooking assistance system according to the embodiment can collect information on ingredients that the user has and special offers from supermarkets, generate optimal recipes, and provide them to the user. Furthermore, recommending the cheapest products enables economical shopping.

[0068] The collection unit can collect information about ingredients that the user owns or supermarket sale items. For example, the collection unit can input ingredients that the user has in the refrigerator. The collection unit can also acquire supermarket sale item information. For example, the collection unit can collect online supermarket price information. The collection unit can also collect in-store price information. Furthermore, the collection unit can also collect discounted price information. In this way, by collecting information about ingredients that the user owns or supermarket sale items, information for generating an optimal recipe can be obtained. Some or all of the above-described 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 online supermarket price information to the generation AI and cause the generation AI to collect price information.

[0069] The generation unit can generate the most suitable recipe using the AI ​​based on the collected ingredient information. The generation unit uses the generation AI to generate the optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also use a multimodal generation AI to generate a recipe based on the ingredient information. For example, the generation AI receives a prompt such as, "Please suggest a dish that can be made with these ingredients," and generates an optimal recipe. The generation unit can also use the generation AI to generate a recipe that takes into account ingredient combinations. For example, the generation AI generates a recipe taking into account the nutritional value and flavor compatibility of the ingredients. This allows the AI ​​to generate the optimal recipe based on the collected ingredient information, thereby enabling it to suggest new dishes to the user. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI. For example, the generation unit can input the collected ingredient information into the generation AI and cause the generation AI to generate a recipe.

[0070] The providing unit can display the generated recipe to the user and provide cooking instructions and necessary ingredients. The providing unit can display the generated recipe to the user and provide cooking instructions and necessary ingredients. For example, the providing unit can display the generated recipe on an application. The providing unit can also send the generated recipe by email. The providing unit can also print and provide the generated recipe. For example, the providing unit can display the generated recipe as step-by-step instructions. The providing unit can also display the generated recipe using videos or images. This allows the user to easily cook a dish by displaying the generated recipe to the user and providing cooking instructions and necessary ingredients. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated recipe to the generation AI and cause the generation AI to execute a recipe display method.

[0071] The recommendation unit can collect price information from nearby supermarkets and recommend the lowest-priced products to the user. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommendation unit collects online price information from nearby supermarkets. The recommendation unit can also collect in-store price information. The recommendation unit can also collect discounted price information. For example, the recommendation unit collects price information from nearby supermarkets and lists the cheapest products. The recommendation unit can also recommend optimal products based on the user's preferences. For example, the recommendation unit recommends optimal products based on information about products the user has previously purchased. This allows the user to shop economically by collecting price information from nearby supermarkets and recommending the cheapest products to the user. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without AI. For example, the recommendation unit can input price information from nearby supermarkets into the generation AI and have the generation AI recommend the cheapest products.

[0072] The collection unit can estimate the user's psychological state and adjust the timing of collecting ingredient information based on the estimated psychological state. For example, if the user is feeling stressed, the collection unit can delay collecting ingredient information and start collecting it when the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can quickly collect ingredient information, saving the user's time. Furthermore, if the user is enjoying themselves, the collection unit can interactively collect ingredient information to attract the user's interest. This allows the timing of collecting ingredient information to be adjusted according to the user's emotions, reducing the user's stress and collecting information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's psychological state into the generation AI and have the generation AI adjust the collection timing.

[0073] The collection unit can analyze the user's past ingredient purchase history and select the most appropriate collection method. For example, the collection unit prioritizes collection of ingredients that the user has frequently purchased in the past. The collection unit can also predict and collect ingredients that the user will purchase in a particular season based on the user's past purchase history. Furthermore, the collection unit can prioritize collection of ingredients needed for a particular dish based on the user's past purchase history. This makes it possible to select a more appropriate collection method and efficiently collect ingredient information by analyzing the user's past ingredient purchase history. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past ingredient purchase history into the generation AI and have the generation AI select the optimal collection method.

[0074] When collecting ingredient information, the collection unit can filter the information based on the user's current meal plan and health condition. For example, if the user is on a diet, the collection unit can prioritize collecting low-calorie ingredient information. Furthermore, if the user has a specific allergy, the collection unit can also collect ingredient information that does not contain that allergen. Furthermore, if the user wants to consume a specific nutrient, the collection unit can collect ingredient information that contains a large amount of that nutrient. By filtering based on the user's current meal plan and health condition, more appropriate ingredient information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's health condition into the generation AI and have the generation AI perform the filtering.

