system

Abstract

The system according to this embodiment aims to allow users to plan trips and business trips and obtain tickets without any hassle. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an acquisition unit. The reception unit receives information from the user. The generation unit analyzes the information received by the reception unit and generates a plan. The acquisition unit acquires a ticket based on the plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when making travel or business trip plans, the user has to manually collect information and obtain tickets, which is time-consuming.

[0005] The system according to the embodiment aims to enable the user to make travel or business trip plans and obtain tickets without taking much time and effort.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an acquisition unit. The reception unit receives information from the user. The generation unit analyzes the information received by the reception unit and generates a plan. The acquisition unit obtains tickets based on the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to plan trips and business trips and obtain tickets without any hassle. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An agent system according to an embodiment of the present invention is a system that proposes travel and business trip itineraries and obtains tickets. In this agent system, when a user informs the agent of multiple destinations and the duration of stay at each location, the user selects their favorite from several proposed plans, and the agent provides a one-stop service, including ticket acquisition. The agent supports ticketless services from various transportation companies, and by informing the itinerary creation agent of any desired changes via voice from a smartphone, ticket changes can be made at the destination without the user having to check timetables themselves. For example, a user informs the agent of multiple destinations and the duration of stay at each location. For instance, the user plans to stay at destination A for 3 hours, destination B for 2.5 hours, destination C for 3 hours, and destination D for 1.5 hours. This information is entered into the agent. Next, the agent collects travel times and proposes several itineraries visiting the destinations. The agent collects travel times and timetables for each mode of transport and considers the travel plans. For example, it collects train and airplane timetables, and if there are multiple destinations, it finds the optimal solution by permuting and combining the order of visits and travel times. When the user selects their favorite from several proposed plans, the agent provides a one-stop service, including ticket acquisition. The agent handles all transportation and hotel reservations in one place and supports ticketless services from various transportation companies. By using their smartphone to voice-inform the agent of any desired changes to their itinerary, users can modify their tickets at their destination without having to check timetables themselves. This system allows users to intuitively create travel or business trip plans and acquire tickets without complex operations. Furthermore, the agent interprets the user's voice and text requests, gathers transportation information from the web, and generates and proposes itineraries that take into account the length of stay. This saves time and effort for people who want to travel spontaneously or for business travelers. As a result, the agent system can automate the entire process of receiving user information, generating plans, and acquiring tickets.

[0029] The agent system according to this embodiment comprises a reception unit, a generation unit, and an acquisition unit. The reception unit receives information from the user. User information includes, but is not limited to, text, voice, and images. For example, the reception unit can receive information from the user by voice using a smartphone. The reception unit can also receive information from the user by text. Furthermore, the reception unit can receive information from the user by uploading images. For example, the reception unit uses speech recognition technology to convert the user's voice information into text. The text information is analyzed using natural language processing technology. Image information is analyzed using image analysis technology. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a plan. The generated plan includes, but is not limited to, travel plans and schedules. For example, the generation AI can generate an optimal travel plan based on the user's input information. The generation unit can also generate an optimal plan considering the user's schedule. Furthermore, the generation unit can generate a customized plan considering the user's preferences. For example, the generation unit uses a generation AI to analyze user input information and propose an optimal travel plan. The generation AI selects the most suitable modes of transportation and accommodations based on the user's input information. The generation AI generates a customized travel plan, taking into account the user's preferences. The acquisition unit retrieves tickets based on the plan generated by the generation unit. The acquired tickets include, but are not limited to, transportation tickets and event tickets. For example, the acquisition unit can acquire transportation tickets online. The acquisition unit can also acquire event tickets online. Furthermore, the acquisition unit can make accommodation reservations online. For example, the acquisition unit acquires transportation tickets online and provides them to the user. The acquisition unit acquires event tickets online and provides them to the user. The acquisition unit makes accommodation reservations online and provides them to the user.This allows the agent system according to the embodiment to automate a series of steps, including receiving user information, generating a plan, and obtaining a ticket.

[0030] The reception desk receives information from users. This information includes, but is not limited to, text, audio, and images. For example, users can input information by voice using their smartphones. Users can also input information by text. Furthermore, users can upload images to provide information. For example, the reception desk uses speech recognition technology to convert the user's voice information into text. The text information is analyzed using natural language processing technology. Image information is analyzed using image analysis technology. The reception desk enables users to input information using devices such as smartphones, tablets, and personal computers. In the case of voice input, speech recognition technology performs noise reduction and optimizes the voice model to convert the user's speech into text with high accuracy. In the case of text input, the text entered by the user is subjected to grammatical and semantic analysis using natural language processing technology to accurately grasp the user's intent. In the case of image input, image analysis technology performs object recognition and text extraction within the image to extract the necessary information from the image provided by the user. For example, to generate travel plans, the system can retrieve relevant information from user-provided photos of destinations or event posters. This allows the reception department to support diverse input formats and improve user convenience. Furthermore, the reception department can centrally manage user input information and smoothly hand it over to subsequent processing departments. This improves the overall efficiency and accuracy of the system.

[0031] The generation unit uses a generation AI to analyze information received by the reception unit and generate a plan. The generated plan may include, but is not limited to, travel plans and schedules. For example, the generation unit can have the generation AI generate the optimal travel plan based on the user's input information. The generation unit can also have the generation AI generate the optimal plan considering the user's schedule. Furthermore, the generation unit can have the generation AI generate a customized plan considering the user's preferences. For example, the generation unit has the generation AI analyze the user's input information and propose the optimal travel plan. The generation AI selects the optimal means of transportation and accommodation based on the user's input information. The generation AI generates a customized travel plan considering the user's preferences. The generation AI uses natural language processing technology to analyze the user's input information and understand the user's wishes and constraints. For example, if the user inputs "I want to go on a hot spring trip on a weekend next month," the generation AI will refer to the calendar information for next month and identify the weekend dates. Furthermore, it will list candidate hot spring resorts and select the optimal hot spring resort considering the user's past travel history and evaluations. The generating AI suggests the optimal mode of transportation from the user's current location to their destination, and selects accommodations that match the user's budget and preferences. The generating AI can also suggest restaurants and tourist spots, taking the user's preferences into account. For example, if the user wants to enjoy gourmet food, it will suggest popular local restaurants and cafes, and for tourist spots, it will list places that match the user's interests. This allows the generating unit to create optimal plans that meet the user's needs, thereby improving user satisfaction.

[0032] The acquisition unit acquires tickets based on the plan generated by the generation unit. The acquired tickets include, but are not limited to, transportation tickets and event tickets. For example, the acquisition unit acquires transportation tickets online. The acquisition unit can also acquire event tickets online. Furthermore, the acquisition unit can also make accommodation reservations online. For example, the acquisition unit acquires transportation tickets online and provides them to the user. The acquisition unit acquires event tickets online and provides them to the user. The acquisition unit makes accommodation reservations online and provides them to the user. The acquisition unit quickly and accurately acquires the necessary tickets and reservations based on the plan generated by the generation unit. For example, when acquiring airline tickets, it searches for the best flight and makes a reservation based on the user's desired date and time, departure point, and destination. The acquisition unit uses airline APIs to acquire real-time seat availability information and select the best seat. When acquiring event tickets, it acquires tickets from ticket sales sites based on the user's desired event date and time and location. The acquisition unit purchases tickets at the optimal time, taking into account the popularity of the event and ticket availability. When booking accommodations, the system searches for and reserves the most suitable accommodations based on the user's desired dates, budget, and preferences. The acquisition unit suggests the most suitable accommodations to the user, taking into account ratings and reviews. This allows the acquisition unit to save the user time and smoothly acquire tickets and reservations. Furthermore, the acquisition unit notifies the user of acquired tickets and reservation information and provides necessary information. This allows users to enjoy their trip or event with peace of mind.

