System

Abstract

An object of the system according to the embodiment is that a user easily plans a menu for one week and efficiently procures necessary food materials.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a list creation unit, an ordering unit, a delivery unit, and a nutrition analysis unit. The reception unit receives input information of a user. The generation unit analyzes the information received by the reception unit and generates a menu for one week. The list creation unit creates a necessary ingredient list based on the menu created by the creation unit. The ordering unit places an order for food materials on the network based on the food material list created by the list creating unit. The delivery unit delivers the food ordered by the ordering unit to the user. The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for users to plan a week's worth of meals and efficiently procure the necessary ingredients.

[0005] The system according to the embodiment aims to enable a user to easily plan a week's worth of menus and efficiently procure the necessary ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a list creation unit, an ordering unit, a delivery unit, and a nutritional analysis unit. The reception unit receives information input by a user. The generation unit analyzes the information received by the reception unit and generates a weekly menu. The list creation unit creates a list of necessary ingredients based on the menu generated by the generation unit. The ordering unit orders ingredients online based on the ingredient list created by the list creation unit. The delivery unit delivers the ingredients ordered by the ordering unit to the user. The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily plan a week's worth of meals and efficiently procure the necessary ingredients. [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) In an embodiment of the present invention, a system allows a user to input their desired meal plan (hearty or healthy), favorite foods, and budget into an AI, which then generates a week's worth of prepared meals and the necessary ingredients. The AI ​​analyzes the user's input, generates a week's worth of prepared meals, and creates a list of ingredients. Furthermore, the system allows users to order ingredients directly online, or offers a delivery plan for the menu they choose if they don't want to cook. For example, if a user inputs information such as "I like healthy meals and my budget is ¥5,000," the AI ​​analyzes the information and suggests a balanced menu. Based on the generated menu, a list of necessary ingredients is created, which the user can order directly online. Furthermore, if the user doesn't want to cook, a delivery plan is also offered. This allows users to enjoy delicious meals without any hassle. This system supports a delicious, enjoyable, and healthy diet. For example, even busy businessmen and housewives raising children can easily enjoy balanced meals. It also reduces food waste and allows for efficient meal preparation.

[0029] The meal suggestion system according to the embodiment includes a reception unit, a generation unit, a list creation unit, an ordering unit, a delivery unit, and a nutritional analysis unit. The reception unit receives user input information. The user input information includes, but is not limited to, food preferences, allergy information, and budget. For example, the reception unit receives user input information such as "I like healthy meals and my budget is 5,000 yen." The generation unit analyzes the information received by the reception unit and generates a weekly menu. The generation unit proposes a balanced menu based on, for example, the user's preferences and budget. The generation unit can analyze the user input information and generate the menu using a generation AI. The list creation unit creates a list of ingredients needed based on the menu generated by the generation unit. For example, the list creation unit lists ingredients needed for the proposed menu, allowing the user to directly order them online. The list creation unit can create the ingredient list using the generation AI. The order unit orders ingredients online based on the ingredient list created by the list creation unit. The ordering unit, for example, works with an online shopping site to order ingredients. The ordering unit can place the order using a generation AI. The delivery unit delivers the ingredients ordered by the ordering unit to the user. The delivery unit provides a plan to deliver the proposed menu, for example, when the user does not want to cook. The delivery unit can provide a delivery plan using the generation AI. The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit. The nutritional analysis unit, for example, takes into account the nutritional balance of the menu to suggest optimal meals to the user. The nutritional analysis unit can analyze the nutritional components using the generation AI. As a result, the meal suggestion system according to the embodiment can support a delicious, enjoyable, and healthy diet by generating a one-week menu of prepared meals and a list of necessary ingredients based on information input by the user, and ordering and delivery can be performed online.

[0030] The generation unit includes a customization unit that customizes the menu based on information input by the user. The customization unit customizes the menu based on the user's preferences and budget. For example, when the user inputs information such as "I like healthy meals and my budget is 5,000 yen," the customization unit adjusts the menu based on that information. The customization unit can analyze the user's input information using a generation AI and customize the menu. For example, the customization unit can suggest vegetable-based menus based on the user's preferences. The customization unit can also select cost-effective ingredients based on the user's budget. Furthermore, the customization unit can suggest allergen-free menus taking into account the user's allergy information. This makes it possible to provide a customized menu based on the user's preferences and budget.

[0031] The list creation unit includes a suggestion unit that suggests ingredient selection and cooking methods. The suggestion unit suggests specific ingredient selection and cooking methods. For example, the suggestion unit lists necessary ingredients based on the generated menu and suggests selection criteria for those ingredients. The suggestion unit can suggest ingredient selection and cooking methods using a generation AI. For example, the suggestion unit can select fresh vegetables and low-calorie ingredients according to the user's preferences. The suggestion unit can also suggest easy cooking methods according to the user's cooking skills. Furthermore, the suggestion unit can suggest allergen-free ingredients taking into account the user's allergy information. As a result, by suggesting specific ingredient selection and cooking methods, the user can efficiently select ingredients and cook.

[0032] The ordering unit includes a linking unit that exchanges data with the online shopping site. The linking unit exchanges data with the online shopping site. For example, the linking unit sends the generated ingredient list to the online shopping site and places an order for the ingredients. The linking unit can exchange data with the online shopping site using the generation AI. For example, the linking unit can automatically add ingredients selected by the user to a cart on the online shopping site. The linking unit can also obtain inventory information from the online shopping site and notify the user of stock status in real time. Furthermore, the linking unit can obtain price information from the online shopping site and provide ingredients at the optimal price to the user. This allows for smooth ordering of ingredients through linking with the online shopping site.

[0033] The delivery unit provides a delivery plan for delivering a menu when the user does not request cooking. The delivery plan provides a plan for delivering a menu when the user does not request cooking. For example, the delivery plan provides a service in which a generated menu is cooked as is and delivered to the user. The delivery plan can use generation AI to analyze the user's input information and provide an optimal delivery plan. For example, if the user inputs information such as "I don't have time to cook," the delivery plan can suggest a pre-cooked menu based on that information. The delivery plan can also provide a customized menu according to the user's preferences. Furthermore, the delivery plan can provide a cost-effective menu according to the user's budget. This allows the user to save time by having the menu delivered even if they do not want to cook.

[0034] The nutritional analysis unit generates a menu based on nutritional components. The nutritional analysis unit generates a menu based on nutritional components. For example, the nutritional analysis unit analyzes the nutritional components of the generated menu and suggests a balanced meal. The nutritional analysis unit can analyze nutritional components and generate a menu using generation AI. For example, the nutritional analysis unit can suggest a menu that takes into account the optimal nutritional balance depending on the user's health condition and dietary restrictions. The nutritional analysis unit can also select ingredients with high nutritional value depending on the user's preferences. Furthermore, the nutritional analysis unit can select ingredients with good cost performance depending on the user's budget. This makes it possible to support a healthy diet by providing a menu that takes into account nutritional balance.