[0075] When collecting ingredient information, the collection unit can select the most appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect ingredient information using voice recognition technology. If the user uses text input, the collection unit can also collect ingredient information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect ingredient information using image recognition technology. This allows ingredient information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0076] The collection unit can estimate the user's psychological state and determine the priority of ingredient information to be collected based on the estimated user's psychological state. For example, if the user is feeling stressed, the collection unit can prioritize collecting ingredient information that has a relaxing effect. Furthermore, if the user is enjoying cooking, the collection unit can prioritize collecting ingredient information that allows the user to try a new dish. Furthermore, if the user is tired, the collection unit can prioritize collecting ingredient information that is easy to prepare. This allows the priority of ingredient information to be collected based on the user's emotions, thereby providing information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's psychological state into the generation AI and have the generation AI determine the priority of ingredient information to be collected.

[0077] When collecting ingredient information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting sale information from supermarkets in that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting ingredient information from travel destinations. Furthermore, if the user is at home, the collection unit can prioritize collecting ingredient information from nearby supermarkets. This allows for the provision of more appropriate ingredient information by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0078] When collecting ingredient information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect necessary ingredient information based on photos of dishes shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related ingredient information. Furthermore, the collection unit can also refer to the activities of the user's friends on social media to collect related ingredient information. In this way, by analyzing the user's social media activities, related ingredient information can be collected efficiently. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related information.

[0079] When collecting ingredient information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes the collection of ingredient information that the user has previously rated highly. The collection unit can also exclude ingredient information that the user has previously rated poorly. Furthermore, the collection unit can improve the collection method based on the user's past feedback and collect more appropriate ingredient information. This makes it possible to collect more appropriate ingredient information by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The generation unit can estimate the user's psychological state and adjust the way the recipe is presented based on the estimated user's psychological state. For example, if the user is relaxed, the generation unit can generate a recipe with detailed instructions. If the user is in a hurry, the generation unit can also generate a concise and to-the-point recipe. Furthermore, if the user is enjoying themselves, the generation unit can also generate a visually appealing recipe. This allows the recipe presentation to be adjusted according to the user's emotions, thereby providing the optimal recipe for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's psychological state into the generation AI and cause the generation AI to adjust the way the recipe is presented.

[0081] When generating a recipe, the generation unit can adjust the level of detail of the recipe based on the importance of the ingredients. For example, the generation unit generates a recipe that includes detailed descriptions of the main ingredients. The generation unit can also generate a recipe that includes concise descriptions of the supplementary ingredients. Furthermore, the generation unit can also provide detailed descriptions of the recipe steps according to the importance of the ingredients. In this way, by adjusting the level of detail of the recipe based on the importance of the ingredients, it is possible to provide an optimal recipe for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the ingredients into the generation AI and cause the generation AI to adjust the level of detail of the recipe.

[0082] When generating a recipe, the generation unit can apply different generation algorithms depending on the category of ingredients. For example, the generation unit can apply a health-oriented algorithm to a recipe whose main ingredient is vegetables. The generation unit can also apply a hearty algorithm to a recipe whose main ingredient is meat. Furthermore, the generation unit can apply an algorithm that emphasizes sweetness to a recipe whose main ingredient is dessert. In this way, by applying different generation algorithms depending on the category of ingredients, more appropriate recipes can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of ingredients into the generation AI and cause the generation AI to apply different generation algorithms.

[0083] When generating a recipe, the generation unit can improve the accuracy of generation by referring to the user's past recipe results. For example, the generation unit generates a new recipe by incorporating features of recipes that the user has previously rated highly. The generation unit can also generate a new recipe by avoiding features of recipes that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past recipe results and generate an optimal recipe. In this way, by referring to the user's past recipe results, the accuracy of generation can be improved and more appropriate recipes can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input the user's past recipe results into the generation AI and cause the generation AI to improve the accuracy of recipe generation.