[0033] The agent system includes a data collection unit that collects information on transportation. The data collection unit collects, for example, transportation schedules and fare information. The collected transportation information includes, but is not limited to, train, bus, and airplane schedules and fare information. The data collection unit can, for example, collect transportation schedules via the internet. It can also collect fare information from the official websites of transportation companies. Furthermore, the data collection unit can collect real-time operation information using transportation APIs. For example, the data collection unit collects train schedules via the internet and provides them to the user. The data collection unit collects bus fare information from official websites and provides it to the user. The data collection unit collects real-time airplane operation information using APIs and provides it to the user. This improves the accuracy of the plan by collecting transportation information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input transportation schedules into an AI, which can then suggest the most suitable mode of transportation.

[0034] The agent system includes a presentation unit that presents the generated plan to the user. The presentation unit presents the generated plan to the user, for example, using screen display or voice guidance. Specific methods and formats of presentation include, but are not limited to, displaying the plan on a smartphone screen or providing voice guidance. The presentation unit can, for example, display the generated plan on a smartphone screen. The presentation unit can also convey the generated plan to the user using voice guidance. Furthermore, the presentation unit can select the optimal presentation method depending on the user's device. For example, the presentation unit can display the plan on a smartphone screen, allowing the user to select it. The presentation unit can convey the plan to the user using voice guidance, allowing the user to select it by voice. The presentation unit selects the optimal presentation method considering the user's device information. This makes it easier for the user to select a plan by presenting it to them. Some or all of the above-described processes in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the generated plan into an AI, which can then suggest the optimal presentation method.

[0035] The agent system includes an interpreter that interprets the user's requests in voice or text. The interpreter interprets the user's requests using, for example, speech recognition technology or text analysis technology. Specific methods and criteria for interpretation include, but are not limited to, methods such as converting the user's voice to text using speech recognition technology and interpreting the requests using text analysis technology. The interpreter can, for example, convert the user's voice to text using speech recognition technology. The interpreter can also interpret the user's requests using text analysis technology. Furthermore, the interpreter can interpret user requests in real time. For example, the interpreter can convert the user's voice to text using speech recognition technology and interpret the requests using text analysis technology. The interpreter interprets user requests in real time and generates an optimal plan. This allows for the generation of more appropriate plans by interpreting user requests. Some or all of the above-described processes in the interpreter may be performed using, for example, AI, or not using AI. For example, the interpreter can input the user's voice into an AI, which can then interpret the requests.

[0036] The reception desk can analyze the user's past travel history and select the optimal information reception method. For example, the reception desk can suggest the optimal information reception method based on places the user has frequently visited in the past. The reception desk can also extract specific patterns from the user's past travel history and select the optimal reception method. Furthermore, the reception desk can analyze the user's past travel history and suggest a reception method suitable for a specific time of day. For example, the reception desk can suggest the optimal information reception method based on places the user has frequently visited in the past. The reception desk can extract specific patterns from the user's past travel history and select the optimal reception method. The reception desk can analyze the user's past travel history and suggest a reception method suitable for a specific time of day. In this way, the optimal information reception method can be selected by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI, which can then select the optimal information reception method.

[0037] The reception unit can filter information based on the user's current interests and preferences when receiving it. For example, the reception unit can filter information based on tourist destinations or events that the user is currently interested in. The reception unit can also prioritize receiving information of a specific genre according to the user's preferences. Furthermore, the reception unit can filter and receive relevant information based on the user's interests. For example, the reception unit can filter information based on tourist destinations or events that the user is currently interested in. The reception unit can prioritize receiving information of a specific genre according to the user's preferences. The reception unit can filter and receive relevant information based on the user's interests. This allows for the reception of more relevant information by filtering information based on the user's interests and preferences. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user interest and preference data into a generating AI, which can then filter the information.

[0038] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also prioritize receiving relevant information based on the user's travel plans. Furthermore, the reception unit can prioritize receiving optimal information based on the user's geographical location. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can prioritize receiving relevant information based on the user's travel plans. The reception unit can prioritize receiving optimal information based on the user's geographical location. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize receiving highly relevant information.

[0039] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information based on the user's social media follow and like history. Furthermore, the reception unit can analyze the user's social media activity patterns and receive optimal information. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can receive relevant information based on the user's social media follow and like history. The reception unit can analyze the user's social media activity patterns and receive optimal information. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can receive relevant information.

[0040] The generation unit can generate the optimal plan by referring to the user's past travel history when generating a plan. For example, the generation unit can generate the optimal plan based on places the user has visited in the past. The generation unit can also extract specific patterns from the user's past travel history and generate the optimal plan. Furthermore, the generation unit can analyze the user's past travel history and generate a plan suitable for a specific time of day. For example, the generation unit can generate the optimal plan based on places the user has visited in the past. The generation unit can extract specific patterns from the user's past travel history and generate the optimal plan. The generation unit can analyze the user's past travel history and generate a plan suitable for a specific time of day. In this way, the optimal plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate the optimal plan.

[0041] The generation unit can customize plans based on the user's current interests and preferences when generating them. For example, the generation unit can customize plans based on tourist destinations or events that the user is currently interested in. The generation unit can also prioritize generating plans of a specific genre to match the user's preferences. Furthermore, the generation unit can customize and generate relevant plans based on the user's interests. For example, the generation unit can customize plans based on tourist destinations or events that the user is currently interested in. The generation unit can prioritize generating plans of a specific genre to match the user's preferences. The generation unit can customize and generate relevant plans based on the user's interests. This allows for the generation of more relevant plans by customizing plans based on the user's interests and preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user interest and preference data into a generation AI, which can then customize the plans.

[0042] The generation unit can generate the optimal plan by considering the user's geographical location information during plan generation. For example, the generation unit can prioritize generating plans related to the user's current location. The generation unit can also prioritize generating relevant plans based on the user's travel plans. Furthermore, the generation unit can generate the optimal plan based on the user's geographical location information. For example, the generation unit can prioritize generating plans related to the user's current location. The generation unit can prioritize generating relevant plans based on the user's travel plans. The generation unit can generate the optimal plan based on the user's geographical location information. In this way, the optimal plan can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, and the generation AI can generate the optimal plan.

[0043] The generation unit can analyze a user's social media activity and generate relevant plans when generating plans. For example, the generation unit can analyze the content of a user's social media posts and generate relevant plans. The generation unit can also generate relevant plans based on a user's social media follow and like history. Furthermore, the generation unit can analyze a user's social media activity patterns and generate the optimal plan. For example, the generation unit can analyze the content of a user's social media posts and generate relevant plans. The generation unit can generate relevant plans based on a user's social media follow and like history. The generation unit can analyze a user's social media activity patterns and generate the optimal plan. In this way, relevant plans can be generated by analyzing a user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user social media activity data into a generation AI, which can then generate relevant plans.

[0044] The acquisition unit can select the optimal acquisition method by referring to the user's past travel history when acquiring tickets. For example, the acquisition unit can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. The acquisition unit can also extract specific patterns from the user's past travel history and select the optimal acquisition method. Furthermore, the acquisition unit can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. For example, the acquisition unit suggests the optimal ticket acquisition method based on the transportation methods the user has used in the past. The acquisition unit extracts specific patterns from the user's past travel history and selects the optimal acquisition method. The acquisition unit analyzes the user's past travel history and suggests an acquisition method suitable for a specific time of day. In this way, the optimal ticket acquisition method can be selected by referring to the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past travel history data into a generating AI, which can then select the optimal acquisition method.

[0045] The acquisition unit can customize the tickets it acquires based on the user's current interests and preferences. For example, it can customize tickets based on events or tourist destinations that the user is currently interested in. It can also prioritize acquiring tickets of a specific genre to match the user's preferences. Furthermore, it can customize and acquire relevant tickets based on the user's interests. For example, it can customize tickets based on events or tourist destinations that the user is currently interested in. It can prioritize acquiring tickets of a specific genre to match the user's preferences. It can customize and acquire relevant tickets based on the user's interests. This allows for the acquisition of more relevant tickets by customizing tickets based on the user's interests and preferences. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit can input user interest and preference data into a generating AI, which can then customize the tickets.

[0046] The acquisition unit can acquire the optimal ticket by considering the user's geographical location information when acquiring tickets. For example, the acquisition unit can prioritize acquiring tickets related to the user's current location. The acquisition unit can also prioritize acquiring tickets related to the user's travel plans. Furthermore, the acquisition unit can acquire the optimal ticket based on the user's geographical location information. For example, the acquisition unit can prioritize acquiring tickets related to the user's current location. The acquisition unit can prioritize acquiring tickets related to the user's travel plans. The acquisition unit can acquire the optimal ticket based on the user's geographical location information. In this way, the optimal ticket can be acquired by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, and the generating AI can acquire the optimal ticket.

