system

Abstract

The system according to this embodiment aims to convert a user's creative culinary ideas into concrete recipes and to provide a food delivery service in cooperation with partner restaurants. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a selection unit, and a linkage unit. The reception unit receives the user's idea for a creative dish. The generation unit analyzes the idea received by the reception unit and generates a specific recipe. The selection unit selects a partner restaurant based on the recipe generated by the generation unit. The linkage unit links the restaurant selected by the selection unit with a food delivery service.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the idea of a user's original dish has not been fully converted into a specific recipe and coordinated with partner restaurants to provide a food delivery service, leaving room for improvement.

[0005] The system according to the embodiment aims to convert the idea of a user's original dish into a specific recipe and coordinate with partner restaurants to provide a food delivery service.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a selection unit, and a linkage unit. The reception unit receives the user's ideas for creative dishes. The generation unit analyzes the ideas received by the reception unit and generates specific recipes. The selection unit selects partner restaurants based on the recipes generated by the generation unit. The linkage unit links the restaurants selected by the selection unit with food delivery services. [Effects of the Invention]

[0007] The system according to this embodiment can convert a user's creative culinary ideas into concrete recipes and provide a food delivery service in cooperation with partner restaurants. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The creative cooking suggestion system according to an embodiment of the present invention is a system in which an AI agent suggests a recipe based on a creative cooking idea entered by the user, selects a restaurant that can handle the request in cooperation with partner restaurants, and delivers the food within the specified time in cooperation with a food delivery service. When a user enters a creative cooking idea, the creative cooking suggestion system has an AI agent that analyzes the idea and generates a specific recipe. Based on the generated recipe, it cooperates with partner restaurants to select a restaurant that can prepare the food. Furthermore, it cooperates with a food delivery service to deliver the food within the specified time. This mechanism allows users to easily realize their ideas and easily enjoy the food they want to eat. For example, a user enters a specific request such as, "I want a chicken dish using miso and curry powder, and a soup to go with it, for under 5000 yen, and I can eat it in one hour." This information is entered into the AI ​​agent. Next, the AI ​​agent analyzes the entered idea and generates a specific recipe. The generating AI proposes the optimal recipe based on the user's request. For example, it generates a recipe such as "Miso-flavored tandoori chicken and chicken egg soup." Based on the generated recipe, the system collaborates with partner restaurants to select a restaurant capable of preparing the dish. The AI ​​agent selects a restaurant from the partner restaurants that can meet the user's request. For example, it might select a restaurant that can serve miso-flavored tandoori chicken. Furthermore, it collaborates with a food delivery service to deliver the dish within the specified time. The AI ​​agent works with a food delivery service to deliver the food within the time specified by the user. For example, it could deliver a meal for 4800 yen including delivery charges in one hour. This system allows users to easily realize their ideas and enjoy the dishes they want to eat. Moreover, even without cooking skills, the AI ​​agent suggests specific recipes, selects restaurants capable of preparing the dish, and supports delivery through a delivery service, allowing users to enjoy cooking without any hassle. In this way, the creative cooking suggestion system can efficiently receive and analyze users' creative cooking ideas, generate recipes, select partner restaurants, and collaborate with food delivery services for delivery.

[0029] The creative cooking suggestion system according to this embodiment comprises a reception unit, a generation unit, a selection unit, and a linkage unit. The reception unit receives creative cooking ideas from users. User creative cooking ideas include, for example, the level of detail in the recipe and the genre of the dish, but are not limited to such examples. The reception unit receives creative cooking ideas entered by the user in text format, for example. The reception unit can also analyze images of dishes uploaded by the user using image analysis technology and accept them as ideas. The generation unit uses generation AI to analyze the ideas received by the reception unit and generate specific recipes. The generation unit generates, for example, the optimal recipe based on the user's requests. The generation AI proposes the optimal recipe based on the user's requests. For example, the generation AI generates recipes such as miso-flavored tandoori chicken and chicken egg soup. The generation AI can also propose the optimal ingredients and cooking procedures based on the user's requests. The selection unit selects partner restaurants based on the recipes generated by the generation unit. The selection unit selects, for example, a restaurant from among the partner restaurants that can meet the user's requests. The selection unit selects the most suitable restaurant based on the selection criteria and conditions of partner restaurants. For example, the selection unit selects a restaurant that can serve miso-flavored tandoori chicken. The coordination unit coordinates the restaurant selected by the selection unit with a food delivery service. The coordination unit, for example, coordinates with the food delivery service to deliver the food within the specified time. The coordination unit can arrange delivery using the API of the food delivery service. For example, the coordination unit delivers the food within the specified budget, including delivery fees. As a result, the creative cooking suggestion system according to this embodiment can efficiently receive and analyze the user's creative cooking ideas, generate recipes, select partner restaurants, and coordinate with food delivery services for delivery.

[0030] The reception desk accepts user-submitted ideas for original dishes. These ideas may include, but are not limited to, the level of detail in the recipe or the genre of the dish. The reception desk accepts user-submitted ideas in text format. Specifically, users can fill in details such as the name of the dish, the ingredients used, the cooking method, and key points of the seasoning through a dedicated input form. The reception desk can also use image analysis technology to analyze images of dishes uploaded by users and accept them as ideas. The image analysis technology uses AI to identify ingredients and cooking methods within the image and extract them as text information. For example, the AI ​​can recognize the types of ingredients and cooking process from an image of a dish uploaded by a user and generate ideas based on that. Furthermore, the reception desk also supports voice input, allowing users to provide ideas by voice. Voice input is converted to text using speech recognition technology and processed in the same way as other input methods. This allows the reception desk to accommodate a variety of user input methods and efficiently receive ideas.

[0031] The generation unit uses a generation AI to analyze ideas received by the reception unit and generate specific recipes. For example, the generation unit generates the optimal recipe based on the user's requests. The generation AI proposes the optimal recipe based on the user's requests. Specifically, the generation AI analyzes information obtained from the ideas and images entered by the user and generates a recipe considering the genre of cuisine, ingredients used, cooking methods, etc. For example, if the user requests "Japanese-style miso-based tandoori chicken," the generation AI will propose in detail how to make the miso marinade, how to prepare the chicken, and how to cook it. The generation AI can also propose the optimal ingredients and cooking procedures considering the user's food preferences and allergy information. For example, if the user has an egg allergy, the generation AI will propose a recipe that does not use eggs. Furthermore, the generation AI can adjust the difficulty level and procedure of the recipe according to the user's cooking skills and the cooking equipment used. In this way, the generation unit can efficiently generate specific recipes that meet the diverse needs of users.