[0084] The generation unit can estimate the user's psychological state and adjust the length of the recipe based on the estimated user's psychological state. For example, if the user is in a hurry, the generation unit can generate a short, concise recipe. Furthermore, if the user is relaxed, the generation unit can generate a longer recipe with detailed instructions. Furthermore, if the user is enjoying themselves, the generation unit can generate a visually appealing recipe. This allows the user to be provided with an optimal recipe by adjusting the length of the recipe according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's psychological state into the generation AI and have the generation AI adjust the length of the recipe.

[0085] When generating a recipe, the generation unit can determine the priority of recipes based on the freshness of ingredients. For example, the generation unit generates recipes that prioritize the use of ingredients with high freshness. The generation unit can also generate recipes that use ingredients with low freshness earlier. Furthermore, the generation unit can generate recipes that suggest the optimal timing to use ingredients depending on the freshness of the ingredients. In this way, by determining the priority of recipes based on the freshness of ingredients, it is possible to provide recipes that prioritize the use of ingredients with high freshness. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the freshness of ingredients into the generation AI and have the generation AI determine the priority of recipes.

[0086] When generating a recipe, the generation unit can adjust the order of the recipe based on the relevance of ingredients. For example, the generation unit generates a recipe that uses a main ingredient first. The generation unit can also generate a recipe that uses a supplementary ingredient later. Furthermore, the generation unit can generate a recipe that suggests an optimal cooking order based on the relevance of ingredients. In this way, by adjusting the order of the recipe based on the relevance of ingredients, more efficient cooking procedures can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of ingredients into the generation AI and cause the generation AI to adjust the order of the recipe.

[0087] When generating a recipe, the generation unit can adjust the difficulty of the recipe according to the user's cooking skill level. The generation unit, for example, generates an easy recipe for beginners. The generation unit can also generate a slightly more difficult recipe for intermediate cooks. Furthermore, the generation unit can generate an advanced recipe for advanced cooks. This makes it possible to provide the optimal recipe for the user by adjusting the difficulty of the recipe according to the user's cooking skill level. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's cooking skill level into the generation AI and have the generation AI adjust the difficulty of the recipe.

[0088] The providing unit can estimate the user's psychological state and adjust the recipe display method based on the estimated user's psychological state. For example, if the user is relaxed, the providing unit can display a recipe with detailed instructions. If the user is in a hurry, the providing unit can also display a concise, to-the-point recipe. Furthermore, if the user is enjoying themselves, the providing unit can display a visually appealing recipe. This allows the recipe display method to be adjusted according to the user's emotions, thereby providing an optimal recipe display for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can input the user's psychological state into the generation AI and cause the generation AI to adjust the recipe display method.

[0089] When providing a recipe, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the providing unit can customize the display method based on the user's past operation history. In this way, by referring to the user's past operation history, a more appropriate recipe display method can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past operation history into the generation AI and cause the generation AI to select the optimal display method.

[0090] When providing a recipe, the providing unit can customize the display content according to the user's current task. For example, when the user is cooking, the providing unit can prioritize displaying cooking steps. Furthermore, when the user is shopping, the providing unit can also prioritize displaying a list of necessary ingredients. Furthermore, when the user is checking a recipe, the providing unit can also prioritize displaying detailed instructions. In this way, by customizing the display content according to the user's current task, a more appropriate recipe display can be provided. Some 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 current task into the generation AI and cause the generation AI to customize the display content.

[0091] The providing unit can improve the display method by reflecting user feedback when providing a recipe. For example, the providing unit can prioritize providing display methods that users have previously rated highly. The providing unit can also exclude display methods that users have previously rated poorly. Furthermore, the providing unit can improve the display method based on user feedback and provide a more appropriate recipe display. In this way, a more appropriate recipe display method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0092] The providing unit can estimate the user's psychological state and adjust the recipe operation procedures based on the estimated user's psychological state. For example, if the user is relaxed, the providing unit can provide detailed operation procedures. If the user is in a hurry, the providing unit can also provide concise and to-the-point operation procedures. Furthermore, if the user is enjoying themselves, the providing unit can also provide visually appealing operation procedures. This allows the recipe operation procedures to be adjusted according to the user's emotions, thereby providing optimal operation procedures for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can input the user's psychological state into the generation AI and have the generation AI adjust the operation procedures.

[0093] When providing a recipe, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This makes it possible to provide a more appropriate recipe display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0094] When providing a recipe, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the recipe based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the recipe in that language. This makes it possible to provide a more appropriate recipe display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language setting into a generation AI and cause the generation AI to execute multilingual display content.

