system

Abstract

The system according to the embodiment aims to propose an itinerary and schedule and automatically make necessary reservations simply by inputting the place the user wants to go. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, and a linking unit. The reception unit accepts input from a user of places the user would like to go. The proposal unit proposes an itinerary or schedule based on the information accepted by the reception unit. The linking unit secures tickets or makes accommodation reservations in conjunction with the websites of various transportation services or hotels based on the itinerary proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when planning a trip or event, users had to check multiple websites individually and make reservations manually, which was a time-consuming process.

[0005] The system according to the embodiment aims to propose an itinerary and schedule and automatically make necessary reservations simply by inputting the place the user wants to go. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a linking unit. The reception unit accepts input of destinations from a user. The proposal unit proposes an itinerary or schedule based on the information accepted by the reception unit. The linking unit secures tickets or makes accommodation reservations in conjunction with the websites of various transportation services or hotels based on the itinerary proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose itineraries and schedules and automatically make necessary reservations simply by inputting the place the user wants to go. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention proposes an itinerary and schedule based on a user's desired destination, and coordinates with the websites of various transportation providers and hotels to secure tickets, make accommodation reservations, and reserve rental cars. This system can be applied not only to travel but also to planning for drinking parties, golf outings, and other events. For example, when a user informs the system of a desired destination, the system proposes an itinerary and schedule based on the user's input. Furthermore, the system coordinates with the websites of various transportation providers and hotels to secure tickets, make accommodation reservations, and reserve rental cars. This system can handle not only travel but also events such as drinking parties and golf outings. The user simply needs to inform the system of the desired destination and event, and the system handles all the necessary arrangements, saving time and effort and enabling efficient planning. This allows the travel planning system to automatically perform everything from proposing an itinerary and schedule to securing tickets and reserving accommodation, simply by providing the user with the desired destination.

[0029] A travel planning system according to an embodiment includes a reception unit, a proposal unit, and an interlocking unit. The reception unit accepts input of desired destinations from a user. For example, the user can input the desired destination in text format. The reception unit can also accept voice input. For example, when the user vocally describes the desired destination, the input can be converted into text using voice recognition technology. The proposal unit proposes an itinerary and schedule based on the information accepted by the reception unit. For example, the proposal unit calculates the optimal means of transportation and travel time based on the user's input and determines the order of visits. The proposal unit can also propose an itinerary customized according to the user's preferences. The interlocking unit interlocks with the websites of various transportation facilities and hotels based on the itinerary proposed by the proposal unit to secure tickets and make hotel reservations. For example, the interlocking unit interlocks with the reservation sites of various transportation facilities and hotels using API integration to automatically secure tickets and make hotel reservations. The interlocking unit can also obtain necessary information from the reservation sites using scraping technology and perform manual operations. As a result, the travel planning system according to the embodiment can automatically carry out everything from proposing itineraries and schedules to securing tickets and booking accommodation, simply by the user telling it where they want to go.

[0030] The interlocking unit may include a reservation unit that reserves transportation tickets. The reservation unit reserves tickets for, for example, airplanes, trains, buses, etc. For example, the reservation unit may link with an airline reservation site to reserve airplane tickets. The reservation unit may also link with a railway company reservation site to reserve train tickets. The reservation unit may also link with a bus company reservation site to reserve bus tickets. By adding a function for reserving transportation tickets, travel arrangements become smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may use an AI model to select the optimal ticket based on user input and make a reservation.

[0031] The interlocking unit may include a reservation unit that makes accommodation reservations. The reservation unit makes reservations for accommodations such as hotels, private lodgings, and guesthouses. For example, the reservation unit may be linked to a hotel reservation site to make hotel reservations. The reservation unit may also be linked to a private lodging reservation site to make private lodging reservations. The reservation unit may also be linked to a guesthouse reservation site to make guesthouse reservations. By adding a function for making accommodation reservations, travel arrangements become smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may use an AI model to select the most suitable accommodation based on user input and make a reservation.

[0032] The interlocking unit may include a reservation unit that reserves a rental car. The reservation unit, for example, links with a rental car company's reservation site to reserve a rental car. For example, the reservation unit links with a rental car company's reservation site to reserve a rental car. The reservation unit can also select and reserve the most suitable rental car based on user input. This adds the ability to reserve a rental car, making travel arrangements smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can use an AI model to select and reserve the most suitable rental car based on user input.

[0033] The proposal unit may include a drinking party unit that arranges drinking parties. The drinking party unit, for example, selects a location for the drinking party, coordinates participants, and sets a budget. For example, the drinking party unit selects a location for the drinking party and makes a reservation. The drinking party unit can also coordinate participants and send notifications to them. The drinking party unit can also set a budget and propose a drinking party plan based on the budget. By adding a function for arranging drinking parties, it becomes possible to plan events other than travel. Some or all of the above-described processing in the drinking party unit may be performed using, for example, AI, or may be performed without using AI. For example, the drinking party unit can use an AI model to propose an optimal drinking party plan based on user input and make the arrangements.

[0034] The suggestion unit may include a golf unit that arranges golf events. The golf unit, for example, selects a golf course, adjusts playing time, and adjusts participants. For example, the golf unit selects a golf course and makes a reservation. The golf unit can also adjust playing time and send notifications to participants. The golf unit can also adjust participants and send notifications to participants. By adding a function for arranging golf events, it becomes possible to arrange events other than travel. Some or all of the above-described processing in the golf unit may be performed using, for example, AI, or may be performed without using AI. For example, the golf unit can use an AI model to propose and arrange an optimal golf plan based on user input.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates destinations that the user has frequently input in the past. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input method.