[0035] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] When receiving input information, the reception unit can select information based on the user's current health condition and dietary restrictions. For example, if the user has diabetes, the reception unit can receive information excluding ingredients with high sugar content. Furthermore, if the user has allergies, the reception unit can receive information excluding ingredients containing allergens. Furthermore, if the user is on a diet, the reception unit can receive information excluding ingredients with high calories. In this way, appropriate information can be received by filtering information according to the user's health condition and dietary restrictions. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0037] When receiving input information, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs by voice, the reception unit receives information using voice recognition technology. Furthermore, when the user inputs by text, the reception unit can also receive information using text analysis technology. Furthermore, when the user inputs by image, the reception unit can also receive information using image recognition technology. This allows information to be received efficiently by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0038] When receiving input information, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving information around the user's home. This makes it possible to prioritize receiving information that is highly relevant based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit, for example, receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. This makes it possible to receive related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal reception timing. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0041] When generating a menu, the generation unit can adjust the level of detail of the menu based on the freshness and season of the ingredients. For example, when the ingredients are fresh, the generation unit generates a menu that includes detailed cooking methods. Furthermore, when the ingredients are out of season, the generation unit can also generate a menu that includes simple cooking methods. Furthermore, the generation unit can generate a menu that includes storage methods according to the freshness of the ingredients. In this way, by providing a level of detail of the menu according to the freshness and season of the ingredients, it is possible to generate a more appropriate menu. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0042] When generating a menu, the generation unit can apply different generation algorithms depending on the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the generation unit generates a menu using ingredients that do not contain gluten. Furthermore, if the user desires a low-calorie diet, the generation unit can also generate a menu using low-calorie ingredients. Furthermore, if the user has an allergy, the generation unit can also generate a menu using ingredients that do not contain allergens. This makes it possible to generate an optimal menu according to the user's dietary restrictions and allergy information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0043] When generating a menu, the generation unit can improve the accuracy of the generation by referring to the user's past menu planning results. The generation unit, for example, generates a similar menu based on a menu that the user has previously preferred. The generation unit can also generate a different menu based on a menu that the user has previously avoided. The generation unit can also generate an optimal menu based on the user's past feedback. In this way, by referring to the user's past menu planning results, the accuracy of the generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0044] When generating a menu, the generation unit can determine the priority of the menu based on the time of acquisition of ingredients. The generation unit, for example, generates a menu that prioritizes the use of seasonal ingredients. The generation unit can also generate a menu that prioritizes the use of ingredients that are easily available. The generation unit can also generate a menu that uses ingredients that are only available at specific times. This allows for the generation of a more appropriate menu by providing a priority order for the menu according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0045] When generating a menu, the generation unit can adjust the order of the menu based on the relevance of ingredients. For example, the generation unit can suggest consecutive dishes that use the same ingredients. The generation unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The generation unit can also suggest a balanced menu, taking into account the combination of ingredients. In this way, by providing a menu order according to the relevance of ingredients, a more appropriate menu can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0046] When generating a menu, the generation unit can adjust the difficulty of the menu according to the user's cooking skill level. For example, the generation unit generates a menu that includes simple cooking methods for beginners. The generation unit can also generate a menu that includes slightly more complicated cooking methods for intermediate cooks. The generation unit can also generate a menu that includes advanced cooking methods for advanced cooks. This allows for the generation of a more appropriate menu by providing a menu difficulty level that corresponds to the user's cooking skill level. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0047] When creating an ingredient list, the list creation unit can adjust the level of detail of the list based on the shelf life of the ingredients. For example, the list creation unit generates a list including detailed storage methods for ingredients with short shelf lives. The list creation unit can also generate a list including concise explanations for ingredients with long shelf lives. The list creation unit can also generate a list including the order of use according to the shelf life of the ingredients. This allows for the creation of a more appropriate ingredient list by providing a level of detail of the list according to the shelf life of the ingredients. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI or without AI.

[0048] When creating an ingredient list, the list creation unit can improve the accuracy of the list by referring to the user's past purchase history. The list creation unit generates the list based on, for example, ingredients previously purchased by the user. The list creation unit can also prioritize frequently used ingredients from the user's past purchase history and include them in the list. The list creation unit can also analyze the user's past purchase history to generate an optimal ingredient list. This allows the accuracy of the list to be improved by referring to the user's past purchase history. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0049] When creating an ingredient list, the list creation unit can customize the list taking into account the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the list creation unit can include gluten-free ingredients in the list. Furthermore, if the user desires a low-calorie diet, the list creation unit can also include low-calorie ingredients in the list. Furthermore, if the user has allergies, the list creation unit can also include allergen-free ingredients in the list. This allows for the creation of a more appropriate ingredient list by providing a list that corresponds to the user's dietary restrictions and allergy information. Some or all of the above-described processing by the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When creating an ingredient list, the list creation unit can determine the priority of the list based on when ingredients are available. For example, the list creation unit can prioritize seasonal ingredients in the list. The list creation unit can also prioritize ingredients that are easily available in the list. The list creation unit can also include ingredients that are only available at specific times in the list. This allows for the creation of a more appropriate ingredient list by providing list priorities according to when ingredients are available. Some or all of the above-described processing in the list creation unit can be performed, for example, using AI, or without using AI.

[0051] When creating an ingredient list, the list creation unit can adjust the order of the list based on the relevance of ingredients. For example, the list creation unit can consecutively suggest dishes that use the same ingredients. The list creation unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The list creation unit can also suggest a balanced list, taking into account the combination of ingredients. In this way, by providing a list order based on the relevance of ingredients, a more appropriate ingredient list can be generated. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0052] When creating an ingredient list, the list creation unit can adjust the difficulty of the list according to the user's cooking skill level. For example, the list creation unit generates a list including simple cooking methods for beginners. The list creation unit can also generate a list including slightly more complicated cooking methods for intermediate cooks. The list creation unit can also generate a list including more advanced cooking methods for advanced cooks. This allows for the creation of a more appropriate ingredient list by providing a list of difficulty according to the user's cooking skill level. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI or without using AI.

[0053] When placing an order, the ordering unit can adjust the level of detail of the order based on the freshness and storage period of the ingredients. For example, if the ingredients are fresh, the ordering unit places an order that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the ordering unit can also place an order that includes simple storage instructions. Furthermore, the ordering unit can also place an order that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate ordering by providing a level of detail of the order that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When placing an order, the ordering unit can improve the accuracy of the order by referring to the user's past order history. The ordering unit places an order, for example, based on ingredients that the user has previously ordered. The ordering unit can also prioritize ordering ingredients that are frequently used based on the user's past order history. The ordering unit can also analyze the user's past order history and place an optimal order. In this way, by referring to the user's past order history, the accuracy of the order can be improved. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0055] When placing an order, the ordering unit can customize the order by taking into consideration the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the ordering unit orders gluten-free ingredients. Furthermore, if the user desires a low-calorie diet, the ordering unit can also order low-calorie ingredients. Furthermore, if the user has an allergy, the ordering unit can also order allergen-free ingredients. This allows for more appropriate ordering by providing an order that takes into account the user's dietary restrictions and allergy information. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When placing an order, the ordering unit can determine the order priority based on the time of acquisition of ingredients. For example, the ordering unit prioritizes ordering seasonal ingredients. The ordering unit can also prioritize ordering ingredients that are easy to obtain. The ordering unit can also order ingredients that are only available at specific times. This allows for more appropriate ordering by providing an order priority according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0057] When placing an order, the ordering unit can adjust the order order based on the relevance of ingredients. For example, the ordering unit can suggest consecutive dishes that use the same ingredients. The ordering unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The ordering unit can also suggest a balanced order, taking into account the combination of ingredients. This allows for more appropriate ordering by providing an order order based on the relevance of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0058] When placing an order, the ordering unit can adjust the difficulty of the order according to the user's cooking skill level. For example, the ordering unit can place an order including simple cooking methods for beginners. The ordering unit can also place an order including slightly more complicated cooking methods for intermediate cooks. The ordering unit can also place an order including advanced cooking methods for advanced cooks. This allows for more appropriate ordering by providing an ordering difficulty level according to the user's cooking skill level. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or without using AI.