[0032] The selection unit selects partner restaurants based on the recipes generated by the generation unit. For example, the selection unit selects a restaurant from among the partner restaurants that can meet the user's requests. Specifically, the selection unit analyzes the content of the generated recipe and searches the database for restaurants that can provide that recipe. The selection unit selects the most suitable restaurant based on the selection criteria and conditions of the partner restaurants. For example, when selecting a restaurant that can provide miso-flavored tandoori chicken, the selection unit considers criteria such as whether the restaurant specializes in Japanese or Indian cuisine, whether the ingredients used are fresh, and whether it has received high ratings from past users. The selection unit also considers the restaurant's location and delivery area to select a restaurant that can deliver to the user's specified delivery address. In this way, the selection unit can efficiently select the restaurant that best suits the user's needs.

[0033] The Integration Department connects restaurants selected by the Selection Department with food delivery services. For example, the Integration Department works with food delivery services to deliver food within a specified time. Specifically, the Integration Department can use the food delivery service's API to arrange deliveries. For example, the Integration Department delivers food within a specified budget, including delivery fees. The Integration Department provides the food delivery service with the delivery time and destination information specified by the user, ensuring smooth delivery. The Integration Department can also monitor the delivery progress in real time and notify the user of the delivery status. This allows the Integration Department to deliver food quickly and reliably to users, providing a highly satisfying service. Furthermore, the Integration Department can collect post-delivery feedback and use it to improve the service. For example, by analyzing user ratings and comments, the department can review delivery quality and restaurant selection criteria to improve the service. This allows the Integration Department to provide high-quality delivery services that meet user needs and improve the overall reliability and satisfaction of the system.

[0034] The generation unit can generate the optimal recipe based on the user's requests. For example, the generation unit can suggest the optimal recipe based on the user's preferences and nutritional balance. The generation unit can also use a generation AI to suggest the optimal ingredients and cooking procedures based on the user's requests. For example, if the user inputs a request such as, "I want to eat a chicken dish using miso and curry powder, and a soup to go with it, within a budget of 5000 yen, in one hour," the generation AI will generate the optimal recipe based on that request. For example, the generation AI might suggest a recipe such as, "Tandoori chicken with miso and chicken egg soup." In this way, by generating the optimal recipe based on the user's requests, it is possible to provide dishes that meet the user's needs. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit inputs the user's requests into the generation AI, and the generation AI outputs the optimal recipe.

[0035] The selection unit can select a restaurant from its network of partner restaurants that can meet the user's request. For example, the selection unit selects a restaurant from its network of partner restaurants that can meet the user's request. The selection unit selects the optimal restaurant based on the selection criteria and conditions of the partner restaurants. For example, the selection unit selects a restaurant that can serve tandoori chicken with miso. The selection unit can also select the optimal restaurant based on the user's request. For example, if the user inputs a request such as, "I want to eat a chicken dish with miso and curry powder, and a soup to go with it, within a budget of 5000 yen, in one hour," the selection unit will select a restaurant from its network of partner restaurants that can meet that request. In this way, by selecting a restaurant from its network of partner restaurants that can meet the user's request, it is possible to provide dishes that meet the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit inputs information about partner restaurants into the AI, and the AI ​​outputs the optimal restaurant.

[0036] The integration unit can work with food delivery services to deliver food within a specified time. For example, the integration unit can work with food delivery services to deliver food within a specified time. The integration unit can use the API of the food delivery service to arrange deliveries. For example, the integration unit can deliver food within a specified budget, including delivery fees. The integration unit can also select the optimal delivery method based on user requests. For example, if a user inputs a request such as, "I want a chicken dish with miso and curry powder, and a soup to go with it, for under 5000 yen, in one hour," the integration unit will work with the food delivery service to arrange delivery to meet that request. In this way, by working with food delivery services and delivering food within a specified time, food can be provided at a time that meets the user's needs. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit inputs information from the food delivery service into the AI, and the AI ​​outputs the optimal delivery method.

[0037] The generation unit can generate recipes such as miso-based tandoori chicken and chicken egg soup. For example, the generation unit generates recipes such as miso-based tandoori chicken and chicken egg soup based on the user's request. The generation unit can also use a generation AI to suggest the optimal ingredients and cooking procedures based on the user's request. For example, if the user inputs a request such as, "I want a chicken dish using miso and curry powder, and a soup to go with it, for under 5000 yen, and I can eat it in an hour," the generation AI will generate recipes such as miso-based tandoori chicken and chicken egg soup based on that request. In this way, by generating specific recipes, it is possible to provide dishes that meet the user's requests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's request as input to the generation AI, and the generation AI outputs recipes such as miso-based tandoori chicken and chicken egg soup.

[0038] The integration unit can deliver food within a specified budget, including delivery fees. For example, the integration unit delivers food within a specified budget, including delivery fees. The integration unit can arrange deliveries using the API of food delivery services. For example, if a user inputs a request such as, "I want a chicken dish with miso and curry powder, and a soup to go with it, for under 5000 yen, and I want it delivered in one hour," the integration unit will work with a food delivery service to arrange a delivery to fulfill that request. This allows the integration unit to deliver food within a specified budget, thereby providing a service that meets the user's budget. Some or all of the above processing in the integration unit may be performed using AI, for example, or not. For example, the integration unit inputs information from a food delivery service into the AI, and the AI ​​outputs the optimal delivery method.

[0039] The reception department can analyze a user's past idea submission history and select the optimal reception method. For example, the reception department can analyze the trends of ideas previously submitted by the user and prioritize accepting similar ideas. The reception department can also adjust the reception method based on the success rate of ideas previously submitted by the user. The reception department can also suggest the optimal reception time based on the time of day when ideas were previously submitted by the user. In this way, the optimal reception method can be selected by analyzing a user's past idea submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department inputs the user's past idea submission history into the AI, and the AI ​​outputs the optimal reception method.

[0040] The reception unit can filter ideas based on the user's current dietary preferences and health status. For example, if the user is currently on a diet, the reception unit will prioritize low-calorie ideas. If the user has an allergy to a particular ingredient, the reception unit can also prioritize ideas that do not contain that ingredient. If the user likes a particular ingredient, the reception unit can also prioritize ideas that contain that ingredient. This allows the reception unit to receive more appropriate ideas by filtering based on the user's current dietary preferences and health status. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit inputs the user's current dietary preferences and health status into the AI, and the AI ​​outputs the optimal filtering result.

[0041] The reception desk can prioritize receiving ideas that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving ideas that use local ingredients. If the user is traveling, the reception desk can also prioritize receiving ideas that use local specialties. If the user is at home, the reception desk can also prioritize receiving ideas that can be prepared at nearby restaurants. By prioritizing highly relevant ideas based on the user's geographical location, the reception desk can receive more appropriate ideas. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​outputs the most suitable ideas.

