system

Abstract

The system according to the embodiment aims to efficiently select a restaurant, make a reservation, and arrange payment when eating out. [Solution] The system according to the embodiment includes a collection unit, a selection unit, a reservation unit, and a payment unit. The collection unit listens to the preferences of participants. The selection unit selects a restaurant based on the information collected by the collection unit. The reservation unit makes a reservation at the restaurant selected by the selection unit. The payment unit creates a group payment after the reservation is completed by the reservation unit.

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] With conventional technology, choosing a restaurant, making a reservation, and coordinating payment when eating out can be complicated and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently select a restaurant, make a reservation, and arrange payment when eating out. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a selection unit, a reservation unit, and a payment unit. The collection unit listens to the preferences of participants. The selection unit selects a restaurant based on the information collected by the collection unit. The reservation unit makes a reservation at the restaurant selected by the selection unit. The payment unit creates a group payment after the reservation is completed by the reservation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select a restaurant, make a reservation, and adjust payment when eating out. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A dining out support system according to an embodiment of the present invention allows a user to select a restaurant when dining out and automates the reservation and payment process. This dining out support system allows a user to ask participants about their preferences through a messaging app, and then suggests and makes a reservation based on the results. Once the reservation is complete, a group payment is automatically created in an electronic payment system. This system allows users to skip the tedious steps of deciding on a restaurant, and by providing feedback on the results, users can receive more personalized recommendations. Restaurants can also easily attract customers at low cost and manage reservations through a messaging app. For example, a user can ask participants about their preferences through a messaging app. For example, the user can send questions such as "How are you feeling today?" or "What would you like to eat?" via the messaging app and collect responses from participants. This information is then entered into the system. The system then suggests an appropriate restaurant based on the results of the interview. The system selects the optimal restaurant based on the participants' preferences and past feedback. For example, if many participants like Japanese food, the system suggests a Japanese restaurant. The user is notified of this suggestion via the messaging app. Once the suggested restaurant is selected, the system automatically makes the reservation. Once a reservation is completed, a group payment is automatically created in the electronic payment system. This saves users the trouble of making payments. Furthermore, by providing feedback on the user's actions, the system can make more personalized suggestions. For example, by inputting feedback such as "This restaurant was good" or "This dish was delicious" into the system, suggestions for future visits will become more accurate. This system allows users to skip the tedious steps of deciding on a restaurant, and by providing feedback on their actions, they can receive more personalized suggestions. It also allows restaurants to easily attract customers at low cost and manage reservations using a messaging app. For example, they can check reservation status and cancel or change reservations through the messaging app. In this way, linking a messaging app with an electronic payment system provides a system that is highly convenient for both users and restaurants.This allows the dining out support system to reduce the hassle of eating out for users and provide a more comfortable dining out experience.

[0029] The dining out support system according to the embodiment includes a collection unit, a selection unit, a reservation unit, and a payment unit. The collection unit asks participants about their preferences via a messaging app. For example, the collection unit can send questions such as "How are you feeling today?" or "What do you want to eat?" via the messaging app and collect responses from the participants. The collection unit can also collect participants' preferences through questionnaires or interviews. For example, the collection unit can send a questionnaire form via the messaging app and collect preferences by having participants respond. The collection unit can also extract preferences from participants' past behavioral data using data mining technology. For example, the collection unit can analyze data on restaurants visited by participants in the past and dishes ordered by participants to identify their preferences. The selection unit selects a restaurant based on the information collected by the collection unit, taking into account the participants' preferences and past feedback. For example, the selection unit can create a list of restaurants that match the participants' preferences based on the collected information and select the most suitable restaurant from the list. The selection unit can also refer to past feedback data to prioritize the selection of highly rated restaurants. For example, the selection unit may prioritize suggesting restaurants that participants have previously rated highly. Furthermore, the selection unit may use AI to analyze participants' preferences and feedback data and select the most suitable restaurant. For example, the selection unit may use an AI model to input participants' preferences and feedback data and output the most suitable restaurant. The reservation unit automatically reserves the restaurant selected by the selection unit. For example, the reservation unit may allow a user to make a reservation for a restaurant through an online reservation system. Alternatively, the reservation unit may make a reservation over the phone. For example, the reservation unit may use an interactive voice response system to call a restaurant to make a reservation. Furthermore, the reservation unit may automate the reservation confirmation procedure. For example, the reservation unit may notify a user via a messaging app that the reservation has been completed. After the reservation unit completes the reservation, the payment unit automatically creates a group payment in an electronic payment system. For example, the payment unit may process payments for all participants in one lump sum through an electronic payment system. Furthermore, the payment unit may set a payment split method.For example, the payment unit may divide the payment amount among each participant and set each participant to make their own payment. Furthermore, the payment unit may also select a payment system to use. For example, the payment unit may select from a number of payment systems, such as credit cards, debit cards, and electronic money. This allows the dining out support system according to the embodiment to reduce the hassle of dining out for users and provide a more comfortable dining out experience.

[0030] The collection unit can hear participants' preferences through a messaging app. For example, the collection unit can send questions such as "How are you feeling today?" or "What do you want to eat?" through the messaging app and collect responses from participants. The collection unit can also collect participants' preferences through questionnaires or interviews. For example, the collection unit can send a questionnaire form through the messaging app and collect preferences by having participants respond. The collection unit can also extract preferences from participants' past behavioral data using data mining technology. For example, the collection unit can analyze data on restaurants visited by participants in the past and dishes ordered by participants to identify their preferences. This allows participants' preferences to be collected efficiently through the messaging app. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected through the messaging app into a generation AI and have the generation AI analyze participants' preferences.

[0031] The selection unit can select a restaurant based on the information collected by the collection unit, the participant's preferences, and past feedback. The selection unit lists restaurants that match the participant's preferences based on the information collected by the collection unit, and selects the optimal restaurant from among them. For example, the selection unit lists restaurants that match the participant's preferences based on the collected information, and selects the optimal restaurant from among them. The selection unit can also refer to past feedback data to prioritize selecting restaurants with high ratings. For example, the selection unit prioritizes suggesting restaurants that participants have previously given high ratings. Furthermore, the selection unit can use AI to analyze the participant's preferences and feedback data and select the optimal restaurant. For example, the selection unit uses an AI model to input the participant's preferences and feedback data and output the optimal restaurant. This allows the optimal restaurant to be selected taking into consideration the participant's preferences and past feedback. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the information collected by the collection unit to a generation AI and cause the generation AI to select the optimal restaurant.