[0036] When a user inputs a desired destination, the reception unit can present input candidates based on the user's current interests and concerns. The reception unit presents related destination candidates based on, for example, places or events recently searched for by the user. For example, the reception unit can analyze posts from accounts the user follows on social media to suggest related destination candidates. The reception unit can also present destination candidates related to products or services the user recently purchased. This allows for more appropriate suggestions by presenting input candidates based on the user's interests and concerns. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into a generation AI and cause the generation AI to present input candidates.

[0037] When the user inputs a desired destination, the reception unit can prioritize presenting highly relevant locations by taking into account the user's geographical location information. The reception unit, for example, can prioritize displaying locations close to the user's current location as candidates. For example, the reception unit can also prioritize suggesting locations the user wants to visit that are close to locations the user has visited in the past. The reception unit can also prioritize presenting locations with good transportation access from the user's current location. This allows more relevant locations to be proposed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant locations.

[0038] When a user inputs a desired destination, the reception unit can analyze the user's social media activity and suggest related places. The reception unit, for example, displays places that the user has "liked" or commented on on social media as candidates. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest related destinations. The reception unit can also present destinations related to places the user has shared on social media as candidates. In this way, more relevant places can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related places.

[0039] When making a proposal, the suggestion unit can analyze the user's past travel history to propose an optimal itinerary. The suggestion unit can, for example, propose related itineraries based on places the user has visited in the past. For example, the suggestion unit can analyze the user's preferred travel style from the user's past travel history and propose an optimal itinerary. The suggestion unit can also propose an optimal itinerary based on the transportation methods and accommodations the user has used in the past. In this way, by analyzing the user's past travel history, a more appropriate itinerary can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past travel history data into the generation AI and cause the generation AI to propose an optimal itinerary.

[0040] When proposing an itinerary, the suggestion unit can customize itineraries based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest relaxing itineraries based on the user's current work situation. For example, the suggestion unit can also suggest related itineraries based on the user's current hobbies and areas of interest. The suggestion unit can also suggest optimal itineraries based on the user's family composition and lifestyle. This enables more appropriate suggestions by customizing the itinerary based on the user's current living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's current living situation and area of ​​interest data into the generation AI and have the generation AI customize the itinerary.

[0041] When proposing, the suggestion unit can propose an optimal itinerary taking into account the user's geographical location information. For example, the suggestion unit prioritizes incorporating places close to the user's current location into the itinerary. For example, the suggestion unit can also incorporate places the user wants to visit that are close to places the user has previously visited into the itinerary. The suggestion unit can also incorporate places the user has good transportation access from their current location into the itinerary. In this way, a more appropriate itinerary can be proposed by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to propose an optimal itinerary.

[0042] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related itineraries. The suggestion unit, for example, incorporates places where the user has "liked" or commented on social media into the itinerary. For example, the suggestion unit can analyze the content of posts from accounts the user follows and suggest related itineraries. The suggestion unit can also incorporate itineraries related to places the user has shared on social media into the itinerary. In this way, by analyzing the user's social media activity, more appropriate itineraries can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related itineraries.

[0043] During linking, the interlocking unit can analyze the user's past reservation history and select the optimal website. The interlocking unit, for example, selects the optimal website based on websites the user has used in the past. For example, the interlocking unit can analyze the user's preferred websites from the user's past reservation history and select the optimal website. The interlocking unit can also select the optimal website based on the user's evaluations of websites they have used in the past. This allows a more appropriate website to be selected by analyzing the user's past reservation history. Some or all of the above-described processing in the interlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the interlocking unit can input the user's past reservation history data into the generation AI and cause the generation AI to select the optimal website.

[0044] During linking, the linking unit can customize the linked website based on the user's current living situation. For example, the linking unit selects a website that allows the user to relax based on their current work situation. For example, the linking unit can select related websites based on the user's current hobbies and areas of interest. The linking unit can also select the optimal website based on the user's family structure and lifestyle. This enables more appropriate linking by customizing the website based on the user's current living situation. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the website.

[0045] During linking, the linking unit can select the optimal website by taking into account the user's geographical location information. For example, the linking unit preferentially selects websites to link that are close to the user's current location. For example, the linking unit can select websites to link that are close to places the user has visited in the past. The linking unit can also select websites to link that are easily accessible from the user's current location. This allows a more appropriate website to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal website.

[0046] During linking, the linking unit can analyze the user's social media activity and select related websites. For example, the linking unit selects websites where the user has "liked" or commented on social media as linked websites. For example, the linking unit can analyze the content posted by accounts the user follows and select related websites. The linking unit can also select websites where the user has shared on social media as linked websites. This allows for the selection of more appropriate websites by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's social media activity data into the generation AI and cause the generation AI to select related websites.

[0047] When reserving a ticket, the reservation unit can analyze the user's past travel history and select the optimal reservation method. The reservation unit selects the optimal ticket reservation method, for example, based on the transportation means the user has used in the past. For example, the reservation unit can analyze the user's preferred ticket reservation method from the user's past travel history and select the optimal method. The reservation unit can also select the optimal reservation method based on the user's evaluation of tickets used in the past. In this way, by analyzing the user's past travel history, a more appropriate ticket reservation method can be selected. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal reservation method.

[0048] When reserving a ticket, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit, for example, prioritizes reserving tickets that are close to the user's current location. For example, the reservation unit can also prioritize reserving tickets that are close to places the user has visited in the past. The reservation unit can also prioritize reserving tickets that are close to places the user has visited in the past. In addition, the reservation unit can prioritize reserving tickets that are close to places that the user has good transportation access from their current location. This allows a more appropriate ticket reservation method to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reservation unit may be performed using, or without, AI, for example. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal reservation method.

