System

Abstract

An object of a system according to an embodiment is to propose an optimal dish according to the mood of a user and to consistently perform a provision method and settlement.SOLUTION: A system includes a reception unit, an analysis unit, a selection unit, and a settlement unit. The reception unit inputs a mood of a user. The analysis unit analyzes the information input by the reception unit and proposes a dish. The selection unit selects a provision method on the basis of the dish proposed by the analysis unit. The settlement unit performs settlement based on the provision method selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not consistently suggest the most appropriate dish or serving method based on the user's mood, and there is room for improvement.

[0005] The system according to the embodiment aims to propose the most suitable dish according to the user's mood and to handle everything from the serving method to payment in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a payment unit. The reception unit inputs the user's mood. The analysis unit analyzes the information input by the reception unit and suggests dishes. The selection unit selects a serving method based on the dishes suggested by the analysis unit. The payment unit performs payment based on the serving method selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable dish according to the user's mood and can handle everything from the delivery method to payment in an integrated manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communicative meal recommendation system according to an embodiment of the present invention allows a user to input their mood at that time, and the system recommends the most suitable dish and handles the delivery method and payment process. In this communicative meal recommendation system, a user inputs their mood into an app, and a generation AI analyzes the input and suggests the most suitable dish. The user can then select a delivery method for the suggested dish. Delivery methods include cooking the meal themselves, delivery, making a restaurant reservation, treating others, and donating to relief efforts. For example, in a communicative meal recommendation system, a user might input, "I want something light today." This information is input into a generation AI. The generation AI then analyzes the input information and suggests the most suitable dish for the user. For example, it might suggest dishes such as "light salad" or "hiyashi chuka." The user can then select a delivery method for the suggested dish. For example, if the user chooses to cook the meal themselves, the generation AI suggests a recipe and the necessary ingredients, which can be arranged at a nearby supermarket or online supermarket. If the user chooses delivery, the generation AI suggests ordering from a nearby restaurant. If the user chooses to treat others, the generation AI suggests sending money or donating to relief efforts. This allows the communicative meal recommendation system to suggest the best dish to suit the user's mood and handle everything from the delivery method to payment.This allows the communicative meal recommendation system to suggest the best dish to suit the user's mood and handle everything from the delivery method to payment.It can also address social issues such as reducing food waste, sharing dining experiences, and supporting areas where it is difficult to get a stable diet.

[0029] A communication-based meal recommendation system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a payment unit. The reception unit inputs a user's mood. The user's mood may include, but is not limited to, emotions such as joy, sadness, and anger. The reception unit provides interfaces such as text input, voice input, and image input. The analysis unit uses a generation AI to analyze the information input by the reception unit and recommend optimal dishes. The analysis may be performed using, but is not limited to, natural language processing technology or a machine learning algorithm. For example, the generation AI may analyze the user's mood using a text generation AI (e.g., LLM) and recommend optimal dishes. The analysis unit may also analyze the user's mood using a multimodal generation AI. The selection unit selects a serving method based on the dishes recommended by the analysis unit. The serving methods may include, but are not limited to, making the meal yourself, ordering delivery, making a restaurant reservation, treating others, or donating to relief efforts. For example, if the user chooses to make the meal themselves, the selection unit uses the generation AI to suggest a recipe and necessary ingredients and arranges for them to be purchased from a nearby supermarket or online supermarket. In the case of delivery, the selection unit can also use the generation AI to suggest ordering from a nearby restaurant. In the case of a restaurant, the selection unit can also use the generation AI to suggest making a reservation at the restaurant. The payment unit makes payment based on the delivery method selected by the selection unit. Payment can be made by, for example, credit card, electronic money, bank transfer, or other methods, but is not limited to these examples. The payment unit enters credit card information and completes payment. Furthermore, the payment unit can also make payment using electronic money. Furthermore, the payment unit can also make payment using bank transfer. This allows the communicative meal recommendation system according to the embodiment to suggest optimal dishes based on the user's mood and consistently handle everything from the delivery method to payment.

[0030] When the user selects "Cold Chinese Noodles," the selection unit uses a generation AI to suggest a recipe and necessary ingredients, which can then be arranged at a supermarket or online supermarket. The generation AI, for example, suggests the optimal recipe based on the user's mood. For example, if the user inputs "I want something light," the generation AI suggests recipes such as "light salad" or "hiyashi chuka." The generation AI can also suggest necessary ingredients and arrange them at a nearby supermarket or online supermarket. For example, if the user selects "hiyashi chuka," the generation AI suggests ingredients such as lettuce, tomato, and cucumber and arranges them at a nearby supermarket. The generation AI can also arrange the ingredients at an online supermarket. For example, if the user selects "hiyashi chuka," the generation AI suggests ingredients such as noodles, ham, and cucumber and arranges them at an online supermarket. This allows the user to easily arrange the necessary ingredients when cooking at home. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI or without AI. For example, the selection unit arranges for the ingredients to be purchased at a supermarket or online supermarket based on the recipe and ingredients suggested by the generation AI.

[0031] In the case of delivery, the selection unit can suggest ordering from a restaurant using the generation AI. The generation AI, for example, suggests the most suitable restaurant based on the user's mood. For example, if the user inputs "I'd like delivery today," the generation AI suggests ordering from a nearby restaurant. The generation AI can also select a restaurant based on the user's preferences. For example, if the user inputs "I want Italian food," the generation AI suggests ordering from a nearby Italian restaurant. The generation AI can also suggest restaurants based on the user's past ordering history. For example, the generation AI suggests the most suitable restaurant based on restaurants the user has previously ordered from. This allows food to be ordered from a nearby restaurant when the user selects delivery. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit orders food from a restaurant suggested by the generation AI.

[0032] In the case of a restaurant, the selection unit can suggest a reservation using a generation AI. The generation AI, for example, suggests the most suitable restaurant based on the user's mood. For example, if the user inputs, "I want to eat at a restaurant today," the generation AI suggests a nearby restaurant. The generation AI can also select a restaurant based on the user's preferences. For example, if the user inputs, "I want to eat French food," the generation AI suggests a nearby French restaurant. The generation AI can also suggest a restaurant based on the user's past reservation history. For example, the generation AI suggests the most suitable restaurant based on restaurants the user has previously booked. This allows the user to easily make a restaurant reservation when they select a restaurant. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit makes a reservation at a restaurant suggested by the generation AI.

[0033] The selection unit can suggest remittance or donations using the generation AI when treating someone. The generation AI, for example, suggests the optimal remittance method or donation destination based on the user's mood. For example, if the user inputs, "I want to treat someone today," the generation AI suggests a remittance method or donation destination. The generation AI can also select a remittance method or donation destination based on the user's preferences. For example, if the user inputs, "I want to treat a friend," the generation AI suggests a method for remittance to the friend. Furthermore, the generation AI can make suggestions based on the user's past remittance history or donation history. For example, the generation AI suggests the optimal remittance method or donation destination based on friends to whom the user has previously remitted money or organizations to which the user has previously donated. This allows the user to easily remit money or donate to relief activities when treating someone. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit selects the remittance method and donation destination suggested by the generation AI.

[0034] The analysis unit can suggest recipes using ingredients as options for addressing food waste. The generation AI, for example, suggests recipes that address food waste based on the user's mood. For example, if the user inputs, "I want to use leftover ingredients," the generation AI suggests recipes using leftover ingredients. The generation AI can also suggest recipes based on the inventory status of the user's refrigerator. For example, the generation AI suggests optimal recipes based on the ingredients left over in the user's refrigerator. The generation AI can also suggest recipes based on the user's past cooking history. For example, the generation AI suggests recipes using leftover ingredients based on dishes the user has made in the past. This makes it possible to effectively utilize leftover ingredients and reduce food waste. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit prepares a dish using leftover ingredients based on a recipe suggested by the generation AI.

