system

Abstract

The system according to this embodiment aims to provide a seamless service that allows users to search for restaurants, make reservations, send information, and handle payment simply by conveying their requests. [Solution] The system according to this embodiment comprises a reception unit, a search unit, a reservation unit, an information unit, and an accounting unit. The reception unit receives requests. The search unit searches for restaurants based on the requests received by the reception unit. The reservation unit selects an appropriate restaurant from those found by the search unit and makes a reservation. The information unit sends information based on the reservation information completed by the reservation unit. The accounting unit makes payments based on the information sent by the information unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is impossible to consistently perform the search, reservation, guidance sending, and accounting of restaurants, which is time-consuming.

[0005] The system according to the embodiment aims to consistently perform the search, reservation, guidance sending, and accounting of restaurants just by conveying a request.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a reservation unit, an information unit, and an accounting unit. The reception unit receives requests. The search unit searches for restaurants based on the requests received by the reception unit. The reservation unit selects an appropriate restaurant from those found by the search unit and makes a reservation. The information unit sends information based on the reservation information completed by the reservation unit. The accounting unit makes payments based on the information sent by the information unit. [Effects of the Invention]

[0007] The system, as exemplified here, allows users to search for restaurants, make reservations, send information, and handle payment all in one go simply by conveying their requests. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system developed to significantly reduce the workload of organizers of events such as drinking parties. This AI agent system performs the following four steps that organizers would normally have to do in one go. First, the organizer simply tells the AI ​​agent their request, and the system automatically searches for a restaurant, makes a reservation, sends an invitation email, and handles the payment (splitting the bill). Specifically, it works as follows: First, the organizer tells the AI ​​agent their request. For example, they input a request such as, "I want to have a drinking party with 10 people. The budget is under 5,000 yen per person, and I'd like you to find a Japanese restaurant." Next, the AI ​​agent analyzes a vast amount of restaurant information and review data to find a restaurant that matches the user's request. For example, it lists highly-rated Japanese restaurants within the budget. Next, the AI ​​agent selects the most suitable restaurant from the list and makes a reservation. Once the reservation is complete, the AI ​​agent sends an invitation email to all participants. This email contains information about the restaurant, reservation details, meeting place, etc. Furthermore, after the drinking party ends, the AI ​​agent automatically handles the payment (splitting the bill). It calculates the amount each participant has to pay and sends invoices according to their respective payment methods. For example, payments are processed automatically according to each person's chosen payment method, such as credit card or electronic money. In this way, the AI ​​agent handles the time-consuming tasks that would normally be done by the organizer, significantly reducing their burden. Furthermore, in the future, the AI ​​agent is planned to have a function that proactively gathers information necessary for reservations. For example, it will be able to make suggestions such as, "Mr. / Ms. A has a meat allergy, so wouldn't a different restaurant be better?" As a result, the AI ​​agent system will greatly reduce the workload for organizers and enable the efficient hosting of events such as drinking parties.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a search unit, a reservation unit, an information unit, and an accounting unit. The reception unit receives requests from the organizer. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception unit analyzes the requests entered by the organizer and passes them to the search unit in an appropriate format. The search unit searches for restaurants based on the requests received by the reception unit. The search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's requirements. For example, the search unit lists highly-rated Japanese restaurants within the budget. The reservation unit selects the most suitable restaurant from those found by the search unit and makes a reservation. For example, the reservation unit selects the highest-rated restaurant from the listed restaurants and makes a reservation. The reservation unit automatically makes reservations using, for example, an online reservation system. The information unit sends an information email to all participants based on the reservation information completed by the reservation unit. The information department sends out informational emails containing details such as the restaurant where the reservation has been completed, reservation details, and meeting place. The information department automatically sends out informational emails using an email sending system. The accounting department handles the accounting (splitting the bill) based on the informational emails sent by the information department. The accounting department calculates the total amount payable by all participants and sends invoices according to each person's payment method. The accounting department automatically processes payments according to each person's chosen payment method, such as credit card or electronic money. As a result, the AI ​​agent system according to this embodiment significantly reduces the workload of the organizer and enables the efficient hosting of events such as drinking parties.

[0030] The reception department receives requests from the organizer. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception department analyzes the requests entered by the organizer and passes them to the search department in an appropriate format. Specifically, the reception department analyzes the information entered by the organizer using natural language processing technology and converts the requests into structured data. For example, if the request is "Japanese food, budget of 5000 yen or less per person, 10 people, around Shinjuku," the reception department breaks this down into keywords such as "Japanese food," "5000 yen or less," "10 people," and "Shinjuku," and converts it into a format that the search department can easily understand. Furthermore, the reception department can also ask additional questions about the organizer's requests to gather more detailed information. For example, if the organizer enters a vague request such as "a restaurant with a quiet atmosphere would be good," the reception department will ask a question such as "What kind of atmosphere are you looking for specifically?" to clarify the request. This allows the reception department to accurately understand the organizer's requests and provide appropriate information to the search department.

[0031] The search unit searches for restaurants based on requests received by the reception unit. For example, the search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's needs. Specifically, the search unit searches the restaurant information stored in the database based on queries and lists restaurants that match the requests. For example, when listing highly-rated Japanese restaurants within a budget, it comprehensively considers information such as the restaurant's menu, price range, ratings, and location. Furthermore, the search unit analyzes review data to narrow down the restaurants that meet the user's needs. For example, it extracts evaluations such as "quiet atmosphere," "private rooms available," and "good service" from the review data and prioritizes listing restaurants that meet these conditions. The search unit uses AI to analyze this data and identify the optimal restaurant. For example, it uses machine learning algorithms to learn from past search results and user ratings to provide more accurate search results. As a result, the search unit can quickly and accurately find the restaurant that best suits the organizer's needs.

[0032] The reservation department selects the most suitable restaurant from those found by the search department and makes the reservation. For example, the reservation department selects the highest-rated restaurant from a list of restaurants and makes the reservation. Specifically, the reservation department ranks the list of restaurants provided by the search department based on evaluation criteria and selects the highest-rated restaurant. Evaluation criteria include user reviews, restaurant popularity, and reservation availability. The reservation department checks the reservation status of the selected restaurant and automatically makes the reservation using an online reservation system. For example, the reservation department accesses the restaurant's reservation system, checks for availability on the desired date and time, and confirms the reservation. Furthermore, the reservation department sends a reservation confirmation email to the organizer, notifying them of the reservation details. This allows the reservation department to save the organizer time and effort and make reservations quickly and reliably.

[0033] The information department sends an invitation email to all participants based on the reservation information completed by the reservation department. For example, the information department sends an invitation email containing information such as the restaurant reservation details and meeting place. Specifically, the information department generates an invitation email template based on the reservation information provided by the reservation department and sends it to all participants. The invitation email includes detailed information such as the restaurant's name, address, phone number, reservation date and time, meeting place, and access instructions. Furthermore, the information department uses an email sending system to automatically send the invitation emails. For example, the information department manages a list of participants' email addresses and sends invitation emails using a mass mailing function. This allows the information department to quickly and accurately convey information to all participants, supporting the smooth running of the event.