[0049] When making a hotel reservation, the reservation unit (accommodation) can analyze the user's past accommodation history and select the optimal reservation method. The reservation unit (accommodation), for example, selects the optimal hotel reservation method based on accommodation facilities used by the user in the past. For example, the reservation unit (accommodation) can analyze the user's preferred hotel reservation method from the user's past accommodation history and select the optimal method. The reservation unit (accommodation) can also select the optimal reservation method based on the user's evaluations of accommodation facilities used in the past. In this way, by analyzing the user's past accommodation history, a more appropriate hotel reservation method can be selected. Some or all of the above-described processing in the reservation unit (accommodation) may be performed using, or without, AI, for example. For example, the reservation unit (accommodation) can input the user's past accommodation history data into a generation AI and have the generation AI select the optimal reservation method.

[0050] When making a hotel reservation, the reservation unit (accommodation) can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit (accommodation), for example, prioritizes reserving accommodations close to the user's current location. For example, the reservation unit (accommodation) can also prioritize reserving accommodations close to places the user has visited in the past. The reservation unit (accommodation) can also prioritize reserving accommodations with good transportation access from the user's current location. This allows a more appropriate hotel reservation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit (accommodation) may be performed using, or without, AI, for example. For example, the reservation unit (accommodation) can input the user's geographical location information into the generation AI and have the generation AI select the optimal reservation method.

[0051] When reserving a rental car, the reservation unit (rental car) can analyze the user's past rental car usage history and select the optimal reservation method. The reservation unit (rental car) selects the optimal rental car reservation method, for example, based on the rental car companies the user has used in the past. For example, the reservation unit (rental car) can analyze the user's preferred rental car reservation method from the user's past rental car usage history and select the optimal method. The reservation unit (rental car) can also select the optimal reservation method based on the user's evaluations of rental car companies used in the past. In this way, by analyzing the user's past rental car usage history, a more appropriate rental car reservation method can be selected. Some or all of the above-mentioned processing in the reservation unit (rental car) can be performed, for example, using AI, or can be performed without using AI. For example, the reservation unit (rental car) can input the user's past rental car usage history data into a generation AI and have the generation AI select the optimal reservation method.

[0052] When reserving a rental car, the reservation unit (rental car) can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit (rental car), for example, prioritizes reserving rental car companies that are close to the user's current location. For example, the reservation unit (rental car) can also prioritize reserving rental car companies that are close to places the user has visited in the past. The reservation unit (rental car) can also prioritize reserving rental car companies that have good transportation access from the user's current location. This allows a more appropriate rental car reservation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit (rental car) may be performed using, or without, AI, for example. For example, the reservation unit (rental car) can input the user's geographical location information into a generation AI and have the generation AI select the optimal reservation method.

[0053] When arranging a drinking party, the drinking party unit can analyze the user's past drinking party history and select the optimal arrangement method. The drinking party unit selects the optimal arrangement method, for example, based on drinking party locations the user has used in the past. For example, the drinking party unit can analyze the user's preferred arrangement methods from the user's past drinking party history and select the optimal method. The drinking party unit can also select the optimal arrangement method based on the user's evaluation of drinking party locations the user has used in the past. In this way, a more appropriate arrangement method can be selected by analyzing the user's past drinking party history. Some or all of the above-described processing in the drinking party unit may be performed using, for example, AI, or may be performed without using AI. For example, the drinking party unit can input the user's past drinking party history data into a generation AI and have the generation AI select the optimal arrangement method.

[0054] When arranging a drinking party, the drinking party unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the drinking party unit prioritizes arranging a drinking party location that is close to the user's current location. For example, the drinking party unit can also prioritize arranging a drinking party location that is close to a place the user has visited in the past. The drinking party unit can also prioritize arranging a drinking party location that is easily accessible from the user's current location. In this way, a more appropriate drinking party arrangement method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the drinking party unit may be performed using, or without, AI, for example. For example, the drinking party unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal arrangement method.

[0055] When preparing for golf, the golf club can analyze the user's past golf history and select the optimal preparation method. The golf club selects the optimal preparation method based on, for example, golf courses the user has used in the past. For example, the golf club can analyze the user's preferred preparation method from the user's past golf history and select the optimal method. The golf club can also select the optimal preparation method based on the user's evaluation of golf courses the user has used in the past. In this way, a more appropriate preparation method can be selected by analyzing the user's past golf history. Some or all of the above-described processing in the golf club may be performed using, for example, AI, or may be performed without using AI. For example, the golf club can input the user's past golf history data into a generation AI and have the generation AI select the optimal preparation method.

[0056] When preparing for a golf game, the golf club can select the optimal preparation method by taking into account the user's geographical location information. For example, the golf club may prioritize preparing golf courses that are close to the user's current location. For example, the golf club may prioritize preparing golf courses that are close to places the user has visited in the past. The golf club may also prioritize preparing golf courses that are easily accessible from the user's current location. In this way, a more appropriate golf preparation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the golf club may be performed using, or without, AI, for example. For example, the golf club may input the user's geographical location information into a generation AI and cause the generation AI to select the optimal preparation method.

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

[0058] The suggestion unit can monitor the user's health condition and adjust the itinerary and schedule based on the health condition. For example, if the user is tired, the suggestion unit can suggest an itinerary that includes many rest periods. Also, if the user is in good health, the suggestion unit can suggest an itinerary that includes many active activities. Furthermore, if the user has a specific health problem, the suggestion unit can suggest an itinerary that takes the problem into consideration. This makes it possible to provide an optimal travel plan based on the user's health condition.