[0059] The delivery unit can adjust the level of detail of delivery based on the freshness and storage period of the ingredients at the time of delivery. For example, if the ingredients are fresh, the delivery unit can provide delivery that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the delivery unit can also provide delivery that includes simple storage instructions. Furthermore, the delivery unit can also provide delivery that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate delivery by providing a level of detail of delivery that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The delivery unit can improve the accuracy of delivery by referring to the user's past delivery history when making a delivery. The delivery unit can, for example, suggest an optimal delivery time based on the time period in which the user received deliveries in the past. The delivery unit can also prioritize the delivery of frequently used ingredients based on the user's past delivery history. The delivery unit can also analyze the user's past delivery history and make an optimal delivery. In this way, by referring to the user's past delivery history, the accuracy of delivery can be improved. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The delivery unit can customize delivery by taking into account the user's dietary restrictions and allergy information. For example, if the user requests a gluten-free meal, the delivery unit can deliver gluten-free ingredients. Furthermore, if the user requests a low-calorie meal, the delivery unit can also deliver low-calorie ingredients. Furthermore, if the user has an allergy, the delivery unit can also deliver allergen-free ingredients. This allows for more appropriate delivery by providing delivery in accordance with the user's dietary restrictions and allergy information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0062] The delivery unit can determine delivery priorities based on when ingredients are obtained at the time of delivery. For example, the delivery unit prioritizes the delivery of seasonal ingredients. The delivery unit can also prioritize the delivery of ingredients that are easily available. The delivery unit can also deliver ingredients that are only available at specific times. This allows for more appropriate delivery by providing delivery priorities according to when ingredients are obtained. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI.

[0063] The delivery unit can adjust the delivery order based on the relevance of ingredients during delivery. For example, the delivery unit can suggest consecutive dishes that use the same ingredients. The delivery unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The delivery unit can also suggest a balanced delivery, taking into account the combination of ingredients. This allows for more appropriate delivery by providing a delivery order based on the relevance of ingredients. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The delivery unit can adjust the difficulty of delivery according to the user's cooking skill level when delivering. For example, the delivery unit can deliver food including simple cooking methods for beginners. The delivery unit can also deliver food including slightly more complicated cooking methods for intermediate cooks. The delivery unit can also deliver food including advanced cooking methods for advanced cooks. This allows for more appropriate delivery by providing a delivery difficulty level according to the user's cooking skill level. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or without using AI.

[0065] During nutritional analysis, the nutritional analysis unit can adjust the level of detail of the analysis based on the nutritional value and calories of the ingredients. For example, the nutritional analysis unit provides detailed nutritional information for high-nutrition ingredients. The nutritional analysis unit can also provide concise nutritional information for low-calorie ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis according to the nutritional value of the ingredients. This allows for more appropriate nutritional analysis by providing a level of detail of analysis according to the nutritional value and calories of the ingredients. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The nutritional analysis unit can improve the accuracy of the analysis by referring to the user's past dietary history during nutritional analysis. The nutritional analysis unit performs nutritional analysis based on, for example, meals the user has eaten in the past. The nutritional analysis unit can also perform analysis that takes nutritional balance into consideration from the user's past dietary history. The nutritional analysis unit can also analyze the user's past dietary history and perform optimal nutritional analysis. This can improve the accuracy of the analysis by referring to the user's past dietary history. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The nutritional analysis unit can customize the nutritional analysis taking into account the user's health condition and dietary restrictions. For example, if the user has diabetes, the nutritional analysis unit can perform nutritional analysis taking into account sugar intake. Furthermore, if the user is on a diet, the nutritional analysis unit can also perform nutritional analysis taking into account calorie intake. Furthermore, if the user has allergies, the nutritional analysis unit can also perform nutritional analysis taking into account ingredients that do not contain allergens. This allows for more appropriate nutritional analysis by providing an analysis that takes into account the user's health condition and dietary restrictions. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0068] During nutritional analysis, the nutritional analysis unit can determine the analysis priority based on the time of acquisition of ingredients. For example, the nutritional analysis unit prioritizes analysis of seasonal ingredients. The nutritional analysis unit can also prioritize analysis of ingredients that are easily available. The nutritional analysis unit can also prioritize analysis of ingredients that are only available at specific times. This allows for more appropriate nutritional analysis by providing analysis priorities according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0069] The nutritional analysis unit can adjust the analysis order based on the relevance of ingredients during nutritional analysis. For example, the nutritional analysis unit can consecutively analyze dishes that use the same ingredients. The nutritional analysis unit can also prioritize the analysis of ingredients that should be used soon, taking into account the shelf life of the ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis, taking into account the combination of ingredients. This allows for more appropriate nutritional analysis by providing an analysis order based on the relevance of ingredients. Some or all of the above-mentioned processing in the nutritional analysis unit can be performed, for example, using AI, or without using AI.

[0070] The nutritional analysis unit can adjust the difficulty of the analysis according to the user's health goals during nutritional analysis. For example, the nutritional analysis unit can perform an analysis that emphasizes calorie intake for a user on a diet. The nutritional analysis unit can also perform an analysis that emphasizes protein intake for a user aiming to build muscle. The nutritional analysis unit can also perform a balanced nutritional analysis for a user aiming to maintain health. This allows for a more appropriate nutritional analysis by providing an analysis difficulty level that corresponds to the user's health goals. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When generating a menu, the generation unit can adjust the level of detail of the menu based on the freshness and season of the ingredients. For example, if the ingredients are fresh, the generation unit generates a menu that includes detailed cooking instructions. Furthermore, if the ingredients are out of season, the generation unit can also generate a menu that includes simple cooking instructions. Furthermore, the generation unit can generate a menu that includes storage instructions according to the freshness of the ingredients. In this way, by providing a level of detail of the menu according to the freshness and season of the ingredients, it is possible to generate a more appropriate menu. Some or all of the above-mentioned processes in the generation unit may be performed, for example, using AI or without using AI.