[0032] The reservation unit can automatically make a reservation for the restaurant selected by the selection unit. The reservation unit can make a reservation for the restaurant through, for example, an online reservation system. The reservation unit can also make a reservation by telephone. For example, the reservation unit can make a reservation by calling the restaurant using an automated voice response system. The reservation unit can also automate the reservation confirmation procedure. For example, the reservation unit notifies the user via a messaging app that the reservation has been completed. This can reduce the user's effort by automatically making a reservation for the selected restaurant. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input information about the restaurant selected by the selection unit into a generation AI and have the generation AI execute the reservation procedure.

[0033] The payment unit can automatically create a group payment in an electronic payment system after the reservation unit completes the reservation. The payment unit can, for example, process payments for all participants in one go through an electronic payment system. The payment unit can also set a payment division method. For example, the payment unit can divide the payment amount among participants and set it so that each participant makes their own payment. The payment unit can also select a payment system to use. For example, the payment unit can select from multiple payment systems, such as credit card, debit card, and electronic money. This can reduce the effort required for payment by automatically creating a group payment after the reservation is completed. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, AI, or can be performed without AI. For example, the payment unit can input information after the reservation unit completes the reservation into a generation AI and have the generation AI create the group payment.

[0034] The selection unit can make subsequent suggestions with higher accuracy based on feedback from the user. For example, the selection unit can make subsequent suggestions more accurate by providing feedback on the user's actions. For example, by inputting feedback such as "This restaurant was good" or "This dish was delicious" into the system, subsequent suggestions will be more accurate. The selection unit can analyze the feedback data and reflect it in subsequent suggestions. For example, the selection unit selects a restaurant that matches the user's preferences based on the feedback data. The selection unit can also use the feedback data to optimize the selection algorithm. For example, the selection unit analyzes the feedback data and adjusts parameters of the selection algorithm. This allows the user's feedback to be utilized to improve the accuracy of subsequent suggestions. 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 can input the feedback data into a generation AI and cause the generation AI to improve the accuracy of subsequent suggestions.

[0035] The collection unit can analyze the user's past answer history and automatically generate optimal questions. The collection unit, for example, analyzes the user's past answer history and automatically generates optimal questions. For example, the collection unit automatically generates related questions based on information about the user's favorite dishes and restaurants in the past. The collection unit can also analyze the user's past answer patterns and automatically generate questions in a format that is easy to answer. Furthermore, the collection unit can automatically generate questions that elicit more specific preferences by referring to the user's past feedback. This allows information to be collected efficiently by generating optimal questions based on the past answer history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past answer history into a generation AI and cause the generation AI to automatically generate optimal questions.

[0036] The collection unit can customize questions based on the user's current activity status and schedule during the interview. The collection unit customizes questions based on the user's current activity status and schedule during the interview, for example. For example, when the user is at work, the collection unit provides questions that can be answered in a short time. Furthermore, when the user is on vacation, the collection unit can provide detailed questions to collect more information. Furthermore, the collection unit can send questions at an appropriate time based on the user's schedule. This allows appropriate information to be collected by customizing questions according to the user's activity status and schedule. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's schedule data into a generation AI and cause the generation AI to customize the questions.

[0037] The collection unit, during the interview, can prioritize highly relevant questions taking into account the user's geographical location information. For example, during the interview, the collection unit prioritizes highly relevant questions taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes questions about shops and dishes related to that area. Furthermore, when the user is traveling, the collection unit prioritizes questions related to the user's travel destination. Furthermore, when the user is at home, the collection unit prioritizes questions about shops near the user's home. This allows appropriate information to be collected by asking highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize highly relevant questions.

[0038] The collection unit can analyze the user's social media activity and ask relevant questions during the interview. For example, the collection unit can ask relevant questions based on photos of food shared by the user on social media. The collection unit can also ask questions about restaurants and cuisines the user follows on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and ask questions that match the user's preferences. This allows appropriate information to be collected by asking relevant questions based on the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to generate relevant questions.

[0039] The selection unit can optimize the selection algorithm by referring to past feedback data when making a selection. The selection unit, for example, optimizes the selection algorithm by referring to past feedback data when making a selection. For example, the selection unit selects a restaurant that matches the user's preferences based on the user's past feedback. The selection unit can also analyze the past feedback data and preferentially select restaurants with high ratings. Furthermore, the selection unit can adjust the selection algorithm by referring to the user's past feedback. This enables more accurate restaurant selection by optimizing the selection algorithm based on the past feedback data. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past feedback data into a generation AI and cause the generation AI to optimize the selection algorithm.

[0040] The selection unit can select a restaurant based on the user's current health condition and dietary restrictions at the time of selection. For example, the selection unit selects a restaurant based on the user's current health condition and dietary restrictions at the time of selection. For example, if the user is looking for a healthy meal, the selection unit can select a restaurant that offers healthy menus. Furthermore, if the user has specific dietary restrictions, the selection unit can select a restaurant that meets those restrictions. Furthermore, the selection unit can select an appropriate restaurant based on the user's health condition. This makes it possible to suggest an appropriate restaurant by selecting a restaurant according to the user's health condition and dietary restrictions. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's health data into the generation AI and cause the generation AI to select an appropriate restaurant.

[0041] The selection unit can select the optimal store based on the user's geographical location information during selection. The selection unit, for example, selects the optimal store based on the user's geographical location information during selection. For example, the selection unit prioritizes selecting stores close to the user's current location. Furthermore, if the user is in a specific area, the selection unit can select stores related to that area. Furthermore, if the user is traveling, the selection unit can select stores related to the user's travel destination. This improves convenience by selecting the optimal store based on the user's geographical location information. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal store.