[0047] The acquisition unit can analyze the user's social media activity when acquiring tickets and acquire relevant tickets. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant tickets. The acquisition unit can also acquire relevant tickets based on the user's social media follow and like history. Furthermore, the acquisition unit can analyze the user's social media activity patterns and acquire the most suitable tickets. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant tickets. The acquisition unit can acquire relevant tickets based on the user's social media follow and like history. The acquisition unit can analyze the user's social media activity patterns and acquire the most suitable tickets. In this way, relevant tickets can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant tickets.

[0048] The data collection unit can collect optimal information by referring to the user's past travel history when collecting transportation information. For example, the data collection unit collects optimal information based on the transportation methods the user has used in the past. The data collection unit can also extract specific patterns from the user's past travel history and collect optimal information. Furthermore, the data collection unit can analyze the user's past travel history and collect information suitable for specific time periods. For example, the data collection unit collects optimal information based on the transportation methods the user has used in the past. The data collection unit extracts specific patterns from the user's past travel history and collects optimal information. The data collection unit analyzes the user's past travel history and collects information suitable for specific time periods. In this way, optimal information can be collected by referring to the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can collect optimal information.

[0049] The data collection unit can customize information based on the user's current interests and preferences when collecting transportation information. For example, the data collection unit can customize information based on the transportation or routes the user is currently interested in. The data collection unit can also prioritize the collection of information in specific genres to match the user's preferences. Furthermore, the data collection unit can customize and collect relevant information based on the user's interests. For example, the data collection unit can customize information based on the transportation or routes the user is currently interested in. The data collection unit can prioritize the collection of information in specific genres to match the user's preferences. The data collection unit can customize and collect relevant information based on the user's interests. By customizing information based on the user's interests and preferences, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user interest and preference data into a generating AI, which can then customize the information.

[0050] The data collection unit can collect optimal information by considering the user's geographical location when collecting transportation information. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can also prioritize collecting relevant information based on the user's travel plans. Furthermore, the data collection unit can collect optimal information based on the user's geographical location. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can prioritize collecting relevant information based on the user's travel plans. The data collection unit can collect optimal information based on the user's geographical location. In this way, optimal information can be collected by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can collect optimal information.

[0051] The presentation unit can select the optimal presentation method by referring to the user's past travel history when presenting plans. For example, the presentation unit proposes the optimal presentation method based on plans the user has used in the past. The presentation unit can also extract specific patterns from the user's past travel history and select the optimal presentation method. Furthermore, the presentation unit can analyze the user's past travel history and propose a presentation method suitable for a specific time of day. For example, the presentation unit proposes the optimal presentation method based on plans the user has used in the past. The presentation unit extracts specific patterns from the user's past travel history and selects the optimal presentation method. The presentation unit analyzes the user's past travel history and proposes a presentation method suitable for a specific time of day. In this way, the optimal presentation method can be selected by referring to the user's past travel history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past travel history data into a generating AI, which can then select the optimal presentation method.

[0052] The presentation unit can customize plans based on the user's current interests and preferences when presenting them. For example, the presentation unit can customize plans based on tourist destinations or events that the user is currently interested in. The presentation unit can also prioritize presenting plans of a specific genre to match the user's preferences. Furthermore, the presentation unit can customize and present relevant plans based on the user's interests. For example, the presentation unit can customize plans based on tourist destinations or events that the user is currently interested in. The presentation unit can prioritize presenting plans of a specific genre to match the user's preferences. The presentation unit can customize and present relevant plans based on the user's interests. This allows for the presentation of more relevant plans by customizing them based on the user's interests and preferences. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can input user interest and preference data into a generating AI, which can then customize the plans.

[0053] The presentation unit can select the optimal presentation method when presenting a plan, taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit provides a presentation method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a presentation method optimized for a larger screen. Additionally, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible presentation method. This allows the optimal presentation method to be selected by considering the user's device information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's device information into a generating AI, which can then select the optimal presentation method.

[0054] The interpretation unit can select the optimal interpretation method by referring to the user's past request history when interpreting requests. For example, the interpretation unit proposes the optimal interpretation method based on requests previously submitted by the user. The interpretation unit can also extract specific patterns from the user's past request history and select the optimal interpretation method. Furthermore, the interpretation unit can analyze the user's past request history and propose an interpretation method suitable for a specific time period. For example, the interpretation unit proposes the optimal interpretation method based on requests previously submitted by the user. The interpretation unit extracts specific patterns from the user's past request history and selects the optimal interpretation method. The interpretation unit analyzes the user's past request history and proposes an interpretation method suitable for a specific time period. In this way, the optimal interpretation method can be selected by referring to the user's past request history. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the user's past request history data into a generating AI, which can then select the optimal interpretation method.

[0055] The interpretation unit can customize its interpretation of requests based on the user's current interests and preferences. For example, the interpretation unit can interpret requests based on what the user is currently interested in. The interpretation unit can also prioritize requests of a specific genre to match the user's preferences. Furthermore, the interpretation unit can customize and interpret related requests based on the user's interests. For example, the interpretation unit interprets requests based on what the user is currently interested in. The interpretation unit prioritizes requests of a specific genre to match the user's preferences. The interpretation unit customizes and interprets related requests based on the user's interests. This allows for more relevant interpretations by customizing the interpretation based on the user's interests and preferences. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input user interest and preference data into a generating AI, which can then customize the interpretation.

[0056] The interpretation unit can analyze the user's social media activity and interpret relevant requests when interpreting requests. For example, the interpretation unit can analyze the content of the user's social media posts and interpret relevant requests. The interpretation unit can also interpret relevant requests based on the user's social media follow and like history. Furthermore, the interpretation unit can analyze the user's social media activity patterns and interpret the most appropriate requests. For example, the interpretation unit can analyze the content of the user's social media posts and interpret relevant requests. The interpretation unit can interpret relevant requests based on the user's social media follow and like history. The interpretation unit can analyze the user's social media activity patterns and interpret the most appropriate requests. In this way, relevant requests can be interpreted by analyzing the user's social media activity. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the user's social media activity data into a generating AI, which can then interpret the relevant requests.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The agent system can be equipped with a health monitoring unit that monitors the user's health status. This unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if a user becomes unwell during a trip, the health monitoring unit can detect the abnormality and prompt the user to take appropriate action. The health monitoring unit can also adjust the travel plan based on the user's health status. For example, if the user is tired, it can suggest a plan that includes more rest time. Furthermore, the health monitoring unit can share the user's health data with medical institutions, enabling a rapid response in emergencies. This allows users to enjoy their trip with peace of mind.

[0059] The agent system can analyze a user's past travel history and propose the optimal travel plan. For example, it can generate a plan tailored to the user's preferences based on their ratings of places and accommodations they have visited in the past. It can also extract specific patterns from the user's past travel history and prioritize suggesting tourist destinations and activities that the user likes. Furthermore, it can analyze the user's past travel history and propose plans tailored to specific seasons or events. For example, it can generate a plan tailored to the user's preferences based on their ratings of places and accommodations they have visited in the past. It can extract specific patterns from the user's past travel history and prioritize suggesting tourist destinations and activities that the user likes. It can analyze the user's past travel history and propose plans tailored to specific seasons or events. In this way, it can leverage the user's past travel history to provide more personalized travel plans.

[0060] The agent system can customize travel plans based on the user's current interests and preferences. For example, it can generate plans based on tourist destinations and events the user is currently interested in. It can also prioritize suggesting tourist destinations and activities of a specific genre to match the user's preferences. Furthermore, it can customize and suggest relevant tourist destinations and activities based on the user's interests. For example, it can generate plans based on tourist destinations and events the user is currently interested in. It can prioritize suggesting tourist destinations and activities of a specific genre to match the user's preferences. It can customize and suggest relevant tourist destinations and activities based on the user's interests. This allows the system to provide more relevant travel plans based on the user's interests and preferences.