[0034] The accounting department handles the accounting (splitting the bill) based on the information email sent by the planning department. For example, the accounting department calculates the total amount payable by all participants and sends invoices according to each person's payment method. Specifically, the accounting department calculates the amount payable per person based on the number of participants and the total amount. For example, if the total amount is 50,000 yen and there are 10 participants, the amount payable per person will be 5,000 yen. The accounting department automatically processes payments according to each person's chosen payment method. For example, it sends invoices according to the payment method chosen by each person, such as credit card, electronic money, or bank transfer. Furthermore, the accounting department can monitor payment status in real time and send reminders to participants who have not paid. This allows the accounting department to significantly reduce the workload of the organizers and ensure smooth accounting processing.

[0035] The search unit can analyze multiple restaurant listings and review data to find restaurants that meet the user's needs. For example, the search unit analyzes information such as restaurant menus, opening hours, location, and ratings. For example, the search unit analyzes review data and lists restaurants that meet the user's needs. For example, the search unit prioritizes searching for restaurants with high ratings. This allows for efficient searching of restaurants that meet the user's needs. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input restaurant listings and review data into a generative AI and have the generative AI output a list of restaurants that meet the user's needs.

[0036] The reservation department can select an appropriate restaurant from the searched restaurants and make a reservation. For example, the reservation department can select the highest-rated restaurant from the listed restaurants and make a reservation. For example, the reservation department can make reservations automatically using an online reservation system. For example, the reservation department can send a reservation confirmation email and notify the user of the reservation details. This allows for the selection of the optimal restaurant and efficient reservation. Some or all of the above processes in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input a list of searched restaurants into a generative AI and have the generative AI select the optimal restaurant and make the reservation.

[0037] The information department can send information to participants based on the completed reservation information. For example, the information department can send an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The information department can, for example, automatically send the information email using an email sending system. The information department can, for example, send the information email to all participants at once. This allows the information email to be sent to all participants efficiently. Some or all of the above processing in the information department may be performed using, for example, a generation AI, or not using a generation AI. For example, the information department can input the completed reservation information into a generation AI and have the generation AI create and send the information email.

[0038] The accounting department can calculate the amount each participant has to pay and issue invoices according to their respective payment methods. For example, the accounting department can calculate the total amount each participant has to pay and issue invoices according to their respective payment methods. The accounting department can automatically process payments according to the payment method each participant has chosen, such as credit card or electronic money. For example, the accounting department can calculate the payment amount by splitting it evenly and notify each participant of their payment amount. This allows for efficient calculation and invoicing of the total amount each participant has to pay. Some or all of the above processing in the accounting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the accounting department can input the participants' payment amounts into a generating AI and have the generating AI perform the calculation of payment amounts and invoices.

[0039] The reception desk can be equipped with a function to proactively hear user requests. For example, the reception desk can ask users questions and hear their requests in detail. For example, the reception desk can confirm user requests and collect necessary information. For example, the reception desk can ask appropriate questions according to user requests. In this way, by proactively hearing user requests, it is possible to receive more appropriate requests. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input user requests into a generative AI and have the generative AI perform the request hearing.

[0040] The reception desk can analyze the user's past request history and select an appropriate reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requests to be used during a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past request history into a generative AI and have the generative AI select the optimal reception method.

[0041] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user inputs their current situation, the reception unit can suggest the most suitable requests based on their areas of interest. For example, if a user has a specific area of ​​interest, the reception unit will prioritize requests related to that area. For example, the reception unit can analyze the user's current situation and areas of interest and suggest the most suitable requests. In this way, the reception unit can suggest the most suitable requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0042] The reception desk can prioritize requests that are highly relevant when receiving requests, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. For example, the reception desk will suggest the most suitable requests based on the user's geographical location information. For example, if the user is on the move, the reception desk will suggest the most suitable requests based on their current location. In this way, by considering the user's geographical location information, highly relevant requests can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI select highly relevant requests.

[0043] The reception department can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the user's social media activity and suggest relevant requests. For example, the reception department can suggest the most suitable requests based on information shared by the user on social media. For example, the reception department can prioritize receiving requests related to the user's areas of interest from the user's social media activity. In this way, relevant requests can be received by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or without generative AI. For example, the reception department can input data on the user's social media activity into a generative AI and have the generative AI select relevant requests.

[0044] The search unit can improve the accuracy of search results by considering restaurant ratings and review data during the search process. For example, the search unit displays the most suitable search results based on restaurant rating data. For example, the search unit analyzes review data and suggests restaurants that meet the user's needs. For example, the search unit comprehensively considers restaurant ratings and review data to provide the most suitable search results. This improves the accuracy of search results by considering restaurant ratings and review data. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input restaurant ratings and review data into a generative AI and have the generative AI perform the task of improving the accuracy of search results.

[0045] The search unit can apply an appropriate search algorithm by referring to the user's past search history during a search. For example, the search unit applies the optimal search algorithm based on the user's past search history. For example, the search unit prioritizes displaying restaurants that the user has searched for in the past. For example, the search unit analyzes the user's past search history and provides the most efficient search results. This allows the optimal search algorithm to be applied by referring to the user's past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history into a generative AI and have the generative AI execute the application of the optimal search algorithm.

[0046] The search unit can display search results while considering the geographical distribution of restaurants. For example, the search unit displays the most suitable search results based on the geographical distribution of restaurants. For example, the search unit prioritizes displaying nearby restaurants based on the user's current location. For example, the search unit analyzes the geographical distribution of restaurants and provides the most efficient search results. In this way, the search unit can provide the most suitable search results by considering the geographical distribution of restaurants. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input geographical distribution data of restaurants into a generative AI and have the generative AI perform the task of displaying the most suitable search results.

[0047] The search unit can improve the accuracy of search results by referring to relevant literature and data on restaurants during a search. For example, the search unit displays the most suitable search results based on relevant literature on restaurants. For example, the search unit analyzes restaurant data and suggests restaurants that meet the user's needs. For example, the search unit comprehensively considers relevant literature and data on restaurants to provide the most suitable search results. As a result, the accuracy of search results is improved by referring to relevant literature and data on restaurants. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant literature and data on restaurants into a generative AI and have the generative AI perform the task of improving the accuracy of search results.

[0048] The reservation department can select an appropriate reservation method at the time of reservation, taking into account the restaurant's availability and reservation history. For example, the reservation department can provide the optimal reservation method based on the restaurant's availability. For example, the reservation department can analyze the restaurant's reservation history and propose a reservation method that meets the user's needs. For example, the reservation department can provide the optimal reservation method by comprehensively considering the restaurant's availability and reservation history. In this way, the optimal reservation method can be provided by considering the restaurant's availability and reservation history. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reservation department can input data on the restaurant's availability and reservation history into a generative AI and have the generative AI select the optimal reservation method.