[0042] The selection unit can analyze the user's social media activity and suggest related restaurants when making a selection. For example, the selection unit can analyze the user's social media activity and suggest related restaurants when making a selection. For example, the selection unit can suggest related restaurants based on photos of food shared by the user on social media. The selection unit can also suggest restaurants related to the restaurants or cuisines the user follows on social media. Furthermore, the selection unit can analyze the content of the user's social media posts and suggest restaurants that match the user's preferences. This allows for more appropriate restaurant selection by suggesting related restaurants based on the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using, or without, AI. For example, the selection unit can input the user's social media data into a generation AI and cause the generation AI to suggest related restaurants.

[0043] The reservation unit can select the optimal reservation method by referring to past reservation history when making a reservation. For example, the reservation unit can select the optimal reservation method by referring to past reservation history when making a reservation. For example, the reservation unit preferentially suggests reservation methods that the user has used in the past. The reservation unit can also select the optimal reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest an efficient reservation method. This enables efficient reservations by selecting the optimal reservation method based on the past reservation history. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input past reservation history into a generation AI and have the generation AI select the optimal reservation method.

[0044] The reservation unit can adjust the reservation time based on the user's current schedule when making a reservation. The reservation unit, for example, adjusts the reservation time based on the user's current schedule when making a reservation. For example, the reservation unit suggests an optimal reservation time based on the user's schedule. The reservation unit can also adjust the reservation time to avoid times when the user is busy. Furthermore, the reservation unit can automatically set the reservation time taking the user's schedule into consideration. This enables appropriate reservations by adjusting the reservation time based on the user's schedule. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into a generation AI and have the generation AI adjust the reservation time.

[0045] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit selects the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes reserving restaurants close to the user's current location. Furthermore, if the user is in a specific area, the reservation unit can reserve restaurants related to that area. Furthermore, if the user is traveling, the reservation unit can reserve restaurants related to the user's travel destination. This improves convenience by selecting the optimal reservation method based on the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal reservation method.

[0046] The reservation unit can analyze the user's social media activity and suggest relevant reservation methods when making a reservation. For example, the reservation unit can reserve a related restaurant based on photos of a meal shared by the user on social media. The reservation unit can also reserve restaurants related to restaurants or cuisines that the user follows on social media. Furthermore, the reservation unit can analyze the content of the user's social media posts and reserve restaurants that match the user's preferences. This allows for more appropriate reservations by suggesting relevant reservation methods based on the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed using, or without, AI. For example, the reservation unit can input the user's social media data into a generation AI and have the generation AI suggest relevant reservation methods.

[0047] The payment unit can select the optimal payment method by referring to past payment history at the time of payment. The payment unit, for example, selects the optimal payment method by referring to past payment history at the time of payment. For example, the payment unit preferentially suggests payment methods that the user has used in the past. The payment unit can also select the optimal payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and suggest an efficient payment method. This enables efficient payments by selecting the optimal payment method based on the 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 can input past payment history into a generation AI and have the generation AI select the optimal payment method.

[0048] The payment unit can propose a payment plan based on the user's current financial situation at the time of payment. The payment unit, for example, proposes a payment plan based on the user's current financial situation at the time of payment. For example, the payment unit can propose an appropriate payment method taking into account the user's current bank balance. The payment unit can also propose an optimal payment plan taking into account the user's credit card usage. Furthermore, the payment unit can propose a reasonable payment plan taking into account the balance between the user's income and expenses. This allows for a reasonable payment by proposing a payment plan based on the user's financial situation. 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 input the user's financial data into a generation AI and have the generation AI execute a payment plan proposal.

[0049] The payment unit can select the optimal payment method at the time of payment by taking into account the user's geographical location information. For example, the payment unit selects the optimal payment method by taking into account the user's geographical location information at the time of payment. For example, the payment unit preferentially suggests payment methods close to the user's current location. Furthermore, if the user is in a specific area, the payment unit can suggest payment methods related to that area. Furthermore, if the user is traveling, the payment unit can suggest payment methods related to the user's travel destination. This improves convenience by selecting the optimal payment method based on the user's geographical location information. 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 input the user's geographical location information into a generation AI and cause the generation AI to select the optimal payment method.

[0050] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on payment methods shared by the user on social media. The payment unit can also suggest payment methods related to payment services the user follows on social media. Furthermore, the payment unit can analyze the content of the user's social media posts and suggest payment methods that match their preferences. This allows for more appropriate payments by suggesting relevant payment methods based on the user's social media activity. Some or all of the above-described processing in the payment unit can be performed using, or without, AI. For example, the payment unit can input the user's social media data into a generation AI and have the generation AI suggest relevant payment methods.

[0051] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on payment methods shared by the user on social media. The payment unit can also suggest payment methods related to payment services the user follows on social media. Furthermore, the payment unit can analyze the content of the user's social media posts and suggest payment methods that match their preferences. This allows for more appropriate payments by suggesting relevant payment methods based on the user's social media activity. Some or all of the above-described processing in the payment unit can be performed using, or without, AI. For example, the payment unit can input the user's social media data into a generation AI and have the generation AI suggest relevant payment methods.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The dining out support system can further include a health management unit that collects the user's health data. The health management unit can suggest appropriate restaurants and menus based on the user's health condition and dietary restrictions. For example, if the user has diabetes, it can suggest restaurants that offer low-carb menus. Also, if the user is on a diet, it can suggest restaurants that offer low-calorie menus. Furthermore, the health management unit can analyze the user's health data and suggest health-conscious meal plans. This makes it possible to suggest dining out options based on the user's health condition, supporting a healthy diet.

[0054] The selection unit can select restaurants based on the user's current activity status and schedule. For example, if the user is at work, it can suggest restaurants where a meal can be eaten in a short time. If the user is on vacation, it can suggest restaurants with a relaxed atmosphere. It can also suggest restaurants at an appropriate time based on the user's schedule. This makes it possible to select restaurants according to the user's activity status and schedule, improving convenience.

[0055] The payment unit can analyze the user's past payment history and suggest the most suitable payment method. For example, it can prioritize the suggestion of payment methods that the user has used in the past. It can also suggest efficient payment methods based on the user's past payment history. It can also analyze the user's payment history and suggest discounts and benefits for specific payment methods. This makes it possible to suggest more appropriate payment methods based on the user's past payment history.