[0095] When providing a recipe, the providing unit can customize the display method according to the user's visual and auditory characteristics. For example, if the user is visually impaired, the providing unit can provide audio guidance preferentially. Furthermore, if the user is hearing impaired, the providing unit can also provide a visually easy-to-understand display method. Furthermore, the providing unit can also provide an optimal display method according to the user's visual and auditory characteristics. This allows for customizing the display method according to the user's visual and auditory characteristics to provide a more appropriate recipe display. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's visual and auditory characteristics into the generation AI and cause the generation AI to customize the display method.

[0096] The recommendation unit can estimate the user's psychological state and determine the priority of recommended products based on the estimated user's psychological state. For example, if the user is relaxed, the recommendation unit can prioritize recommending products that have a relaxing effect. Furthermore, if the user is in a hurry, the recommendation unit can prioritize recommending products that can be quickly prepared. Furthermore, if the user is enjoying themselves, the recommendation unit can prioritize recommending products that allow the user to try new dishes. This allows the optimal product to be recommended by determining the priority of recommended products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the user's psychological state into the generation AI and have the generation AI determine the priority of products.

[0097] The recommendation unit can improve the accuracy of recommendations by taking into account the interrelationships between products when making recommendations. For example, the recommendation unit makes recommendations by taking into account combinations of main ingredients and supplementary ingredients. The recommendation unit can also make recommendations based on combinations of products previously purchased by the user. Furthermore, the recommendation unit can analyze the interrelationships between products and recommend optimal combinations. In this way, by taking the interrelationships between products into consideration, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the interrelationships between products into the generation AI and cause the generation AI to improve the accuracy of recommendations.

[0098] When making a recommendation, the recommendation unit can take into consideration the attribute information of the product provider. For example, the recommendation unit can preferentially recommend products provided by local producers. The recommendation unit can also preferentially recommend providers of organically grown products. Furthermore, the recommendation unit can recommend products from specific brands or providers. In this way, by taking into consideration the attribute information of the product provider, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the attribute information of the product provider into the generation AI and have the generation AI execute the recommendation.

[0099] When making a recommendation, the recommendation unit can weight the recommendation based on the frequency of product provision. For example, the recommendation unit prioritizes recommending products that the user frequently purchases. The recommendation unit can also recommend products that the user has never purchased in the past. Furthermore, the recommendation unit can adjust the weighting of the recommendation based on the frequency of product provision. In this way, by weighting the recommendation based on the frequency of product provision, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit can be performed using a generation AI. For example, the recommendation unit can input the frequency of product provision into the generation AI and have the generation AI perform weighting of the recommendations.

[0100] The recommendation unit can estimate the user's psychological state and adjust the display method of recommended products based on the estimated user's psychological state. For example, if the user is relaxed, the recommendation unit can display products with detailed descriptions. Furthermore, if the user is in a hurry, the recommendation unit can display products that are concise and to the point. Furthermore, if the user is enjoying themselves, the recommendation unit can display products that are visually appealing. This allows the display method of recommended products to be adjusted according to the user's emotions, thereby providing optimal product display for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recommendation unit is performed using the generation AI. For example, the recommendation unit can input the user's psychological state into the generation AI and have the generation AI adjust the display method of the products.

[0101] The recommendation unit can make recommendations taking into account the geographical distribution of products. For example, if the user is in a specific region, the recommendation unit can prioritize recommending products in that region. Also, if the user is traveling, the recommendation unit can prioritize recommending products in the travel destination. Furthermore, if the user is at home, the recommendation unit can prioritize recommending products from nearby supermarkets. In this way, by taking the geographical distribution of products into consideration, more appropriate products can be recommended. Some or all of the above-mentioned processing in the recommendation unit can be performed using a generation AI. For example, the recommendation unit can input the geographical distribution of products into the generation AI and have the generation AI execute recommendations.

[0102] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to literature related to the product. The recommendation unit makes recommendations based on, for example, reviews and ratings about the product. The recommendation unit can also make recommendations by referring to research papers and articles about the product. Furthermore, the recommendation unit can analyze literature related to the product and make optimal recommendations. This makes it possible to recommend more appropriate products by referring to literature related to the product. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input literature related to the product into the generation AI and have the generation AI improve the accuracy of the recommendations.