[0061] The agent system can propose optimal travel plans by considering the user's geographical location. For example, it can prioritize suggesting tourist destinations and activities related to the user's current location. It can also suggest relevant tourist destinations and activities based on the user's travel plans. Furthermore, it can suggest the optimal route and mode of transportation based on the user's geographical location. For example, it can prioritize suggesting tourist destinations and activities related to the user's current location. It can suggest relevant tourist destinations and activities based on the user's travel plans. It can suggest the optimal route and mode of transportation based on the user's geographical location. In this way, by considering the user's geographical location, it can provide more efficient and convenient travel plans.

[0062] The agent system can analyze a user's social media activity and suggest relevant travel plans. For example, it can analyze a user's social media posts and suggest tourist destinations and activities that the user might be interested in. It can also suggest relevant tourist destinations and activities based on a user's social media following and liking history. Furthermore, it can analyze a user's social media activity patterns and suggest the optimal travel plan. For example, it can analyze a user's social media posts and suggest tourist destinations and activities that the user might be interested in. It can suggest relevant tourist destinations and activities based on a user's social media following and liking history. It can analyze a user's social media activity patterns and suggest the optimal travel plan. This allows the system to leverage a user's social media activity to provide more personalized travel plans.

[0063] The agent system can select the optimal ticket acquisition method by referring to the user's past travel history. For example, it can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. It can also extract specific patterns from the user's past travel history and select the optimal acquisition method. Furthermore, it can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. For example, it can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. It can extract specific patterns from the user's past travel history and select the optimal acquisition method. It can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. In this way, the optimal ticket acquisition method can be selected by referring to the user's past travel history.

[0064] The agent system can customize tickets based on the user's current interests and preferences. For example, it can customize tickets based on events or tourist destinations the user is currently interested in. It can also prioritize retrieving tickets of specific genres to match the user's preferences. Furthermore, it can customize and retrieve relevant tickets based on the user's interests. For example, it can customize tickets based on events or tourist destinations the user is currently interested in. It can prioritize retrieving tickets of specific genres to match the user's preferences. It can customize and retrieve relevant tickets based on the user's interests. This allows for the retrieval of more relevant tickets by customizing tickets based on the user's interests and preferences.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives information from the user. This information includes text, audio, and images. For example, a user can input information by voice using their smartphone, and this information is converted to text using speech recognition technology. The text information is analyzed using natural language processing technology, and the image information is analyzed using image analysis technology. Step 2: The generation unit analyzes the information received by the reception unit and generates a plan. The generated plan includes travel plans and schedules. The generation AI generates the optimal travel plan based on the user's input information and creates a customized plan that takes into account the user's schedule and preferences. Step 3: The acquisition unit acquires tickets based on the plan generated by the generation unit. The acquired tickets include transportation tickets and event tickets. The acquisition unit acquires transportation tickets and event tickets online and provides them to the user. It also makes accommodation reservations online and provides them to the user.

[0067] (Example of form 2) An agent system according to an embodiment of the present invention is a system that proposes travel and business trip itineraries and obtains tickets. In this agent system, when a user informs the agent of multiple destinations and the duration of stay at each location, the user selects their favorite from several proposed plans, and the agent provides a one-stop service, including ticket acquisition. The agent supports ticketless services from various transportation companies, and by informing the itinerary creation agent of any desired changes via voice from a smartphone, ticket changes can be made at the destination without the user having to check timetables themselves. For example, a user informs the agent of multiple destinations and the duration of stay at each location. For instance, the user plans to stay at destination A for 3 hours, destination B for 2.5 hours, destination C for 3 hours, and destination D for 1.5 hours. This information is entered into the agent. Next, the agent collects travel times and proposes several itineraries visiting the destinations. The agent collects travel times and timetables for each mode of transport and considers the travel plans. For example, it collects train and airplane timetables, and if there are multiple destinations, it finds the optimal solution by permuting and combining the order of visits and travel times. When the user selects their favorite from several proposed plans, the agent provides a one-stop service, including ticket acquisition. The agent handles all transportation and hotel reservations in one place and supports ticketless services from various transportation companies. By using their smartphone to voice-inform the agent of any desired changes to their itinerary, users can modify their tickets at their destination without having to check timetables themselves. This system allows users to intuitively create travel or business trip plans and acquire tickets without complex operations. Furthermore, the agent interprets the user's voice and text requests, gathers transportation information from the web, and generates and proposes itineraries that take into account the length of stay. This saves time and effort for people who want to travel spontaneously or for business travelers. As a result, the agent system can automate the entire process of receiving user information, generating plans, and acquiring tickets.

[0068] The agent system according to this embodiment comprises a reception unit, a generation unit, and an acquisition unit. The reception unit receives information from the user. User information includes, but is not limited to, text, voice, and images. For example, the reception unit can receive information from the user by voice using a smartphone. The reception unit can also receive information from the user by text. Furthermore, the reception unit can receive information from the user by uploading images. For example, the reception unit uses speech recognition technology to convert the user's voice information into text. The text information is analyzed using natural language processing technology. Image information is analyzed using image analysis technology. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a plan. The generated plan includes, but is not limited to, travel plans and schedules. For example, the generation AI can generate an optimal travel plan based on the user's input information. The generation unit can also generate an optimal plan considering the user's schedule. Furthermore, the generation unit can generate a customized plan considering the user's preferences. For example, the generation unit uses a generation AI to analyze user input information and propose an optimal travel plan. The generation AI selects the most suitable modes of transportation and accommodations based on the user's input information. The generation AI generates a customized travel plan, taking into account the user's preferences. The acquisition unit retrieves tickets based on the plan generated by the generation unit. The acquired tickets include, but are not limited to, transportation tickets and event tickets. For example, the acquisition unit can acquire transportation tickets online. The acquisition unit can also acquire event tickets online. Furthermore, the acquisition unit can make accommodation reservations online. For example, the acquisition unit acquires transportation tickets online and provides them to the user. The acquisition unit acquires event tickets online and provides them to the user. The acquisition unit makes accommodation reservations online and provides them to the user.This allows the agent system according to the embodiment to automate a series of steps, including receiving user information, generating a plan, and obtaining a ticket.

[0069] The reception desk receives information from users. This information includes, but is not limited to, text, audio, and images. For example, users can input information by voice using their smartphones. Users can also input information by text. Furthermore, users can upload images to provide information. For example, the reception desk uses speech recognition technology to convert the user's voice information into text. The text information is analyzed using natural language processing technology. Image information is analyzed using image analysis technology. The reception desk enables users to input information using devices such as smartphones, tablets, and personal computers. In the case of voice input, speech recognition technology performs noise reduction and optimizes the voice model to convert the user's speech into text with high accuracy. In the case of text input, the text entered by the user is subjected to grammatical and semantic analysis using natural language processing technology to accurately grasp the user's intent. In the case of image input, image analysis technology performs object recognition and text extraction within the image to extract the necessary information from the image provided by the user. For example, to generate travel plans, the system can retrieve relevant information from user-provided photos of destinations or event posters. This allows the reception department to support diverse input formats and improve user convenience. Furthermore, the reception department can centrally manage user input information and smoothly hand it over to subsequent processing departments. This improves the overall efficiency and accuracy of the system.

[0070] The generation unit uses a generation AI to analyze information received by the reception unit and generate a plan. The generated plan may include, but is not limited to, travel plans and schedules. For example, the generation unit can have the generation AI generate the optimal travel plan based on the user's input information. The generation unit can also have the generation AI generate the optimal plan considering the user's schedule. Furthermore, the generation unit can have the generation AI generate a customized plan considering the user's preferences. For example, the generation unit has the generation AI analyze the user's input information and propose the optimal travel plan. The generation AI selects the optimal means of transportation and accommodation based on the user's input information. The generation AI generates a customized travel plan considering the user's preferences. The generation AI uses natural language processing technology to analyze the user's input information and understand the user's wishes and constraints. For example, if the user inputs "I want to go on a hot spring trip on a weekend next month," the generation AI will refer to the calendar information for next month and identify the weekend dates. Furthermore, it will list candidate hot spring resorts and select the optimal hot spring resort considering the user's past travel history and evaluations. The generating AI suggests the optimal mode of transportation from the user's current location to their destination, and selects accommodations that match the user's budget and preferences. The generating AI can also suggest restaurants and tourist spots, taking the user's preferences into account. For example, if the user wants to enjoy gourmet food, it will suggest popular local restaurants and cafes, and for tourist spots, it will list places that match the user's interests. This allows the generating unit to create optimal plans that meet the user's needs, thereby improving user satisfaction.