[0042] The reception desk can analyze the user's social media activity when receiving ideas and accept relevant ideas. For example, the reception desk can prioritize accepting cooking ideas that the user has shared on social media. The reception desk can also accept ideas that are based on recipes from chefs that the user follows on social media. The reception desk can also prioritize accepting cooking ideas that the user has "liked" on social media. In this way, relevant ideas can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​outputs the most suitable ideas.

[0043] The generation unit can adjust the level of detail in a recipe based on the importance of the idea during recipe generation. For example, for a highly important idea, the generation unit generates a recipe that includes detailed steps and ingredient lists. For a less important idea, the generation unit can also generate a recipe that includes concise steps and ingredient lists. For an idea of ​​moderate importance, the generation unit can generate a recipe with a moderate level of detail. This allows for the provision of more appropriate recipes by adjusting the level of detail based on the importance of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the importance of the idea to the generation AI, and the generation AI outputs the optimal level of recipe detail.

[0044] The generation unit can apply different generation algorithms depending on the category of the idea when generating a recipe. For example, if the idea is for a dessert, the generation unit will apply a generation algorithm specializing in sweets. If the idea is for a main dish, the generation unit can also apply a generation algorithm specializing in main dishes. If the idea is for a soup, the generation unit can also apply a generation algorithm specializing in soups. By applying different generation algorithms depending on the category of the idea, it is possible to provide more appropriate recipes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the category of the idea to the generation AI, and the generation AI outputs the optimal generation algorithm.

[0045] The generation unit can determine the priority of recipes based on when the ideas were submitted. For example, the generation unit can generate a recipe immediately after an idea is submitted. The generation unit can also generate a recipe after a certain amount of time has passed since the idea was submitted. The generation unit can also determine the order in which recipes are generated based on the time of day when the ideas were submitted. This allows for the provision of more appropriate recipes by determining the priority of recipes based on when the ideas were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the idea submission time to the generation AI, and the generation AI outputs the optimal recipe priority order.

[0046] The generation unit can adjust the order of recipes based on the relevance of the ideas during recipe generation. For example, the generation unit can prioritize recipeizing highly relevant ideas. The generation unit can also postpone recipeizing less relevant ideas. The generation unit can also appropriately recipeize ideas of moderate relevance. By adjusting the order of recipes based on the relevance of ideas, it can provide more appropriate recipes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the relevance of ideas as input to the generation AI, and the generation AI outputs the optimal order of recipes.

[0047] The selection unit can improve the accuracy of restaurant selection by considering the interrelationships of ideas. For example, if an idea includes multiple dishes, the selection unit can select restaurants that can accommodate each dish. If an idea includes a specific ingredient, the selection unit can also select restaurants that specialize in that ingredient. If an idea includes a specific cooking method, the selection unit can also select restaurants that are proficient in that cooking method. This allows for more accurate restaurant selection by considering the interrelationships of ideas. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the interrelationships of ideas into the AI, and the AI ​​outputs the optimal restaurant.

[0048] The selection unit can consider the attribute information of the idea submitter when selecting a restaurant. For example, if the submitter is a vegetarian, the selection unit will select a restaurant that offers vegetarian menus. If the submitter has allergies, the selection unit can also select a restaurant that can accommodate those allergies. If the submitter has a preference for a particular dish, the selection unit can also select a restaurant that specializes in that dish. In this way, a more appropriate restaurant can be selected by considering the submitter's attribute information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the submitter's attribute information into the AI, and the AI ​​outputs the optimal restaurant.

[0049] The selection unit can consider the geographical distribution of ideas when selecting a restaurant. For example, if the user is in a specific region, the selection unit will prioritize restaurants in that region. If the user is traveling, the selection unit can also select restaurants that offer local specialties. If the user is at home, the selection unit can also prioritize nearby restaurants. This allows for the selection of a more appropriate restaurant by considering the geographical distribution of ideas. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit inputs the geographical distribution of ideas into the AI, and the AI ​​outputs the optimal restaurant.

[0050] The selection unit can improve the accuracy of its restaurant selection by referring to relevant literature related to the idea. For example, if the idea relates to a specific dish, the selection unit can select a restaurant by referring to literature related to that dish. If the idea relates to a specific ingredient, the selection unit can also select a restaurant by referring to literature related to that ingredient. If the idea relates to a specific cooking method, the selection unit can also select a restaurant by referring to literature related to that cooking method. This makes it possible to select a restaurant with higher accuracy by referring to relevant literature related to the idea. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit inputs relevant literature related to the idea into the AI, and the AI ​​outputs the optimal restaurant.

[0051] The integration unit can select the optimal integration method by referring to past integration data when integrating with delivery services. For example, the integration unit can select the optimal service based on evaluations of delivery services used in the past. The integration unit can also select a service that can deliver quickly by referring to past delivery time data. The integration unit can also select a highly reliable delivery service based on past user feedback. In this way, the optimal integration method for delivery services can be selected by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit inputs past integration data into the AI, and the AI ​​outputs the optimal integration method.

[0052] The integration unit can customize the means of integration based on the user's current living situation when integrating with a delivery service. For example, if the user is at home, the integration unit will use a standard delivery service. If the user is out, the integration unit can also use a delivery service that allows pickup at a specified location. If the user is attending a specific event, the integration unit can also suggest delivery to the event venue. This allows for the provision of more appropriate delivery services by customizing the means of integration based on the user's current living situation. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's current living situation as input to the AI, and the AI ​​outputs the optimal means of integration.

[0053] The integration unit can select the optimal integration method when integrating with delivery services, taking into account the user's geographical location information. For example, if the user is in a specific region, the integration unit will prioritize using delivery services in that region. If the user is traveling, the integration unit can also utilize delivery services that offer local specialties. If the user is at home, the integration unit can also prioritize using nearby delivery services. This allows for the provision of more appropriate delivery services by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's geographical location information to the AI, and the AI ​​outputs the optimal integration method.

[0054] The integration unit can analyze a user's social media activity and propose integration methods when integrating with delivery services. For example, the integration unit can propose the most suitable delivery service based on food ideas shared by the user on social media. The integration unit can also prioritize the use of delivery services that the user follows on social media. The integration unit can also propose delivery services that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate delivery services. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's social media activity as input to the AI, and the AI ​​outputs the most suitable integration method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception department can analyze a user's past idea submission history and select the optimal reception method. For example, it can analyze the trends of ideas previously submitted by the user and prioritize accepting similar ideas. It can also adjust the reception method based on the success rate of ideas previously submitted by the user. Furthermore, it can suggest the optimal reception time based on the time of day when ideas were previously submitted by the user. In this way, the optimal reception method can be selected by analyzing a user's past idea submission history.

[0057] The reception system can filter ideas based on the user's current dietary preferences and health status. For example, if a user is currently on a diet, the reception system can prioritize low-calorie ideas. If a user has an allergy to a particular ingredient, the reception system can prioritize ideas that do not contain that ingredient. Furthermore, if a user has a preference for a particular ingredient, the reception system can prioritize ideas that include that ingredient. This allows for the reception of more relevant ideas by filtering based on the user's current dietary preferences and health status.