[0049] The reservation unit can apply an appropriate reservation algorithm by referring to the user's past reservation history when a reservation is made. For example, the reservation unit applies the optimal reservation algorithm based on the user's past reservation history. For example, the reservation unit prioritizes displaying restaurants that the user has previously reserved. For example, the reservation unit analyzes the user's past reservation history and provides the most efficient reservation method. This allows the optimal reservation algorithm to be applied by referring to the user's past reservation history. Some or all of the above processes in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's past reservation history into a generative AI and have the generative AI execute the application of the optimal reservation algorithm.

[0050] The reservation unit can select an appropriate reservation method when a reservation is made, taking into account the geographical distribution of restaurants. For example, the reservation unit provides the optimal reservation method based on the geographical distribution of restaurants. For example, the reservation unit prioritizes reservations at nearby restaurants based on the user's current location. For example, the reservation unit analyzes the geographical distribution of restaurants and provides the most efficient reservation method. In this way, the optimal reservation method can be provided by taking into account the geographical distribution of restaurants. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reservation unit can input geographical distribution data of restaurants into a generative AI and have the generative AI select the optimal reservation method.

[0051] The reservation department can improve the accuracy of reservations by referring to relevant literature and data on restaurants during the reservation process. For example, the reservation department can provide the optimal reservation method based on relevant literature on restaurants. For example, the reservation department can analyze restaurant data and propose a reservation method that meets the user's needs. For example, the reservation department can provide the optimal reservation method by comprehensively considering relevant literature and data on restaurants. As a result, the accuracy of reservations is improved by referring to relevant literature and data on restaurants. Some or all of the above processes in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input relevant literature and data on restaurants into a generative AI and have the generative AI perform the task of improving reservation accuracy.

[0052] The guidance unit can select an appropriate guidance method by referring to the user's past guidance history when sending guidance emails. For example, the guidance unit can provide the optimal guidance method based on the content of guidance emails the user has received in the past. For example, the guidance unit can analyze the user's past guidance history and propose the most effective guidance method. For example, the guidance unit can prioritize providing guidance methods that the user has preferred in the past. In this way, the optimal guidance method can be provided by referring to the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the user's past guidance history into a generative AI and have the generative AI select the optimal guidance method.

[0053] The guidance unit can filter the information sent via email based on the user's current situation and areas of interest. For example, the guidance unit can send the most relevant information email based on the user's current situation. For example, the guidance unit can send information emails containing relevant information based on the user's areas of interest. For example, the guidance unit can analyze the user's current situation and areas of interest and provide the most effective information email. This allows the guidance unit to send the most relevant information email by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input data on the user's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0054] The guidance unit can prioritize sending highly relevant guidance emails by considering the user's geographical location when sending guidance emails. For example, if the user is in a specific region, the guidance unit will prioritize sending guidance emails related to that region. For example, the guidance unit will provide the most relevant guidance email based on the user's geographical location. For example, if the user is on the move, the guidance unit will provide the most relevant guidance email based on their current location. In this way, by considering the user's geographical location, the guidance unit can prioritize sending highly relevant guidance emails. Some or all of the above processing in the guidance unit may be performed using, for example, a generation AI, or without a generation AI. For example, the guidance unit can input the user's geographical location information into a generation AI and have the generation AI select highly relevant guidance.

[0055] The guidance unit can analyze a user's social media activity when sending guidance emails and send relevant guidance. For example, the guidance unit can analyze a user's social media activity and send relevant guidance emails. For example, the guidance unit can provide optimal guidance emails based on information shared by the user on social media. For example, the guidance unit can prioritize sending guidance emails related to the user's areas of interest based on their social media activity. In this way, relevant guidance emails can be sent by analyzing the user's social media activity. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the guidance unit can input data on the user's social media activity into a generative AI and have the generative AI select relevant guidance.

[0056] The accounting department can select an appropriate accounting method by referring to the user's past payment history at the time of accounting. For example, the accounting department can provide the optimal accounting method based on the user's past payment history. For example, the accounting department can prioritize suggesting payment methods that the user has used in the past. For example, the accounting department can analyze the user's past payment history and provide the most efficient accounting method. In this way, the optimal accounting method can be provided by referring to the user's past payment history. Some or all of the above processes in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input the user's past payment history into a generative AI and have the generative AI select the optimal accounting method.

[0057] The accounting department can perform filtering based on the user's current status and payment method during accounting. For example, the accounting department can provide the optimal accounting method based on the user's current status. For example, the accounting department can prioritize providing relevant accounting methods based on the user's payment method. For example, the accounting department can analyze the user's current status and payment method and provide the most efficient accounting method. This allows the accounting department to provide the optimal accounting method by filtering based on the user's current status and payment method. Some or all of the above processing in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input data on the user's current status and payment method into a generative AI and have the generative AI perform the filtering.

[0058] The accounting department can select an appropriate accounting method at the time of accounting, taking into account the user's geographical location information. For example, the accounting department can provide the optimal accounting method based on the user's geographical location information. For example, if the user is in a specific region, the accounting department can prioritize providing an accounting method related to that region. For example, the accounting department can analyze the user's geographical location information and provide the most efficient accounting method. In this way, the accounting department can provide the optimal accounting method by taking the user's geographical location information into account. Some or all of the above processing in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input the user's geographical location information into a generative AI and have the generative AI select the optimal accounting method.

[0059] The accounting department can analyze users' social media activity and provide relevant accounting methods at the time of accounting. For example, the accounting department can analyze users' social media activity and propose relevant accounting methods. For example, the accounting department can provide the optimal accounting method based on information shared by users on social media. For example, the accounting department can prioritize providing accounting methods related to users' areas of interest based on their social media activity. In this way, relevant accounting methods can be provided by analyzing users' social media activity. Some or all of the above processes in the accounting department may be performed using, for example, generative AI, or not using generative AI. For example, the accounting department can input data on users' social media activity into generative AI and have the generative AI select relevant accounting methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The search function can apply an appropriate search algorithm by referring to the user's past search history. For example, it can apply the optimal search algorithm based on the user's past search history. It can also prioritize displaying restaurants that the user has searched for in the past. Furthermore, it can analyze the user's past search history and provide the most efficient search results. In this way, by referring to the user's past search history, the optimal search algorithm can be applied.

[0062] The guidance system can select the appropriate guidance method by referring to the user's past guidance history. For example, it can provide the most suitable guidance method based on the content of guidance emails the user has received in the past. It can also analyze the user's past guidance history and suggest the most effective guidance method. Furthermore, it can prioritize providing guidance methods that the user has preferred in the past. In this way, the system can provide the most suitable guidance method by referring to the user's past guidance history.

[0063] The reception desk can prioritize requests that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, requests related to that region can be prioritized. It can also suggest the most suitable requests based on the user's geographical location. Furthermore, if a user is on the move, the system can suggest the most suitable requests based on their current location. This allows for the prioritization of highly relevant requests by considering the user's geographical location.