[0071] The acquisition unit acquires tickets based on the plan generated by the generation unit. The acquired tickets include, but are not limited to, transportation tickets and event tickets. For example, the acquisition unit acquires transportation tickets online. The acquisition unit can also acquire event tickets online. Furthermore, the acquisition unit can also make accommodation reservations online. For example, the acquisition unit acquires transportation tickets online and provides them to the user. The acquisition unit acquires event tickets online and provides them to the user. The acquisition unit makes accommodation reservations online and provides them to the user. The acquisition unit quickly and accurately acquires the necessary tickets and reservations based on the plan generated by the generation unit. For example, when acquiring airline tickets, it searches for the best flight and makes a reservation based on the user's desired date and time, departure point, and destination. The acquisition unit uses airline APIs to acquire real-time seat availability information and select the best seat. When acquiring event tickets, it acquires tickets from ticket sales sites based on the user's desired event date and time and location. The acquisition unit purchases tickets at the optimal time, taking into account the popularity of the event and ticket availability. When booking accommodations, the system searches for and reserves the most suitable accommodations based on the user's desired dates, budget, and preferences. The acquisition unit suggests the most suitable accommodations to the user, taking into account ratings and reviews. This allows the acquisition unit to save the user time and smoothly acquire tickets and reservations. Furthermore, the acquisition unit notifies the user of acquired tickets and reservation information and provides necessary information. This allows users to enjoy their trip or event with peace of mind.

[0072] The agent system includes a data collection unit that collects information on transportation. The data collection unit collects, for example, transportation schedules and fare information. The collected transportation information includes, but is not limited to, train, bus, and airplane schedules and fare information. The data collection unit can, for example, collect transportation schedules via the internet. It can also collect fare information from the official websites of transportation companies. Furthermore, the data collection unit can collect real-time operation information using transportation APIs. For example, the data collection unit collects train schedules via the internet and provides them to the user. The data collection unit collects bus fare information from official websites and provides it to the user. The data collection unit collects real-time airplane operation information using APIs and provides it to the user. This improves the accuracy of the plan by collecting transportation information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input transportation schedules into an AI, which can then suggest the most suitable mode of transportation.

[0073] The agent system includes a presentation unit that presents the generated plan to the user. The presentation unit presents the generated plan to the user, for example, using screen display or voice guidance. Specific methods and formats of presentation include, but are not limited to, displaying the plan on a smartphone screen or providing voice guidance. The presentation unit can, for example, display the generated plan on a smartphone screen. The presentation unit can also convey the generated plan to the user using voice guidance. Furthermore, the presentation unit can select the optimal presentation method depending on the user's device. For example, the presentation unit can display the plan on a smartphone screen, allowing the user to select it. The presentation unit can convey the plan to the user using voice guidance, allowing the user to select it by voice. The presentation unit selects the optimal presentation method considering the user's device information. This makes it easier for the user to select a plan by presenting it to them. Some or all of the above-described processes in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the generated plan into an AI, which can then suggest the optimal presentation method.

[0074] The agent system includes an interpreter that interprets the user's requests in voice or text. The interpreter interprets the user's requests using, for example, speech recognition technology or text analysis technology. Specific methods and criteria for interpretation include, but are not limited to, methods such as converting the user's voice to text using speech recognition technology and interpreting the requests using text analysis technology. The interpreter can, for example, convert the user's voice to text using speech recognition technology. The interpreter can also interpret the user's requests using text analysis technology. Furthermore, the interpreter can interpret user requests in real time. For example, the interpreter can convert the user's voice to text using speech recognition technology and interpret the requests using text analysis technology. The interpreter interprets user requests in real time and generates an optimal plan. This allows for the generation of more appropriate plans by interpreting user requests. Some or all of the above-described processes in the interpreter may be performed using, for example, AI, or not using AI. For example, the interpreter can input the user's voice into an AI, which can then interpret the requests.

[0075] The reception system can estimate the user's emotions and adjust how information is received based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to ensure quick information reception. This allows for more appropriate information reception by adjusting how information is received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of receiving the information.

[0076] The reception desk can analyze the user's past travel history and select the optimal information reception method. For example, the reception desk can suggest the optimal information reception method based on places the user has frequently visited in the past. The reception desk can also extract specific patterns from the user's past travel history and select the optimal reception method. Furthermore, the reception desk can analyze the user's past travel history and suggest a reception method suitable for a specific time of day. For example, the reception desk can suggest the optimal information reception method based on places the user has frequently visited in the past. The reception desk can extract specific patterns from the user's past travel history and select the optimal reception method. The reception desk can analyze the user's past travel history and suggest a reception method suitable for a specific time of day. In this way, the optimal information reception method can be selected by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI, which can then select the optimal information reception method.

[0077] The reception unit can filter information based on the user's current interests and preferences when receiving it. For example, the reception unit can filter information based on tourist destinations or events that the user is currently interested in. The reception unit can also prioritize receiving information of a specific genre according to the user's preferences. Furthermore, the reception unit can filter and receive relevant information based on the user's interests. For example, the reception unit can filter information based on tourist destinations or events that the user is currently interested in. The reception unit can prioritize receiving information of a specific genre according to the user's preferences. The reception unit can filter and receive relevant information based on the user's interests. This allows for the reception of more relevant information by filtering information based on the user's interests and preferences. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user interest and preference data into a generating AI, which can then filter the information.

[0078] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. If the user is relaxed, the reception desk may also prioritize receiving detailed information. Furthermore, if the user is in a hurry, the reception desk may also prioritize receiving information that requires a quick response. For example, if the user is stressed, the reception desk will prioritize receiving important information. If the user is relaxed, the reception desk will prioritize receiving detailed information. If the user is in a hurry, the reception desk will prioritize receiving information that requires a quick response. This allows for the priority of receiving important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the information.

[0079] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also prioritize receiving relevant information based on the user's travel plans. Furthermore, the reception unit can prioritize receiving optimal information based on the user's geographical location. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can prioritize receiving relevant information based on the user's travel plans. The reception unit can prioritize receiving optimal information based on the user's geographical location. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI, which can then prioritize receiving highly relevant information.

[0080] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information based on the user's social media follow and like history. Furthermore, the reception unit can analyze the user's social media activity patterns and receive optimal information. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can receive relevant information based on the user's social media follow and like history. The reception unit can analyze the user's social media activity patterns and receive optimal information. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can receive relevant information.

[0081] The generation unit can estimate the user's emotions and adjust the plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a plan with visually stimulating effects. For example, if the user is relaxed, the generation unit will generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit will generate a plan that emphasizes the shortest route. If the user is excited, the generation unit will generate a plan with visually stimulating effects. This allows for the generation of more appropriate plans by adjusting the plan generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotion and adjust the plan generation method.

[0082] The generation unit can generate the optimal plan by referring to the user's past travel history when generating a plan. For example, the generation unit can generate the optimal plan based on places the user has visited in the past. The generation unit can also extract specific patterns from the user's past travel history and generate the optimal plan. Furthermore, the generation unit can analyze the user's past travel history and generate a plan suitable for a specific time of day. For example, the generation unit can generate the optimal plan based on places the user has visited in the past. The generation unit can extract specific patterns from the user's past travel history and generate the optimal plan. The generation unit can analyze the user's past travel history and generate a plan suitable for a specific time of day. In this way, the optimal plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate the optimal plan.