[0035] The analysis unit can suggest that the user share photos and impressions of their meal. The generation AI, for example, suggests sharing a meal experience based on the user's mood. For example, if the user inputs "I want to share photos of my meal," the generation AI suggests sharing photos and impressions of their meal. The generation AI can also make suggestions based on the user's past sharing history. For example, the generation AI can suggest an optimal sharing method based on photos and impressions of meals shared by the user in the past. Furthermore, the generation AI can also make suggestions based on the user's social media activity. For example, the generation AI can suggest a sharing method based on photos and impressions of meals shared by the user on social media. This allows users to share their meal experiences with others, promoting communication. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit shares photos and impressions of meals based on the sharing method suggested by the generation AI.

[0036] The analysis unit can suggest that the user donate a meal. The generation AI, for example, suggests a meal donation based on the user's mood. For example, if the user inputs "I want to donate a meal," the generation AI suggests a meal donation. The generation AI can also make suggestions based on the user's past donation history. For example, the generation AI suggests the most suitable donation recipient based on organizations to which the user has donated in the past. Furthermore, the generation AI can also make suggestions based on the user's social media activity. For example, the generation AI suggests a donation recipient based on donation activities shared by the user on social media. This makes it possible to address social issues by providing support to areas where food security is difficult. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit donates meals based on the donation recipients suggested by the generation AI.

[0037] The reception unit can analyze the user's past mood input history and provide an input interface. The reception unit, for example, uses a data analysis algorithm to analyze the user's past mood input history. For example, the reception unit provides an optimal input interface based on the moods and emotions the user has input in the past. The reception unit can also provide an interface based on the user's past input method. For example, the reception unit provides an optimal input interface based on the input method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict the mood to be input during a specific time period from the user's past input history and provide an interface. For example, the reception unit provides an optimal input interface based on the moods the user has input during a specific time period in the past. In this way, by analyzing the past mood input history, the optimal input interface can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit provides an optimal input interface based on the past mood input history analyzed by the generation AI.

[0038] When inputting the mood, the reception unit can adjust the input method based on the user's current activity status and environment. The reception unit, for example, uses sensors and data collection algorithms to acquire the user's current activity status and environment. For example, when the user is out, the reception unit prioritizes voice input to enable quick mood input. Furthermore, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is exercising, the reception unit can provide an interface that allows the user to input the mood with a simple tap operation. For example, when the user inputs the mood while exercising, the reception unit provides an interface that allows easy input with a tap operation. This allows for more appropriate input by customizing the input method according to the user's activity status and environment. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit adjusts the input method based on the activity status and environmental data acquired by the generation AI.

[0039] When a user inputs their mood, the reception unit can select an input means according to the user's input method. The reception unit, for example, uses an input device and a data analysis algorithm to detect the user's input method. For example, when a user inputs their mood by voice, the reception unit supports the input using voice recognition technology. Furthermore, when a user inputs their mood by text, the reception unit can provide a predictive conversion function to simplify the input. Furthermore, when a user inputs their mood using an image, the reception unit can estimate the user's mood using image analysis technology and support the input. For example, when a user uploads an image, the reception unit estimates the user's mood using image analysis technology and supports the input. This enables more appropriate input by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit selects the optimal input means based on the input method detected by the generation AI.

[0040] When inputting a mood, the reception unit can automatically suggest a location by referring to the user's past travel history. The reception unit, for example, uses a location information service or a data analysis algorithm to acquire the user's past travel history. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit suggests optimal candidate locations based on data on places the user has visited in the past. This allows more appropriate candidate locations to be suggested by referring to the user's past travel history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit suggests optimal candidate locations based on travel history data acquired by the generation AI.

[0041] When the user inputs the mood, the reception unit can refer to the user's calendar information and make a suggestion based on the user's schedule. The reception unit, for example, uses a calendar app or a data analysis algorithm to acquire the user's calendar information. For example, the reception unit refers to the schedule registered in the user's calendar and makes a suggestion related to the mood. The reception unit can also suggest a mood related to a specific event from the user's calendar information. Furthermore, the reception unit can suggest an optimal mood to match the schedule based on the user's calendar information. For example, the reception unit can suggest an optimal mood based on the schedule registered in the user's calendar. This makes it possible to make a suggestion based on the schedule by referring to the user's calendar information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit makes a suggestion based on the schedule based on the calendar information acquired by the generation AI.

[0042] When a mood is input, the reception unit can analyze the user's social media activity and suggest related moods. The reception unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the reception unit can suggest moods related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related moods. Furthermore, the reception unit can suggest related moods by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related moods based on the content of posts shared by the user's friends on social media. In this way, related moods can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit suggests related moods based on the social media activity analyzed by the generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of suggestions by referring to the user's past dish selection history. The analysis unit, for example, uses a data analysis algorithm to analyze the user's past dish selection history. For example, the analysis unit suggests optimal dishes based on dishes selected by the user in the past. The analysis unit can also suggest dishes containing the user's favorite ingredients from the user's past selection history. Furthermore, the analysis unit can analyze the user's past selection history and suggest dishes that provide the highest satisfaction. For example, the analysis unit suggests optimal dishes based on dishes that the user has previously rated highly. This improves the accuracy of suggestions by referring to the user's past dish selection history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit suggests optimal dishes based on the user's past dish selection history analyzed by the generation AI.

[0044] During analysis, the analysis unit can change the content of suggestions based on the user's current health condition and dietary restrictions. The analysis unit, for example, uses a health management app or a data analysis algorithm to acquire the user's health condition and dietary restrictions. For example, if the user is on a diet, the analysis unit can suggest low-calorie dishes. Furthermore, if the user has allergies, the analysis unit can suggest dishes that do not contain allergens. Furthermore, if the user needs a specific nutrient, the analysis unit can suggest dishes that contain that nutrient. For example, if the user needs a dish that is high in vitamin C, the analysis unit can suggest dishes that are high in vitamin C. This allows the suggestion content to be customized according to the user's health condition and dietary restrictions, thereby suggesting more appropriate dishes. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit changes the content of suggestions based on the health condition and dietary restriction data acquired by the generation AI.

[0045] During analysis, the analysis unit can change the content of the suggestions taking into account the user's ingredient inventory. The analysis unit, for example, uses an inventory management system or a data analysis algorithm to obtain the user's ingredient inventory. For example, the analysis unit can suggest optimal dishes based on the ingredients in the user's refrigerator. The analysis unit can also suggest dishes that use up the ingredients the user has. Furthermore, the analysis unit can suggest dishes that make up for ingredients that the user is running low on. For example, the analysis unit can suggest dishes that use up the ingredients the user has in the refrigerator. This makes it possible to suggest dishes that do not waste food by taking into account the user's ingredient inventory. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit changes the content of the suggestions based on ingredient inventory data obtained by the generation AI.

[0046] During analysis, the analysis unit can suggest regional dishes taking into account the user's geographical location information. The analysis unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest dishes using local specialties. Also, if the user is traveling, the analysis unit can suggest local specialty dishes. Furthermore, if the user is in their hometown, the analysis unit can suggest dishes using local ingredients. For example, if the user is in their hometown, the analysis unit can suggest dishes using local ingredients. In this way, regional dishes can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit suggests regional dishes based on the geographical location information acquired by the generation AI.

[0047] During the analysis, the analysis unit can analyze the user's social media activity and suggest related dishes. The analysis unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the analysis unit can suggest dishes related to dishes shared by the user on social media. The analysis unit can also analyze the user's social media posts to suggest related dishes. Furthermore, the analysis unit can suggest related dishes based on the activities of the user's friends on social media. For example, the analysis unit can suggest related dishes based on dishes shared by the user's friends on social media. In this way, related dishes can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit suggests related dishes based on the social media activity analyzed by the generation AI.