[0056] The collection unit can prioritize highly relevant questions taking into account the user's geographical location information. For example, if the user is in a specific area, questions about shops and dishes related to that area are prioritized. If the user is traveling, questions related to the travel destination are prioritized. If the user is at home, questions about shops near the home are prioritized. This allows appropriate information to be collected by asking highly relevant questions based on the user's geographical location information.

[0057] The selection unit can analyze the user's social media activity and suggest related restaurants. For example, it can suggest related restaurants based on photos of food the user has shared on social media. It can also suggest restaurants related to the restaurants or cuisines the user follows on social media. It can also analyze the content of the user's social media posts and suggest restaurants that match the user's preferences. This allows the selection of more appropriate restaurants by suggesting related restaurants based on the user's social media activity.

[0058] The payment unit can propose a payment plan based on the user's current financial situation. For example, it can propose an appropriate payment method taking into account the user's current bank balance. It can also propose an optimal payment plan taking into account the user's credit card usage. It can also propose a reasonable payment plan taking into account the balance between the user's income and expenses. This allows the user to make reasonable payments by proposing a payment plan according to their financial situation.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit asks participants about their preferences through a messaging app. For example, the user sends questions such as "How are you feeling today?" or "What do you want to eat?" via the messaging app and collects responses from participants. Participants' preferences can also be collected through questionnaires or interviews. Furthermore, preferences can be extracted from participants' past behavioral data using data mining technology. Step 2: The selection unit selects restaurants based on the information collected by the collection unit, taking into consideration the participants' preferences and past feedback. For example, based on the collected information, it creates a list of restaurants that match the participants' preferences and selects the most suitable restaurant from that list. It can also refer to past feedback data to prioritize the selection of restaurants with high ratings. Furthermore, it can use AI to analyze the participants' preferences and feedback data to select the most suitable restaurant. Step 3: The reservation unit automatically reserves the restaurant selected by the selection unit. For example, the restaurant reservation can be made through an online reservation system. Alternatively, the reservation can be made over the phone using an automated voice response system. The reservation unit then notifies the user via a messaging app that the reservation has been completed. Step 4: The payment unit automatically creates a group payment in the electronic payment system after the reservation unit completes the reservation. For example, you can process all the payments for all participants in one payment through the electronic payment system. You can also set how to split the payment. You can also select the payment system to use.

[0061] (Example 2) A dining out support system according to an embodiment of the present invention allows a user to select a restaurant when dining out and automates the reservation and payment process. This dining out support system allows a user to ask participants about their preferences through a messaging app, and then suggests and makes a reservation based on the results. Once the reservation is complete, a group payment is automatically created in an electronic payment system. This system allows users to skip the tedious steps of deciding on a restaurant, and by providing feedback on the results, users can receive more personalized recommendations. Restaurants can also easily attract customers at low cost and manage reservations through a messaging app. For example, a user can ask participants about their preferences through a messaging app. For example, the user can send questions such as "How are you feeling today?" or "What would you like to eat?" via the messaging app and collect responses from participants. This information is then entered into the system. The system then suggests an appropriate restaurant based on the results of the interview. The system selects the optimal restaurant based on the participants' preferences and past feedback. For example, if many participants like Japanese food, the system suggests a Japanese restaurant. The user is notified of this suggestion via the messaging app. Once the suggested restaurant is selected, the system automatically makes the reservation. Once a reservation is completed, a group payment is automatically created in the electronic payment system. This saves users the trouble of making payments. Furthermore, by providing feedback on the user's actions, the system can make more personalized suggestions. For example, by inputting feedback such as "This restaurant was good" or "This dish was delicious" into the system, suggestions for future visits will become more accurate. This system allows users to skip the tedious steps of deciding on a restaurant, and by providing feedback on their actions, they can receive more personalized suggestions. It also allows restaurants to easily attract customers at low cost and manage reservations using a messaging app. For example, they can check reservation status and cancel or change reservations through the messaging app. In this way, linking a messaging app with an electronic payment system provides a system that is highly convenient for both users and restaurants.This allows the dining out support system to reduce the hassle of eating out for users and provide a more comfortable dining out experience.

[0062] The dining out support system according to the embodiment includes a collection unit, a selection unit, a reservation unit, and a payment unit. The collection unit asks participants about their preferences via a messaging app. For example, the collection unit can send questions such as "How are you feeling today?" or "What do you want to eat?" via the messaging app and collect responses from the participants. The collection unit can also collect participants' preferences through questionnaires or interviews. For example, the collection unit can send a questionnaire form via the messaging app and collect preferences by having participants respond. The collection unit can also extract preferences from participants' past behavioral data using data mining technology. For example, the collection unit can analyze data on restaurants visited by participants in the past and dishes ordered by participants to identify their preferences. The selection unit selects a restaurant based on the information collected by the collection unit, taking into account the participants' preferences and past feedback. For example, the selection unit can create a list of restaurants that match the participants' preferences based on the collected information and select the most suitable restaurant from the list. The selection unit can also refer to past feedback data to prioritize the selection of highly rated restaurants. For example, the selection unit may prioritize suggesting restaurants that participants have previously rated highly. Furthermore, the selection unit may use AI to analyze participants' preferences and feedback data and select the most suitable restaurant. For example, the selection unit may use an AI model to input participants' preferences and feedback data and output the most suitable restaurant. The reservation unit automatically reserves the restaurant selected by the selection unit. For example, the reservation unit may allow a user to make a reservation for a restaurant through an online reservation system. Alternatively, the reservation unit may make a reservation over the phone. For example, the reservation unit may use an interactive voice response system to call a restaurant to make a reservation. Furthermore, the reservation unit may automate the reservation confirmation procedure. For example, the reservation unit may notify a user via a messaging app that the reservation has been completed. After the reservation unit completes the reservation, the payment unit automatically creates a group payment in an electronic payment system. For example, the payment unit may process payments for all participants in one lump sum through an electronic payment system. Furthermore, the payment unit may set a payment split method.For example, the payment unit may divide the payment amount among each participant and set each participant to make their own payment. Furthermore, the payment unit may also select a payment system to use. For example, the payment unit may select from a number of payment systems, such as credit cards, debit cards, and electronic money. This allows the dining out support system according to the embodiment to reduce the hassle of dining out for users and provide a more comfortable dining out experience.