[0058] The generation unit can adjust the level of detail in a recipe based on the importance of the idea during recipe generation. For example, for a highly important idea, the generation unit can generate a recipe with detailed instructions and ingredient lists. For a less important idea, the generation unit can generate a recipe with concise instructions and ingredient lists. Furthermore, for an idea of ​​moderate importance, the generation unit can generate a recipe with a moderate level of detail. By adjusting the level of detail in a recipe based on the importance of the idea, it is possible to provide a more appropriate recipe.

[0059] The selection process can improve the accuracy of restaurant selection by considering the interrelationships between ideas. For example, if an idea includes multiple dishes, the selection process can select restaurants that can accommodate each dish. If an idea includes a specific ingredient, the selection process can select restaurants that specialize in that ingredient. Furthermore, if an idea includes a specific cooking method, the selection process can select restaurants that are proficient in that method. This allows for more accurate restaurant selection by considering the interrelationships between ideas.

[0060] The integration unit can customize the integration method based on the user's current living situation when integrating with delivery services. For example, if the user is at home, the integration unit can use a standard delivery service. If the user is out, the integration unit can use a delivery service that allows pickup at a specified location. Furthermore, if the user is attending a specific event, the integration unit can suggest delivery to the event venue. By customizing the integration method based on the user's current living situation, a more appropriate delivery service can be provided.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the user's creative dish ideas. These ideas may include, but are not limited to, the level of detail in the recipe or the genre of the dish. The reception desk accepts the user's creative dish ideas in text format, for example. The reception desk can also use image analysis technology to analyze images of dishes uploaded by the user and accept them as ideas. Step 2: The generation unit uses a generation AI to analyze the ideas received by the reception unit and generate specific recipes. For example, the generation unit generates the optimal recipe based on the user's requests. The generation AI suggests the optimal recipe based on the user's requests. For example, the generation AI generates recipes such as miso-based tandoori chicken and chicken egg soup. The generation AI can also suggest the optimal ingredients and cooking procedures based on the user's requests. Step 3: The selection unit selects partner restaurants based on the recipes generated by the generation unit. The selection unit selects, for example, a restaurant from among the partner restaurants that can meet the user's requests. The selection unit selects the optimal restaurant based on the selection criteria and conditions of the partner restaurants. For example, the selection unit selects a restaurant that can serve miso-flavored tandoori chicken. Step 4: The Integration Department connects the restaurants selected by the Selection Department with food delivery services. For example, the Integration Department connects with food delivery services to deliver food within a specified time. The Integration Department can use the food delivery service's API to arrange deliveries. For example, the Integration Department delivers food within a specified budget, including delivery fees.

[0063] (Example of form 2) The creative cooking suggestion system according to an embodiment of the present invention is a system in which an AI agent suggests a recipe based on a creative cooking idea entered by the user, selects a restaurant that can handle the request in cooperation with partner restaurants, and delivers the food within the specified time in cooperation with a food delivery service. When a user enters a creative cooking idea, the creative cooking suggestion system has an AI agent that analyzes the idea and generates a specific recipe. Based on the generated recipe, it cooperates with partner restaurants to select a restaurant that can prepare the food. Furthermore, it cooperates with a food delivery service to deliver the food within the specified time. This mechanism allows users to easily realize their ideas and easily enjoy the food they want to eat. For example, a user enters a specific request such as, "I want a chicken dish using miso and curry powder, and a soup to go with it, for under 5000 yen, and I can eat it in one hour." This information is entered into the AI ​​agent. Next, the AI ​​agent analyzes the entered idea and generates a specific recipe. The generating AI proposes the optimal recipe based on the user's request. For example, it generates a recipe such as "Miso-flavored tandoori chicken and chicken egg soup." Based on the generated recipe, the system collaborates with partner restaurants to select a restaurant capable of preparing the dish. The AI ​​agent selects a restaurant from the partner restaurants that can meet the user's request. For example, it might select a restaurant that can serve miso-flavored tandoori chicken. Furthermore, it collaborates with a food delivery service to deliver the dish within the specified time. The AI ​​agent works with a food delivery service to deliver the food within the time specified by the user. For example, it could deliver a meal for 4800 yen including delivery charges in one hour. This system allows users to easily realize their ideas and enjoy the dishes they want to eat. Moreover, even without cooking skills, the AI ​​agent suggests specific recipes, selects restaurants capable of preparing the dish, and supports delivery through a delivery service, allowing users to enjoy cooking without any hassle. In this way, the creative cooking suggestion system can efficiently receive and analyze users' creative cooking ideas, generate recipes, select partner restaurants, and collaborate with food delivery services for delivery.

[0064] The creative cooking suggestion system according to this embodiment comprises a reception unit, a generation unit, a selection unit, and a linkage unit. The reception unit receives creative cooking ideas from users. User creative cooking ideas include, for example, the level of detail in the recipe and the genre of the dish, but are not limited to such examples. The reception unit receives creative cooking ideas entered by the user in text format, for example. The reception unit can also analyze images of dishes uploaded by the user using image analysis technology and accept them as ideas. The generation unit uses generation AI to analyze the ideas received by the reception unit and generate specific recipes. The generation unit generates, for example, the optimal recipe based on the user's requests. The generation AI proposes the optimal recipe based on the user's requests. For example, the generation AI generates recipes such as miso-flavored tandoori chicken and chicken egg soup. The generation AI can also propose the optimal ingredients and cooking procedures based on the user's requests. The selection unit selects partner restaurants based on the recipes generated by the generation unit. The selection unit selects, for example, a restaurant from among the partner restaurants that can meet the user's requests. The selection unit selects the most suitable restaurant based on the selection criteria and conditions of partner restaurants. For example, the selection unit selects a restaurant that can serve miso-flavored tandoori chicken. The coordination unit coordinates the restaurant selected by the selection unit with a food delivery service. The coordination unit, for example, coordinates with the food delivery service to deliver the food within the specified time. The coordination unit can arrange delivery using the API of the food delivery service. For example, the coordination unit delivers the food within the specified budget, including delivery fees. As a result, the creative cooking suggestion system according to this embodiment can efficiently receive and analyze the user's creative cooking ideas, generate recipes, select partner restaurants, and coordinate with food delivery services for delivery.