[0048] During analysis, the analysis unit can change the content of suggestions to reflect the user's past feedback. The analysis unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the analysis unit can suggest optimal dishes based on dishes that the user has previously given high ratings. The analysis unit can also suggest dishes that include the user's preferred seasonings based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past feedback to suggest the most satisfying dish. For example, the analysis unit can suggest optimal dishes based on dishes that the user has previously given high ratings. This improves the accuracy of the suggestions by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit changes the content of suggestions based on the past feedback analyzed by the generation AI.

[0049] When selecting a delivery method, the selection unit can provide options by referring to the user's past selection history. The selection unit, for example, uses a data analysis algorithm to analyze the user's past selection history. For example, the selection unit proposes an optimal option based on delivery methods selected by the user in the past. The selection unit can also propose a preferred delivery method from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and propose a delivery method that provides the highest satisfaction. For example, the selection unit proposes an optimal option based on delivery methods that the user has previously rated highly. In this way, the optimal delivery method can be proposed by referring to the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit proposes an optimal option based on the past selection history analyzed by the generation AI.

[0050] When selecting a delivery method, the selection unit can change the options based on the user's current lifestyle and schedule. The selection unit, for example, uses a schedule management app or a data analysis algorithm to acquire the user's lifestyle and schedule. For example, the selection unit can suggest a quick delivery method if the user is busy. The selection unit can also suggest a delivery method that proceeds at a leisurely pace if the user has a lot of free time. Furthermore, the selection unit can suggest an optimal delivery method based on the user's schedule. For example, the selection unit can suggest an optimal delivery method based on the user's schedule. This allows the delivery method to be customized according to the user's lifestyle and schedule, making it possible to suggest a more appropriate delivery method. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the delivery method based on lifestyle and schedule data acquired by the generation AI.

[0051] When selecting a serving method, the selection unit can change the options taking into account the user's ingredient inventory. The selection unit, for example, uses an inventory management system or a data analysis algorithm to obtain the user's ingredient inventory. For example, the selection unit suggests the optimal serving method for cooking at home based on the ingredients in the user's refrigerator. The selection unit can also suggest a serving method that allows the user to use up the ingredients they have. Furthermore, the selection unit can also suggest a serving method that allows the user to make up for ingredients that are running low. For example, the selection unit suggests a serving method that allows the user to use up the ingredients in the refrigerator. In this way, by taking into account the user's ingredient inventory, a serving method that does not waste food can be suggested. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the serving method based on ingredient inventory data obtained by the generation AI.

[0052] When selecting a delivery method, the selection unit can propose a delivery method taking into account the user's geographical location information. The selection unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the selection unit can propose restaurants and delivery services in that area. Also, if the user is traveling, the selection unit can propose restaurants that serve local specialties. Furthermore, if the user is in their hometown, the selection unit can propose a method of serving dishes using local ingredients. For example, if the user is in their hometown, the selection unit can propose a method of serving dishes using local ingredients. In this way, the optimal delivery method can be proposed by taking the user's geographical location information into account. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit proposes a delivery method based on the geographical location information acquired by the generation AI.

[0053] When selecting a delivery method, the selection unit can analyze the user's social media activity and suggest a relevant delivery method. The selection unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the selection unit can suggest a delivery method related to a dish shared by the user on social media. The selection unit can also analyze the content of the user's social media posts and suggest a relevant delivery method. Furthermore, the selection unit can suggest a relevant delivery method by referring to the activity of the user's friends on social media. For example, the selection unit can suggest a relevant delivery method based on a dish shared by the user's friends on social media. In this way, a relevant delivery method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit suggests a relevant delivery method based on the social media activity analyzed by the generation AI.

[0054] When selecting a delivery method, the selection unit can change the options by reflecting the user's past feedback. The selection unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the selection unit suggests an optimal delivery method based on delivery methods that the user has previously given high ratings. The selection unit can also suggest a preferred delivery method based on the user's past feedback. Furthermore, the selection unit can analyze the user's past feedback and suggest a delivery method that provides the highest level of satisfaction. For example, the selection unit suggests an optimal delivery method based on delivery methods that the user has previously given high ratings. This improves the accuracy of the delivery method by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the delivery method based on past feedback analyzed by the generation AI.

[0055] At the time of payment, the payment unit can provide a payment method by referring to the user's past payment history. The payment unit, for example, uses a data analysis algorithm to analyze the user's past payment history. For example, the payment unit can suggest the optimal payment method based on payment methods the user has used in the past. The payment unit can also suggest a preferred payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and suggest the payment method with the highest satisfaction. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has given high ratings in the past. In this way, the optimal payment method can be suggested by referring to the user's past payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit provides the optimal payment method based on the past payment history analyzed by the generation AI.

[0056] The payment unit can change the payment method based on the user's current economic situation and payment ability at the time of payment. The payment unit, for example, uses an economic information management system or a data analysis algorithm to obtain the user's economic situation and payment ability. For example, the payment unit can suggest a more expensive payment method if the user is financially well off. The payment unit can also suggest a less expensive payment method if the user is financially struggling. Furthermore, the payment unit can suggest the optimal payment method based on the user's payment ability. For example, the payment unit can suggest the optimal payment method based on the user's income information and expenditure information. This enables more appropriate payment by customizing the payment method based on the user's economic situation and payment ability. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit changes the payment method based on the economic situation and payment ability data obtained by the generation AI.

[0057] The payment unit can analyze the user's payment history at the time of payment and provide optimal discounts and benefits. The payment unit, for example, uses a data analysis algorithm to analyze the user's payment history. For example, the payment unit can provide a special discount if the user has made large payments in the past. The payment unit can also provide a loyalty program if the user is a frequent user. Furthermore, the payment unit can provide optimal benefits based on the user's payment history. For example, the payment unit can provide optimal discounts and benefits based on payment methods that the user has given high ratings in the past. In this way, optimal discounts and benefits can be provided by analyzing the user's payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can provide optimal discounts and benefits based on the payment history analyzed by the generation AI.

[0058] The payment unit can propose a payment method taking into account the user's geographical location information at the time of payment. The payment unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the payment unit proposes payment methods available in that area. Also, if the user is traveling, the payment unit can propose payment methods available in that area. Furthermore, if the user is in their hometown, the payment unit can propose local payment methods. For example, if the user is in their hometown, the payment unit proposes local payment methods. In this way, the optimal payment method can be proposed by taking the user's geographical location information into account. Some or all of the above-described processing in the payment unit may be performed, for example, using AI, or may be performed without using AI. For example, the payment unit proposes a payment method based on the geographical location information acquired by the generation AI.

[0059] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. The payment unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the payment unit can suggest payment methods related to payment methods shared by the user on social media. The payment unit can also analyze the user's social media posts to suggest relevant payment methods. Furthermore, the payment unit can suggest relevant payment methods based on the activity of the user's friends on social media. For example, the payment unit can suggest relevant payment methods based on the payment methods shared by the user's friends on social media. In this way, relevant payment methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can suggest relevant payment methods based on the social media activity analyzed by the generation AI.

[0060] The payment unit can change the payment method at the time of payment by reflecting the user's past feedback. The payment unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has previously given high ratings. The payment unit can also suggest a preferred payment method based on the user's past feedback. Furthermore, the payment unit can analyze the user's past feedback and suggest the payment method that provides the highest satisfaction. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has previously given high ratings. This improves the accuracy of the payment method by reflecting the user's past feedback. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit changes the payment method based on the past feedback analyzed by the generation AI.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can customize the suggestions taking into account the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, it can suggest dishes that do not contain that ingredient. Also, if the user prefers a particular dietary style (e.g., vegetarian, vegan, gluten-free, etc.), it can suggest dishes that suit that style. Furthermore, the analysis unit can analyze the user's past eating history and suggest dishes that reflect the user's preferred seasonings and cooking methods. This makes it possible to make suggestions that meet the user's individual needs.