[0064] The reservation department can select the appropriate reservation method by considering the restaurant's availability and reservation history. For example, it can provide the optimal reservation method based on the restaurant's availability. It can also analyze the restaurant's reservation history and propose a reservation method that meets the user's needs. Furthermore, it can provide the optimal reservation method by comprehensively considering the restaurant's availability and reservation history. In this way, by considering the restaurant's availability and reservation history, it can provide the most suitable reservation method.

[0065] The accounting department can select the appropriate accounting method by referring to the user's past payment history. For example, it can provide the optimal accounting method based on the user's past payment history. It can also prioritize suggesting payment methods the user has used in the past. Furthermore, it can analyze the user's past payment history and provide the most efficient accounting method. In this way, the accounting department can provide the optimal accounting method by referring to the user's past payment history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception department receives the organizer's requests. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception department analyzes the requests entered by the organizer and passes them to the search department in an appropriate format. Step 2: The search unit searches for restaurants based on the requests received by the reception unit. The search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's requirements. For example, it might list highly-rated Japanese restaurants within a given budget. Step 3: The reservation department selects the most suitable restaurant from those found by the search department and makes a reservation. The reservation department selects the highest-rated restaurant from the list and automatically makes a reservation using the online reservation system. Step 4: The information department sends an information email to all participants based on the reservation information completed by the reservation department. The information department sends an information email containing details of the restaurant where the reservation has been completed, reservation details, meeting place, etc. The information email is sent automatically using an email sending system. Step 5: The accounting department will handle the accounting (splitting the bill) based on the information email sent by the guidance department. The accounting department will calculate the total amount payable by all participants and send invoices according to each person's payment method. The system will automatically process payments according to each person's chosen payment method, such as credit card or electronic money.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system developed to significantly reduce the workload of organizers of events such as drinking parties. This AI agent system performs the following four steps that organizers would normally have to do in one go. First, the organizer simply tells the AI ​​agent their request, and the system automatically searches for a restaurant, makes a reservation, sends an invitation email, and handles the payment (splitting the bill). Specifically, it works as follows: First, the organizer tells the AI ​​agent their request. For example, they input a request such as, "I want to have a drinking party with 10 people. The budget is under 5,000 yen per person, and I'd like you to find a Japanese restaurant." Next, the AI ​​agent analyzes a vast amount of restaurant information and review data to find a restaurant that matches the user's request. For example, it lists highly-rated Japanese restaurants within the budget. Next, the AI ​​agent selects the most suitable restaurant from the list and makes a reservation. Once the reservation is complete, the AI ​​agent sends an invitation email to all participants. This email contains information about the restaurant, reservation details, meeting place, etc. Furthermore, after the drinking party ends, the AI ​​agent automatically handles the payment (splitting the bill). It calculates the amount each participant has to pay and sends invoices according to their respective payment methods. For example, payments are processed automatically according to each person's chosen payment method, such as credit card or electronic money. In this way, the AI ​​agent handles the time-consuming tasks that would normally be done by the organizer, significantly reducing their burden. Furthermore, in the future, the AI ​​agent is planned to have a function that proactively gathers information necessary for reservations. For example, it will be able to make suggestions such as, "Mr. / Ms. A has a meat allergy, so wouldn't a different restaurant be better?" As a result, the AI ​​agent system will greatly reduce the workload for organizers and enable the efficient hosting of events such as drinking parties.

[0069] The AI ​​agent system according to this embodiment comprises a reception unit, a search unit, a reservation unit, an information unit, and an accounting unit. The reception unit receives requests from the organizer. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception unit analyzes the requests entered by the organizer and passes them to the search unit in an appropriate format. The search unit searches for restaurants based on the requests received by the reception unit. The search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's requirements. For example, the search unit lists highly-rated Japanese restaurants within the budget. The reservation unit selects the most suitable restaurant from those found by the search unit and makes a reservation. For example, the reservation unit selects the highest-rated restaurant from the listed restaurants and makes a reservation. The reservation unit automatically makes reservations using, for example, an online reservation system. The information unit sends an information email to all participants based on the reservation information completed by the reservation unit. The information department sends out informational emails containing details such as the restaurant where the reservation has been completed, reservation details, and meeting place. The information department automatically sends out informational emails using an email sending system. The accounting department handles the accounting (splitting the bill) based on the informational emails sent by the information department. The accounting department calculates the total amount payable by all participants and sends invoices according to each person's payment method. The accounting department automatically processes payments according to each person's chosen payment method, such as credit card or electronic money. As a result, the AI ​​agent system according to this embodiment significantly reduces the workload of the organizer and enables the efficient hosting of events such as drinking parties.

[0070] The reception department receives requests from the organizer. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception department analyzes the requests entered by the organizer and passes them to the search department in an appropriate format. Specifically, the reception department analyzes the information entered by the organizer using natural language processing technology and converts the requests into structured data. For example, if the request is "Japanese food, budget of 5000 yen or less per person, 10 people, around Shinjuku," the reception department breaks this down into keywords such as "Japanese food," "5000 yen or less," "10 people," and "Shinjuku," and converts it into a format that the search department can easily understand. Furthermore, the reception department can also ask additional questions about the organizer's requests to gather more detailed information. For example, if the organizer enters a vague request such as "a restaurant with a quiet atmosphere would be good," the reception department will ask a question such as "What kind of atmosphere are you looking for specifically?" to clarify the request. This allows the reception department to accurately understand the organizer's requests and provide appropriate information to the search department.

[0071] The search unit searches for restaurants based on requests received by the reception unit. For example, the search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's needs. Specifically, the search unit searches the restaurant information stored in the database based on queries and lists restaurants that match the requests. For example, when listing highly-rated Japanese restaurants within a budget, it comprehensively considers information such as the restaurant's menu, price range, ratings, and location. Furthermore, the search unit analyzes review data to narrow down the restaurants that meet the user's needs. For example, it extracts evaluations such as "quiet atmosphere," "private rooms available," and "good service" from the review data and prioritizes listing restaurants that meet these conditions. The search unit uses AI to analyze this data and identify the optimal restaurant. For example, it uses machine learning algorithms to learn from past search results and user ratings to provide more accurate search results. As a result, the search unit can quickly and accurately find the restaurant that best suits the organizer's needs.

[0072] The reservation department selects the most suitable restaurant from those found by the search department and makes the reservation. For example, the reservation department selects the highest-rated restaurant from a list of restaurants and makes the reservation. Specifically, the reservation department ranks the list of restaurants provided by the search department based on evaluation criteria and selects the highest-rated restaurant. Evaluation criteria include user reviews, restaurant popularity, and reservation availability. The reservation department checks the reservation status of the selected restaurant and automatically makes the reservation using an online reservation system. For example, the reservation department accesses the restaurant's reservation system, checks for availability on the desired date and time, and confirms the reservation. Furthermore, the reservation department sends a reservation confirmation email to the organizer, notifying them of the reservation details. This allows the reservation department to save the organizer time and effort and make reservations quickly and reliably.