[0063] The collection unit can hear participants' preferences through a messaging app. For example, the collection unit can send questions such as "How are you feeling today?" or "What do you want to eat?" through the messaging app and collect responses from participants. The collection unit can also collect participants' preferences through questionnaires or interviews. For example, the collection unit can send a questionnaire form through the messaging app and collect preferences by having participants respond. The collection unit can also extract preferences from participants' past behavioral data using data mining technology. For example, the collection unit can analyze data on restaurants visited by participants in the past and dishes ordered by participants to identify their preferences. This allows participants' preferences to be collected efficiently through the messaging app. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected through the messaging app into a generation AI and have the generation AI analyze participants' preferences.

[0064] The selection unit can select a restaurant based on the information collected by the collection unit, the participant's preferences, and past feedback. The selection unit lists restaurants that match the participant's preferences based on the information collected by the collection unit, and selects the optimal restaurant from among them. For example, the selection unit lists restaurants that match the participant's preferences based on the collected information, and selects the optimal restaurant from among them. The selection unit can also refer to past feedback data to prioritize selecting restaurants with high ratings. For example, the selection unit prioritizes suggesting restaurants that participants have previously given high ratings. Furthermore, the selection unit can use AI to analyze the participant's preferences and feedback data and select the optimal restaurant. For example, the selection unit uses an AI model to input the participant's preferences and feedback data and output the optimal restaurant. This allows the optimal restaurant to be selected taking into consideration the participant's preferences and past feedback. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the information collected by the collection unit to a generation AI and cause the generation AI to select the optimal restaurant.

[0065] The reservation unit can automatically make a reservation for the restaurant selected by the selection unit. The reservation unit can make a reservation for the restaurant through, for example, an online reservation system. The reservation unit can also make a reservation by telephone. For example, the reservation unit can make a reservation by calling the restaurant using an automated voice response system. The reservation unit can also automate the reservation confirmation procedure. For example, the reservation unit notifies the user via a messaging app that the reservation has been completed. This can reduce the user's effort by automatically making a reservation for the selected restaurant. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input information about the restaurant selected by the selection unit into a generation AI and have the generation AI execute the reservation procedure.

[0066] The payment unit can automatically create a group payment in an electronic payment system after the reservation unit completes the reservation. The payment unit can, for example, process payments for all participants in one go through an electronic payment system. The payment unit can also set a payment division method. For example, the payment unit can divide the payment amount among participants and set it so that each participant makes their own payment. The payment unit can also select a payment system to use. For example, the payment unit can select from multiple payment systems, such as credit card, debit card, and electronic money. This can reduce the effort required for payment by automatically creating a group payment after the reservation is completed. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, AI, or can be performed without AI. For example, the payment unit can input information after the reservation unit completes the reservation into a generation AI and have the generation AI create the group payment.

[0067] The selection unit can make subsequent suggestions with higher accuracy based on feedback from the user. For example, the selection unit can make subsequent suggestions more accurate by providing feedback on the user's actions. For example, by inputting feedback such as "This restaurant was good" or "This dish was delicious" into the system, subsequent suggestions will be more accurate. The selection unit can analyze the feedback data and reflect it in subsequent suggestions. For example, the selection unit selects a restaurant that matches the user's preferences based on the feedback data. The selection unit can also use the feedback data to optimize the selection algorithm. For example, the selection unit analyzes the feedback data and adjusts parameters of the selection algorithm. This allows the user's feedback to be utilized to improve the accuracy of subsequent suggestions. 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 can input the feedback data into a generation AI and cause the generation AI to improve the accuracy of subsequent suggestions.

[0068] The collection unit can estimate the user's emotions and adjust the content of the questions asked in the hearing based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the content of the questions asked in the hearing based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit provides simple questions with fewer options to reduce the burden of answering. Furthermore, if the user is relaxed, the collection unit can provide detailed questions to collect more specific preferences. Furthermore, if the user is in a hurry, the collection unit can provide questions that can be answered in a short time to quickly collect information. This allows for more appropriate information to be collected by adjusting the content of the questions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the questions.

[0069] The collection unit can analyze the user's past answer history and automatically generate optimal questions. The collection unit, for example, analyzes the user's past answer history and automatically generates optimal questions. For example, the collection unit automatically generates related questions based on information about the user's favorite dishes and restaurants in the past. The collection unit can also analyze the user's past answer patterns and automatically generate questions in a format that is easy to answer. Furthermore, the collection unit can automatically generate questions that elicit more specific preferences by referring to the user's past feedback. This allows information to be collected efficiently by generating optimal questions based on the past answer history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past answer history into a generation AI and cause the generation AI to automatically generate optimal questions.

[0070] The collection unit can customize questions based on the user's current activity status and schedule during the interview. The collection unit customizes questions based on the user's current activity status and schedule during the interview, for example. For example, when the user is at work, the collection unit provides questions that can be answered in a short time. Furthermore, when the user is on vacation, the collection unit can provide detailed questions to collect more information. Furthermore, the collection unit can send questions at an appropriate time based on the user's schedule. This allows appropriate information to be collected by customizing questions according to the user's activity status and schedule. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's schedule data into a generation AI and cause the generation AI to customize the questions.

[0071] The collection unit can estimate the user's emotions and adjust the timing of the hearing based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of the hearing based on the estimated user emotions. For example, the collection unit can send questions during times when the user is relaxed. The collection unit can also send questions that avoid times when the user is feeling stressed. Furthermore, if the user is in a hurry, the collection unit can send questions that can be answered in a short time. This allows information to be collected at an appropriate time by adjusting the timing of the hearing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of the hearing.