[0063] The selection unit can provide nutritional information for the dishes selected by the user in cooperation with a health management app. For example, nutritional information such as calories, protein, fat, and carbohydrates for the dishes selected by the user can be displayed. If the user wants to consume a specific nutrient, the selection unit can also suggest dishes that are rich in that nutrient. Furthermore, the selection unit can also suggest optimal dishes based on the user's health goals (e.g., dieting, muscle building, diabetes management, etc.). This can support the user's health management.

[0064] The selection unit can display the cooking difficulty and cooking time for the dish selected by the user. For example, if the user is busy, it can suggest dishes that can be cooked in a short time. It can also suggest simple recipes for beginners and advanced recipes for experienced cooks depending on the cooking difficulty. Furthermore, if the user uses specific cooking equipment (e.g., an oven, frying pan, microwave, etc.), the selection unit can also suggest recipes that are suitable for that cooking equipment. This makes it possible to suggest recipes that are suited to the user's cooking environment.

[0065] The selection unit can display storage methods and expiration dates for ingredients for the dishes selected by the user. For example, it can suggest storage methods (refrigerated, frozen, room temperature, etc.) for ingredients purchased by the user. It can also display expiration dates for ingredients and recommend using them early. Furthermore, the selection unit can suggest recipes that will allow the user to use up leftover ingredients. This can reduce food waste and contribute to reducing food loss.

[0066] The selection unit can introduce the cultural background and history of the dish selected by the user. For example, it can explain the origin and traditional cooking methods of the dish selected by the user. It can also introduce how the dish is eaten in a particular region or culture. Furthermore, the selection unit can provide the user with an opportunity to learn about different cultures through cooking. This can enrich the user's dining experience and deepen their understanding of food culture.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs the user's mood. The user's mood may include, but is not limited to, emotions such as joy, sadness, and anger. The reception unit provides an interface for inputting, for example, text, voice, or image. Step 2: The analysis unit uses the generation AI to analyze the information input by the reception unit and propose the most suitable dish. The analysis is performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the generation AI analyzes the user's mood using a text generation AI (e.g., LLM) and proposes the most suitable dish. The analysis unit can also analyze the user's mood using a multimodal generation AI. Step 3: The selection unit selects a delivery method based on the food suggested by the analysis unit. Delivery methods include, but are not limited to, cooking the food yourself, delivery, making a restaurant reservation, treating others, and donating to relief efforts. For example, if the user chooses to cook the food themselves, the selection unit uses the generation AI to suggest a recipe and necessary ingredients, and arranges for them to be ordered from a nearby supermarket or online supermarket. In addition, in the case of delivery, the selection unit can also use the generation AI to suggest ordering from a nearby restaurant. Furthermore, in the case of a restaurant, the selection unit can also use the generation AI to suggest making a restaurant reservation. Step 4: The payment unit makes payment based on the delivery method selected by the selection unit. Payment can be made by, for example, credit card, electronic money, bank transfer, or other methods, but is not limited to these examples. The payment unit, for example, inputs credit card information and completes payment. The payment unit can also make payment using electronic money. The payment unit can also make payment using bank transfer.

[0069] (Example 2) The communicative meal recommendation system according to an embodiment of the present invention allows a user to input their mood at that time, and the system recommends the most suitable dish and handles the delivery method and payment process. In this communicative meal recommendation system, a user inputs their mood into an app, and a generation AI analyzes the input and suggests the most suitable dish. The user can then select a delivery method for the suggested dish. Delivery methods include cooking the meal themselves, delivery, making a restaurant reservation, treating others, and donating to relief efforts. For example, in a communicative meal recommendation system, a user might input, "I want something light today." This information is input into a generation AI. The generation AI then analyzes the input information and suggests the most suitable dish for the user. For example, it might suggest dishes such as "light salad" or "hiyashi chuka." The user can then select a delivery method for the suggested dish. For example, if the user chooses to cook the meal themselves, the generation AI suggests a recipe and the necessary ingredients, which can be arranged at a nearby supermarket or online supermarket. If the user chooses delivery, the generation AI suggests ordering from a nearby restaurant. If the user chooses to treat others, the generation AI suggests sending money or donating to relief efforts. This allows the communicative meal recommendation system to suggest the best dish to suit the user's mood and handle everything from the delivery method to payment.This allows the communicative meal recommendation system to suggest the best dish to suit the user's mood and handle everything from the delivery method to payment.It can also address social issues such as reducing food waste, sharing dining experiences, and supporting areas where it is difficult to get a stable diet.

[0070] A communication-based meal recommendation system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a payment unit. The reception unit inputs a user's mood. The user's mood may include, but is not limited to, emotions such as joy, sadness, and anger. The reception unit provides interfaces such as text input, voice input, and image input. The analysis unit uses a generation AI to analyze the information input by the reception unit and recommend optimal dishes. The analysis may be performed using, but is not limited to, natural language processing technology or a machine learning algorithm. For example, the generation AI may analyze the user's mood using a text generation AI (e.g., LLM) and recommend optimal dishes. The analysis unit may also analyze the user's mood using a multimodal generation AI. The selection unit selects a serving method based on the dishes recommended by the analysis unit. The serving methods may include, but are not limited to, making the meal yourself, ordering delivery, making a restaurant reservation, treating others, or donating to relief efforts. For example, if the user chooses to make the meal themselves, the selection unit uses the generation AI to suggest a recipe and necessary ingredients and arranges for them to be purchased from a nearby supermarket or online supermarket. In the case of delivery, the selection unit can also use the generation AI to suggest ordering from a nearby restaurant. In the case of a restaurant, the selection unit can also use the generation AI to suggest making a reservation at the restaurant. The payment unit makes payment based on the delivery method selected by the selection unit. Payment can be made by, for example, credit card, electronic money, bank transfer, or other methods, but is not limited to these examples. The payment unit enters credit card information and completes payment. Furthermore, the payment unit can also make payment using electronic money. Furthermore, the payment unit can also make payment using bank transfer. This allows the communicative meal recommendation system according to the embodiment to suggest optimal dishes based on the user's mood and consistently handle everything from the delivery method to payment.

[0071] When the user selects "Cold Chinese Noodles," the selection unit uses a generation AI to suggest a recipe and necessary ingredients, which can then be arranged at a supermarket or online supermarket. The generation AI, for example, suggests the optimal recipe based on the user's mood. For example, if the user inputs "I want something light," the generation AI suggests recipes such as "light salad" or "hiyashi chuka." The generation AI can also suggest necessary ingredients and arrange them at a nearby supermarket or online supermarket. For example, if the user selects "hiyashi chuka," the generation AI suggests ingredients such as lettuce, tomato, and cucumber and arranges them at a nearby supermarket. The generation AI can also arrange the ingredients at an online supermarket. For example, if the user selects "hiyashi chuka," the generation AI suggests ingredients such as noodles, ham, and cucumber and arranges them at an online supermarket. This allows the user to easily arrange the necessary ingredients when cooking at home. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI or without AI. For example, the selection unit arranges for the ingredients to be purchased at a supermarket or online supermarket based on the recipe and ingredients suggested by the generation AI.

[0072] In the case of delivery, the selection unit can suggest ordering from a restaurant using the generation AI. The generation AI, for example, suggests the most suitable restaurant based on the user's mood. For example, if the user inputs "I'd like delivery today," the generation AI suggests ordering from a nearby restaurant. The generation AI can also select a restaurant based on the user's preferences. For example, if the user inputs "I want Italian food," the generation AI suggests ordering from a nearby Italian restaurant. The generation AI can also suggest restaurants based on the user's past ordering history. For example, the generation AI suggests the most suitable restaurant based on restaurants the user has previously ordered from. This allows food to be ordered from a nearby restaurant when the user selects delivery. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit orders food from a restaurant suggested by the generation AI.