[0073] When creating an ingredient list, the list creation unit can adjust the level of detail of the list based on the shelf life of the ingredients. For example, for ingredients with short shelf lives, the list creation unit generates a list that includes detailed storage methods. The list creation unit can also generate a list that includes concise explanations for ingredients with long shelf lives. The list creation unit can also generate a list that includes the order of use of ingredients according to their shelf lives. This allows for the creation of a more appropriate ingredient list by providing a level of detail of the list according to the shelf life of the ingredients. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0074] When placing an order, the ordering unit can adjust the level of detail of the order based on the freshness and storage period of the ingredients. For example, if the ingredients are fresh, the ordering unit can place an order that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the ordering unit can also place an order that includes simple storage instructions. Furthermore, the ordering unit can also place an order that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate ordering by providing a level of detail of the order that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0075] The delivery unit can adjust the level of detail of delivery based on the freshness and storage period of the ingredients at the time of delivery. For example, if the ingredients are fresh, the delivery unit can provide delivery that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the delivery unit can also provide delivery that includes simple storage instructions. Furthermore, the delivery unit can also provide delivery that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate delivery by providing delivery details that correspond to the freshness and storage period of the ingredients. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0076] During nutritional analysis, the nutritional analysis unit can adjust the level of detail of the analysis based on the nutritional value and calories of the ingredients. For example, detailed nutritional information is provided for high-nutrition ingredients. The nutritional analysis unit can also provide concise nutritional information for low-calorie ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis according to the nutritional value of the ingredients. This allows for more appropriate nutritional analysis by providing a level of detail of analysis according to the nutritional value and calories of the ingredients. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives information input by the user. The user's input information includes food preferences, allergy information, budget, etc. For example, the reception unit receives information such as "I like healthy food and my budget is 5,000 yen." Step 2: The generation unit analyzes the information received by the reception unit and generates a weekly menu. The generation unit proposes a balanced menu based on the user's preferences and budget. The generation unit can use generation AI to analyze the information input by the user and generate a menu. Step 3: The list creation unit creates a list of ingredients needed based on the menu generated by the generation unit. The list creation unit lists the ingredients needed for the proposed menu, allowing the user to order them directly online. The list creation unit can create the ingredient list using generation AI. Step 4: The ordering unit orders ingredients online based on the ingredient list created by the list creation unit. The ordering unit links with an online shopping site to order ingredients. The ordering unit can use the generation AI to place the order. Step 5: The delivery unit delivers the ingredients ordered by the ordering unit to the user. If the user does not want to cook, the delivery unit provides a delivery plan for the menu item. The delivery unit can provide a delivery plan using the generation AI. Step 6: The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit. The nutritional analysis unit takes into account the nutritional balance of the menu and suggests the optimal meal for the user. The nutritional analysis unit can analyze the nutritional components using the generation AI.

[0079] (Example 2) In an embodiment of the present invention, a system allows a user to input their desired meal plan (hearty or healthy), favorite foods, and budget into an AI, which then generates a week's worth of prepared meals and the necessary ingredients. The AI ​​analyzes the user's input, generates a week's worth of prepared meals, and creates a list of ingredients. Furthermore, the system allows users to order ingredients directly online, or offers a delivery plan for the menu they choose if they don't want to cook. For example, if a user inputs information such as "I like healthy meals and my budget is ¥5,000," the AI ​​analyzes the information and suggests a balanced menu. Based on the generated menu, a list of necessary ingredients is created, which the user can order directly online. Furthermore, if the user doesn't want to cook, a delivery plan is also offered. This allows users to enjoy delicious meals without any hassle. This system supports a delicious, enjoyable, and healthy diet. For example, even busy businessmen and housewives raising children can easily enjoy balanced meals. It also reduces food waste and allows for efficient meal preparation.

[0080] The meal suggestion system according to the embodiment includes a reception unit, a generation unit, a list creation unit, an ordering unit, a delivery unit, and a nutritional analysis unit. The reception unit receives user input information. The user input information includes, but is not limited to, food preferences, allergy information, and budget. For example, the reception unit receives user input information such as "I like healthy meals and my budget is 5,000 yen." The generation unit analyzes the information received by the reception unit and generates a weekly menu. The generation unit proposes a balanced menu based on, for example, the user's preferences and budget. The generation unit can analyze the user input information and generate the menu using a generation AI. The list creation unit creates a list of ingredients needed based on the menu generated by the generation unit. For example, the list creation unit lists ingredients needed for the proposed menu, allowing the user to directly order them online. The list creation unit can create the ingredient list using the generation AI. The order unit orders ingredients online based on the ingredient list created by the list creation unit. The ordering unit, for example, works with an online shopping site to order ingredients. The ordering unit can place the order using a generation AI. The delivery unit delivers the ingredients ordered by the ordering unit to the user. The delivery unit provides a plan to deliver the proposed menu, for example, when the user does not want to cook. The delivery unit can provide a delivery plan using the generation AI. The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit. The nutritional analysis unit, for example, takes into account the nutritional balance of the menu to suggest optimal meals to the user. The nutritional analysis unit can analyze the nutritional components using the generation AI. As a result, the meal suggestion system according to the embodiment can support a delicious, enjoyable, and healthy diet by generating a one-week menu of prepared meals and a list of necessary ingredients based on information input by the user, and ordering and delivery can be performed online.

[0081] The generation unit includes a customization unit that customizes the menu based on information input by the user. The customization unit customizes the menu based on the user's preferences and budget. For example, when the user inputs information such as "I like healthy meals and my budget is 5,000 yen," the customization unit adjusts the menu based on that information. The customization unit can analyze the user's input information using a generation AI and customize the menu. For example, the customization unit can suggest vegetable-based menus based on the user's preferences. The customization unit can also select cost-effective ingredients based on the user's budget. Furthermore, the customization unit can suggest allergen-free menus taking into account the user's allergy information. This makes it possible to provide a customized menu based on the user's preferences and budget.

[0082] The list creation unit includes a suggestion unit that suggests ingredient selection and cooking methods. The suggestion unit suggests specific ingredient selection and cooking methods. For example, the suggestion unit lists necessary ingredients based on the generated menu and suggests selection criteria for those ingredients. The suggestion unit can suggest ingredient selection and cooking methods using a generation AI. For example, the suggestion unit can select fresh vegetables and low-calorie ingredients according to the user's preferences. The suggestion unit can also suggest easy cooking methods according to the user's cooking skills. Furthermore, the suggestion unit can suggest allergen-free ingredients taking into account the user's allergy information. As a result, by suggesting specific ingredient selection and cooking methods, the user can efficiently select ingredients and cook.

[0083] The ordering unit includes a linking unit that exchanges data with the online shopping site. The linking unit exchanges data with the online shopping site. For example, the linking unit sends the generated ingredient list to the online shopping site and places an order for the ingredients. The linking unit can exchange data with the online shopping site using the generation AI. For example, the linking unit can automatically add ingredients selected by the user to a cart on the online shopping site. The linking unit can also obtain inventory information from the online shopping site and notify the user of stock status in real time. Furthermore, the linking unit can obtain price information from the online shopping site and provide ingredients at the optimal price to the user. This allows for smooth ordering of ingredients through linking with the online shopping site.