[0073] The information department sends an invitation email to all participants based on the reservation information completed by the reservation department. For example, the information department sends an invitation email containing information such as the restaurant reservation details and meeting place. Specifically, the information department generates an invitation email template based on the reservation information provided by the reservation department and sends it to all participants. The invitation email includes detailed information such as the restaurant's name, address, phone number, reservation date and time, meeting place, and access instructions. Furthermore, the information department uses an email sending system to automatically send the invitation emails. For example, the information department manages a list of participants' email addresses and sends invitation emails using a mass mailing function. This allows the information department to quickly and accurately convey information to all participants, supporting the smooth running of the event.

[0074] The accounting department handles the accounting (splitting the bill) based on the information email sent by the planning department. For example, the accounting department calculates the total amount payable by all participants and sends invoices according to each person's payment method. Specifically, the accounting department calculates the amount payable per person based on the number of participants and the total amount. For example, if the total amount is 50,000 yen and there are 10 participants, the amount payable per person will be 5,000 yen. The accounting department automatically processes payments according to each person's chosen payment method. For example, it sends invoices according to the payment method chosen by each person, such as credit card, electronic money, or bank transfer. Furthermore, the accounting department can monitor payment status in real time and send reminders to participants who have not paid. This allows the accounting department to significantly reduce the workload of the organizers and ensure smooth accounting processing.

[0075] The search unit can analyze multiple restaurant listings and review data to find restaurants that meet the user's needs. For example, the search unit analyzes information such as restaurant menus, opening hours, location, and ratings. For example, the search unit analyzes review data and lists restaurants that meet the user's needs. For example, the search unit prioritizes searching for restaurants with high ratings. This allows for efficient searching of restaurants that meet the user's needs. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input restaurant listings and review data into a generative AI and have the generative AI output a list of restaurants that meet the user's needs.

[0076] The reservation department can select an appropriate restaurant from the searched restaurants and make a reservation. For example, the reservation department can select the highest-rated restaurant from the listed restaurants and make a reservation. For example, the reservation department can make reservations automatically using an online reservation system. For example, the reservation department can send a reservation confirmation email and notify the user of the reservation details. This allows for the selection of the optimal restaurant and efficient reservation. Some or all of the above processes in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input a list of searched restaurants into a generative AI and have the generative AI select the optimal restaurant and make the reservation.

[0077] The information department can send information to participants based on the completed reservation information. For example, the information department can send an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The information department can, for example, automatically send the information email using an email sending system. The information department can, for example, send the information email to all participants at once. This allows the information email to be sent to all participants efficiently. Some or all of the above processing in the information department may be performed using, for example, a generation AI, or not using a generation AI. For example, the information department can input the completed reservation information into a generation AI and have the generation AI create and send the information email.

[0078] The accounting department can calculate the amount each participant has to pay and issue invoices according to their respective payment methods. For example, the accounting department can calculate the total amount each participant has to pay and issue invoices according to their respective payment methods. The accounting department can automatically process payments according to the payment method each participant has chosen, such as credit card or electronic money. For example, the accounting department can calculate the payment amount by splitting it evenly and notify each participant of their payment amount. This allows for efficient calculation and invoicing of the total amount each participant has to pay. Some or all of the above processing in the accounting department may be performed using, for example, a generating AI, or not using a generating AI. For example, the accounting department can input the participants' payment amounts into a generating AI and have the generating AI perform the calculation of payment amounts and invoices.

[0079] The reception desk can be equipped with a function to proactively hear user requests. For example, the reception desk can ask users questions and hear their requests in detail. For example, the reception desk can confirm user requests and collect necessary information. For example, the reception desk can ask appropriate questions according to user requests. In this way, by proactively hearing user requests, it is possible to receive more appropriate requests. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input user requests into a generative AI and have the generative AI perform the request hearing.

[0080] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk may provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk may prioritize voice input to allow for quick request input. This allows for more appropriate requests to be received by adjusting the request processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the processing method.

[0081] The reception desk can analyze the user's past request history and select an appropriate reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requests to be used during a specific time period based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past request history into a generative AI and have the generative AI select the optimal reception method.

[0082] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user inputs their current situation, the reception unit can suggest the most suitable requests based on their areas of interest. For example, if a user has a specific area of ​​interest, the reception unit will prioritize requests related to that area. For example, the reception unit can analyze the user's current situation and areas of interest and suggest the most suitable requests. In this way, the reception unit can suggest the most suitable requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0083] The reception desk can estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is tense, the reception desk will prioritize important requests. If the user is relaxed, the reception desk will prioritize detailed requests. If the user is in a hurry, the reception desk will prioritize requests that require a quick response. This allows important requests to be prioritized by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation and request prioritization.

[0084] The reception desk can prioritize requests that are highly relevant when receiving requests, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. For example, the reception desk will suggest the most suitable requests based on the user's geographical location information. For example, if the user is on the move, the reception desk will suggest the most suitable requests based on their current location. In this way, by considering the user's geographical location information, highly relevant requests can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI select highly relevant requests.

[0085] The reception department can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the user's social media activity and suggest relevant requests. For example, the reception department can suggest the most suitable requests based on information shared by the user on social media. For example, the reception department can prioritize receiving requests related to the user's areas of interest from the user's social media activity. In this way, relevant requests can be received by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or without generative AI. For example, the reception department can input data on the user's social media activity into a generative AI and have the generative AI select relevant requests.

[0086] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit may display search results containing detailed information. If the user is in a hurry, the search unit may display concise search results. If the user is excited, the search unit may display search results with visually stimulating effects. By adjusting how search results are displayed according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust how search results are displayed.

[0087] The search unit can improve the accuracy of search results by considering restaurant ratings and review data during the search process. For example, the search unit displays the most suitable search results based on restaurant rating data. For example, the search unit analyzes review data and suggests restaurants that meet the user's needs. For example, the search unit comprehensively considers restaurant ratings and review data to provide the most suitable search results. This improves the accuracy of search results by considering restaurant ratings and review data. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input restaurant ratings and review data into a generative AI and have the generative AI perform the task of improving the accuracy of search results.

[0088] The search unit can apply an appropriate search algorithm by referring to the user's past search history during a search. For example, the search unit applies the optimal search algorithm based on the user's past search history. For example, the search unit prioritizes displaying restaurants that the user has searched for in the past. For example, the search unit analyzes the user's past search history and provides the most efficient search results. This allows the optimal search algorithm to be applied by referring to the user's past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history into a generative AI and have the generative AI execute the application of the optimal search algorithm.

[0089] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is stressed, the search unit will prioritize important search results. If the user is relaxed, the search unit will prioritize detailed search results. If the user is in a hurry, the search unit will prioritize search results that require immediate attention. This allows for the prioritization of important search results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and search result prioritization.