[0073] In the case of a restaurant, the selection unit can suggest a reservation using a generation AI. The generation AI, for example, suggests the most suitable restaurant based on the user's mood. For example, if the user inputs, "I want to eat at a restaurant today," the generation AI suggests a nearby restaurant. The generation AI can also select a restaurant based on the user's preferences. For example, if the user inputs, "I want to eat French food," the generation AI suggests a nearby French restaurant. The generation AI can also suggest a restaurant based on the user's past reservation history. For example, the generation AI suggests the most suitable restaurant based on restaurants the user has previously booked. This allows the user to easily make a restaurant reservation when they select a restaurant. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit makes a reservation at a restaurant suggested by the generation AI.

[0074] The selection unit can suggest remittance or donations using the generation AI when treating someone. The generation AI, for example, suggests the optimal remittance method or donation destination based on the user's mood. For example, if the user inputs, "I want to treat someone today," the generation AI suggests a remittance method or donation destination. The generation AI can also select a remittance method or donation destination based on the user's preferences. For example, if the user inputs, "I want to treat a friend," the generation AI suggests a method for remittance to the friend. Furthermore, the generation AI can make suggestions based on the user's past remittance history or donation history. For example, the generation AI suggests the optimal remittance method or donation destination based on friends to whom the user has previously remitted money or organizations to which the user has previously donated. This allows the user to easily remit money or donate to relief activities when treating someone. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit selects the remittance method and donation destination suggested by the generation AI.

[0075] The analysis unit can suggest recipes using ingredients as options for addressing food waste. The generation AI, for example, suggests recipes that address food waste based on the user's mood. For example, if the user inputs, "I want to use leftover ingredients," the generation AI suggests recipes using leftover ingredients. The generation AI can also suggest recipes based on the inventory status of the user's refrigerator. For example, the generation AI suggests optimal recipes based on the ingredients left over in the user's refrigerator. The generation AI can also suggest recipes based on the user's past cooking history. For example, the generation AI suggests recipes using leftover ingredients based on dishes the user has made in the past. This makes it possible to effectively utilize leftover ingredients and reduce food waste. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit prepares a dish using leftover ingredients based on a recipe suggested by the generation AI.

[0076] The analysis unit can suggest that the user share photos and impressions of their meal. The generation AI, for example, suggests sharing a meal experience based on the user's mood. For example, if the user inputs "I want to share photos of my meal," the generation AI suggests sharing photos and impressions of their meal. The generation AI can also make suggestions based on the user's past sharing history. For example, the generation AI can suggest an optimal sharing method based on photos and impressions of meals shared by the user in the past. Furthermore, the generation AI can also make suggestions based on the user's social media activity. For example, the generation AI can suggest a sharing method based on photos and impressions of meals shared by the user on social media. This allows users to share their meal experiences with others, promoting communication. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit shares photos and impressions of meals based on the sharing method suggested by the generation AI.

[0077] The analysis unit can suggest that the user donate a meal. The generation AI, for example, suggests a meal donation based on the user's mood. For example, if the user inputs "I want to donate a meal," the generation AI suggests a meal donation. The generation AI can also make suggestions based on the user's past donation history. For example, the generation AI suggests the most suitable donation recipient based on organizations to which the user has donated in the past. Furthermore, the generation AI can also make suggestions based on the user's social media activity. For example, the generation AI suggests a donation recipient based on donation activities shared by the user on social media. This makes it possible to address social issues by providing support to areas where food security is difficult. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit donates meals based on the donation recipients suggested by the generation AI.

[0078] The reception unit can estimate the user's emotion and adjust the mood input method based on the estimated emotion. The reception unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the reception unit analyzes the user's facial expressions and voice to estimate the emotion. The reception unit can also estimate the emotion based on the user's past input history. For example, the reception unit estimates the user's current emotion based on moods and emotions previously input by the user. The reception unit can also estimate the emotion based on the user's social media activity. For example, the reception unit estimates the emotion based on the content of posts the user shared on social media. This allows for more appropriate mood input by adjusting the mood input method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit adjusts the method of inputting the mood based on the emotion estimated by the generation AI.

[0079] The reception unit can analyze the user's past mood input history and provide an input interface. The reception unit, for example, uses a data analysis algorithm to analyze the user's past mood input history. For example, the reception unit provides an optimal input interface based on the moods and emotions the user has input in the past. The reception unit can also provide an interface based on the user's past input method. For example, the reception unit provides an optimal input interface based on the input method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict the mood to be input during a specific time period from the user's past input history and provide an interface. For example, the reception unit provides an optimal input interface based on the moods the user has input during a specific time period in the past. In this way, by analyzing the past mood input history, the optimal input interface can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit provides an optimal input interface based on the past mood input history analyzed by the generation AI.

[0080] When inputting the mood, the reception unit can adjust the input method based on the user's current activity status and environment. The reception unit, for example, uses sensors and data collection algorithms to acquire the user's current activity status and environment. For example, when the user is out, the reception unit prioritizes voice input to enable quick mood input. Furthermore, when the user is at home, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is exercising, the reception unit can provide an interface that allows the user to input the mood with a simple tap operation. For example, when the user inputs the mood while exercising, the reception unit provides an interface that allows easy input with a tap operation. This allows for more appropriate input by customizing the input method according to the user's activity status and environment. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit adjusts the input method based on the activity status and environmental data acquired by the generation AI.

[0081] When a user inputs their mood, the reception unit can select an input means according to the user's input method. The reception unit, for example, uses an input device and a data analysis algorithm to detect the user's input method. For example, when a user inputs their mood by voice, the reception unit supports the input using voice recognition technology. Furthermore, when a user inputs their mood by text, the reception unit can provide a predictive conversion function to simplify the input. Furthermore, when a user inputs their mood using an image, the reception unit can estimate the user's mood using image analysis technology and support the input. For example, when a user uploads an image, the reception unit estimates the user's mood using image analysis technology and supports the input. This enables more appropriate input by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit selects the optimal input means based on the input method detected by the generation AI.

[0082] The reception unit can estimate the user's emotion and change the design of the input interface based on the estimated emotion. The reception unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the reception unit analyzes the user's facial expressions and voice to estimate the emotion. The reception unit can also estimate the emotion based on the user's past input history. For example, the reception unit estimates the user's current emotion based on the moods and emotions input by the user in the past. The reception unit can also estimate the emotion based on the user's social media activity. For example, the reception unit estimates the emotion based on the content of posts shared by the user on social media. This enables more appropriate input by adjusting the design of the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit adjusts the design of the input interface based on the emotion estimated by the generation AI.

[0083] When inputting a mood, the reception unit can automatically suggest a location by referring to the user's past travel history. The reception unit, for example, uses a location information service or a data analysis algorithm to acquire the user's past travel history. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. Furthermore, the reception unit can analyze the user's past travel patterns and suggest optimal candidate locations. For example, the reception unit suggests optimal candidate locations based on data on places the user has visited in the past. This allows more appropriate candidate locations to be suggested by referring to the user's past travel history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit suggests optimal candidate locations based on travel history data acquired by the generation AI.

[0084] When the user inputs the mood, the reception unit can refer to the user's calendar information and make a suggestion based on the user's schedule. The reception unit, for example, uses a calendar app or a data analysis algorithm to acquire the user's calendar information. For example, the reception unit refers to the schedule registered in the user's calendar and makes a suggestion related to the mood. The reception unit can also suggest a mood related to a specific event from the user's calendar information. Furthermore, the reception unit can suggest an optimal mood to match the schedule based on the user's calendar information. For example, the reception unit can suggest an optimal mood based on the schedule registered in the user's calendar. This makes it possible to make a suggestion based on the schedule by referring to the user's calendar information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit makes a suggestion based on the schedule based on the calendar information acquired by the generation AI.