[0059] The interlocking unit can select optimal tickets and accommodations taking into account the user's budget. For example, if the user has a low budget, the interlocking unit can suggest cost-effective options. Alternatively, if the user has a high budget, the interlocking unit can suggest more luxurious options. Furthermore, the interlocking unit can apply specific discounts and promotions depending on the user's budget. This allows the user to be provided with an optimal travel plan that suits their budget.

[0060] The reception unit can customize the input interface based on the user's language setting. For example, if the user selects English, the reception unit can provide an English interface. Also, if the user selects Japanese, the reception unit can provide a Japanese interface. Furthermore, if the user uses multiple languages, the reception unit can support input in multiple languages. This makes it possible to provide an optimal input interface according to the user's language setting.

[0061] The suggestion unit can customize the itinerary and schedule based on the user's travel purpose. For example, if the user's travel purpose is sightseeing, the suggestion unit can suggest an itinerary that includes many tourist spots. If the user's travel purpose is business, the suggestion unit can also suggest an itinerary that takes business meetings and conferences into consideration. Furthermore, if the user's travel purpose is relaxation, the suggestion unit can also suggest an itinerary that includes many relaxing places and activities. This makes it possible to provide an optimal travel plan that matches the user's travel purpose.

[0062] The interlocking unit can suggest optimal options taking into consideration the user's preferred brands and services. For example, if the user prefers a particular hotel chain, the interlocking unit can preferentially suggest accommodations from that hotel chain. Also, if the user prefers a particular airline, the interlocking unit can preferentially suggest tickets from that airline. Furthermore, if the user prefers a particular rental car company, the interlocking unit can preferentially suggest vehicles from that rental car company. This makes it possible to provide an optimal travel plan based on the user's preferences.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives input of a destination from the user. For example, the user can input the destination in text format. The reception unit can also receive voice input. For example, when the user speaks the destination, it can be converted into text using voice recognition technology. Step 2: The suggestion unit proposes an itinerary or schedule based on the information received by the reception unit. For example, the suggestion unit calculates the optimal means of transportation and travel time based on the user's input and determines the order of visits. The suggestion unit can also propose a customized itinerary based on the user's preferences. Step 3: The interlocking unit secures tickets and makes accommodation reservations by interlocking with the websites of each transportation company and hotel based on the itinerary proposed by the proposal unit. For example, the interlocking unit uses API integration to connect with the reservation sites of each transportation company and hotel, automatically securing tickets and making accommodation reservations. The interlocking unit can also use scraping technology to obtain necessary information from the reservation sites and perform manual operations.

[0065] (Example 2) A travel planning system according to an embodiment of the present invention proposes an itinerary and schedule based on a user's desired destination, and coordinates with the websites of various transportation providers and hotels to secure tickets, make accommodation reservations, and reserve rental cars. This system can be applied not only to travel but also to planning for drinking parties, golf outings, and other events. For example, when a user informs the system of a desired destination, the system proposes an itinerary and schedule based on the user's input. Furthermore, the system coordinates with the websites of various transportation providers and hotels to secure tickets, make accommodation reservations, and reserve rental cars. This system can handle not only travel but also events such as drinking parties and golf outings. The user simply needs to inform the system of the desired destination and event, and the system handles all the necessary arrangements, saving time and effort and enabling efficient planning. This allows the travel planning system to automatically perform everything from proposing an itinerary and schedule to securing tickets and reserving accommodation, simply by providing the user with the desired destination.

[0066] A travel planning system according to an embodiment includes a reception unit, a proposal unit, and an interlocking unit. The reception unit accepts input of desired destinations from a user. For example, the user can input the desired destination in text format. The reception unit can also accept voice input. For example, when the user vocally describes the desired destination, the input can be converted into text using voice recognition technology. The proposal unit proposes an itinerary and schedule based on the information accepted by the reception unit. For example, the proposal unit calculates the optimal means of transportation and travel time based on the user's input and determines the order of visits. The proposal unit can also propose an itinerary customized according to the user's preferences. The interlocking unit interlocks with the websites of various transportation facilities and hotels based on the itinerary proposed by the proposal unit to secure tickets and make hotel reservations. For example, the interlocking unit interlocks with the reservation sites of various transportation facilities and hotels using API integration to automatically secure tickets and make hotel reservations. The interlocking unit can also obtain necessary information from the reservation sites using scraping technology and perform manual operations. As a result, the travel planning system according to the embodiment can automatically carry out everything from proposing itineraries and schedules to securing tickets and booking accommodation, simply by the user telling it where they want to go.

[0067] The interlocking unit may include a reservation unit that reserves transportation tickets. The reservation unit reserves tickets for, for example, airplanes, trains, buses, etc. For example, the reservation unit may link with an airline reservation site to reserve airplane tickets. The reservation unit may also link with a railway company reservation site to reserve train tickets. The reservation unit may also link with a bus company reservation site to reserve bus tickets. By adding a function for reserving transportation tickets, travel arrangements become smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may use an AI model to select the optimal ticket based on user input and make a reservation.

[0068] The interlocking unit may include a reservation unit that makes accommodation reservations. The reservation unit makes reservations for accommodations such as hotels, private lodgings, and guesthouses. For example, the reservation unit may be linked to a hotel reservation site to make hotel reservations. The reservation unit may also be linked to a private lodging reservation site to make private lodging reservations. The reservation unit may also be linked to a guesthouse reservation site to make guesthouse reservations. By adding a function for making accommodation reservations, travel arrangements become smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may use an AI model to select the most suitable accommodation based on user input and make a reservation.