[0084] The delivery unit provides a delivery plan for delivering a menu when the user does not request cooking. The delivery plan provides a plan for delivering a menu when the user does not request cooking. For example, the delivery plan provides a service in which a generated menu is cooked as is and delivered to the user. The delivery plan can use generation AI to analyze the user's input information and provide an optimal delivery plan. For example, if the user inputs information such as "I don't have time to cook," the delivery plan can suggest a pre-cooked menu based on that information. The delivery plan can also provide a customized menu according to the user's preferences. Furthermore, the delivery plan can provide a cost-effective menu according to the user's budget. This allows the user to save time by having the menu delivered even if they do not want to cook.

[0085] The nutritional analysis unit generates a menu based on nutritional components. The nutritional analysis unit generates a menu based on nutritional components. For example, the nutritional analysis unit analyzes the nutritional components of the generated menu and suggests a balanced meal. The nutritional analysis unit can analyze nutritional components and generate a menu using generation AI. For example, the nutritional analysis unit can suggest a menu that takes into account the optimal nutritional balance depending on the user's health condition and dietary restrictions. The nutritional analysis unit can also select ingredients with high nutritional value depending on the user's preferences. Furthermore, the nutritional analysis unit can select ingredients with good cost performance depending on the user's budget. This makes it possible to support a healthy diet by providing a menu that takes into account nutritional balance.

[0086] The reception unit can analyze the user's emotions and adjust the timing of receiving input information based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can delay the timing at which the AI ​​prompts the user to enter information, allowing the user to enter information in a relaxed state. Furthermore, if the user is in a hurry, the reception unit can prompt the user to enter information quickly, allowing the information to be received in a short time. Furthermore, if the user is relaxed, the reception unit can prompt the user to enter detailed information, allowing the information to be received at a more appropriate time by adjusting the timing of receiving input information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0088] When receiving input information, the reception unit can select information based on the user's current health condition and dietary restrictions. For example, if the user has diabetes, the reception unit can receive information excluding ingredients with high sugar content. Furthermore, if the user has allergies, the reception unit can receive information excluding ingredients containing allergens. Furthermore, if the user is on a diet, the reception unit can receive information excluding ingredients with high calories. In this way, appropriate information can be received by filtering information according to the user's health condition and dietary restrictions. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0089] When receiving input information, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs by voice, the reception unit receives information using voice recognition technology. Furthermore, when the user inputs by text, the reception unit can also receive information using text analysis technology. Furthermore, when the user inputs by image, the reception unit can also receive information using image recognition technology. This allows information to be received efficiently by providing the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0090] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving important information. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving detailed information. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving information that can be processed quickly. In this way, by determining the priority of information according to the user's emotions, important information can be received preferentially. Emotion estimation is realized using an emotion estimation function using, 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.

[0091] When receiving input information, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving information around the user's home. This makes it possible to prioritize receiving information that is highly relevant based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit, for example, receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. This makes it possible to receive related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal reception timing. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0094] The generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a menu that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a menu that includes concise explanations. If the user is excited, the generation unit can also generate a visually appealing menu. This allows for the generation of a more appropriate menu by providing a menu presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] When generating a menu, the generation unit can adjust the level of detail of the menu based on the freshness and season of the ingredients. For example, when the ingredients are fresh, the generation unit generates a menu that includes detailed cooking methods. Furthermore, when the ingredients are out of season, the generation unit can also generate a menu that includes simple cooking methods. Furthermore, the generation unit can generate a menu that includes storage methods according to the freshness of the ingredients. In this way, by providing a level of detail of the menu according to the freshness and season of the ingredients, it is possible to generate a more appropriate menu. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0096] When generating a menu, the generation unit can apply different generation algorithms depending on the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the generation unit generates a menu using ingredients that do not contain gluten. Furthermore, if the user desires a low-calorie diet, the generation unit can also generate a menu using low-calorie ingredients. Furthermore, if the user has an allergy, the generation unit can also generate a menu using ingredients that do not contain allergens. This makes it possible to generate an optimal menu according to the user's dietary restrictions and allergy information. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0097] When generating a menu, the generation unit can improve the accuracy of the generation by referring to the user's past menu planning results. The generation unit, for example, generates a similar menu based on a menu that the user has previously preferred. The generation unit can also generate a different menu based on a menu that the user has previously avoided. The generation unit can also generate an optimal menu based on the user's past feedback. In this way, by referring to the user's past menu planning results, the accuracy of the generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The generation unit can estimate the user's emotions and adjust the length of the menu based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a menu that can be prepared in a short time. Furthermore, if the user is relaxed, the generation unit can also generate a menu that can be prepared over a long period of time. Furthermore, if the user is excited, the generation unit can also generate a menu that includes multiple dishes. This allows for the generation of a more appropriate menu by providing a menu length that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When generating a menu, the generation unit can determine the priority of the menu based on the time of acquisition of ingredients. The generation unit, for example, generates a menu that prioritizes the use of seasonal ingredients. The generation unit can also generate a menu that prioritizes the use of ingredients that are easily available. The generation unit can also generate a menu that uses ingredients that are only available at specific times. This allows for the generation of a more appropriate menu by providing a priority order for the menu according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0100] When generating a menu, the generation unit can adjust the order of the menu based on the relevance of ingredients. For example, the generation unit can suggest consecutive dishes that use the same ingredients. The generation unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The generation unit can also suggest a balanced menu, taking into account the combination of ingredients. In this way, by providing a menu order according to the relevance of ingredients, a more appropriate menu can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0101] When generating a menu, the generation unit can adjust the difficulty of the menu according to the user's cooking skill level. For example, the generation unit generates a menu that includes simple cooking methods for beginners. The generation unit can also generate a menu that includes slightly more complicated cooking methods for intermediate cooks. The generation unit can also generate a menu that includes advanced cooking methods for advanced cooks. This allows for the generation of a more appropriate menu by providing a menu difficulty level that corresponds to the user's cooking skill level. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0102] The list creation unit can estimate the user's emotions and adjust the presentation method of the ingredient list based on the estimated user emotions. For example, if the user is relaxed, the list creation unit generates an ingredient list with detailed descriptions. If the user is in a hurry, the list creation unit can also generate an ingredient list with concise descriptions. If the user is excited, the list creation unit can also generate a visually appealing ingredient list. This allows for the generation of a more appropriate ingredient list by providing an ingredient list presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When creating an ingredient list, the list creation unit can adjust the level of detail of the list based on the shelf life of the ingredients. For example, the list creation unit generates a list including detailed storage methods for ingredients with short shelf lives. The list creation unit can also generate a list including concise explanations for ingredients with long shelf lives. The list creation unit can also generate a list including the order of use according to the shelf life of the ingredients. This allows for the creation of a more appropriate ingredient list by providing a level of detail of the list according to the shelf life of the ingredients. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI or without AI.