[0083] The generation unit can customize plans based on the user's current interests and preferences when generating them. For example, the generation unit can customize plans based on tourist destinations or events that the user is currently interested in. The generation unit can also prioritize generating plans of a specific genre to match the user's preferences. Furthermore, the generation unit can customize and generate relevant plans based on the user's interests. For example, the generation unit can customize plans based on tourist destinations or events that the user is currently interested in. The generation unit can prioritize generating plans of a specific genre to match the user's preferences. The generation unit can customize and generate relevant plans based on the user's interests. This allows for the generation of more relevant plans by customizing plans based on the user's interests and preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user interest and preference data into a generation AI, which can then customize the plans.

[0084] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important plans. It can also prioritize generating detailed plans if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can prioritize generating plans that require immediate attention. This allows for the prioritization of important plans by determining the priority of plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotion and determine the priority of the plan.

[0085] The generation unit can generate the optimal plan by considering the user's geographical location information during plan generation. For example, the generation unit can prioritize generating plans related to the user's current location. The generation unit can also prioritize generating relevant plans based on the user's travel plans. Furthermore, the generation unit can generate the optimal plan based on the user's geographical location information. For example, the generation unit can prioritize generating plans related to the user's current location. The generation unit can prioritize generating relevant plans based on the user's travel plans. The generation unit can generate the optimal plan based on the user's geographical location information. In this way, the optimal plan can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI, and the generation AI can generate the optimal plan.

[0086] The generation unit can analyze a user's social media activity and generate relevant plans when generating plans. For example, the generation unit can analyze the content of a user's social media posts and generate relevant plans. The generation unit can also generate relevant plans based on a user's social media follow and like history. Furthermore, the generation unit can analyze a user's social media activity patterns and generate the optimal plan. For example, the generation unit can analyze the content of a user's social media posts and generate relevant plans. The generation unit can generate relevant plans based on a user's social media follow and like history. The generation unit can analyze a user's social media activity patterns and generate the optimal plan. In this way, relevant plans can be generated by analyzing a user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user social media activity data into a generation AI, which can then generate relevant plans.

[0087] The acquisition unit can estimate the user's emotions and adjust the ticket acquisition method based on the estimated emotions. For example, if the user is stressed, the acquisition unit can provide a simple interface and minimize the ticket acquisition procedure. If the user is relaxed, the acquisition unit can also provide detailed ticket acquisition options and suggest a customizable acquisition method. Furthermore, if the user is in a hurry, the acquisition unit can prioritize voice input to enable quick ticket acquisition. For example, if the acquisition unit is stressed, it can provide a simple interface and minimize the ticket acquisition procedure. If the user is relaxed, it can provide detailed ticket acquisition options and suggest a customizable acquisition method. If the user is in a hurry, it can prioritize voice input to enable quick ticket acquisition. This allows for more appropriate ticket acquisition by adjusting the ticket acquisition method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the ticket acquisition method.

[0088] The acquisition unit can select the optimal acquisition method by referring to the user's past travel history when acquiring tickets. For example, the acquisition unit can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. The acquisition unit can also extract specific patterns from the user's past travel history and select the optimal acquisition method. Furthermore, the acquisition unit can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. For example, the acquisition unit suggests the optimal ticket acquisition method based on the transportation methods the user has used in the past. The acquisition unit extracts specific patterns from the user's past travel history and selects the optimal acquisition method. The acquisition unit analyzes the user's past travel history and suggests an acquisition method suitable for a specific time of day. In this way, the optimal ticket acquisition method can be selected by referring to the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past travel history data into a generating AI, which can then select the optimal acquisition method.

[0089] The acquisition unit can customize the tickets it acquires based on the user's current interests and preferences. For example, it can customize tickets based on events or tourist destinations that the user is currently interested in. It can also prioritize acquiring tickets of a specific genre to match the user's preferences. Furthermore, it can customize and acquire relevant tickets based on the user's interests. For example, it can customize tickets based on events or tourist destinations that the user is currently interested in. It can prioritize acquiring tickets of a specific genre to match the user's preferences. It can customize and acquire relevant tickets based on the user's interests. This allows for the acquisition of more relevant tickets by customizing tickets based on the user's interests and preferences. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit can input user interest and preference data into a generating AI, which can then customize the tickets.

[0090] The acquisition unit can acquire the optimal ticket by considering the user's geographical location information when acquiring tickets. For example, the acquisition unit can prioritize acquiring tickets related to the user's current location. The acquisition unit can also prioritize acquiring tickets related to the user's travel plans. Furthermore, the acquisition unit can acquire the optimal ticket based on the user's geographical location information. For example, the acquisition unit can prioritize acquiring tickets related to the user's current location. The acquisition unit can prioritize acquiring tickets related to the user's travel plans. The acquisition unit can acquire the optimal ticket based on the user's geographical location information. In this way, the optimal ticket can be acquired by considering the user's geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, and the generating AI can acquire the optimal ticket.

[0091] The acquisition unit can analyze the user's social media activity when acquiring tickets and acquire relevant tickets. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant tickets. The acquisition unit can also acquire relevant tickets based on the user's social media follow and like history. Furthermore, the acquisition unit can analyze the user's social media activity patterns and acquire the most suitable tickets. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant tickets. The acquisition unit can acquire relevant tickets based on the user's social media follow and like history. The acquisition unit can analyze the user's social media activity patterns and acquire the most suitable tickets. In this way, relevant tickets can be acquired by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant tickets.

[0092] The data collection unit can estimate the user's emotions and adjust the method of collecting transportation information based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple interface and minimize the collection procedure. If the user is relaxed, the data collection unit can also provide detailed collection options and suggest a customizable collection method. Furthermore, if the user is in a hurry, the data collection unit can prioritize voice input to enable faster information collection. For example, if the user is stressed, the data collection unit can provide a simple interface and minimize the collection procedure. If the user is relaxed, the data collection unit can provide detailed collection options and suggest a customizable collection method. If the user is in a hurry, the data collection unit can prioritize voice input to enable faster information collection. This allows for more appropriate information collection by adjusting the method of collecting transportation information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of collecting transportation information.

[0093] The data collection unit can collect optimal information by referring to the user's past travel history when collecting transportation information. For example, the data collection unit collects optimal information based on the transportation methods the user has used in the past. The data collection unit can also extract specific patterns from the user's past travel history and collect optimal information. Furthermore, the data collection unit can analyze the user's past travel history and collect information suitable for specific time periods. For example, the data collection unit collects optimal information based on the transportation methods the user has used in the past. The data collection unit extracts specific patterns from the user's past travel history and collects optimal information. The data collection unit analyzes the user's past travel history and collects information suitable for specific time periods. In this way, optimal information can be collected by referring to the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can collect optimal information.

[0094] The data collection unit can customize information based on the user's current interests and preferences when collecting transportation information. For example, the data collection unit can customize information based on the transportation or routes the user is currently interested in. The data collection unit can also prioritize the collection of information in specific genres to match the user's preferences. Furthermore, the data collection unit can customize and collect relevant information based on the user's interests. For example, the data collection unit can customize information based on the transportation or routes the user is currently interested in. The data collection unit can prioritize the collection of information in specific genres to match the user's preferences. The data collection unit can customize and collect relevant information based on the user's interests. By customizing information based on the user's interests and preferences, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user interest and preference data into a generating AI, which can then customize the information.

[0095] The data collection unit can estimate the user's emotions and determine the priority of transportation information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit can also prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that requires immediate attention. For example, if the user is stressed, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit will prioritize collecting detailed information. If the user is in a hurry, the data collection unit will prioritize collecting information that requires immediate attention. This allows for the priority collection of important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the information.

[0096] The data collection unit can collect optimal information by considering the user's geographical location when collecting transportation information. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can also prioritize collecting relevant information based on the user's travel plans. Furthermore, the data collection unit can collect optimal information based on the user's geographical location. For example, the data collection unit can prioritize collecting information related to the user's current location. The data collection unit can prioritize collecting relevant information based on the user's travel plans. The data collection unit can collect optimal information based on the user's geographical location. In this way, optimal information can be collected by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, and the generating AI can collect optimal information.