[0069] The interlocking unit may include a reservation unit that reserves a rental car. The reservation unit, for example, links with a rental car company's reservation site to reserve a rental car. For example, the reservation unit links with a rental car company's reservation site to reserve a rental car. The reservation unit can also select and reserve the most suitable rental car based on user input. This adds the ability to reserve a rental car, making travel arrangements smoother. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can use an AI model to select and reserve the most suitable rental car based on user input.

[0070] The proposal unit may include a drinking party unit that arranges drinking parties. The drinking party unit, for example, selects a location for the drinking party, coordinates participants, and sets a budget. For example, the drinking party unit selects a location for the drinking party and makes a reservation. The drinking party unit can also coordinate participants and send notifications to them. The drinking party unit can also set a budget and propose a drinking party plan based on the budget. By adding a function for arranging drinking parties, it becomes possible to plan events other than travel. Some or all of the above-described processing in the drinking party unit may be performed using, for example, AI, or may be performed without using AI. For example, the drinking party unit can use an AI model to propose an optimal drinking party plan based on user input and make the arrangements.

[0071] The suggestion unit may include a golf unit that arranges golf events. The golf unit, for example, selects a golf course, adjusts playing time, and adjusts participants. For example, the golf unit selects a golf course and makes a reservation. The golf unit can also adjust playing time and send notifications to participants. The golf unit can also adjust participants and send notifications to participants. By adding a function for arranging golf events, it becomes possible to arrange events other than travel. Some or all of the above-described processing in the golf unit may be performed using, for example, AI, or may be performed without using AI. For example, the golf unit can use an AI model to propose and arrange an optimal golf plan based on user input.

[0072] The reception unit can estimate the user's emotions and adjust the input method for the destination based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the destination. This adjusts the input method according to the user's emotions, providing a more comfortable user experience. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates destinations that the user has frequently input in the past. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. In this way, by analyzing the past input history, the optimal input method can be suggested to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to a generation AI and have the generation AI suggest the optimal input method.

[0074] When a user inputs a desired destination, the reception unit can present input candidates based on the user's current interests and concerns. The reception unit presents related destination candidates based on, for example, places or events recently searched for by the user. For example, the reception unit can analyze posts from accounts the user follows on social media to suggest related destination candidates. The reception unit can also present destination candidates related to products or services the user recently purchased. This allows for more appropriate suggestions by presenting input candidates based on the user's interests and concerns. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into a generation AI and cause the generation AI to present input candidates.

[0075] The reception unit can estimate the user's emotions and prioritize input based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying important input items and postpone displaying other items. For example, if the user is relaxed, the reception unit can display all input items evenly, allowing the user to select freely. Furthermore, if the user is in a hurry, the reception unit can display only the most important input items, allowing the user to complete input quickly. This enables more efficient input by prioritizing input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0076] When the user inputs a desired destination, the reception unit can prioritize presenting highly relevant locations by taking into account the user's geographical location information. The reception unit, for example, can prioritize displaying locations close to the user's current location as candidates. For example, the reception unit can also prioritize suggesting locations the user wants to visit that are close to locations the user has visited in the past. The reception unit can also prioritize presenting locations with good transportation access from the user's current location. This allows more relevant locations to be proposed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant locations.

[0077] When a user inputs a desired destination, the reception unit can analyze the user's social media activity and suggest related places. The reception unit, for example, displays places that the user has "liked" or commented on on social media as candidates. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest related destinations. The reception unit can also present destinations related to places the user has shared on social media as candidates. In this way, more relevant places can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related places.

[0078] The suggestion unit can estimate the user's emotions and adjust the method of proposing an itinerary or schedule based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest a leisurely itinerary. For example, if the user is in a hurry, the suggestion unit can suggest an efficient schedule. Furthermore, if the user is excited, the suggestion unit can suggest an active itinerary. This enables more appropriate suggestions by adjusting the method of proposing an itinerary or schedule according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of proposing an itinerary or schedule.

[0079] When making a proposal, the suggestion unit can analyze the user's past travel history to propose an optimal itinerary. The suggestion unit can, for example, propose related itineraries based on places the user has visited in the past. For example, the suggestion unit can analyze the user's preferred travel style from the user's past travel history and propose an optimal itinerary. The suggestion unit can also propose an optimal itinerary based on the transportation methods and accommodations the user has used in the past. In this way, by analyzing the user's past travel history, a more appropriate itinerary can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past travel history data into the generation AI and cause the generation AI to propose an optimal itinerary.

[0080] When proposing an itinerary, the suggestion unit can customize itineraries based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest relaxing itineraries based on the user's current work situation. For example, the suggestion unit can also suggest related itineraries based on the user's current hobbies and areas of interest. The suggestion unit can also suggest optimal itineraries based on the user's family composition and lifestyle. This enables more appropriate suggestions by customizing the itinerary based on the user's current living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's current living situation and area of ​​interest data into the generation AI and have the generation AI customize the itinerary.

[0081] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can prioritize important suggestions and postpone other suggestions. For example, if the user is relaxed, the suggestion unit can display all suggestions evenly, allowing the user to freely select. Alternatively, if the user is in a hurry, the suggestion unit can display only the most important suggestions, allowing the user to quickly select. This enables more efficient suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0082] When proposing, the suggestion unit can propose an optimal itinerary taking into account the user's geographical location information. For example, the suggestion unit prioritizes incorporating places close to the user's current location into the itinerary. For example, the suggestion unit can also incorporate places the user wants to visit that are close to places the user has previously visited into the itinerary. The suggestion unit can also incorporate places the user has good transportation access from their current location into the itinerary. In this way, a more appropriate itinerary can be proposed by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to propose an optimal itinerary.