[0104] When creating an ingredient list, the list creation unit can improve the accuracy of the list by referring to the user's past purchase history. The list creation unit generates the list based on, for example, ingredients previously purchased by the user. The list creation unit can also prioritize frequently used ingredients from the user's past purchase history and include them in the list. The list creation unit can also analyze the user's past purchase history to generate an optimal ingredient list. This allows the accuracy of the list to be improved by referring to the user's past purchase history. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0105] When creating an ingredient list, the list creation unit can customize the list taking into account the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the list creation unit can include gluten-free ingredients in the list. Furthermore, if the user desires a low-calorie diet, the list creation unit can also include low-calorie ingredients in the list. Furthermore, if the user has allergies, the list creation unit can also include allergen-free ingredients in the list. This allows for the creation of a more appropriate ingredient list by providing a list that corresponds to the user's dietary restrictions and allergy information. Some or all of the above-described processing by the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The list creation unit can estimate the user's emotions and adjust the length of the ingredient list based on the estimated user emotions. For example, if the user is in a hurry, the list creation unit can generate a short list containing the bare minimum of ingredients. Furthermore, if the user is relaxed, the list creation unit can generate a long list with detailed descriptions. Furthermore, if the user is excited, the list creation unit can generate a visually appealing list. This allows for the creation of a more appropriate ingredient list by providing an ingredient list length that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When creating an ingredient list, the list creation unit can determine the priority of the list based on when ingredients are available. For example, the list creation unit can prioritize seasonal ingredients in the list. The list creation unit can also prioritize ingredients that are easily available in the list. The list creation unit can also include ingredients that are only available at specific times in the list. This allows for the creation of a more appropriate ingredient list by providing list priorities according to when ingredients are available. Some or all of the above-described processing in the list creation unit can be performed, for example, using AI, or without using AI.

[0108] When creating an ingredient list, the list creation unit can adjust the order of the list based on the relevance of ingredients. For example, the list creation unit can consecutively suggest dishes that use the same ingredients. The list creation unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The list creation unit can also suggest a balanced list, taking into account the combination of ingredients. In this way, by providing a list order based on the relevance of ingredients, a more appropriate ingredient list can be generated. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When creating an ingredient list, the list creation unit can adjust the difficulty of the list according to the user's cooking skill level. For example, the list creation unit generates a list including simple cooking methods for beginners. The list creation unit can also generate a list including slightly more complicated cooking methods for intermediate cooks. The list creation unit can also generate a list including more advanced cooking methods for advanced cooks. This allows for the creation of a more appropriate ingredient list by providing a list of difficulty according to the user's cooking skill level. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI or without using AI.

[0110] The order unit can estimate the user's emotions and adjust the timing of the order based on the estimated user emotions. For example, if the user is feeling stressed, the order unit can delay the timing of the order so that the user can place the order in a relaxed state. The order unit can also place an order quickly if the user is in a hurry. The order unit can also place an order including detailed instructions if the user is relaxed. This allows the order to be placed at a more appropriate time by providing the timing of the order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] When placing an order, the ordering unit can adjust the level of detail of the order based on the freshness and storage period of the ingredients. For example, if the ingredients are fresh, the ordering unit places an order that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the ordering unit can also place an order that includes simple storage instructions. Furthermore, the ordering unit can also place an order that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate ordering by providing a level of detail of the order that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0112] When placing an order, the ordering unit can improve the accuracy of the order by referring to the user's past order history. The ordering unit places an order, for example, based on ingredients that the user has previously ordered. The ordering unit can also prioritize ordering ingredients that are frequently used based on the user's past order history. The ordering unit can also analyze the user's past order history and place an optimal order. In this way, by referring to the user's past order history, the accuracy of the order can be improved. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0113] When placing an order, the ordering unit can customize the order by taking into consideration the user's dietary restrictions and allergy information. For example, if the user desires a gluten-free diet, the ordering unit orders gluten-free ingredients. Furthermore, if the user desires a low-calorie diet, the ordering unit can also order low-calorie ingredients. Furthermore, if the user has an allergy, the ordering unit can also order allergen-free ingredients. This allows for more appropriate ordering by providing an order that takes into account the user's dietary restrictions and allergy information. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0114] The ordering unit can estimate the user's emotions and determine the order priority based on the estimated user emotions. For example, if the user is feeling stressed, the ordering unit can prioritize ordering important ingredients. Furthermore, if the user is relaxed, the ordering unit can place an order including detailed instructions. Furthermore, if the user is in a hurry, the ordering unit can place an order quickly. This allows for more appropriate ordering by providing order priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] When placing an order, the ordering unit can determine the order priority based on the time of acquisition of ingredients. For example, the ordering unit prioritizes ordering seasonal ingredients. The ordering unit can also prioritize ordering ingredients that are easy to obtain. The ordering unit can also order ingredients that are only available at specific times. This allows for more appropriate ordering by providing an order priority according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0116] When placing an order, the ordering unit can adjust the order order based on the relevance of ingredients. For example, the ordering unit can suggest consecutive dishes that use the same ingredients. The ordering unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The ordering unit can also suggest a balanced order, taking into account the combination of ingredients. This allows for more appropriate ordering by providing an order order based on the relevance of ingredients. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0117] When placing an order, the ordering unit can adjust the difficulty of the order according to the user's cooking skill level. For example, the ordering unit can place an order including simple cooking methods for beginners. The ordering unit can also place an order including slightly more complicated cooking methods for intermediate cooks. The ordering unit can also place an order including advanced cooking methods for advanced cooks. This allows for more appropriate ordering by providing an ordering difficulty level according to the user's cooking skill level. Some or all of the above-mentioned processing in the ordering unit may be performed, for example, using AI or without using AI.

[0118] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated user emotions. For example, if the user is feeling stressed, the delivery unit can delay the timing of delivery so that the user can receive the item in a relaxed state. The delivery unit can also deliver quickly if the user is in a hurry. The delivery unit can also deliver with detailed explanations if the user is relaxed. This allows delivery to be timed more appropriately by providing delivery timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0119] The delivery unit can adjust the level of detail of delivery based on the freshness and storage period of the ingredients at the time of delivery. For example, if the ingredients are fresh, the delivery unit can provide delivery that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the delivery unit can also provide delivery that includes simple storage instructions. Furthermore, the delivery unit can also provide delivery that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate delivery by providing a level of detail of delivery that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0120] The delivery unit can improve the accuracy of delivery by referring to the user's past delivery history when making a delivery. The delivery unit can, for example, suggest an optimal delivery time based on the time period in which the user received deliveries in the past. The delivery unit can also prioritize the delivery of frequently used ingredients based on the user's past delivery history. The delivery unit can also analyze the user's past delivery history and make an optimal delivery. In this way, by referring to the user's past delivery history, the accuracy of delivery can be improved. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0121] The delivery unit can customize delivery by taking into account the user's dietary restrictions and allergy information. For example, if the user requests a gluten-free meal, the delivery unit can deliver gluten-free ingredients. Furthermore, if the user requests a low-calorie meal, the delivery unit can also deliver low-calorie ingredients. Furthermore, if the user has an allergy, the delivery unit can also deliver allergen-free ingredients. This allows for more appropriate delivery by providing delivery in accordance with the user's dietary restrictions and allergy information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0122] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, if the user is feeling stressed, the delivery unit can prioritize delivery of important ingredients. Furthermore, if the user is relaxed, the delivery unit can also provide delivery with detailed instructions. Furthermore, if the user is in a hurry, the delivery unit can also provide quick delivery. This allows for more appropriate delivery by providing delivery priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The delivery unit can determine delivery priorities based on when ingredients are obtained at the time of delivery. For example, the delivery unit prioritizes the delivery of seasonal ingredients. The delivery unit can also prioritize the delivery of ingredients that are easily available. The delivery unit can also deliver ingredients that are only available at specific times. This allows for more appropriate delivery by providing delivery priorities according to when ingredients are obtained. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI.