[0072] The collection unit, during the interview, can prioritize highly relevant questions taking into account the user's geographical location information. For example, during the interview, the collection unit prioritizes highly relevant questions taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes questions about shops and dishes related to that area. Furthermore, when the user is traveling, the collection unit prioritizes questions related to the user's travel destination. Furthermore, when the user is at home, the collection unit prioritizes questions about shops near the user's home. This allows appropriate information to be collected by asking highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize highly relevant questions.

[0073] The collection unit can analyze the user's social media activity and ask relevant questions during the interview. For example, the collection unit can ask relevant questions based on photos of food shared by the user on social media. The collection unit can also ask questions about restaurants and cuisines the user follows on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and ask questions that match the user's preferences. This allows appropriate information to be collected by asking relevant questions based on the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to generate relevant questions.

[0074] The selection unit can estimate the user's emotions and adjust the restaurant selection criteria based on the estimated user emotions. For example, the selection unit can estimate the user's emotions and adjust the restaurant selection criteria based on the estimated user emotions. For example, if the user is relaxed, the selection unit can prioritize selecting restaurants with a relaxed atmosphere. Furthermore, if the user is stressed, the selection unit can prioritize selecting quiet and calm restaurants. Furthermore, if the user is in a hurry, the selection unit can prioritize selecting restaurants that provide quick service. This allows the restaurant selection criteria to be adjusted according to the user's emotions, thereby selecting a more appropriate restaurant. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using an AI, or may be performed without an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the restaurant selection criteria.

[0075] The selection unit can optimize the selection algorithm by referring to past feedback data when making a selection. The selection unit, for example, optimizes the selection algorithm by referring to past feedback data when making a selection. For example, the selection unit selects a restaurant that matches the user's preferences based on the user's past feedback. The selection unit can also analyze the past feedback data and preferentially select restaurants with high ratings. Furthermore, the selection unit can adjust the selection algorithm by referring to the user's past feedback. This enables more accurate restaurant selection by optimizing the selection algorithm based on the past feedback data. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past feedback data into a generation AI and cause the generation AI to optimize the selection algorithm.

[0076] The selection unit can select a restaurant based on the user's current health condition and dietary restrictions at the time of selection. For example, the selection unit selects a restaurant based on the user's current health condition and dietary restrictions at the time of selection. For example, if the user is looking for a healthy meal, the selection unit can select a restaurant that offers healthy menus. Furthermore, if the user has specific dietary restrictions, the selection unit can select a restaurant that meets those restrictions. Furthermore, the selection unit can select an appropriate restaurant based on the user's health condition. This makes it possible to suggest an appropriate restaurant by selecting a restaurant according to the user's health condition and dietary restrictions. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's health data into the generation AI and cause the generation AI to select an appropriate restaurant.

[0077] The selection unit can estimate the user's emotions and adjust the order of suggested restaurants based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and adjusts the order of suggested restaurants based on the estimated user emotions. For example, if the user is relaxed, the selection unit can first suggest restaurants with a laid-back atmosphere. Furthermore, if the user is stressed, the selection unit can first suggest quiet and calm restaurants. Furthermore, if the user is in a hurry, the selection unit can first suggest restaurants that provide quick service. This allows the order of suggested restaurants to be adjusted according to the user's emotions, thereby suggesting more appropriate restaurants. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the order of suggested restaurants.

[0078] The selection unit can select the optimal store based on the user's geographical location information during selection. The selection unit, for example, selects the optimal store based on the user's geographical location information during selection. For example, the selection unit prioritizes selecting stores close to the user's current location. Furthermore, if the user is in a specific area, the selection unit can select stores related to that area. Furthermore, if the user is traveling, the selection unit can select stores related to the user's travel destination. This improves convenience by selecting the optimal store based on the user's geographical location information. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal store.

[0079] The selection unit can analyze the user's social media activity and suggest related restaurants when making a selection. For example, the selection unit can analyze the user's social media activity and suggest related restaurants when making a selection. For example, the selection unit can suggest related restaurants based on photos of food shared by the user on social media. The selection unit can also suggest restaurants related to the restaurants or cuisines the user follows on social media. Furthermore, the selection unit can analyze the content of the user's social media posts and suggest restaurants that match the user's preferences. This allows for more appropriate restaurant selection by suggesting related restaurants based on the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using, or without, AI. For example, the selection unit can input the user's social media data into a generation AI and cause the generation AI to suggest related restaurants.

[0080] The reservation unit can estimate a user's emotions and adjust the reservation confirmation method based on the estimated user emotions. The reservation unit, for example, estimates a user's emotions and adjusts the reservation confirmation method based on the estimated user emotions. For example, the reservation unit can provide detailed reservation confirmation information when the user is relaxed. Furthermore, the reservation unit can provide concise reservation confirmation information when the user is stressed. Furthermore, the reservation unit can provide quick reservation confirmation information when the user is in a hurry. This enables more appropriate reservation confirmation by adjusting the reservation confirmation method according to the user's emotions. 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 such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reservation confirmation method.

[0081] The reservation unit can select the optimal reservation method by referring to past reservation history when making a reservation. For example, the reservation unit can select the optimal reservation method by referring to past reservation history when making a reservation. For example, the reservation unit preferentially suggests reservation methods that the user has used in the past. The reservation unit can also select the optimal reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history and suggest an efficient reservation method. This enables efficient reservations by selecting the optimal reservation method based on the past reservation history. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input past reservation history into a generation AI and have the generation AI select the optimal reservation method.

[0082] The reservation unit can adjust the reservation time based on the user's current schedule when making a reservation. The reservation unit, for example, adjusts the reservation time based on the user's current schedule when making a reservation. For example, the reservation unit suggests an optimal reservation time based on the user's schedule. The reservation unit can also adjust the reservation time to avoid times when the user is busy. Furthermore, the reservation unit can automatically set the reservation time taking the user's schedule into consideration. This enables appropriate reservations by adjusting the reservation time based on the user's schedule. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's schedule data into a generation AI and have the generation AI adjust the reservation time.