[0083] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related itineraries. The suggestion unit, for example, incorporates places where the user has "liked" or commented on social media into the itinerary. For example, the suggestion unit can analyze the content of posts from accounts the user follows and suggest related itineraries. The suggestion unit can also incorporate itineraries related to places the user has shared on social media into the itinerary. In this way, by analyzing the user's social media activity, more appropriate itineraries can be suggested. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related itineraries.

[0084] The linking unit can estimate the user's emotions and select a website to link to based on the estimated user's emotions. For example, if the user is relaxed, the linking unit can select a website with a relaxed atmosphere. For example, if the user is in a hurry, the linking unit can select a website that allows for quick reservation completion. Furthermore, if the user is excited, the linking unit can select a visually stimulating website. This enables more appropriate linking by selecting a website to link to based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the linking unit can be performed using AI, for example, or without AI. For example, the linking unit can input the user's emotion data into the generation AI and cause the generation AI to select a website to link to.

[0085] During linking, the interlocking unit can analyze the user's past reservation history and select the optimal website. The interlocking unit, for example, selects the optimal website based on websites the user has used in the past. For example, the interlocking unit can analyze the user's preferred websites from the user's past reservation history and select the optimal website. The interlocking unit can also select the optimal website based on the user's evaluations of websites they have used in the past. This allows a more appropriate website to be selected by analyzing the user's past reservation history. Some or all of the above-described processing in the interlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the interlocking unit can input the user's past reservation history data into the generation AI and cause the generation AI to select the optimal website.

[0086] During linking, the linking unit can customize the linked website based on the user's current living situation. For example, the linking unit selects a website that allows the user to relax based on their current work situation. For example, the linking unit can select related websites based on the user's current hobbies and areas of interest. The linking unit can also select the optimal website based on the user's family structure and lifestyle. This enables more appropriate linking by customizing the website based on the user's current living situation. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the website.

[0087] The interlocking unit can estimate the user's emotions and determine the priority of interlocking operations based on the estimated user's emotions. For example, if the user is nervous, the interlocking unit can prioritize important interlocking operations and postpone other interlocking operations. For example, if the user is relaxed, the interlocking unit can perform all interlocking operations equally, allowing the user to freely select which one to perform. Furthermore, if the user is in a hurry, the interlocking unit can perform only the most important interlocking operations, allowing the user to complete them quickly. This enables more efficient interlocking operations by determining the priority of interlocking operations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the interlocking unit can be performed using, for example, an AI. For example, the interlocking unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of interlocking operations.

[0088] During linking, the linking unit can select the optimal website by taking into account the user's geographical location information. For example, the linking unit preferentially selects websites to link that are close to the user's current location. For example, the linking unit can select websites to link that are close to places the user has visited in the past. The linking unit can also select websites to link that are easily accessible from the user's current location. This allows a more appropriate website to be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal website.

[0089] During linking, the linking unit can analyze the user's social media activity and select related websites. For example, the linking unit selects websites where the user has "liked" or commented on social media as linked websites. For example, the linking unit can analyze the content posted by accounts the user follows and select related websites. The linking unit can also select websites where the user has shared on social media as linked websites. This allows for the selection of more appropriate websites by analyzing the user's social media activity. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the user's social media activity data into the generation AI and cause the generation AI to select related websites.

[0090] The reservation unit can estimate the user's emotions and adjust the ticket reservation method based on the estimated user emotions. For example, if the user is relaxed, the reservation unit can reserve the ticket at a leisurely pace. For example, if the user is in a hurry, the reservation unit can quickly reserve the ticket. Furthermore, if the user is excited, the reservation unit can provide a ticket reservation method that adds visually stimulating effects. This allows for more appropriate ticket reservation by adjusting the ticket reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit can be performed using AI, for example, or without AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI adjust the ticket reservation method.

[0091] When reserving a ticket, the reservation unit can analyze the user's past travel history and select the optimal reservation method. The reservation unit selects the optimal ticket reservation method, for example, based on the transportation means the user has used in the past. For example, the reservation unit can analyze the user's preferred ticket reservation method from the user's past travel history and select the optimal method. The reservation unit can also select the optimal reservation method based on the user's evaluation of tickets used in the past. In this way, by analyzing the user's past travel history, a more appropriate ticket reservation method can be selected. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal reservation method.

[0092] The reservation unit can estimate the user's emotions and determine the priority of ticket reservation based on the estimated user emotions. For example, if the user is nervous, the reservation unit can prioritize the reservation of important tickets and postpone other tickets. For example, if the user is relaxed, the reservation unit can reserve all tickets equally, allowing the user to freely select. Furthermore, if the user is in a hurry, the reservation unit can reserve only the most important tickets, allowing the user to complete the process quickly. This enables more efficient ticket reservation by determining the priority of ticket reservation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit can be performed using, for example, an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of ticket reservation.

[0093] When reserving a ticket, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit, for example, prioritizes reserving tickets that are close to the user's current location. For example, the reservation unit can also prioritize reserving tickets that are close to places the user has visited in the past. The reservation unit can also prioritize reserving tickets that are close to places the user has visited in the past. In addition, the reservation unit can prioritize reserving tickets that are close to places that the user has good transportation access from their current location. This allows a more appropriate ticket reservation method to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reservation unit may be performed using, or without, AI, for example. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal reservation method.

[0094] The reservation unit (accommodation) can estimate a user's emotions and adjust the hotel reservation method based on the estimated user emotions. For example, if the user is relaxed, the reservation unit (accommodation) can make a hotel reservation at a leisurely pace. For example, if the user is in a hurry, the reservation unit (accommodation) can make a hotel reservation quickly. Furthermore, if the user is excited, the reservation unit (accommodation) can provide a hotel reservation method that adds visually stimulating effects. This allows for more appropriate reservations by adjusting the hotel reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit (accommodation) can be performed using, for example, AI, or without AI. For example, the reservation unit (accommodation) can input the user's emotion data into the generation AI and have the generation AI adjust the hotel reservation method.