[0065] The reception desk accepts user-submitted ideas for original dishes. These ideas may include, but are not limited to, the level of detail in the recipe or the genre of the dish. The reception desk accepts user-submitted ideas in text format. Specifically, users can fill in details such as the name of the dish, the ingredients used, the cooking method, and key points of the seasoning through a dedicated input form. The reception desk can also use image analysis technology to analyze images of dishes uploaded by users and accept them as ideas. The image analysis technology uses AI to identify ingredients and cooking methods within the image and extract them as text information. For example, the AI ​​can recognize the types of ingredients and cooking process from an image of a dish uploaded by a user and generate ideas based on that. Furthermore, the reception desk also supports voice input, allowing users to provide ideas by voice. Voice input is converted to text using speech recognition technology and processed in the same way as other input methods. This allows the reception desk to accommodate a variety of user input methods and efficiently receive ideas.

[0066] The generation unit uses a generation AI to analyze ideas received by the reception unit and generate specific recipes. For example, the generation unit generates the optimal recipe based on the user's requests. The generation AI proposes the optimal recipe based on the user's requests. Specifically, the generation AI analyzes information obtained from the ideas and images entered by the user and generates a recipe considering the genre of cuisine, ingredients used, cooking methods, etc. For example, if the user requests "Japanese-style miso-based tandoori chicken," the generation AI will propose in detail how to make the miso marinade, how to prepare the chicken, and how to cook it. The generation AI can also propose the optimal ingredients and cooking procedures considering the user's food preferences and allergy information. For example, if the user has an egg allergy, the generation AI will propose a recipe that does not use eggs. Furthermore, the generation AI can adjust the difficulty level and procedure of the recipe according to the user's cooking skills and the cooking equipment used. In this way, the generation unit can efficiently generate specific recipes that meet the diverse needs of users.

[0067] The selection unit selects partner restaurants based on the recipes generated by the generation unit. For example, the selection unit selects a restaurant from among the partner restaurants that can meet the user's requests. Specifically, the selection unit analyzes the content of the generated recipe and searches the database for restaurants that can provide that recipe. The selection unit selects the most suitable restaurant based on the selection criteria and conditions of the partner restaurants. For example, when selecting a restaurant that can provide miso-flavored tandoori chicken, the selection unit considers criteria such as whether the restaurant specializes in Japanese or Indian cuisine, whether the ingredients used are fresh, and whether it has received high ratings from past users. The selection unit also considers the restaurant's location and delivery area to select a restaurant that can deliver to the user's specified delivery address. In this way, the selection unit can efficiently select the restaurant that best suits the user's needs.

[0068] The Integration Department connects restaurants selected by the Selection Department with food delivery services. For example, the Integration Department works with food delivery services to deliver food within a specified time. Specifically, the Integration Department can use the food delivery service's API to arrange deliveries. For example, the Integration Department delivers food within a specified budget, including delivery fees. The Integration Department provides the food delivery service with the delivery time and destination information specified by the user, ensuring smooth delivery. The Integration Department can also monitor the delivery progress in real time and notify the user of the delivery status. This allows the Integration Department to deliver food quickly and reliably to users, providing a highly satisfying service. Furthermore, the Integration Department can collect post-delivery feedback and use it to improve the service. For example, by analyzing user ratings and comments, the department can review delivery quality and restaurant selection criteria to improve the service. This allows the Integration Department to provide high-quality delivery services that meet user needs and improve the overall reliability and satisfaction of the system.

[0069] The generation unit can generate the optimal recipe based on the user's requests. For example, the generation unit can suggest the optimal recipe based on the user's preferences and nutritional balance. The generation unit can also use a generation AI to suggest the optimal ingredients and cooking procedures based on the user's requests. For example, if the user inputs a request such as, "I want to eat a chicken dish using miso and curry powder, and a soup to go with it, within a budget of 5000 yen, in one hour," the generation AI will generate the optimal recipe based on that request. For example, the generation AI might suggest a recipe such as, "Tandoori chicken with miso and chicken egg soup." In this way, by generating the optimal recipe based on the user's requests, it is possible to provide dishes that meet the user's needs. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit inputs the user's requests into the generation AI, and the generation AI outputs the optimal recipe.

[0070] The selection unit can select a restaurant from its network of partner restaurants that can meet the user's request. For example, the selection unit selects a restaurant from its network of partner restaurants that can meet the user's request. The selection unit selects the optimal restaurant based on the selection criteria and conditions of the partner restaurants. For example, the selection unit selects a restaurant that can serve tandoori chicken with miso. The selection unit can also select the optimal restaurant based on the user's request. For example, if the user inputs a request such as, "I want to eat a chicken dish with miso and curry powder, and a soup to go with it, within a budget of 5000 yen, in one hour," the selection unit will select a restaurant from its network of partner restaurants that can meet that request. In this way, by selecting a restaurant from its network of partner restaurants that can meet the user's request, it is possible to provide dishes that meet the user's needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit inputs information about partner restaurants into the AI, and the AI ​​outputs the optimal restaurant.

[0071] The integration unit can work with food delivery services to deliver food within a specified time. For example, the integration unit can work with food delivery services to deliver food within a specified time. The integration unit can use the API of the food delivery service to arrange deliveries. For example, the integration unit can deliver food within a specified budget, including delivery fees. The integration unit can also select the optimal delivery method based on user requests. For example, if a user inputs a request such as, "I want a chicken dish with miso and curry powder, and a soup to go with it, for under 5000 yen, in one hour," the integration unit will work with the food delivery service to arrange delivery to meet that request. In this way, by working with food delivery services and delivering food within a specified time, food can be provided at a time that meets the user's needs. Some or all of the above processes in the integration unit may be performed using AI, for example, or not. For example, the integration unit inputs information from the food delivery service into the AI, and the AI ​​outputs the optimal delivery method.

[0072] The generation unit can generate recipes such as miso-based tandoori chicken and chicken egg soup. For example, the generation unit generates recipes such as miso-based tandoori chicken and chicken egg soup based on the user's request. The generation unit can also use a generation AI to suggest the optimal ingredients and cooking procedures based on the user's request. For example, if the user inputs a request such as, "I want a chicken dish using miso and curry powder, and a soup to go with it, for under 5000 yen, and I can eat it in an hour," the generation AI will generate recipes such as miso-based tandoori chicken and chicken egg soup based on that request. In this way, by generating specific recipes, it is possible to provide dishes that meet the user's requests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's request as input to the generation AI, and the generation AI outputs recipes such as miso-based tandoori chicken and chicken egg soup.

[0073] The integration unit can deliver food within a specified budget, including delivery fees. For example, the integration unit delivers food within a specified budget, including delivery fees. The integration unit can arrange deliveries using the API of food delivery services. For example, if a user inputs a request such as, "I want a chicken dish with miso and curry powder, and a soup to go with it, for under 5000 yen, and I want it delivered in one hour," the integration unit will work with a food delivery service to arrange a delivery to fulfill that request. This allows the integration unit to deliver food within a specified budget, thereby providing a service that meets the user's budget. Some or all of the above processing in the integration unit may be performed using AI, for example, or not. For example, the integration unit inputs information from a food delivery service into the AI, and the AI ​​outputs the optimal delivery method.