[0124] The delivery unit can adjust the delivery order based on the relevance of ingredients during delivery. For example, the delivery unit can suggest consecutive dishes that use the same ingredients. The delivery unit can also prioritize ingredients that should be used soon, taking into account the shelf life of the ingredients. The delivery unit can also suggest a balanced delivery, taking into account the combination of ingredients. This allows for more appropriate delivery by providing a delivery order based on the relevance of ingredients. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0125] The delivery unit can adjust the difficulty of delivery according to the user's cooking skill level when delivering. For example, the delivery unit can deliver food including simple cooking methods for beginners. The delivery unit can also deliver food including slightly more complicated cooking methods for intermediate cooks. The delivery unit can also deliver food including advanced cooking methods for advanced cooks. This allows for more appropriate delivery by providing a delivery difficulty level according to the user's cooking skill level. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or without using AI.

[0126] The nutritional analysis unit can estimate the user's emotions and adjust the nutritional analysis method based on the estimated user's emotions. For example, the nutritional analysis unit can perform a detailed nutritional analysis when the user is relaxed. The nutritional analysis unit can also perform a brief nutritional analysis when the user is in a hurry. The nutritional analysis unit can also perform a visually appealing nutritional analysis when the user is excited. This allows for more appropriate nutritional analysis by providing a nutritional analysis method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] During nutritional analysis, the nutritional analysis unit can adjust the level of detail of the analysis based on the nutritional value and calories of the ingredients. For example, the nutritional analysis unit provides detailed nutritional information for high-nutrition ingredients. The nutritional analysis unit can also provide concise nutritional information for low-calorie ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis according to the nutritional value of the ingredients. This allows for more appropriate nutritional analysis by providing a level of detail of analysis according to the nutritional value and calories of the ingredients. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0128] The nutritional analysis unit can improve the accuracy of the analysis by referring to the user's past dietary history during nutritional analysis. The nutritional analysis unit performs nutritional analysis based on, for example, meals the user has eaten in the past. The nutritional analysis unit can also perform analysis that takes nutritional balance into consideration from the user's past dietary history. The nutritional analysis unit can also analyze the user's past dietary history and perform optimal nutritional analysis. This can improve the accuracy of the analysis by referring to the user's past dietary history. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0129] The nutritional analysis unit can customize the nutritional analysis taking into account the user's health condition and dietary restrictions. For example, if the user has diabetes, the nutritional analysis unit can perform nutritional analysis taking into account sugar intake. Furthermore, if the user is on a diet, the nutritional analysis unit can also perform nutritional analysis taking into account calorie intake. Furthermore, if the user has allergies, the nutritional analysis unit can also perform nutritional analysis taking into account ingredients that do not contain allergens. This allows for more appropriate nutritional analysis by providing an analysis that takes into account the user's health condition and dietary restrictions. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0130] The nutritional analysis unit can estimate the user's emotions and determine the priority of nutritional analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the nutritional analysis unit can prioritize analyzing important nutritional information. Furthermore, if the user is relaxed, the nutritional analysis unit can prioritize analyzing detailed nutritional information. Furthermore, if the user is in a hurry, the nutritional analysis unit can prioritize analyzing concise nutritional information. This allows for more appropriate nutritional analysis by providing a priority order for nutritional analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] During nutritional analysis, the nutritional analysis unit can determine the analysis priority based on the time of acquisition of ingredients. For example, the nutritional analysis unit prioritizes analysis of seasonal ingredients. The nutritional analysis unit can also prioritize analysis of ingredients that are easily available. The nutritional analysis unit can also prioritize analysis of ingredients that are only available at specific times. This allows for more appropriate nutritional analysis by providing analysis priorities according to the time of acquisition of ingredients. Some or all of the above-mentioned processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0132] The nutritional analysis unit can adjust the analysis order based on the relevance of ingredients during nutritional analysis. For example, the nutritional analysis unit can consecutively analyze dishes that use the same ingredients. The nutritional analysis unit can also prioritize the analysis of ingredients that should be used soon, taking into account the shelf life of the ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis, taking into account the combination of ingredients. This allows for more appropriate nutritional analysis by providing an analysis order based on the relevance of ingredients. Some or all of the above-mentioned processing in the nutritional analysis unit can be performed, for example, using AI, or without using AI.

[0133] The nutritional analysis unit can adjust the difficulty of the analysis according to the user's health goals during nutritional analysis. For example, the nutritional analysis unit can perform an analysis that emphasizes calorie intake for a user on a diet. The nutritional analysis unit can also perform an analysis that emphasizes protein intake for a user aiming to build muscle. The nutritional analysis unit can also perform a balanced nutritional analysis for a user aiming to maintain health. This allows for a more appropriate nutritional analysis by providing an analysis difficulty level that corresponds to the user's health goals. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, list creation unit, ordering unit, delivery unit, and nutritional analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives user input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input information to generate a menu. The list creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an ingredient list based on the generated menu. The ordering unit is realized by the specific processing unit 290 of the data processing device 12 and orders ingredients online based on the ingredient list. The delivery unit is realized by the output device 40 of the smart device 14 and delivers ingredients and menu items to the user. The nutritional analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the nutritional components of the generated menu. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, list creation unit, ordering unit, delivery unit, and nutritional analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives user input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input information to generate a menu. The list creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an ingredient list based on the generated menu. The ordering unit is realized by the specific processing unit 290 of the data processing device 12 and orders ingredients online based on the ingredient list. The delivery unit is realized by the speaker 240 of the smart glasses 214 and delivers ingredients and menu items to the user. The nutritional analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the nutritional components of the generated menu. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, list creation unit, ordering unit, delivery unit, and nutritional analysis unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives user input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input information to generate a menu. The list creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an ingredient list based on the generated menu. The ordering unit is realized by the specific processing unit 290 of the data processing device 12 and orders ingredients online based on the ingredient list. The delivery unit is realized by the speaker 240 of the headset terminal 314 and delivers ingredients and menu items to the user. The nutritional analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the nutritional components of the generated menu. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, list creation unit, ordering unit, delivery unit, and nutritional analysis unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives user input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input information to generate a menu. The list creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an ingredient list based on the generated menu. The ordering unit is realized by the specific processing unit 290 of the data processing device 12 and orders ingredients online based on the ingredient list. The delivery unit is realized by the speaker 240 of the robot 414 and delivers ingredients and menu items to the user. The nutritional analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the nutritional components of the generated menu.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can delay the timing of prompting the user to enter information, allowing the user to enter information in a relaxed state. Furthermore, if the user is in a hurry, the reception unit can prompt the user to enter information quickly, allowing the user to receive information in a short period of time. Furthermore, if the user is relaxed, the reception unit can prompt the user to enter more detailed information, allowing the user to receive more information. By adjusting the timing of receiving input information according to the user's emotions, the user can receive 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.