[0095] When making a hotel reservation, the reservation unit (accommodation) can analyze the user's past accommodation history and select the optimal reservation method. The reservation unit (accommodation), for example, selects the optimal hotel reservation method based on accommodation facilities used by the user in the past. For example, the reservation unit (accommodation) can analyze the user's preferred hotel reservation method from the user's past accommodation history and select the optimal method. The reservation unit (accommodation) can also select the optimal reservation method based on the user's evaluations of accommodation facilities used in the past. In this way, by analyzing the user's past accommodation history, a more appropriate hotel reservation method can be selected. Some or all of the above-described processing in the reservation unit (accommodation) may be performed using, or without, AI, for example. For example, the reservation unit (accommodation) can input the user's past accommodation history data into a generation AI and have the generation AI select the optimal reservation method.

[0096] The reservation unit (accommodation) can estimate a user's emotions and prioritize hotel reservations based on the estimated user emotions. For example, if a user is nervous, the reservation unit (accommodation) can prioritize important hotel reservations and postpone other reservations. For example, if a user is relaxed, the reservation unit (accommodation) can make all hotel reservations equally, allowing the user to freely choose. Furthermore, if a user is in a hurry, the reservation unit (accommodation) can make only the most important hotel reservations and allow them to be completed quickly. This enables more efficient reservations by prioritizing hotel reservations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit (accommodation) can be performed using, for example, AI, or without AI. For example, the reservation unit (accommodation) can input user emotion data into a generation AI and have the generation AI determine the priority of hotel reservations.

[0097] When making a hotel reservation, the reservation unit (accommodation) can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit (accommodation), for example, prioritizes reserving accommodations close to the user's current location. For example, the reservation unit (accommodation) can also prioritize reserving accommodations close to places the user has visited in the past. The reservation unit (accommodation) can also prioritize reserving accommodations with good transportation access from the user's current location. This allows a more appropriate hotel reservation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit (accommodation) may be performed using, or without, AI, for example. For example, the reservation unit (accommodation) can input the user's geographical location information into the generation AI and have the generation AI select the optimal reservation method.

[0098] The reservation unit (rental car) can estimate the user's emotions and adjust the rental car reservation method based on the estimated user emotions. For example, if the user is relaxed, the reservation unit (rental car) can make a rental car reservation at a leisurely pace. For example, if the user is in a hurry, the reservation unit (rental car) can make a rental car reservation quickly. Furthermore, if the user is excited, the reservation unit (rental car) can provide a rental car reservation method that adds visually stimulating effects. This allows for more appropriate reservations by adjusting the rental car reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit (rental car) can be performed using, for example, AI, or without AI. For example, the reservation unit (rental car) can input the user's emotion data into the generation AI and have the generation AI adjust the rental car reservation method.

[0099] When reserving a rental car, the reservation unit (rental car) can analyze the user's past rental car usage history and select the optimal reservation method. The reservation unit (rental car) selects the optimal rental car reservation method, for example, based on the rental car companies the user has used in the past. For example, the reservation unit (rental car) can analyze the user's preferred rental car reservation method from the user's past rental car usage history and select the optimal method. The reservation unit (rental car) can also select the optimal reservation method based on the user's evaluations of rental car companies used in the past. In this way, by analyzing the user's past rental car usage history, a more appropriate rental car reservation method can be selected. Some or all of the above-mentioned processing in the reservation unit (rental car) can be performed, for example, using AI, or can be performed without using AI. For example, the reservation unit (rental car) can input the user's past rental car usage history data into a generation AI and have the generation AI select the optimal reservation method.

[0100] The reservation unit (rental car) can estimate the user's emotions and prioritize rental car reservations based on the estimated user emotions. For example, if the user is nervous, the reservation unit (rental car) can prioritize important rental car reservations and postpone other reservations. For example, if the user is relaxed, the reservation unit (rental car) can make all rental car reservations equally, allowing the user to freely choose. Also, if the user is in a hurry, the reservation unit (rental car) can make only the most important rental car reservations so that they can be completed quickly. This enables more efficient reservations by prioritizing rental car reservations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit (rental car) can be performed using, for example, AI, or without AI. For example, the reservation unit (rental car) can input the user's emotion data into a generation AI and have the generation AI determine the priority of rental car reservations.

[0101] When reserving a rental car, the reservation unit (rental car) can select the optimal reservation method by taking into account the user's geographical location information. The reservation unit (rental car), for example, prioritizes reserving rental car companies that are close to the user's current location. For example, the reservation unit (rental car) can also prioritize reserving rental car companies that are close to places the user has visited in the past. The reservation unit (rental car) can also prioritize reserving rental car companies that have good transportation access from the user's current location. This allows a more appropriate rental car reservation method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit (rental car) may be performed using, or without, AI, for example. For example, the reservation unit (rental car) can input the user's geographical location information into a generation AI and have the generation AI select the optimal reservation method.

[0102] The drinking party unit can estimate the user's emotions and adjust the drinking party planning method based on the estimated user's emotions. For example, if the user is relaxed, the drinking party unit can plan the drinking party at a leisurely pace. For example, if the user is in a hurry, the drinking party unit can quickly plan the drinking party. Furthermore, if the user is excited, the drinking party unit can provide a drinking party planning method that adds visually stimulating effects. This allows for more appropriate planning by adjusting the drinking party planning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the drinking party unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the drinking party unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the drinking party planning method.