[0074] The reception unit can estimate the user's emotions and adjust the timing of idea submission based on the estimated emotions. For example, if the user is stressed, the reception unit will accept ideas during a time when the user can relax. If the user is excited, the reception unit can accept ideas immediately. If the user is tired, the reception unit can accept ideas after the user has rested. By adjusting the timing of idea submission according to the user's emotions, ideas can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit inputs user emotion data into the AI, and the AI ​​outputs the optimal timing for submission.

[0075] The reception department can analyze a user's past idea submission history and select the optimal reception method. For example, the reception department can analyze the trends of ideas previously submitted by the user and prioritize accepting similar ideas. The reception department can also adjust the reception method based on the success rate of ideas previously submitted by the user. The reception department can also suggest the optimal reception time based on the time of day when ideas were previously submitted by the user. In this way, the optimal reception method can be selected by analyzing a user's past idea submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department inputs the user's past idea submission history into the AI, and the AI ​​outputs the optimal reception method.

[0076] The reception unit can filter ideas based on the user's current dietary preferences and health status. For example, if the user is currently on a diet, the reception unit will prioritize low-calorie ideas. If the user has an allergy to a particular ingredient, the reception unit can also prioritize ideas that do not contain that ingredient. If the user likes a particular ingredient, the reception unit can also prioritize ideas that contain that ingredient. This allows the reception unit to receive more appropriate ideas by filtering based on the user's current dietary preferences and health status. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit inputs the user's current dietary preferences and health status into the AI, and the AI ​​outputs the optimal filtering result.

[0077] The reception unit can estimate the user's emotions and determine the priority of ideas to receive based on the estimated emotions. For example, if the user is excited, the reception unit will prioritize that idea. If the user is relaxed, the reception unit may accept that idea equally. If the user is stressed, the reception unit may postpone that idea. This allows for prioritizing more appropriate ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs user emotion data into the AI, and the AI ​​outputs the optimal priority.

[0078] The reception desk can prioritize receiving ideas that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can prioritize receiving ideas that use local ingredients. If the user is traveling, the reception desk can also prioritize receiving ideas that use local specialties. If the user is at home, the reception desk can also prioritize receiving ideas that can be prepared at nearby restaurants. By prioritizing highly relevant ideas based on the user's geographical location, the reception desk can receive more appropriate ideas. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​outputs the most suitable ideas.

[0079] The reception desk can analyze the user's social media activity when receiving ideas and accept relevant ideas. For example, the reception desk can prioritize accepting cooking ideas that the user has shared on social media. The reception desk can also accept ideas that are based on recipes from chefs that the user follows on social media. The reception desk can also prioritize accepting cooking ideas that the user has "liked" on social media. In this way, relevant ideas can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​outputs the most suitable ideas.

[0080] The generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a recipe with detailed instructions. If the user is in a hurry, the generation unit can also generate a recipe with concise instructions. If the user is excited, the generation unit can also generate a visually appealing recipe. This allows for the provision of more appropriate recipes by adjusting the way the recipe is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal way to present the recipe.

[0081] The generation unit can adjust the level of detail in a recipe based on the importance of the idea during recipe generation. For example, for a highly important idea, the generation unit generates a recipe that includes detailed steps and ingredient lists. For a less important idea, the generation unit can also generate a recipe that includes concise steps and ingredient lists. For an idea of ​​moderate importance, the generation unit can generate a recipe with a moderate level of detail. This allows for the provision of more appropriate recipes by adjusting the level of detail based on the importance of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the importance of the idea to the generation AI, and the generation AI outputs the optimal level of recipe detail.

[0082] The generation unit can apply different generation algorithms depending on the category of the idea when generating a recipe. For example, if the idea is for a dessert, the generation unit will apply a generation algorithm specializing in sweets. If the idea is for a main dish, the generation unit can also apply a generation algorithm specializing in main dishes. If the idea is for a soup, the generation unit can also apply a generation algorithm specializing in soups. By applying different generation algorithms depending on the category of the idea, it is possible to provide more appropriate recipes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the category of the idea to the generation AI, and the generation AI outputs the optimal generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the recipe length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise recipe. If the user is relaxed, the generation unit can also generate a longer recipe with detailed explanations. If the user is excited, the generation unit can also generate a recipe with visually stimulating effects. This allows for the provision of more appropriate recipes by adjusting the recipe length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI or not. For example, the generation unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal recipe length.

[0084] The generation unit can determine the priority of recipes based on when the ideas were submitted. For example, the generation unit can generate a recipe immediately after an idea is submitted. The generation unit can also generate a recipe after a certain amount of time has passed since the idea was submitted. The generation unit can also determine the order in which recipes are generated based on the time of day when the ideas were submitted. This allows for the provision of more appropriate recipes by determining the priority of recipes based on when the ideas were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the idea submission time to the generation AI, and the generation AI outputs the optimal recipe priority order.

[0085] The generation unit can adjust the order of recipes based on the relevance of the ideas during recipe generation. For example, the generation unit can prioritize recipeizing highly relevant ideas. The generation unit can also postpone recipeizing less relevant ideas. The generation unit can also appropriately recipeize ideas of moderate relevance. By adjusting the order of recipes based on the relevance of ideas, it can provide more appropriate recipes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the relevance of ideas as input to the generation AI, and the generation AI outputs the optimal order of recipes.

[0086] The selection unit can estimate the user's emotions and adjust the restaurant selection criteria based on the estimated emotions. For example, if the user is relaxed, the selection unit may prioritize highly-rated restaurants. If the user is in a hurry, the selection unit may also prioritize restaurants that can provide quick service. If the user is excited, the selection unit may also prioritize restaurants that offer unique menus. This allows for the selection of a more appropriate restaurant by adjusting the restaurant selection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs user emotion data into the AI, and the AI ​​outputs the optimal restaurant selection criteria.

[0087] The selection unit can improve the accuracy of restaurant selection by considering the interrelationships of ideas. For example, if an idea includes multiple dishes, the selection unit can select restaurants that can accommodate each dish. If an idea includes a specific ingredient, the selection unit can also select restaurants that specialize in that ingredient. If an idea includes a specific cooking method, the selection unit can also select restaurants that are proficient in that cooking method. This allows for more accurate restaurant selection by considering the interrelationships of ideas. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the interrelationships of ideas into the AI, and the AI ​​outputs the optimal restaurant.

[0088] The selection unit can consider the attribute information of the idea submitter when selecting a restaurant. For example, if the submitter is a vegetarian, the selection unit will select a restaurant that offers vegetarian menus. If the submitter has allergies, the selection unit can also select a restaurant that can accommodate those allergies. If the submitter has a preference for a particular dish, the selection unit can also select a restaurant that specializes in that dish. In this way, a more appropriate restaurant can be selected by considering the submitter's attribute information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the submitter's attribute information into the AI, and the AI ​​outputs the optimal restaurant.