[0136] The generation unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a menu with detailed explanations. If the user is in a hurry, the generation unit can also generate a menu with concise explanations. If the user is excited, the generation unit can also generate a visually appealing menu. This allows for the generation of a more appropriate menu by providing a menu presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0137] The list creation unit can estimate the user's emotions and adjust the presentation method of the ingredient list based on the estimated user emotions. For example, if the user is relaxed, the list creation unit can generate an ingredient list with detailed descriptions. If the user is in a hurry, the list creation unit can also generate an ingredient list with concise descriptions. If the user is excited, the list creation unit can also generate a visually appealing ingredient list. This allows for the generation of a more appropriate ingredient list by providing an ingredient list presentation method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0138] The order unit can estimate the user's emotions and adjust the timing of an order based on the estimated user emotions. For example, if the user is feeling stressed, the order timing can be delayed to allow the user to place the order in a relaxed state. The order unit can also place an order quickly if the user is in a hurry. The order unit can also place an order including detailed instructions if the user is relaxed. This allows the order to be placed at a more appropriate time by providing an order timing that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0139] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated user emotions. For example, if the user is feeling stressed, the timing of delivery can be delayed so that the user can receive the package in a relaxed state. The delivery unit can also deliver quickly if the user is in a hurry. The delivery unit can also deliver with detailed explanations if the user is relaxed. This allows delivery to be timed more appropriately by providing delivery timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0140] When generating a menu, the generation unit can adjust the level of detail of the menu based on the freshness and season of the ingredients. For example, if the ingredients are fresh, the generation unit generates a menu that includes detailed cooking instructions. Furthermore, if the ingredients are out of season, the generation unit can also generate a menu that includes simple cooking instructions. Furthermore, the generation unit can generate a menu that includes storage instructions according to the freshness of the ingredients. In this way, by providing a level of detail of the menu according to the freshness and season of the ingredients, it is possible to generate a more appropriate menu. Some or all of the above-mentioned processes in the generation unit may be performed, for example, using AI or without using AI.

[0141] When creating an ingredient list, the list creation unit can adjust the level of detail of the list based on the shelf life of the ingredients. For example, for ingredients with short shelf lives, the list creation unit generates a list that includes detailed storage methods. The list creation unit can also generate a list that includes concise explanations for ingredients with long shelf lives. The list creation unit can also generate a list that includes the order of use of ingredients according to their shelf lives. This allows for the creation of a more appropriate ingredient list by providing a level of detail of the list according to the shelf life of the ingredients. Some or all of the above-described processing in the list creation unit may be performed, for example, using AI, or may be performed without using AI.

[0142] When placing an order, the ordering unit can adjust the level of detail of the order based on the freshness and storage period of the ingredients. For example, if the ingredients are fresh, the ordering unit can place an order that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the ordering unit can also place an order that includes simple storage instructions. Furthermore, the ordering unit can also place an order that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate ordering by providing a level of detail of the order that corresponds to the freshness and storage period of the ingredients. Some or all of the above-described processing in the ordering unit may be performed, for example, using AI, or may be performed without using AI.

[0143] The delivery unit can adjust the level of detail of delivery based on the freshness and storage period of the ingredients at the time of delivery. For example, if the ingredients are fresh, the delivery unit can provide delivery that includes detailed storage instructions. Furthermore, if the ingredients are out of season, the delivery unit can also provide delivery that includes simple storage instructions. Furthermore, the delivery unit can also provide delivery that includes storage instructions depending on the freshness of the ingredients. This allows for more appropriate delivery by providing delivery details that correspond to the freshness and storage period of the ingredients. Some or all of the above-mentioned processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI.

[0144] During nutritional analysis, the nutritional analysis unit can adjust the level of detail of the analysis based on the nutritional value and calories of the ingredients. For example, detailed nutritional information is provided for high-nutrition ingredients. The nutritional analysis unit can also provide concise nutritional information for low-calorie ingredients. The nutritional analysis unit can also perform a balanced nutritional analysis according to the nutritional value of the ingredients. This allows for more appropriate nutritional analysis by providing a level of detail of analysis according to the nutritional value and calories of the ingredients. Some or all of the above-described processing in the nutritional analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives information input by the user. The user's input information includes food preferences, allergy information, budget, etc. For example, the reception unit receives information such as "I like healthy food and my budget is 5,000 yen." Step 2: The generation unit analyzes the information received by the reception unit and generates a weekly menu. The generation unit proposes a balanced menu based on the user's preferences and budget. The generation unit can use generation AI to analyze the information input by the user and generate a menu. Step 3: The list creation unit creates a list of ingredients needed based on the menu generated by the generation unit. The list creation unit lists the ingredients needed for the proposed menu, allowing the user to order them directly online. The list creation unit can create the ingredient list using generation AI. Step 4: The ordering unit orders ingredients online based on the ingredient list created by the list creation unit. The ordering unit links with an online shopping site to order ingredients. The ordering unit can use the generation AI to place the order. Step 5: The delivery unit delivers the ingredients ordered by the ordering unit to the user. If the user does not want to cook, the delivery unit provides a delivery plan for the menu item. The delivery unit can provide a delivery plan using the generation AI. Step 6: The nutritional analysis unit analyzes the nutritional components of the menu generated by the generation unit. The nutritional analysis unit takes into account the nutritional balance of the menu and suggests the optimal meal for the user. The nutritional analysis unit can analyze the nutritional components using the generation AI.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input information from a user; A generation unit that analyzes the information received by the reception unit and generates a menu for one week; a list creation unit that creates a list of ingredients required based on the menu created by the creation unit; an ordering unit that orders ingredients online based on the ingredient list created by the list creation unit; a delivery unit that delivers the ingredients ordered by the ordering unit to the user; a nutritional analysis unit that analyzes nutritional components of the menu generated by the generation unit; Equipped with A system characterized by:

2. The generation unit A customization unit is provided to customize the menu based on user input information.

2. The system of claim 1.

3. The list creation unit Equipped with a proposal department that suggests ingredients and cooking methods 2. The system of claim 1.

4. The ordering unit Equipped with a linking section that exchanges data with online shopping sites 2. The system of claim 1.

5. The delivery unit Offer delivery options to deliver menu items if the user does not want to order food 2. The system of claim 1.

6. The nutritional analysis unit Generate menus based on nutritional information 2. The system of claim 1.

7. The reception unit Analyzes user emotions and adjusts the timing of accepting input information based on the analyzed user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit When accepting input information, filter it based on the user's current health status and dietary restrictions.

2. The system of claim 1.

10. The reception unit When accepting input information, select an appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

Images