[0097] The presentation unit can estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is stressed, the presentation unit can provide a simple interface and minimize the presentation steps. If the user is relaxed, the presentation unit can also provide detailed presentation options and suggest a customizable presentation method. Furthermore, if the user is in a hurry, the presentation unit can prioritize voice input to quickly present the plan. For example, if the user is stressed, the presentation unit can provide a simple interface and minimize the presentation steps. If the user is relaxed, the presentation unit can provide detailed presentation options and suggest a customizable presentation method. If the user is in a hurry, the presentation unit can prioritize voice input to quickly present the plan. This allows for more appropriate plan presentation by adjusting the presentation method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of presenting the plan.

[0098] The presentation unit can select the optimal presentation method by referring to the user's past travel history when presenting plans. For example, the presentation unit proposes the optimal presentation method based on plans the user has used in the past. The presentation unit can also extract specific patterns from the user's past travel history and select the optimal presentation method. Furthermore, the presentation unit can analyze the user's past travel history and propose a presentation method suitable for a specific time of day. For example, the presentation unit proposes the optimal presentation method based on plans the user has used in the past. The presentation unit extracts specific patterns from the user's past travel history and selects the optimal presentation method. The presentation unit analyzes the user's past travel history and proposes a presentation method suitable for a specific time of day. In this way, the optimal presentation method can be selected by referring to the user's past travel history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past travel history data into a generating AI, which can then select the optimal presentation method.

[0099] The presentation unit can customize plans based on the user's current interests and preferences when presenting them. For example, the presentation unit can customize plans based on tourist destinations or events that the user is currently interested in. The presentation unit can also prioritize presenting plans of a specific genre to match the user's preferences. Furthermore, the presentation unit can customize and present relevant plans based on the user's interests. For example, the presentation unit can customize plans based on tourist destinations or events that the user is currently interested in. The presentation unit can prioritize presenting plans of a specific genre to match the user's preferences. The presentation unit can customize and present relevant plans based on the user's interests. This allows for the presentation of more relevant plans by customizing them based on the user's interests and preferences. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can input user interest and preference data into a generating AI, which can then customize the plans.

[0100] The presentation unit can estimate the user's emotions and adjust the order in which plans are presented based on the estimated emotions. For example, if the user is stressed, the presentation unit will prioritize presenting important plans. If the user is relaxed, the presentation unit can also prioritize presenting detailed plans. Furthermore, if the user is in a hurry, the presentation unit can prioritize presenting plans that require immediate attention. For example, if the user is stressed, the presentation unit will prioritize presenting important plans. If the user is relaxed, the presentation unit will prioritize presenting detailed plans. If the user is in a hurry, the presentation unit will prioritize presenting plans that require immediate attention. This allows for the prioritization of important plans by adjusting the order in which plans are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the order in which the plans are presented.

[0101] The presentation unit can select the optimal presentation method when presenting a plan, taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit provides a presentation method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can provide a presentation method optimized for a larger screen. Additionally, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible presentation method. This allows the optimal presentation method to be selected by considering the user's device information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's device information into a generating AI, which can then select the optimal presentation method.

[0102] The interpreter can estimate the user's emotions and adjust how requests are interpreted based on those emotions. For example, if the user is stressed, the interpreter can provide a simple interface and minimize the interpretation process. If the user is relaxed, the interpreter can also provide detailed interpretation options and suggest customizable interpretation methods. Furthermore, if the user is in a hurry, the interpreter can prioritize voice input to enable quick interpretation of requests. For example, if the user is stressed, the interpreter can provide a simple interface and minimize the interpretation process. If the user is relaxed, the interpreter can provide detailed interpretation options and suggest customizable interpretation methods. If the user is in a hurry, the interpreter can prioritize voice input to enable quick interpretation of requests. This allows for more appropriate request interpretation by adjusting how requests are interpreted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of interpreting the request.

[0103] The interpretation unit can select the optimal interpretation method by referring to the user's past request history when interpreting requests. For example, the interpretation unit proposes the optimal interpretation method based on requests previously submitted by the user. The interpretation unit can also extract specific patterns from the user's past request history and select the optimal interpretation method. Furthermore, the interpretation unit can analyze the user's past request history and propose an interpretation method suitable for a specific time period. For example, the interpretation unit proposes the optimal interpretation method based on requests previously submitted by the user. The interpretation unit extracts specific patterns from the user's past request history and selects the optimal interpretation method. The interpretation unit analyzes the user's past request history and proposes an interpretation method suitable for a specific time period. In this way, the optimal interpretation method can be selected by referring to the user's past request history. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the user's past request history data into a generating AI, which can then select the optimal interpretation method.

[0104] The interpretation unit can customize its interpretation of requests based on the user's current interests and preferences. For example, the interpretation unit can interpret requests based on what the user is currently interested in. The interpretation unit can also prioritize requests of a specific genre to match the user's preferences. Furthermore, the interpretation unit can customize and interpret related requests based on the user's interests. For example, the interpretation unit interprets requests based on what the user is currently interested in. The interpretation unit prioritizes requests of a specific genre to match the user's preferences. The interpretation unit customizes and interprets related requests based on the user's interests. This allows for more relevant interpretations by customizing the interpretation based on the user's interests and preferences. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input user interest and preference data into a generating AI, which can then customize the interpretation.

[0105] The interpreter can estimate the user's emotions and prioritize requests based on those emotions. For example, if the user is stressed, the interpreter will prioritize important requests. If the user is relaxed, the interpreter can also prioritize detailed requests. Furthermore, if the user is in a hurry, the interpreter can prioritize requests that require a quick response. For example, if the user is stressed, the interpreter will prioritize important requests. If the user is relaxed, the interpreter will prioritize detailed requests. If the user is in a hurry, the interpreter will prioritize requests that require a quick response. This allows for prioritizing important requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interpreter may be performed using AI, for example, or without AI. For example, the interpretation unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the requests.

[0106] The interpretation unit can analyze the user's social media activity and interpret relevant requests when interpreting requests. For example, the interpretation unit can analyze the content of the user's social media posts and interpret relevant requests. The interpretation unit can also interpret relevant requests based on the user's social media follow and like history. Furthermore, the interpretation unit can analyze the user's social media activity patterns and interpret the most appropriate requests. For example, the interpretation unit can analyze the content of the user's social media posts and interpret relevant requests. The interpretation unit can interpret relevant requests based on the user's social media follow and like history. The interpretation unit can analyze the user's social media activity patterns and interpret the most appropriate requests. In this way, relevant requests can be interpreted by analyzing the user's social media activity. Some or all of the above processing in the interpretation unit may be performed using AI, for example, or without AI. For example, the interpretation unit can input the user's social media activity data into a generating AI, which can then interpret the relevant requests.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The agent system can be equipped with a health monitoring unit that monitors the user's health status. This unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and monitors the user's health in real time. For example, if a user becomes unwell during a trip, the health monitoring unit can detect the abnormality and prompt the user to take appropriate action. The health monitoring unit can also adjust the travel plan based on the user's health status. For example, if the user is tired, it can suggest a plan that includes more rest time. Furthermore, the health monitoring unit can share the user's health data with medical institutions, enabling a rapid response in emergencies. This allows users to enjoy their trip with peace of mind.

[0109] The agent system can estimate the user's emotions and customize travel plans based on those emotions. For example, if the user is excited, it can suggest a plan that includes many active activities. If the user is relaxed, it can prioritize suggesting relaxing tourist destinations and accommodations. Furthermore, if the user is stressed, it can suggest relaxation plans to alleviate stress. In this way, the system can provide the optimal travel plan tailored to the user's emotions.

[0110] The agent system can analyze a user's past travel history and propose the optimal travel plan. For example, it can generate a plan tailored to the user's preferences based on their ratings of places and accommodations they have visited in the past. It can also extract specific patterns from the user's past travel history and prioritize suggesting tourist destinations and activities that the user likes. Furthermore, it can analyze the user's past travel history and propose plans tailored to specific seasons or events. For example, it can generate a plan tailored to the user's preferences based on their ratings of places and accommodations they have visited in the past. It can extract specific patterns from the user's past travel history and prioritize suggesting tourist destinations and activities that the user likes. It can analyze the user's past travel history and propose plans tailored to specific seasons or events. In this way, it can leverage the user's past travel history to provide more personalized travel plans.