[0085] When a mood is input, the reception unit can analyze the user's social media activity and suggest related moods. The reception unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the reception unit can suggest moods related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related moods. Furthermore, the reception unit can suggest related moods by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related moods based on the content of posts shared by the user's friends on social media. In this way, related moods can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit suggests related moods based on the social media activity analyzed by the generation AI.

[0086] The analysis unit can estimate the user's emotions and change the method of suggesting dishes based on the estimated emotions. The analysis unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also estimate emotions based on the user's past input history. For example, the analysis unit estimates the user's current emotions based on the moods and emotions input by the user in the past. The analysis unit can also estimate emotions based on the user's social media activities. For example, the analysis unit estimates emotions based on the content of posts the user has shared on social media. This allows the system to adjust the method of suggesting dishes based on the user's emotions, thereby suggesting more appropriate dishes. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit adjusts the way it suggests dishes based on the emotions estimated by the generation AI.

[0087] During analysis, the analysis unit can improve the accuracy of suggestions by referring to the user's past dish selection history. The analysis unit, for example, uses a data analysis algorithm to analyze the user's past dish selection history. For example, the analysis unit suggests optimal dishes based on dishes selected by the user in the past. The analysis unit can also suggest dishes containing the user's favorite ingredients from the user's past selection history. Furthermore, the analysis unit can analyze the user's past selection history and suggest dishes that provide the highest satisfaction. For example, the analysis unit suggests optimal dishes based on dishes that the user has previously rated highly. This improves the accuracy of suggestions by referring to the user's past dish selection history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit suggests optimal dishes based on the user's past dish selection history analyzed by the generation AI.

[0088] During analysis, the analysis unit can change the content of suggestions based on the user's current health condition and dietary restrictions. The analysis unit, for example, uses a health management app or a data analysis algorithm to acquire the user's health condition and dietary restrictions. For example, if the user is on a diet, the analysis unit can suggest low-calorie dishes. Furthermore, if the user has allergies, the analysis unit can suggest dishes that do not contain allergens. Furthermore, if the user needs a specific nutrient, the analysis unit can suggest dishes that contain that nutrient. For example, if the user needs a dish that is high in vitamin C, the analysis unit can suggest dishes that are high in vitamin C. This allows the suggestion content to be customized according to the user's health condition and dietary restrictions, thereby suggesting more appropriate dishes. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit changes the content of suggestions based on the health condition and dietary restriction data acquired by the generation AI.

[0089] During analysis, the analysis unit can change the content of the suggestions taking into account the user's ingredient inventory. The analysis unit, for example, uses an inventory management system or a data analysis algorithm to obtain the user's ingredient inventory. For example, the analysis unit can suggest optimal dishes based on the ingredients in the user's refrigerator. The analysis unit can also suggest dishes that use up the ingredients the user has. Furthermore, the analysis unit can suggest dishes that make up for ingredients that the user is running low on. For example, the analysis unit can suggest dishes that use up the ingredients the user has in the refrigerator. This makes it possible to suggest dishes that do not waste food by taking into account the user's ingredient inventory. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit changes the content of the suggestions based on ingredient inventory data obtained by the generation AI.

[0090] The analysis unit can estimate the user's emotions and determine the order of dishes to be suggested based on the estimated emotions. The analysis unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also estimate emotions based on the user's past input history. For example, the analysis unit estimates the user's current emotions based on the moods and emotions previously input by the user. The analysis unit can also estimate emotions based on the user's social media activities. For example, the analysis unit estimates emotions based on the content of posts the user shared on social media. This allows for more appropriate dishes to be suggested by determining the priority of dishes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit determines the order of dishes to suggest based on the emotions estimated by the generation AI.

[0091] During analysis, the analysis unit can suggest regional dishes taking into account the user's geographical location information. The analysis unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific region, the analysis unit can suggest dishes using local specialties. Also, if the user is traveling, the analysis unit can suggest local specialty dishes. Furthermore, if the user is in their hometown, the analysis unit can suggest dishes using local ingredients. For example, if the user is in their hometown, the analysis unit can suggest dishes using local ingredients. In this way, regional dishes can be suggested by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit suggests regional dishes based on the geographical location information acquired by the generation AI.

[0092] During the analysis, the analysis unit can analyze the user's social media activity and suggest related dishes. The analysis unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the analysis unit can suggest dishes related to dishes shared by the user on social media. The analysis unit can also analyze the user's social media posts to suggest related dishes. Furthermore, the analysis unit can suggest related dishes based on the activities of the user's friends on social media. For example, the analysis unit can suggest related dishes based on dishes shared by the user's friends on social media. In this way, related dishes can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit suggests related dishes based on the social media activity analyzed by the generation AI.

[0093] During analysis, the analysis unit can change the content of suggestions to reflect the user's past feedback. The analysis unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the analysis unit can suggest optimal dishes based on dishes that the user has previously given high ratings. The analysis unit can also suggest dishes that include the user's preferred seasonings based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past feedback to suggest the most satisfying dish. For example, the analysis unit can suggest optimal dishes based on dishes that the user has previously given high ratings. This improves the accuracy of the suggestions by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit changes the content of suggestions based on the past feedback analyzed by the generation AI.

[0094] The selection unit can estimate the user's emotion and change the delivery method options based on the estimated emotion. The selection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the selection unit analyzes the user's facial expressions and voice to estimate the emotion. The selection unit can also estimate the emotion based on the user's past input history. For example, the selection unit estimates the user's current emotion based on moods and emotions previously input by the user. The selection unit can also estimate the emotion based on the user's social media activity. For example, the selection unit estimates the emotion based on the content of posts the user shared on social media. This allows the system to suggest a more appropriate delivery method by adjusting the delivery method options according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit adjusts the options for delivery methods based on the emotions estimated by the generation AI.

[0095] When selecting a delivery method, the selection unit can provide options by referring to the user's past selection history. The selection unit, for example, uses a data analysis algorithm to analyze the user's past selection history. For example, the selection unit proposes an optimal option based on delivery methods selected by the user in the past. The selection unit can also propose a preferred delivery method from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and propose a delivery method that provides the highest satisfaction. For example, the selection unit proposes an optimal option based on delivery methods that the user has previously rated highly. In this way, the optimal delivery method can be proposed by referring to the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit proposes an optimal option based on the past selection history analyzed by the generation AI.

[0096] When selecting a delivery method, the selection unit can change the options based on the user's current lifestyle and schedule. The selection unit, for example, uses a schedule management app or a data analysis algorithm to acquire the user's lifestyle and schedule. For example, the selection unit can suggest a quick delivery method if the user is busy. The selection unit can also suggest a delivery method that proceeds at a leisurely pace if the user has a lot of free time. Furthermore, the selection unit can suggest an optimal delivery method based on the user's schedule. For example, the selection unit can suggest an optimal delivery method based on the user's schedule. This allows the delivery method to be customized according to the user's lifestyle and schedule, making it possible to suggest a more appropriate delivery method. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the delivery method based on lifestyle and schedule data acquired by the generation AI.

[0097] When selecting a serving method, the selection unit can change the options taking into account the user's ingredient inventory. The selection unit, for example, uses an inventory management system or a data analysis algorithm to obtain the user's ingredient inventory. For example, the selection unit suggests the optimal serving method for cooking at home based on the ingredients in the user's refrigerator. The selection unit can also suggest a serving method that allows the user to use up the ingredients they have. Furthermore, the selection unit can also suggest a serving method that allows the user to make up for ingredients that are running low. For example, the selection unit suggests a serving method that allows the user to use up the ingredients in the refrigerator. In this way, by taking into account the user's ingredient inventory, a serving method that does not waste food can be suggested. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the serving method based on ingredient inventory data obtained by the generation AI.