[0083] The reservation unit can estimate a user's emotions and determine reservation priorities based on the estimated user emotions. The reservation unit, for example, estimates a user's emotions and determines reservation priorities based on the estimated user emotions. For example, the reservation unit performs a normal reservation procedure when the user is relaxed. Furthermore, the reservation unit can quickly perform reservation procedures when the user is stressed. Furthermore, the reservation unit can give top priority to reservation procedures when the user is in a hurry. This enables more appropriate reservations by determining reservation 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation unit can input user emotion data into the generation AI and have the generation AI determine reservation priorities.

[0084] The reservation unit can select the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit selects the optimal reservation method by taking into account the user's geographical location information when making a reservation. For example, the reservation unit prioritizes reserving restaurants close to the user's current location. Furthermore, if the user is in a specific area, the reservation unit can reserve restaurants related to that area. Furthermore, if the user is traveling, the reservation unit can reserve restaurants related to the user's travel destination. This improves convenience by selecting the optimal reservation method based on the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal reservation method.

[0085] The reservation unit can analyze the user's social media activity and suggest relevant reservation methods when making a reservation. For example, the reservation unit can reserve a related restaurant based on photos of a meal shared by the user on social media. The reservation unit can also reserve restaurants related to restaurants or cuisines that the user follows on social media. Furthermore, the reservation unit can analyze the content of the user's social media posts and reserve restaurants that match the user's preferences. This allows for more appropriate reservations by suggesting relevant reservation methods based on the user's social media activity. Some or all of the above-described processing in the reservation unit can be performed using, or without, AI. For example, the reservation unit can input the user's social media data into a generation AI and have the generation AI suggest relevant reservation methods.

[0086] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated user emotions. For example, the payment unit can estimate the user's emotions and adjust the payment method based on the estimated user emotions. For example, if the user is relaxed, the payment unit can provide a regular payment method. Furthermore, if the user is stressed, the payment unit can provide a simple and quick payment method. Furthermore, if the user is in a hurry, the payment unit can provide a method that completes payment in the shortest time. This enables more appropriate payment by adjusting the payment method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, an AI. For example, the payment unit can input the user's emotion data into the generation AI and have the generation AI adjust the payment method.

[0087] The payment unit can select the optimal payment method by referring to past payment history at the time of payment. The payment unit, for example, selects the optimal payment method by referring to past payment history at the time of payment. For example, the payment unit preferentially suggests payment methods that the user has used in the past. The payment unit can also select the optimal payment method based on the user's past payment history. Furthermore, the payment unit can analyze the user's past payment history and suggest an efficient payment method. This enables efficient payments by selecting the optimal payment method based on the 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 can input past payment history into a generation AI and have the generation AI select the optimal payment method.

[0088] The payment unit can propose a payment plan based on the user's current financial situation at the time of payment. The payment unit, for example, proposes a payment plan based on the user's current financial situation at the time of payment. For example, the payment unit can propose an appropriate payment method taking into account the user's current bank balance. The payment unit can also propose an optimal payment plan taking into account the user's credit card usage. Furthermore, the payment unit can propose a reasonable payment plan taking into account the balance between the user's income and expenses. This allows for a reasonable payment by proposing a payment plan based on the user's financial situation. 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 input the user's financial data into a generation AI and have the generation AI execute a payment plan proposal.

[0089] The payment unit can estimate the user's emotions and determine payment priorities based on the estimated user emotions. The payment unit, for example, estimates the user's emotions and determines payment priorities based on the estimated user emotions. For example, the payment unit performs a normal payment procedure when the user is relaxed. Furthermore, the payment unit can perform a quick payment procedure when the user is stressed. Furthermore, the payment unit can perform a payment procedure with the highest priority when the user is in a hurry. This enables more appropriate payments by determining payment priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the payment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the payment unit can input the user's emotion data into the generation AI and have the generation AI determine the payment priorities.

[0090] The payment unit can select the optimal payment method at the time of payment by taking into account the user's geographical location information. For example, the payment unit selects the optimal payment method by taking into account the user's geographical location information at the time of payment. For example, the payment unit preferentially suggests payment methods close to the user's current location. Furthermore, if the user is in a specific area, the payment unit can suggest payment methods related to that area. Furthermore, if the user is traveling, the payment unit can suggest payment methods related to the user's travel destination. This improves convenience by selecting the optimal payment method based on the user's geographical location information. 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 input the user's geographical location information into a generation AI and cause the generation AI to select the optimal payment method.

[0091] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on payment methods shared by the user on social media. The payment unit can also suggest payment methods related to payment services the user follows on social media. Furthermore, the payment unit can analyze the content of the user's social media posts and suggest payment methods that match their preferences. This allows for more appropriate payments by suggesting relevant payment methods based on the user's social media activity. Some or all of the above-described processing in the payment unit can be performed using, or without, AI. For example, the payment unit can input the user's social media data into a generation AI and have the generation AI suggest relevant payment methods.

[0092] The payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can analyze the user's social media activity and suggest relevant payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on payment methods shared by the user on social media. The payment unit can also suggest payment methods related to payment services the user follows on social media. Furthermore, the payment unit can analyze the content of the user's social media posts and suggest payment methods that match their preferences. This allows for more appropriate payments by suggesting relevant payment methods based on the user's social media activity. Some or all of the above-described processing in the payment unit can be performed using, or without, AI. For example, the payment unit can input the user's social media data into a generation AI and have the generation AI suggest relevant payment methods. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, selection unit, reservation unit, and payment unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and listens to participants' preferences through a messaging app. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the collected information. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically reserves the selected restaurant. The payment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically creates a group payment in an electronic payment system. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, selection unit, reservation unit, and payment unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and listens to participants' preferences through a messaging app. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the collected information. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically reserves the selected restaurant. The payment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically creates a group payment for an electronic payment system. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, selection unit, reservation 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 collection unit is realized by the control unit 46A of the headset type terminal 314 and listens to participants' preferences through a messaging app. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the collected information. The reservation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and automatically reserves the selected restaurant. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically creates a group payment in an electronic payment system. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, selection unit, reservation 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 collection unit is realized by the control unit 46A of the robot 414 and listens to participants' preferences through a messaging app. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects a restaurant based on the collected information. The reservation unit is realized, for example, by the control unit 46A of the robot 414 and automatically makes a reservation for the selected restaurant. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically creates a group payment for an electronic payment system.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The dining out support system can further include a health management unit that collects the user's health data. The health management unit can suggest appropriate restaurants and menus based on the user's health condition and dietary restrictions. For example, if the user has diabetes, it can suggest restaurants that offer low-carb menus. Also, if the user is on a diet, it can suggest restaurants that offer low-calorie menus. Furthermore, the health management unit can analyze the user's health data and suggest health-conscious meal plans. This makes it possible to suggest dining out options based on the user's health condition, supporting a healthy diet.