[0090] The search unit can display search results while considering the geographical distribution of restaurants. For example, the search unit displays the most suitable search results based on the geographical distribution of restaurants. For example, the search unit prioritizes displaying nearby restaurants based on the user's current location. For example, the search unit analyzes the geographical distribution of restaurants and provides the most efficient search results. In this way, the search unit can provide the most suitable search results by considering the geographical distribution of restaurants. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input geographical distribution data of restaurants into a generative AI and have the generative AI perform the task of displaying the most suitable search results.

[0091] The search unit can improve the accuracy of search results by referring to relevant literature and data on restaurants during a search. For example, the search unit displays the most suitable search results based on relevant literature on restaurants. For example, the search unit analyzes restaurant data and suggests restaurants that meet the user's needs. For example, the search unit comprehensively considers relevant literature and data on restaurants to provide the most suitable search results. As a result, the accuracy of search results is improved by referring to relevant literature and data on restaurants. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant literature and data on restaurants into a generative AI and have the generative AI perform the task of improving the accuracy of search results.

[0092] The reservation unit can estimate the user's emotions and adjust the way the reservation is presented based on the estimated emotions. For example, if the user is relaxed, the reservation unit may provide a reservation method that includes detailed information. If the user is in a hurry, the reservation unit may provide a reservation method that gets straight to the point. If the user is excited, the reservation unit may provide a reservation method that includes visually stimulating effects. In this way, by adjusting the way the reservation is presented according to the user's emotions, a more appropriate reservation method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the way the reservation is presented.

[0093] The reservation department can select an appropriate reservation method at the time of reservation, taking into account the restaurant's availability and reservation history. For example, the reservation department can provide the optimal reservation method based on the restaurant's availability. For example, the reservation department can analyze the restaurant's reservation history and propose a reservation method that meets the user's needs. For example, the reservation department can provide the optimal reservation method by comprehensively considering the restaurant's availability and reservation history. In this way, the optimal reservation method can be provided by considering the restaurant's availability and reservation history. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or without using a generative AI. For example, the reservation department can input data on the restaurant's availability and reservation history into a generative AI and have the generative AI select the optimal reservation method.

[0094] The reservation unit can apply an appropriate reservation algorithm by referring to the user's past reservation history when a reservation is made. For example, the reservation unit applies the optimal reservation algorithm based on the user's past reservation history. For example, the reservation unit prioritizes displaying restaurants that the user has previously reserved. For example, the reservation unit analyzes the user's past reservation history and provides the most efficient reservation method. This allows the optimal reservation algorithm to be applied by referring to the user's past reservation history. Some or all of the above processes in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's past reservation history into a generative AI and have the generative AI execute the application of the optimal reservation algorithm.

[0095] The reservation unit can estimate the user's emotions and determine reservation priorities based on those emotions. For example, if the user is nervous, the reservation unit will prioritize important reservations. If the user is relaxed, the reservation unit will prioritize detailed reservations. If the user is in a hurry, the reservation unit will prioritize reservations that require immediate attention. This allows important reservations to be prioritized by determining reservation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI or not. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and reservation priority determination.

[0096] The reservation unit can select an appropriate reservation method when a reservation is made, taking into account the geographical distribution of restaurants. For example, the reservation unit provides the optimal reservation method based on the geographical distribution of restaurants. For example, the reservation unit prioritizes reservations at nearby restaurants based on the user's current location. For example, the reservation unit analyzes the geographical distribution of restaurants and provides the most efficient reservation method. In this way, the optimal reservation method can be provided by taking into account the geographical distribution of restaurants. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reservation unit can input geographical distribution data of restaurants into a generative AI and have the generative AI select the optimal reservation method.

[0097] The reservation department can improve the accuracy of reservations by referring to relevant literature and data on restaurants during the reservation process. For example, the reservation department can provide the optimal reservation method based on relevant literature on restaurants. For example, the reservation department can analyze restaurant data and propose a reservation method that meets the user's needs. For example, the reservation department can provide the optimal reservation method by comprehensively considering relevant literature and data on restaurants. As a result, the accuracy of reservations is improved by referring to relevant literature and data on restaurants. Some or all of the above processes in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input relevant literature and data on restaurants into a generative AI and have the generative AI perform the task of improving reservation accuracy.

[0098] The guidance unit can estimate the user's emotions and adjust the wording of the guidance email based on the estimated emotions. For example, if the user is relaxed, the guidance unit will send a guidance email containing detailed information. If the user is in a hurry, the guidance unit will send a guidance email that gets straight to the point. If the user is excited, the guidance unit will send a guidance email with visually stimulating effects. By adjusting the wording of the guidance email according to the user's emotions, it is possible to send more appropriate guidance emails. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the wording of the guidance email.

[0099] The guidance unit can select an appropriate guidance method by referring to the user's past guidance history when sending guidance emails. For example, the guidance unit can provide the optimal guidance method based on the content of guidance emails the user has received in the past. For example, the guidance unit can analyze the user's past guidance history and propose the most effective guidance method. For example, the guidance unit can prioritize providing guidance methods that the user has preferred in the past. In this way, the optimal guidance method can be provided by referring to the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input the user's past guidance history into a generative AI and have the generative AI select the optimal guidance method.

[0100] The guidance unit can filter the information sent via email based on the user's current situation and areas of interest. For example, the guidance unit can send the most relevant information email based on the user's current situation. For example, the guidance unit can send information emails containing relevant information based on the user's areas of interest. For example, the guidance unit can analyze the user's current situation and areas of interest and provide the most effective information email. This allows the guidance unit to send the most relevant information email by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or without a generative AI. For example, the guidance unit can input data on the user's current situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0101] The guidance unit can estimate the user's emotions and determine the priority of guidance emails based on the estimated emotions. For example, if the user is stressed, the guidance unit will prioritize sending important guidance emails. For example, if the user is relaxed, the guidance unit will prioritize sending detailed guidance emails. For example, if the user is in a hurry, the guidance unit will prioritize sending guidance emails that require immediate attention. In this way, by determining the priority of guidance emails according to the user's emotions, important guidance emails can be sent preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of guidance emails.

[0102] The guidance unit can prioritize sending highly relevant guidance emails by considering the user's geographical location when sending guidance emails. For example, if the user is in a specific region, the guidance unit will prioritize sending guidance emails related to that region. For example, the guidance unit will provide the most relevant guidance email based on the user's geographical location. For example, if the user is on the move, the guidance unit will provide the most relevant guidance email based on their current location. In this way, by considering the user's geographical location, the guidance unit can prioritize sending highly relevant guidance emails. Some or all of the above processing in the guidance unit may be performed using, for example, a generation AI, or without a generation AI. For example, the guidance unit can input the user's geographical location information into a generation AI and have the generation AI select highly relevant guidance.