[0098] The selection unit can estimate the user's emotion and determine the order of delivery methods based on the estimated emotion. The selection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the selection unit analyzes the user's facial expressions and voice to estimate the emotion. The selection unit can also estimate the emotion based on the user's past input history. For example, the selection unit estimates the user's current emotion based on moods and emotions previously input by the user. The selection unit can also estimate the emotion based on the user's social media activity. For example, the selection unit estimates the emotion based on the content of posts the user shared on social media. This allows the system to prioritize delivery methods according to the user's emotion, thereby proposing a more appropriate delivery method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit determines the order of the presentation methods based on the emotion estimated by the generation AI.

[0099] When selecting a delivery method, the selection unit can propose a delivery method taking into account the user's geographical location information. The selection unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the selection unit can propose restaurants and delivery services in that area. Also, if the user is traveling, the selection unit can propose restaurants that serve local specialties. Furthermore, if the user is in their hometown, the selection unit can propose a method of serving dishes using local ingredients. For example, if the user is in their hometown, the selection unit can propose a method of serving dishes using local ingredients. In this way, the optimal delivery method can be proposed by taking the user's geographical location information into account. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit proposes a delivery method based on the geographical location information acquired by the generation AI.

[0100] When selecting a delivery method, the selection unit can analyze the user's social media activity and suggest a relevant delivery method. The selection unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the selection unit can suggest a delivery method related to a dish shared by the user on social media. The selection unit can also analyze the content of the user's social media posts and suggest a relevant delivery method. Furthermore, the selection unit can suggest a relevant delivery method by referring to the activity of the user's friends on social media. For example, the selection unit can suggest a relevant delivery method based on a dish shared by the user's friends on social media. In this way, a relevant delivery method can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit suggests a relevant delivery method based on the social media activity analyzed by the generation AI.

[0101] When selecting a delivery method, the selection unit can change the options by reflecting the user's past feedback. The selection unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the selection unit suggests an optimal delivery method based on delivery methods that the user has previously given high ratings. The selection unit can also suggest a preferred delivery method based on the user's past feedback. Furthermore, the selection unit can analyze the user's past feedback and suggest a delivery method that provides the highest level of satisfaction. For example, the selection unit suggests an optimal delivery method based on delivery methods that the user has previously given high ratings. This improves the accuracy of the delivery method by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit changes the delivery method based on past feedback analyzed by the generation AI.

[0102] The payment unit can estimate the user's emotions and change the payment method based on the estimated emotions. The payment unit, for example, uses an emotion estimation algorithm to estimate the user's emotions. For example, the payment unit analyzes the user's facial expressions and voice to estimate the emotions. The payment unit can also estimate the emotions based on the user's past input history. For example, the payment unit estimates the user's current emotions based on the moods and emotions input by the user in the past. The payment unit can also estimate the emotions based on the user's social media activities. For example, the payment unit estimates the emotions based on the content of posts the user shared on social media. This enables more appropriate payments by adjusting the payment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the payment unit can be performed using AI, for example, or without AI. For example, the payment unit adjusts the payment method based on the emotions estimated by the generation AI.

[0103] At the time of payment, the payment unit can provide a payment method by referring to the user's past payment history. The payment unit, for example, uses a data analysis algorithm to analyze the user's past payment history. For example, the payment unit can suggest the optimal payment method based on payment methods the user has used in the past. The payment unit can also suggest a preferred payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and suggest the payment method with the highest satisfaction. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has given high ratings in the past. In this way, the optimal payment method can be suggested by referring to the user's past payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit provides the optimal payment method based on the past payment history analyzed by the generation AI.

[0104] The payment unit can change the payment method based on the user's current economic situation and payment ability at the time of payment. The payment unit, for example, uses an economic information management system or a data analysis algorithm to obtain the user's economic situation and payment ability. For example, the payment unit can suggest a more expensive payment method if the user is financially well off. The payment unit can also suggest a less expensive payment method if the user is financially struggling. Furthermore, the payment unit can suggest the optimal payment method based on the user's payment ability. For example, the payment unit can suggest the optimal payment method based on the user's income information and expenditure information. This enables more appropriate payment by customizing the payment method based on the user's economic situation and payment ability. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit changes the payment method based on the economic situation and payment ability data obtained by the generation AI.

[0105] The payment unit can analyze the user's payment history at the time of payment and provide optimal discounts and benefits. The payment unit, for example, uses a data analysis algorithm to analyze the user's payment history. For example, the payment unit can provide a special discount if the user has made large payments in the past. The payment unit can also provide a loyalty program if the user is a frequent user. Furthermore, the payment unit can provide optimal benefits based on the user's payment history. For example, the payment unit can provide optimal discounts and benefits based on payment methods that the user has given high ratings in the past. In this way, optimal discounts and benefits can be provided by analyzing the user's payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can provide optimal discounts and benefits based on the payment history analyzed by the generation AI.

[0106] The payment unit can estimate a user's emotions and determine the order of payments based on the estimated emotions. The payment unit, for example, uses an emotion estimation algorithm to estimate a user's emotions. For example, the payment unit analyzes the user's facial expressions and voice to estimate emotions. The payment unit can also estimate emotions based on the user's past input history. For example, the payment unit estimates the user's current emotions based on moods and emotions previously input by the user. The payment unit can also estimate emotions based on the user's social media activities. For example, the payment unit estimates emotions based on the content of posts the user shared on social media. This enables more appropriate payments by determining payment priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the payment unit can be performed using, for example, AI, or without AI. For example, the payment unit determines the order of payments based on the emotions estimated by the generation AI.

[0107] The payment unit can propose a payment method taking into account the user's geographical location information at the time of payment. The payment unit, for example, uses GPS data or location information services to acquire the user's geographical location information. For example, if the user is in a specific area, the payment unit proposes payment methods available in that area. Also, if the user is traveling, the payment unit can propose payment methods available in that area. Furthermore, if the user is in their hometown, the payment unit can propose local payment methods. For example, if the user is in their hometown, the payment unit proposes local payment methods. In this way, the optimal payment method can be proposed by taking the user's geographical location information into account. Some or all of the above-described processing in the payment unit may be performed, for example, using AI, or may be performed without using AI. For example, the payment unit proposes a payment method based on the geographical location information acquired by the generation AI.

[0108] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. The payment unit, for example, uses a data analysis algorithm to analyze the user's social media activity. For example, the payment unit can suggest payment methods related to payment methods shared by the user on social media. The payment unit can also analyze the user's social media posts to suggest relevant payment methods. Furthermore, the payment unit can suggest relevant payment methods based on the activity of the user's friends on social media. For example, the payment unit can suggest relevant payment methods based on the payment methods shared by the user's friends on social media. In this way, relevant payment methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can suggest relevant payment methods based on the social media activity analyzed by the generation AI.