[0103] The recommendation unit can make recommendations taking into account the market value of the product. For example, the recommendation unit preferentially recommends products with high market value. The recommendation unit can also recommend products that exclude products with low market value. Furthermore, the recommendation unit can analyze the market value of the product and make optimal recommendations. This makes it possible to recommend more appropriate products by taking the market value of the product into consideration. Some or all of the above-mentioned processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit can input the market value of the product into the generation AI and have the generation AI execute the recommendation. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, provision unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects ingredient information using the camera 42 or sensors of the smart device 14 and processes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI generates an optimal recipe based on the collected ingredient information. The provision unit displays the generated recipe to the user, for example, using the output device 40 of the smart device 14. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects price information from nearby supermarkets and recommends the cheapest products to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, provision unit, and recommendation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects ingredient information using the camera 42 or sensors of the smart glasses 214 and processes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI generates an optimal recipe based on the collected ingredient information. The provision unit displays the generated recipe to the user, for example, using the display of the smart glasses 214. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects price information from nearby supermarkets and recommends the cheapest products to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, provision unit, and recommendation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects ingredient information using the camera 42 or sensors of the headset terminal 314 and processes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI generates an optimal recipe based on the collected ingredient information. The provision unit displays the generated recipe to the user, for example, using the display 343 of the headset terminal 314. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects price information from nearby supermarkets and recommends the cheapest products to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, provision unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects ingredient information using the camera 42 and sensors of the robot 414, and processes the information using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI generates an optimal recipe based on the collected ingredient information. The provision unit displays the generated recipe to the user, for example, using the display and speaker 240 of the robot 414. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects price information from nearby supermarkets and recommends the cheapest products to the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can also analyze the user's ingredient consumption patterns and prioritize the ingredient information to be collected based on the ingredient's expiration date. For example, the collection unit can prioritize the collection of ingredients with an approaching expiration date, thereby reducing waste. The collection unit can also achieve efficient ingredient management by postponing ingredients with a long expiration date. Furthermore, the collection unit can predict the expiration date of specific ingredients based on the user's past consumption patterns and adjust the information to be collected. This makes the user's ingredient management more efficient and reduces ingredient waste.

[0106] The generation unit can also generate recipes taking into account the user's ingredient allergy information. For example, the generation unit can prioritize generating recipes that do not include ingredients to which the user has allergies. The generation unit can also suggest substitutes for ingredients to which the user has allergies. Furthermore, the generation unit can generate recipes that do not cause allergic reactions based on the user's allergy information. This allows the user to enjoy cooking with peace of mind while protecting their health.

[0107] The providing unit can also adjust the difficulty of the recipes according to the user's cooking skill. For example, the providing unit can prioritize displaying easy recipes for beginners. The providing unit can also display slightly more difficult recipes for intermediate cooks. Furthermore, the providing unit can display more advanced recipes for advanced cooks. This allows the user to be provided with optimal recipes according to their cooking skill, increasing the enjoyment of cooking.

[0108] The recommendation unit can also analyze the user's past purchase history and prioritize recommend products that the user likes. For example, the recommendation unit can analyze the trends of products that the user has purchased in the past and recommend similar products. The recommendation unit can also prioritize recommending products that the user has given high ratings to in the past. Furthermore, the recommendation unit can also suggest new products based on the user's purchase history. This makes it possible to recommend products that match the user's preferences, thereby increasing shopping satisfaction.

[0109] The collection unit can also estimate the user's emotions and adjust the method of collecting ingredient information based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting ingredient information that has a relaxing effect. If the user is having fun, the collection unit can also collect ingredient information that allows the user to try a new dish. Furthermore, if the user is tired, the collection unit can also collect ingredient information that is easy to prepare. This makes it possible to provide optimal ingredient information according to the user's emotions.

[0110] The generator can also estimate the user's emotions and adjust the way the recipe is presented based on the estimated emotions. For example, if the user is relaxed, the generator can generate a recipe with detailed instructions. If the user is in a hurry, the generator can generate a concise and to-the-point recipe. Furthermore, if the user is having fun, the generator can generate a visually appealing recipe. This allows the generator to provide the optimal recipe according to the user's emotions.