[0089] The selection unit can estimate the user's emotions and adjust the order in which the selection results are displayed based on the estimated emotions. For example, if the user is relaxed, the selection unit may display highly-rated restaurants at the top. If the user is in a hurry, the selection unit may also display restaurants that can provide quick service at the top. If the user is excited, the selection unit may also display restaurants that offer unique menus at the top. This allows for the selection of a more appropriate restaurant by adjusting the display order of the selection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not using AI. For example, the selection unit inputs user emotion data into the AI, and the AI ​​outputs the optimal display order.

[0090] The selection unit can consider the geographical distribution of ideas when selecting a restaurant. For example, if the user is in a specific region, the selection unit will prioritize restaurants in that region. If the user is traveling, the selection unit can also select restaurants that offer local specialties. If the user is at home, the selection unit can also prioritize nearby restaurants. This allows for the selection of a more appropriate restaurant by considering the geographical distribution of ideas. Some or all of the above processing in the selection unit may be performed using AI, for example, or not. For example, the selection unit inputs the geographical distribution of ideas into the AI, and the AI ​​outputs the optimal restaurant.

[0091] The selection unit can improve the accuracy of its restaurant selection by referring to relevant literature related to the idea. For example, if the idea relates to a specific dish, the selection unit can select a restaurant by referring to literature related to that dish. If the idea relates to a specific ingredient, the selection unit can also select a restaurant by referring to literature related to that ingredient. If the idea relates to a specific cooking method, the selection unit can also select a restaurant by referring to literature related to that cooking method. This makes it possible to select a restaurant with higher accuracy by referring to relevant literature related to the idea. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit inputs relevant literature related to the idea into the AI, and the AI ​​outputs the optimal restaurant.

[0092] The integration unit can estimate the user's emotions and adjust the delivery service integration method based on the estimated user emotions. For example, if the user is relaxed, the integration unit will use the standard delivery service. If the user is in a hurry, the integration unit can also prioritize the use of the expedited delivery service. If the user is excited, the integration unit can also suggest special delivery options. This allows for the provision of more appropriate delivery services by adjusting the delivery service integration method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI. For example, the integration unit inputs user emotion data into the AI, and the AI ​​outputs the optimal integration method.

[0093] The integration unit can select the optimal integration method by referring to past integration data when integrating with delivery services. For example, the integration unit can select the optimal service based on evaluations of delivery services used in the past. The integration unit can also select a service that can deliver quickly by referring to past delivery time data. The integration unit can also select a highly reliable delivery service based on past user feedback. In this way, the optimal integration method for delivery services can be selected by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit inputs past integration data into the AI, and the AI ​​outputs the optimal integration method.

[0094] The integration unit can customize the means of integration based on the user's current living situation when integrating with a delivery service. For example, if the user is at home, the integration unit will use a standard delivery service. If the user is out, the integration unit can also use a delivery service that allows pickup at a specified location. If the user is attending a specific event, the integration unit can also suggest delivery to the event venue. This allows for the provision of more appropriate delivery services by customizing the means of integration based on the user's current living situation. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's current living situation as input to the AI, and the AI ​​outputs the optimal means of integration.

[0095] The collaboration unit can estimate the user's emotions and determine collaboration priorities based on the estimated emotions. For example, if the user is relaxed, the collaboration unit will use a normal collaboration method. If the user is in a hurry, the collaboration unit may also prioritize a fast collaboration method. If the user is excited, the collaboration unit may also suggest special collaboration options. This allows for a more appropriate delivery service by determining collaboration priorities 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit inputs user emotion data into the AI, and the AI ​​outputs the optimal collaboration priority.

[0096] The integration unit can select the optimal integration method when integrating with delivery services, taking into account the user's geographical location information. For example, if the user is in a specific region, the integration unit will prioritize using delivery services in that region. If the user is traveling, the integration unit can also utilize delivery services that offer local specialties. If the user is at home, the integration unit can also prioritize using nearby delivery services. This allows for the provision of more appropriate delivery services by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's geographical location information to the AI, and the AI ​​outputs the optimal integration method.

[0097] The integration unit can analyze a user's social media activity and propose integration methods when integrating with delivery services. For example, the integration unit can propose the most suitable delivery service based on food ideas shared by the user on social media. The integration unit can also prioritize the use of delivery services that the user follows on social media. The integration unit can also propose delivery services that the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate delivery services. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit inputs the user's social media activity as input to the AI, and the AI ​​outputs the most suitable integration method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and adjust the idea reception method based on the estimated emotions. For example, if the user is stressed, the reception desk can accept ideas during a time when the user is relaxed. If the user is excited, the reception desk can accept ideas immediately. Furthermore, if the user is tired, the reception desk can accept ideas after the user has rested. In this way, by adjusting the idea reception method according to the user's emotions, ideas can be received at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0100] The generation unit can estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate a recipe with detailed instructions. If the user is in a hurry, the generation unit can generate a recipe with concise instructions. Furthermore, if the user is excited, the generation unit can generate a visually appealing recipe. This allows for the provision of more appropriate recipes by adjusting the presentation of the recipe according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0101] The selection unit can estimate the user's emotions and adjust the restaurant selection criteria based on those emotions. For example, if the user is relaxed, the selection unit can prioritize highly-rated restaurants. If the user is in a hurry, the selection unit can prioritize restaurants that can provide quick service. Furthermore, if the user is excited, the selection unit can prioritize restaurants that offer unique menus. In this way, by adjusting the restaurant selection criteria according to the user's emotions, a more appropriate restaurant can be selected. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0102] The integration unit can estimate the user's emotions and adjust the delivery service integration method based on the estimated emotions. For example, if the user is relaxed, the integration unit can use the standard delivery service. If the user is in a hurry, the integration unit can prioritize the use of the expedited delivery service. Furthermore, if the user is excited, the integration unit can suggest special delivery options. In this way, by adjusting the delivery service integration method according to the user's emotions, a more appropriate delivery service can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0103] The reception desk can estimate the user's emotions and prioritize ideas based on those emotions. For example, if the user is excited, the reception desk can prioritize that idea. If the user is relaxed, the reception desk can consider it equally. Furthermore, if the user is stressed, the reception desk can postpone that idea. By prioritizing ideas according to the user's emotions, more appropriate ideas can be prioritized. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0104] The reception department can analyze a user's past idea submission history and select the optimal reception method. For example, it can analyze the trends of ideas previously submitted by the user and prioritize accepting similar ideas. It can also adjust the reception method based on the success rate of ideas previously submitted by the user. Furthermore, it can suggest the optimal reception time based on the time of day when ideas were previously submitted by the user. In this way, the optimal reception method can be selected by analyzing a user's past idea submission history.