[0109] The payment unit can change the payment method at the time of payment by reflecting the user's past feedback. The payment unit, for example, uses a data analysis algorithm to analyze the user's past feedback. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has previously given high ratings. The payment unit can also suggest a preferred payment method based on the user's past feedback. Furthermore, the payment unit can analyze the user's past feedback and suggest the payment method that provides the highest satisfaction. For example, the payment unit can suggest the optimal payment method based on payment methods that the user has previously given high ratings. This improves the accuracy of the payment method by reflecting the user's past feedback. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit changes the payment method based on the past feedback analyzed by the generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, analysis unit, selection unit, and payment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's mood using the reception device 38 of the smart device 14. For example, text input is performed using the touch panel 38A, and voice input is performed using the microphone 38B. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's mood using a generation AI and suggests the optimal dish. The selection unit is realized by the control unit 46A of the smart device 14, and selects a serving method based on the dish suggested by the analysis unit. The payment unit is realized by the specific processing unit 290 of the data processing device 12, and makes a payment based on the serving method selected by the selection unit. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and payment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's mood using the microphone 238 of the smart glasses 214. For example, voice input is performed using the microphone 238. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's mood using a generation AI and suggests the optimal dish. The selection unit is realized by the control unit 46A of the smart glasses 214 and selects a serving method based on the dish suggested by the analysis unit. The payment unit is realized by the specific processing unit 290 of the data processing device 12 and makes payment based on the serving method selected by the selection unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and payment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the user's mood using the microphone 238 of the headset-type terminal 314. For example, voice input is performed using the microphone 238. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's mood using a generation AI and suggests the most suitable dish. The selection unit is realized by the control unit 46A of the headset-type terminal 314, and selects a serving method based on the dish suggested by the analysis unit. The payment unit is realized by the specific processing unit 290 of the data processing device 12, and makes payment based on the serving method selected by the selection unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and payment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's mood using the microphone 238 of the robot 414. For example, voice input is performed using the microphone 238. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's mood using a generative AI and suggests the most suitable dish. The selection unit is realized by the control unit 46A of the robot 414, and selects a serving method based on the dish suggested by the analysis unit. The payment unit is realized by the specific processing unit 290 of the data processing device 12, and makes payment based on the serving method selected by the selection unit.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can customize the suggestions taking into account the user's dietary preferences and allergy information. For example, if the user is allergic to a particular ingredient, it can suggest dishes that do not contain that ingredient. Also, if the user prefers a particular dietary style (e.g., vegetarian, vegan, gluten-free, etc.), it can suggest dishes that suit that style. Furthermore, the analysis unit can analyze the user's past eating history and suggest dishes that reflect the user's preferred seasonings and cooking methods. This makes it possible to make suggestions that meet the user's individual needs.

[0112] The selection unit can provide nutritional information for the dishes selected by the user in cooperation with a health management app. For example, nutritional information such as calories, protein, fat, and carbohydrates for the dishes selected by the user can be displayed. If the user wants to consume a specific nutrient, the selection unit can also suggest dishes that are rich in that nutrient. Furthermore, the selection unit can also suggest optimal dishes based on the user's health goals (e.g., dieting, muscle building, diabetes management, etc.). This can support the user's health management.

[0113] The selection unit can display the cooking difficulty and cooking time for the dish selected by the user. For example, if the user is busy, it can suggest dishes that can be cooked in a short time. It can also suggest simple recipes for beginners and advanced recipes for experienced cooks depending on the cooking difficulty. Furthermore, if the user uses specific cooking equipment (e.g., an oven, frying pan, microwave, etc.), the selection unit can also suggest recipes that are suitable for that cooking equipment. This makes it possible to suggest recipes that are suited to the user's cooking environment.

[0114] The selection unit can display storage methods and expiration dates for ingredients for the dishes selected by the user. For example, it can suggest storage methods (refrigerated, frozen, room temperature, etc.) for ingredients purchased by the user. It can also display expiration dates for ingredients and recommend using them early. Furthermore, the selection unit can suggest recipes that will allow the user to use up leftover ingredients. This can reduce food waste and contribute to reducing food loss.

[0115] The selection unit can introduce the cultural background and history of the dish selected by the user. For example, it can explain the origin and traditional cooking methods of the dish selected by the user. It can also introduce how the dish is eaten in a particular region or culture. Furthermore, the selection unit can provide the user with an opportunity to learn about different cultures through cooking. This can enrich the user's dining experience and deepen their understanding of food culture.

[0116] The analysis unit can estimate the user's emotions and change the way the food is presented based on the estimated emotions. For example, if the user is happy, it can suggest a more glamorous presentation. If the user is depressed, it can suggest a more heartwarming presentation that will soothe the heart. Furthermore, the analysis unit can provide ideas for food presentation and decoration according to the user's emotions. This makes it possible to provide a dining experience that is tailored to the user's emotions.

[0117] The analysis unit can estimate the user's emotions and change the suggested dishes based on the estimated emotions. For example, if the user is feeling stressed, it can suggest dishes that have a relaxing effect. Also, if the user wants to cheer up, it can suggest dishes that will replenish energy. Furthermore, the analysis unit can suggest dishes that use specific ingredients or seasonings according to the user's emotions. This makes it possible to suggest meals that match the user's emotions.

[0118] The analysis unit can estimate the user's emotions and customize cooking recipes based on the estimated emotions. For example, if the user is tired, it can suggest easy-to-make recipes. If the user wants to enjoy cooking on a special occasion, it can suggest luxurious recipes. Furthermore, the analysis unit can also adjust the recipe quantities and cooking times according to the user's emotions. This makes it possible to suggest recipes that match the user's emotions.

[0119] The analysis unit can estimate the user's emotions and change the way food is served based on the estimated emotions. For example, if the user is busy, delivery or takeout can be suggested. If the user wants to relax, it can also suggest eating at a restaurant. Furthermore, the analysis unit can adjust the timing of food serving according to the user's emotions. This makes it possible to suggest a food serving method that matches the user's emotions.

[0120] The analysis unit can estimate the user's emotions and adjust the frequency of recipe suggestions based on the estimated emotions. For example, if the user wants to enjoy cooking frequently, daily suggestions can be made. Also, if the user has lost interest in cooking, the frequency of suggestions can be reduced. Furthermore, the analysis unit can make suggestions at specific time periods depending on the user's emotions. This allows the frequency of suggestions to be adjusted to match the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit inputs the user's mood. The user's mood may include, but is not limited to, emotions such as joy, sadness, and anger. The reception unit provides an interface for inputting, for example, text, voice, or image. Step 2: The analysis unit uses the generation AI to analyze the information input by the reception unit and propose the most suitable dish. The analysis is performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the generation AI analyzes the user's mood using a text generation AI (e.g., LLM) and proposes the most suitable dish. The analysis unit can also analyze the user's mood using a multimodal generation AI. Step 3: The selection unit selects a delivery method based on the food suggested by the analysis unit. Delivery methods include, but are not limited to, cooking the food yourself, delivery, making a restaurant reservation, treating others, and donating to relief efforts. For example, if the user chooses to cook the food themselves, the selection unit uses the generation AI to suggest a recipe and necessary ingredients, and arranges for them to be ordered from a nearby supermarket or online supermarket. In addition, in the case of delivery, the selection unit can also use the generation AI to suggest ordering from a nearby restaurant. Furthermore, in the case of a restaurant, the selection unit can also use the generation AI to suggest making a restaurant reservation. Step 4: The payment unit makes payment based on the delivery method selected by the selection unit. Payment can be made by, for example, credit card, electronic money, bank transfer, or other methods, but is not limited to these examples. The payment unit, for example, inputs credit card information and completes payment. The payment unit can also make payment using electronic money. The payment unit can also make payment using bank transfer.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's mood; an analysis unit that analyzes the information input by the reception unit and suggests dishes; a selection unit that selects a serving method based on the dish proposed by the analysis unit; a payment unit that performs payment based on the provision method selected by the selection unit. A system characterized by:

2. The selection unit If you choose to make it yourself, the AI ​​will suggest a recipe and the ingredients you need, and you can arrange them at a supermarket or online supermarket.

2. The system of claim 1.

3. The selection unit For delivery, generative AI suggests ordering from the restaurant.

2. The system of claim 1.

4. The selection unit For restaurants, generative AI can suggest reservations 2. The system of claim 1.

5. The selection unit If you treat someone, AI will suggest sending money or making a donation 2. The system of claim 1.

6. The analysis unit Proposing recipes using ingredients as an option to address food waste 2. The system of claim 1.

7. The analysis unit Suggests that users share photos and reviews of their meals 2. The system of claim 1.

8. The analysis unit Users suggest donating meals 2. The system of claim 1.

9. The reception unit Inferring the user's emotions and adjusting the mood input method based on the inferred emotions 2. The system of claim 1.

Images