[0111] The agent system can customize travel plans based on the user's current interests and preferences. For example, it can generate plans based on tourist destinations and events the user is currently interested in. It can also prioritize suggesting tourist destinations and activities of a specific genre to match the user's preferences. Furthermore, it can customize and suggest relevant tourist destinations and activities based on the user's interests. For example, it can generate plans based on tourist destinations and events the user is currently interested in. It can prioritize suggesting tourist destinations and activities of a specific genre to match the user's preferences. It can customize and suggest relevant tourist destinations and activities based on the user's interests. This allows the system to provide more relevant travel plans based on the user's interests and preferences.

[0112] The agent system can estimate the user's emotions and prioritize travel plans based on those emotions. For example, if the user is stressed, it can prioritize suggesting relaxing tourist destinations and activities. If the user is relaxed, it can prioritize suggesting active activities. Furthermore, if the user is in a hurry, it can prioritize suggesting plans that require quick attention. By prioritizing travel plans according to the user's emotions, the system can provide more appropriate travel plans.

[0113] The agent system can propose optimal travel plans by considering the user's geographical location. For example, it can prioritize suggesting tourist destinations and activities related to the user's current location. It can also suggest relevant tourist destinations and activities based on the user's travel plans. Furthermore, it can suggest the optimal route and mode of transportation based on the user's geographical location. For example, it can prioritize suggesting tourist destinations and activities related to the user's current location. It can suggest relevant tourist destinations and activities based on the user's travel plans. It can suggest the optimal route and mode of transportation based on the user's geographical location. In this way, by considering the user's geographical location, it can provide more efficient and convenient travel plans.

[0114] The agent system can analyze a user's social media activity and suggest relevant travel plans. For example, it can analyze a user's social media posts and suggest tourist destinations and activities that the user might be interested in. It can also suggest relevant tourist destinations and activities based on a user's social media following and liking history. Furthermore, it can analyze a user's social media activity patterns and suggest the optimal travel plan. For example, it can analyze a user's social media posts and suggest tourist destinations and activities that the user might be interested in. It can suggest relevant tourist destinations and activities based on a user's social media following and liking history. It can analyze a user's social media activity patterns and suggest the optimal travel plan. This allows the system to leverage a user's social media activity to provide more personalized travel plans.

[0115] The agent system can estimate the user's emotions and adjust the ticket acquisition method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the ticket acquisition process. If the user is relaxed, it can provide detailed ticket acquisition options and suggest a customizable acquisition method. Furthermore, if the user is in a hurry, it can prioritize voice input to enable quick ticket acquisition. This allows for more appropriate ticket acquisition by adjusting the ticket acquisition method according to the user's emotions.

[0116] The agent system can select the optimal ticket acquisition method by referring to the user's past travel history. For example, it can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. It can also extract specific patterns from the user's past travel history and select the optimal acquisition method. Furthermore, it can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. For example, it can suggest the optimal ticket acquisition method based on the transportation methods the user has used in the past. It can extract specific patterns from the user's past travel history and select the optimal acquisition method. It can analyze the user's past travel history and suggest an acquisition method suitable for a specific time of day. In this way, the optimal ticket acquisition method can be selected by referring to the user's past travel history.

[0117] The agent system can customize tickets based on the user's current interests and preferences. For example, it can customize tickets based on events or tourist destinations the user is currently interested in. It can also prioritize retrieving tickets of specific genres to match the user's preferences. Furthermore, it can customize and retrieve relevant tickets based on the user's interests. For example, it can customize tickets based on events or tourist destinations the user is currently interested in. It can prioritize retrieving tickets of specific genres to match the user's preferences. It can customize and retrieve relevant tickets based on the user's interests. This allows for the retrieval of more relevant tickets by customizing tickets based on the user's interests and preferences.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk receives information from the user. This information includes text, audio, and images. For example, a user can input information by voice using their smartphone, and this information is converted to text using speech recognition technology. The text information is analyzed using natural language processing technology, and the image information is analyzed using image analysis technology. Step 2: The generation unit analyzes the information received by the reception unit and generates a plan. The generated plan includes travel plans and schedules. The generation AI generates the optimal travel plan based on the user's input information and creates a customized plan that takes into account the user's schedule and preferences. Step 3: The acquisition unit acquires tickets based on the plan generated by the generation unit. The acquired tickets include transportation tickets and event tickets. The acquisition unit acquires transportation tickets and event tickets online and provides them to the user. It also makes accommodation reservations online and provides them to the user.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, generation unit, acquisition unit, collection unit, presentation unit, and interpretation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts the user's voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an optimal travel plan using generation AI. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12 and obtains tickets online. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects transportation schedules and fare information. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the generated plan to the user via screen display or voice guidance. The interpretation unit is implemented by the specific processing unit 290 of the data processing device 12 and interprets the user's requests using speech recognition technology and text analysis technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, generation unit, acquisition unit, collection unit, presentation unit, and interpretation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts the user's voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains tickets online. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects transportation schedules and fare information. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents the generated plan to the user via screen display or voice guidance. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and interprets the user's requests using speech recognition technology and text analysis technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, generation unit, acquisition unit, collection unit, presentation unit, and interpretation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts the user's voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains tickets online. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects transportation schedules and fare information. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents the generated plan to the user via screen display or voice guidance. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and interprets the user's requests using speech recognition technology and text analysis technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, generation unit, acquisition unit, collection unit, presentation unit, and interpretation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts voice and text input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains tickets online. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects transportation schedules and fare information. The presentation unit is implemented by the control unit 46A of the robot 414 and presents the generated plan to the user via screen display or voice guidance. The interpretation unit is implemented by the specific processing unit 290 of the data processing unit 12 and interprets the user's requests using speech recognition technology and text analysis technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A reception desk that receives information from users, A generation unit analyzes the information received by the reception unit and generates a plan, An acquisition unit that acquires tickets based on the plan generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a collection unit that collects information on transportation. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a display unit that presents the generated plan to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an interpreter that interprets user requests in voice or text. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past travel history to select the most suitable method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We estimate the user's emotions and adjust the plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a plan, customize it based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a plan, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a plan, the system analyzes the user's social media activity and generates a relevant plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, We estimate the user's emotions and adjust the ticket acquisition method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, When acquiring tickets, the system selects the most suitable acquisition method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, When acquiring tickets, the system customizes the tickets acquired based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The acquisition unit is, When obtaining a ticket, the system takes the user's geographical location into consideration to select the most suitable ticket. The system described in Appendix 1, characterized by the features described herein. (Note 21) The acquisition unit is, When acquiring tickets, the system analyzes the user's social media activity and retrieves relevant tickets. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting transportation information based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting transportation information, we refer to the user's past travel history to collect the most relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting transportation information, the information is customized based on the user's current interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned collection unit is It estimates the user's emotions and prioritizes the transportation information to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting transportation information, the system takes the user's geographical location into consideration to collect the most relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned display unit is, It estimates the user's emotions and adjusts how the plan is presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting a plan, the system selects the most suitable presentation method by referring to the user's past travel history. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting a plan, customize it based on the user's current interests and preferences. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned display unit is, It estimates the user's emotions and adjusts the order in which plans are presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned display unit is, When presenting a plan, the optimal presentation method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned interpretation section is: It estimates the user's emotions and adjusts how requests are interpreted based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned interpretation section is: When interpreting a request, the system selects the most appropriate interpretation method by referring to the user's past request history. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned interpretation section is: When interpreting requests, the interpretation is customized based on the user's current interests and preferences. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned interpretation section is: It estimates the user's emotions and prioritizes requests based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned interpretation section is: When interpreting requests, we analyze the user's social media activity and interpret relevant requests. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, A generation unit analyzes the information received by the reception unit and generates a plan, An acquisition unit that acquires tickets based on the plan generated by the generation unit, Equipped with A system characterized by the following features.

2. It is equipped with a collection unit that collects information on transportation. The system according to feature 1.

3. It includes a display unit that presents the generated plan to the user. The system according to feature 1.

4. It includes an interpreter that interprets user requests in voice or text. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past travel history to select the most suitable method for receiving information. The system according to feature 1.

7. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

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