[0105] The reception system can filter ideas based on the user's current dietary preferences and health status. For example, if a user is currently on a diet, the reception system can prioritize low-calorie ideas. If a user has an allergy to a particular ingredient, the reception system can prioritize ideas that do not contain that ingredient. Furthermore, if a user has a preference for a particular ingredient, the reception system can prioritize ideas that include that ingredient. This allows for the reception of more relevant ideas by filtering based on the user's current dietary preferences and health status.

[0106] The generation unit can adjust the level of detail in a recipe based on the importance of the idea during recipe generation. For example, for a highly important idea, the generation unit can generate a recipe with detailed instructions and ingredient lists. For a less important idea, the generation unit can generate a recipe with concise instructions and ingredient lists. Furthermore, for an idea of ​​moderate importance, the generation unit can generate a recipe with a moderate level of detail. By adjusting the level of detail in a recipe based on the importance of the idea, it is possible to provide a more appropriate recipe.

[0107] The selection process can improve the accuracy of restaurant selection by considering the interrelationships between ideas. For example, if an idea includes multiple dishes, the selection process can select restaurants that can accommodate each dish. If an idea includes a specific ingredient, the selection process can select restaurants that specialize in that ingredient. Furthermore, if an idea includes a specific cooking method, the selection process can select restaurants that are proficient in that method. This allows for more accurate restaurant selection by considering the interrelationships between ideas.

[0108] The integration unit can customize the integration method based on the user's current living situation when integrating with delivery services. For example, if the user is at home, the integration unit can use a standard delivery service. If the user is out, the integration unit can use a delivery service that allows pickup at a specified location. Furthermore, if the user is attending a specific event, the integration unit can suggest delivery to the event venue. By customizing the integration method based on the user's current living situation, a more appropriate delivery service can be provided.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives the user's creative dish ideas. These ideas may include, but are not limited to, the level of detail in the recipe or the genre of the dish. The reception desk accepts the user's creative dish ideas in text format, for example. The reception desk can also use image analysis technology to analyze images of dishes uploaded by the user and accept them as ideas. Step 2: The generation unit uses a generation AI to analyze the ideas received by the reception unit and generate specific recipes. For example, the generation unit generates the optimal recipe based on the user's requests. The generation AI suggests the optimal recipe based on the user's requests. For example, the generation AI generates recipes such as miso-based tandoori chicken and chicken egg soup. The generation AI can also suggest the optimal ingredients and cooking procedures based on the user's requests. Step 3: The selection unit selects partner restaurants based on the recipes generated by the generation unit. The selection unit selects, for example, a restaurant from among the partner restaurants that can meet the user's requests. The selection unit selects the optimal restaurant based on the selection criteria and conditions of the partner restaurants. For example, the selection unit selects a restaurant that can serve miso-flavored tandoori chicken. Step 4: The Integration Department connects the restaurants selected by the Selection Department with food delivery services. For example, the Integration Department connects with food delivery services to deliver food within a specified time. The Integration Department can use the food delivery service's API to arrange deliveries. For example, the Integration Department delivers food within a specified budget, including delivery fees.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the reception unit, generation unit, selection unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives creative dish ideas entered by the user in text format. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a specific recipe using a generation AI. The selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects a partner restaurant based on the generated recipe. The collaboration unit is implemented by the control unit 46A of the smart device 14 and arranges delivery in cooperation with a food delivery service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, generation unit, selection unit, and coordination unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives creative dish ideas entered by the user in text format. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a specific recipe using a generation AI. The selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects a partner restaurant based on the generated recipe. The coordination unit is implemented by the control unit 46A of the smart glasses 214 and arranges delivery in cooperation with a food delivery service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, generation unit, selection unit, and coordination unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives creative dish ideas entered by the user in text format. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a specific recipe using a generation AI. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects a partner restaurant based on the generated recipe. The coordination unit is implemented by the control unit 46A of the headset terminal 314 and arranges delivery in cooperation with a food delivery service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, generation unit, selection unit, and coordination unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives creative dish ideas entered by the user in text format. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a specific recipe using a generation AI. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects a partner restaurant based on the generated recipe. The coordination unit is implemented by, for example, the control unit 46A of the robot 414 and arranges delivery in cooperation with a food delivery service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception desk that accepts user-created recipe ideas, A generation unit analyzes the ideas received by the reception unit and generates a specific recipe, A selection unit selects partner restaurants based on the recipes generated by the generation unit, The aforementioned selection unit connects the restaurants selected with the food delivery service, Equipped with A system characterized by the following features. (Note 2) The generating unit is Generates the optimal recipe based on user requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is We select a restaurant from our partner restaurants that can meet the user's requests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, We partner with food delivery services to deliver meals within a specified time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is This generates recipes such as miso-flavored tandoori chicken and chicken egg soup. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, We deliver food within the specified budget, including delivery fees. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past idea submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving ideas, filtering is performed based on the user's current dietary preferences and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving ideas, we prioritize accepting highly relevant ideas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting ideas, we analyze users' social media activity and accept relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating recipes, adjust the level of detail in the recipe based on the importance of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating recipes, different generation algorithms are applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the recipe length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating recipes, prioritize recipes based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating recipes, adjust the order of recipes based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is The system estimates user sentiment and adjusts restaurant selection criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When selecting a restaurant, consider the interrelationships of ideas to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When selecting a restaurant, we will take into account the attribute information of the person who submitted the idea. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and adjusts the order in which selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When selecting a restaurant, consider the geographical distribution of ideas. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When selecting a restaurant, refer to relevant literature related to the idea to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the delivery service's integration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, When integrating with delivery services, the system selects the optimal integration method by referring to past integration data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, When integrating with delivery services, the integration method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When integrating with delivery services, the optimal integration method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, When integrating with delivery services, we analyze users' social media activity and propose methods for integration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user-created recipe ideas, A generation unit analyzes the ideas received by the reception unit and generates a specific recipe, A selection unit selects partner restaurants based on the recipes generated by the generation unit, The aforementioned selection unit connects the restaurants selected with the food delivery service, Equipped with A system characterized by the following features.

2. The generating unit is Generates the optimal recipe based on user requests. The system according to feature 1.

3. The aforementioned selection unit is We select a restaurant from our partner restaurants that can meet the user's requests. The system according to feature 1.

4. The aforementioned linkage unit is, We partner with food delivery services to deliver meals within a specified time. The system according to feature 1.

5. The generating unit is This generates recipes such as miso-flavored tandoori chicken and chicken egg soup. The system according to feature 1.

6. The aforementioned linkage unit is, We deliver food within the specified budget, including delivery fees. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past idea submission history to select the most suitable submission method. The system according to feature 1.

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