[0111] The providing unit can also estimate the user's emotions and adjust the way in which the recipe is displayed based on the estimated emotions. For example, if the user is relaxed, a recipe with detailed instructions can be displayed. If the user is in a hurry, a concise recipe that gets to the point can be displayed. Furthermore, if the user is enjoying themselves, a visually appealing recipe can be displayed. This makes it possible to provide an optimal recipe display according to the user's emotions.

[0112] The recommendation unit can also estimate the user's emotions and prioritize recommended products based on the estimated emotions. For example, if the user is relaxed, priority can be given to recommending products that have a relaxing effect. If the user is in a hurry, priority can be given to recommending products that can be cooked quickly. Furthermore, if the user is having fun, priority can be given to recommending products that allow the user to try new dishes. This makes it possible to recommend optimal products according to the user's emotions.

[0113] The recommendation unit can also estimate the user's emotions and adjust the display method of recommended products based on the estimated emotions. For example, if the user is relaxed, products with detailed descriptions can be displayed. If the user is in a hurry, products can be displayed that are concise and to the point. Furthermore, if the user is enjoying themselves, products that are visually appealing can be displayed. This makes it possible to provide optimal product display according to the user's emotions.

[0114] The collection unit can also adjust the ingredient information to be collected taking into account the user's ingredient storage method. For example, the collection unit can prioritize collection of ingredients that require refrigeration. The collection unit can also postpone collection of ingredients that can be stored at room temperature. Furthermore, the collection unit can adjust the ingredient information to be collected depending on the user's storage space. This makes it possible to provide optimal ingredient information according to the user's ingredient storage method.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information about ingredients that the user has and supermarket sale items. For example, the collection unit allows the user to input ingredients that are in the refrigerator. The collection unit can also obtain supermarket sale item information. Furthermore, the collection unit can automatically collect ingredient information using a sensor. For example, the collection unit detects ingredients in the refrigerator with a sensor and collects that information. Step 2: The generation unit generates a recipe based on the information collected by the collection unit. The generation unit uses a generation AI to generate an optimal recipe based on the collected ingredient information. For example, the generation AI generates a recipe using a text generation AI (e.g., LLM). The generation unit can also generate a recipe based on ingredient information using a multimodal generation AI. For example, the generation AI receives a prompt such as "Please suggest a dish that can be made with these ingredients" and generates an optimal recipe. Step 3: The providing unit provides the recipe generated by the generating unit to the user. The providing unit displays the generated recipe to the user and provides cooking steps and necessary ingredients. For example, the providing unit displays the generated recipe on an application. The providing unit can also send the generated recipe by email. Furthermore, the providing unit can also print and provide the generated recipe. Step 4: The recommendation unit collects information about nearby supermarkets and recommends the cheapest products. The recommendation unit collects price information from nearby supermarkets and recommends the cheapest products to the user. For example, the recommendation unit collects online price information from nearby supermarkets. The recommendation unit can also collect in-store price information. Furthermore, the recommendation unit can collect price information after discounts.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 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 collection unit that collects food ingredient information; a generation unit that generates a recipe based on the information collected by the collection unit; a providing unit that provides a user with the recipe generated by the generating unit; A recommendation unit that collects information on nearby supermarkets and recommends the lowest priced products. A system characterized by:

2. The collecting unit Collect information about ingredients the user owns or supermarket specials 2. The system of claim 1.

3. The generation unit Based on the collected ingredient information, AI generates the most suitable recipe.

2. The system of claim 1.

4. The providing unit Display the generated recipe to the user and provide cooking instructions and required ingredients 2. The system of claim 1.

5. The recommendation unit Collects price information from nearby supermarkets and recommends the lowest priced items to users 2. The system of claim 1.

6. The collecting unit The psychological state of the user is estimated, and the timing of collecting ingredient information is adjusted based on the estimated psychological state of the user.

2. The system of claim 1.

7. The collecting unit Analyze the user's past food purchase history and select the most suitable collection method 2. The system of claim 1.

8. The collecting unit When collecting food information, filter it based on the user's current diet plan and health status.

2. The system of claim 1.

9. The collecting unit When collecting food ingredient information, select the most appropriate collection method according to the user's input method.

2. The system of claim 1.

10. The collecting unit The user's mental state is estimated, and the priority of the food ingredient information to be collected is determined based on the estimated user's mental state.

2. The system of claim 1.

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