[0103] The guidance unit can analyze a user's social media activity when sending guidance emails and send relevant guidance. For example, the guidance unit can analyze a user's social media activity and send relevant guidance emails. For example, the guidance unit can provide optimal guidance emails based on information shared by the user on social media. For example, the guidance unit can prioritize sending guidance emails related to the user's areas of interest based on their social media activity. In this way, relevant guidance emails can be sent by analyzing the user's social media activity. Some or all of the above processing in the guidance unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the guidance unit can input data on the user's social media activity into a generative AI and have the generative AI select relevant guidance.

[0104] The accounting department can estimate the user's emotions and adjust the accounting method based on the estimated emotions. For example, if the user is relaxed, the accounting department may provide an accounting method that includes detailed information. If the user is in a hurry, the accounting department may provide an accounting method that gets straight to the point. If the user is excited, the accounting department may provide an accounting method that includes visually stimulating effects. By adjusting the accounting method according to the user's emotions, a more appropriate accounting method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the accounting department may be performed using AI or not using AI. For example, the accounting department can input user emotion data into a generative AI and have the generative AI perform emotion estimation and accounting method adjustment.

[0105] The accounting department can select an appropriate accounting method by referring to the user's past payment history at the time of accounting. For example, the accounting department can provide the optimal accounting method based on the user's past payment history. For example, the accounting department can prioritize suggesting payment methods that the user has used in the past. For example, the accounting department can analyze the user's past payment history and provide the most efficient accounting method. In this way, the optimal accounting method can be provided by referring to the user's past payment history. Some or all of the above processes in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input the user's past payment history into a generative AI and have the generative AI select the optimal accounting method.

[0106] The accounting department can perform filtering based on the user's current status and payment method during accounting. For example, the accounting department can provide the optimal accounting method based on the user's current status. For example, the accounting department can prioritize providing relevant accounting methods based on the user's payment method. For example, the accounting department can analyze the user's current status and payment method and provide the most efficient accounting method. This allows the accounting department to provide the optimal accounting method by filtering based on the user's current status and payment method. Some or all of the above processing in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input data on the user's current status and payment method into a generative AI and have the generative AI perform the filtering.

[0107] The accounting department can estimate the user's emotions and determine accounting priorities based on those estimated emotions. For example, if the user is stressed, the accounting department will prioritize important accounting tasks. If the user is relaxed, the accounting department will prioritize detailed accounting tasks. If the user is in a hurry, the accounting department will prioritize accounting tasks that require immediate attention. This allows for prioritizing important accounting tasks by determining accounting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the accounting department may be performed using AI or not. For example, the accounting department can input user emotion data into a generative AI and have the generative AI perform emotion estimation and accounting priority determination.

[0108] The accounting department can select an appropriate accounting method at the time of accounting, taking into account the user's geographical location information. For example, the accounting department can provide the optimal accounting method based on the user's geographical location information. For example, if the user is in a specific region, the accounting department can prioritize providing an accounting method related to that region. For example, the accounting department can analyze the user's geographical location information and provide the most efficient accounting method. In this way, the accounting department can provide the optimal accounting method by taking the user's geographical location information into account. Some or all of the above processing in the accounting department may be performed using, for example, a generative AI, or not using a generative AI. For example, the accounting department can input the user's geographical location information into a generative AI and have the generative AI select the optimal accounting method.

[0109] The accounting department can analyze users' social media activity and provide relevant accounting methods at the time of accounting. For example, the accounting department can analyze users' social media activity and propose relevant accounting methods. For example, the accounting department can provide the optimal accounting method based on information shared by users on social media. For example, the accounting department can prioritize providing accounting methods related to users' areas of interest based on their social media activity. In this way, relevant accounting methods can be provided by analyzing users' social media activity. Some or all of the above processes in the accounting department may be performed using, for example, generative AI, or not using generative AI. For example, the accounting department can input data on users' social media activity into generative AI and have the generative AI select relevant accounting methods.

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

[0111] The reception system can estimate the user's emotions and adjust how requests are received based on those estimates. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick request entry. By adjusting the request reception process according to the user's emotions, more appropriate requests can be received.

[0112] The search function can apply an appropriate search algorithm by referring to the user's past search history. For example, it can apply the optimal search algorithm based on the user's past search history. It can also prioritize displaying restaurants that the user has searched for in the past. Furthermore, it can analyze the user's past search history and provide the most efficient search results. In this way, by referring to the user's past search history, the optimal search algorithm can be applied.

[0113] The reservation system can estimate the user's emotions and adjust the way reservations are presented based on those emotions. For example, if the user is relaxed, it can provide a reservation method that includes detailed information. If the user is in a hurry, it can provide a reservation method that gets straight to the point. Furthermore, if the user is excited, it can provide a reservation method that includes visually stimulating effects. In this way, by adjusting the way reservations are presented according to the user's emotions, a more appropriate reservation method can be provided.

[0114] The guidance system can select the appropriate guidance method by referring to the user's past guidance history. For example, it can provide the most suitable guidance method based on the content of guidance emails the user has received in the past. It can also analyze the user's past guidance history and suggest the most effective guidance method. Furthermore, it can prioritize providing guidance methods that the user has preferred in the past. In this way, the system can provide the most suitable guidance method by referring to the user's past guidance history.

[0115] The accounting department can estimate the user's emotions and adjust the accounting method based on that estimation. For example, if the user is relaxed, it can provide an accounting method that includes detailed information. If the user is in a hurry, it can provide an accounting method that gets straight to the point. Furthermore, if the user is excited, it can provide an accounting method that incorporates visually stimulating effects. By adjusting the accounting method according to the user's emotions, a more appropriate accounting method can be provided.

[0116] The reception desk can prioritize requests that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, requests related to that region can be prioritized. It can also suggest the most suitable requests based on the user's geographical location. Furthermore, if a user is on the move, the system can suggest the most suitable requests based on their current location. This allows for the prioritization of highly relevant requests by considering the user's geographical location.

[0117] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is relaxed, it can display search results with detailed information. If the user is in a hurry, it can display concise search results. Furthermore, if the user is excited, it can display search results with visually stimulating effects. By adjusting how search results are displayed according to the user's emotions, the system can provide more relevant search results.

[0118] The reservation department can select the appropriate reservation method by considering the restaurant's availability and reservation history. For example, it can provide the optimal reservation method based on the restaurant's availability. It can also analyze the restaurant's reservation history and propose a reservation method that meets the user's needs. Furthermore, it can provide the optimal reservation method by comprehensively considering the restaurant's availability and reservation history. In this way, by considering the restaurant's availability and reservation history, it can provide the most suitable reservation method.

[0119] The guidance system can estimate the user's emotions and adjust the wording of guidance emails based on those estimates. For example, if the user is relaxed, a guidance email containing detailed information can be sent. If the user is in a hurry, a guidance email that gets straight to the point can be sent. Furthermore, if the user is excited, a guidance email with visually stimulating effects can be sent. By adjusting the wording of guidance emails according to the user's emotions, more appropriate guidance emails can be sent.