[0095] The collection unit can estimate the user's emotions and adjust the content of the interview questions based on the estimated emotions. For example, if the user is feeling stressed, simple questions with fewer options are provided to reduce the burden of answering. If the user is relaxed, detailed questions are provided to collect more specific preferences. Furthermore, if the user is in a hurry, questions that can be answered in a short amount of time are provided to quickly collect information. In this way, more appropriate information can be collected by adjusting the content of the questions according to the user's emotions.

[0096] The selection unit can select restaurants based on the user's current activity status and schedule. For example, if the user is at work, it can suggest restaurants where a meal can be eaten in a short time. If the user is on vacation, it can suggest restaurants with a relaxed atmosphere. It can also suggest restaurants at an appropriate time based on the user's schedule. This makes it possible to select restaurants according to the user's activity status and schedule, improving convenience.

[0097] The reservation unit can estimate the user's emotions and adjust the reservation confirmation method based on the estimated emotions. For example, if the user is relaxed, detailed reservation confirmation information can be provided. If the user is stressed, concise reservation confirmation information can be provided. Furthermore, if the user is in a hurry, quick reservation confirmation information can be provided. This allows for more appropriate reservation confirmation by adjusting the reservation confirmation method according to the user's emotions.

[0098] The payment unit can analyze the user's past payment history and suggest the most suitable payment method. For example, it can prioritize the suggestion of payment methods that the user has used in the past. It can also suggest efficient payment methods based on the user's past payment history. It can also analyze the user's payment history and suggest discounts and benefits for specific payment methods. This makes it possible to suggest more appropriate payment methods based on the user's past payment history.

[0099] The selection unit can estimate the user's emotions and adjust the store selection criteria based on the estimated emotions. For example, if the user is relaxed, stores with a laid-back atmosphere can be preferentially selected. Also, if the user is stressed, quiet and calm stores can be preferentially selected. Furthermore, if the user is in a hurry, stores that provide quick service can be preferentially selected. In this way, by adjusting the store selection criteria according to the user's emotions, a more appropriate store can be selected.

[0100] The collection unit can prioritize highly relevant questions taking into account the user's geographical location information. For example, if the user is in a specific area, questions about shops and dishes related to that area are prioritized. If the user is traveling, questions related to the travel destination are prioritized. If the user is at home, questions about shops near the home are prioritized. This allows appropriate information to be collected by asking highly relevant questions based on the user's geographical location information.

[0101] The selection unit can analyze the user's social media activity and suggest related restaurants. For example, it can suggest related restaurants based on photos of food the user has shared on social media. It can also suggest restaurants related to the restaurants or cuisines the user follows on social media. It can also analyze the content of the user's social media posts and suggest restaurants that match the user's preferences. This allows the selection of more appropriate restaurants by suggesting related restaurants based on the user's social media activity.

[0102] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is relaxed, a regular payment method can be provided. If the user is stressed, a simple and quick payment method can be provided. Furthermore, if the user is in a hurry, a method that allows payment to be completed in the shortest time can be provided. In this way, adjusting the payment method according to the user's emotions enables more appropriate payments.

[0103] The payment unit can propose a payment plan based on the user's current financial situation. For example, it can propose an appropriate payment method taking into account the user's current bank balance. It can also propose an optimal payment plan taking into account the user's credit card usage. It can also propose a reasonable payment plan taking into account the balance between the user's income and expenses. This allows the user to make reasonable payments by proposing a payment plan according to their financial situation.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit asks participants about their preferences through a messaging app. For example, the user sends questions such as "How are you feeling today?" or "What do you want to eat?" via the messaging app and collects responses from participants. Participants' preferences can also be collected through questionnaires or interviews. Furthermore, preferences can be extracted from participants' past behavioral data using data mining technology. Step 2: The selection unit selects restaurants based on the information collected by the collection unit, taking into consideration the participants' preferences and past feedback. For example, based on the collected information, it creates a list of restaurants that match the participants' preferences and selects the most suitable restaurant from that list. It can also refer to past feedback data to prioritize the selection of restaurants with high ratings. Furthermore, it can use AI to analyze the participants' preferences and feedback data to select the most suitable restaurant. Step 3: The reservation unit automatically reserves the restaurant selected by the selection unit. For example, the restaurant reservation can be made through an online reservation system. Alternatively, the reservation can be made over the phone using an automated voice response system. The reservation unit then notifies the user via a messaging app that the reservation has been completed. Step 4: The payment unit automatically creates a group payment in the electronic payment system after the reservation unit completes the reservation. For example, you can process all the payments for all participants in one payment through the electronic payment system. You can also set how to split the payment. You can also select the payment system to use.

[0106] 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.

[0107] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[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] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that listens to participants' preferences, a selection unit that selects a store based on the information collected by the collection unit; a reservation unit for making a reservation at the restaurant selected by the selection unit; a payment unit that generates a group payment after a reservation is completed by the reservation unit; Equipped with A system characterized by:

2. The collecting unit Ask participants about their preferences through messaging apps 2. The system of claim 1.

3. The selection unit Based on the information collected by the collection unit, a restaurant is selected based on the participant's preferences and past feedback.

2. The system of claim 1.

4. The reservation unit Automatically make a reservation for the restaurant selected by the selection unit 2. The system of claim 1.

5. The payment unit After the reservation is completed by the reservation unit, a group payment is automatically generated in an electronic payment system.

2. The system of claim 1.

6. The selection unit Based on user feedback, future suggestions will be more accurate 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the interview questions based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past answer history and automatically generate the most appropriate questions 2. The system of claim 1.

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