[0120] The accounting department can select the appropriate accounting method by referring to the user's past payment history. For example, it can provide the optimal accounting method based on the user's past payment history. It can also prioritize suggesting payment methods the user has used in the past. Furthermore, it can analyze the user's past payment history and provide the most efficient accounting method. In this way, the accounting department can provide the optimal accounting method by referring to the user's past payment history.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception department receives the organizer's requests. These requests may include, but are not limited to, the type of restaurant, budget, number of people, and location. The reception department analyzes the requests entered by the organizer and passes them to the search department in an appropriate format. Step 2: The search unit searches for restaurants based on the requests received by the reception unit. The search unit analyzes a vast amount of restaurant information and review data to find restaurants that meet the user's requirements. For example, it might list highly-rated Japanese restaurants within a given budget. Step 3: The reservation department selects the most suitable restaurant from those found by the search department and makes a reservation. The reservation department selects the highest-rated restaurant from the list and automatically makes a reservation using the online reservation system. Step 4: The information department sends an information email to all participants based on the reservation information completed by the reservation department. The information department sends an information email containing details of the restaurant where the reservation has been completed, reservation details, meeting place, etc. The information email is sent automatically using an email sending system. Step 5: The accounting department will handle the accounting (splitting the bill) based on the information email sent by the guidance department. The accounting department will calculate the total amount payable by all participants and send invoices according to each person's payment method. The system will automatically process payments according to each person's chosen payment method, such as credit card or electronic money.

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

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0126] Each of the multiple elements described above, including the reception unit, search unit, reservation unit, information unit, and accounting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives requests from the organizer. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a vast amount of restaurant information and review data to search for restaurants that meet the user's requirements. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the highest-rated restaurant from the listed restaurants and makes a reservation. The information unit is implemented by, for example, the output device 40 of the smart device 14 and sends an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The accounting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the payment amount for all participants and sends invoices according to each person's payment method. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0142] Each of the multiple elements described above, including the reception, search, reservation, information, and accounting departments, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the reception department is implemented by the microphone 238 of the smart glasses 214 and receives requests from the organizer. The search department is implemented by the specific processing unit 290 of the data processing device 12 and analyzes a vast amount of restaurant information and review data to search for restaurants that meet the user's needs. The reservation department is implemented by the specific processing unit 290 of the data processing device 12 and selects the highest-rated restaurant from the listed restaurants and makes a reservation. The information department is implemented by the speaker 240 of the smart glasses 214 and sends an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The accounting department is implemented by the specific processing unit 290 of the data processing device 12 and calculates the payment amount for all participants and sends invoices according to each person's payment method. The correspondence between each department and the devices and control units is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0158] Each of the multiple elements described above, including the reception, search, reservation, information, and accounting departments, is implemented by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the reception department is implemented by the microphone 238 of the headset terminal 314 and receives requests from the organizer. The search department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes a vast amount of restaurant information and review data to search for restaurants that meet the user's needs. The reservation department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and selects the highest-rated restaurant from the listed restaurants and makes a reservation. The information department is implemented by, for example, the display 343 of the headset terminal 314 and sends an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The accounting department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and calculates the payment amount for all participants and sends invoices according to each person's payment method. The correspondence between each department and the devices and control units is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0175] Each of the multiple elements described above, including the reception, search, reservation, information, and accounting departments, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception department is implemented by the microphone 238 of the robot 414 and receives requests from the organizer. The search department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes a vast amount of restaurant information and review data to search for restaurants that meet the user's needs. The reservation department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the highest-rated restaurant from the listed restaurants and makes a reservation. The information department is implemented by, for example, the speaker 240 of the robot 414 and sends an information email containing information about the restaurant where the reservation has been completed, reservation details, meeting place, etc. The accounting department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the payment amount for all participants and sends invoices according to each person's payment method. The correspondence between each department and the devices and control units is not limited to the example described above and can be changed in various ways.

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

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception desk that takes requests, A search unit that searches for restaurants based on requests received by the reception unit, A reservation unit selects an appropriate restaurant from among those found by the search unit and makes a reservation. The information department sends out information based on the reservation completed by the aforementioned reservation department, The system includes an accounting department that makes payments based on the information sent by the aforementioned information department. A system characterized by the following features. (Note 2) The aforementioned search unit, By analyzing information and reviews from multiple restaurants, we search for restaurants that meet the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reservation section is, Select a suitable restaurant from the search results and make a reservation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is Based on the information confirming the reservation, we will send out instructions to the participants. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned accounting department, We will calculate the amount each participant has to pay and send them an invoice according to their respective payment method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It features a function to proactively gather user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the appropriate method for receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requests, we narrow them down based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and sets the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, we analyze the user's social media activity and accept related requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, we consider restaurant ratings and customer review data to improve the accuracy of search results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When a search is performed, the system applies an appropriate search algorithm by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, the search results will be displayed taking into account the geographical distribution of restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, we refer to relevant literature and data on restaurants to improve the accuracy of search results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the way reservations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, When making a reservation, the appropriate reservation method is selected considering the restaurant's availability and reservation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When a reservation is made, the appropriate reservation algorithm is applied by referring to the user's past reservation history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When making a reservation, select the appropriate reservation method considering the geographical distribution of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, When making a reservation, we refer to relevant literature and data on restaurants to improve the accuracy of the reservation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is The system estimates the user's emotions and adjusts the wording of the notification email based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When sending notification emails, the system will refer to the user's past notification history to select the appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When sending out notification emails, the system will narrow down the recipients based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is The system estimates the user's emotions and prioritizes notification emails based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When sending notification emails, the system prioritizes sending highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When sending out notification emails, we analyze the user's social media activity and send relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned accounting department, We estimate the user's emotions and adjust accounting methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned accounting department, During accounting, the system selects the appropriate accounting method by referring to the user's past payment history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned accounting department, At checkout, filters are applied based on the user's current status and payment method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned accounting department, It estimates user sentiment and determines accounting priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned accounting department, During accounting, the appropriate accounting method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned accounting department, At the time of accounting, we analyze the user's social media activity and provide relevant accounting methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The reception desk that takes requests, A search unit that searches for restaurants based on requests received by the reception unit, A reservation unit selects an appropriate restaurant from among those found by the search unit and makes a reservation. The information department sends out information based on the reservation completed by the aforementioned reservation department, The system includes an accounting department that makes payments based on the information sent by the aforementioned information department. A system characterized by the following features.

2. The aforementioned search unit, By analyzing information and reviews from multiple restaurants, we search for restaurants that meet the user's needs. The system according to feature 1.

3. The aforementioned reservation section is, Select a suitable restaurant from the search results and make a reservation. The system according to feature 1.

4. The aforementioned guide section is Based on the information confirming the reservation, we will send out instructions to the participants. The system according to feature 1.

5. The aforementioned accounting department, We will calculate the amount each participant has to pay and send them an invoice according to their respective payment method. The system according to feature 1.

6. The aforementioned reception unit is It features a function to proactively gather user feedback. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the appropriate method for receiving requests. The system according to feature 1.

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