[0103] When arranging a drinking party, the drinking party unit can analyze the user's past drinking party history and select the optimal arrangement method. The drinking party unit selects the optimal arrangement method, for example, based on drinking party locations the user has used in the past. For example, the drinking party unit can analyze the user's preferred arrangement methods from the user's past drinking party history and select the optimal method. The drinking party unit can also select the optimal arrangement method based on the user's evaluation of drinking party locations the user has used in the past. In this way, a more appropriate arrangement method can be selected by analyzing the user's past drinking party history. Some or all of the above-described processing in the drinking party unit may be performed using, for example, AI, or may be performed without using AI. For example, the drinking party unit can input the user's past drinking party history data into a generation AI and have the generation AI select the optimal arrangement method.

[0104] The drinking party unit can estimate the user's emotions and determine the priorities for the drinking party arrangements based on the estimated user's emotions. For example, if the user is nervous, the drinking party unit can prioritize important arrangements and postpone other arrangements. For example, if the user is relaxed, the drinking party unit can perform all arrangements equally, allowing the user to freely choose. Furthermore, if the user is in a hurry, the drinking party unit can perform only the most important arrangements so that they can be completed quickly. This enables more efficient arrangements by determining the priorities for the drinking party arrangements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the drinking party unit can be performed using, for example, an AI. For example, the drinking party unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities for the drinking party arrangements.

[0105] When arranging a drinking party, the drinking party unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the drinking party unit prioritizes arranging a drinking party location that is close to the user's current location. For example, the drinking party unit can also prioritize arranging a drinking party location that is close to a place the user has visited in the past. The drinking party unit can also prioritize arranging a drinking party location that is easily accessible from the user's current location. In this way, a more appropriate drinking party arrangement method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the drinking party unit may be performed using, or without, AI, for example. For example, the drinking party unit can input the user's geographical location information into a generation AI and cause the generation AI to select the optimal arrangement method.

[0106] The golf club can estimate the user's emotions and adjust the golf preparation method based on the estimated user's emotions. For example, if the user is relaxed, the golf club can perform golf preparation at a leisurely pace. For example, if the user is in a hurry, the golf club can perform golf preparation quickly. Furthermore, if the user is excited, the golf club can provide a golf preparation method that adds visually stimulating effects. This allows for more appropriate preparation by adjusting the golf preparation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the golf club can be performed using, for example, an AI, or can be performed without using an AI. For example, the golf club can input the user's emotion data into the generation AI and cause the generation AI to adjust the golf preparation method.

[0107] When preparing for golf, the golf club can analyze the user's past golf history and select the optimal preparation method. The golf club selects the optimal preparation method based on, for example, golf courses the user has used in the past. For example, the golf club can analyze the user's preferred preparation method from the user's past golf history and select the optimal method. The golf club can also select the optimal preparation method based on the user's evaluation of golf courses the user has used in the past. In this way, a more appropriate preparation method can be selected by analyzing the user's past golf history. Some or all of the above-described processing in the golf club may be performed using, for example, AI, or may be performed without using AI. For example, the golf club can input the user's past golf history data into a generation AI and have the generation AI select the optimal preparation method.

[0108] The golf club can estimate the user's emotions and determine the priority of golf preparations based on the estimated user's emotions. For example, if the user is nervous, the golf club can prioritize important preparations and postpone other preparations. For example, if the user is relaxed, the golf club can perform all preparations equally, allowing the user to freely choose. Also, if the user is in a hurry, the golf club can perform only the most important preparations and allow them to be completed quickly. This enables more efficient preparations by determining the priority of golf preparations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the golf club can be performed using, for example, an AI. For example, the golf club can input the user's emotion data into the generation AI and have the generation AI determine the priority of golf preparations.

[0109] When preparing for a golf game, the golf club can select the optimal preparation method by taking into account the user's geographical location information. For example, the golf club may prioritize preparing golf courses that are close to the user's current location. For example, the golf club may prioritize preparing golf courses that are close to places the user has visited in the past. The golf club may also prioritize preparing golf courses that are easily accessible from the user's current location. In this way, a more appropriate golf preparation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the golf club may be performed using, or without, AI, for example. For example, the golf club may input the user's geographical location information into a generation AI and cause the generation AI to select the optimal preparation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, suggestion unit, and linkage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts user input using the touch panel 38A or microphone 38B of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an itinerary or schedule based on the user input. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and links with the websites of each transportation facility and hotel to secure tickets and make accommodation reservations. The linkage unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, suggestion unit, and linkage unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts user input using the microphone 238 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an itinerary or schedule based on the user input. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and links with the websites of each transportation facility and hotel to secure tickets and make accommodation reservations. The linkage unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, suggestion unit, and interlocking unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit accepts user input using the microphone 238 of the headset terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an itinerary or schedule based on the user input. The interlocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and interlocks with the websites of each transportation facility and hotel to secure tickets and make accommodation reservations. The interlocking unit may be realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, and interlocking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts user input using the microphone 238 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an itinerary or schedule based on the user input. The interlocking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and interlocks with the websites of each transportation facility and hotel to secure tickets and make accommodation reservations. The interlocking unit may be realized, for example, by the control unit 46A of the robot 414.

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

[0111] The suggestion unit can monitor the user's health condition and adjust the itinerary and schedule based on the health condition. For example, if the user is tired, the suggestion unit can suggest an itinerary that includes many rest periods. Also, if the user is in good health, the suggestion unit can suggest an itinerary that includes many active activities. Furthermore, if the user has a specific health problem, the suggestion unit can suggest an itinerary that takes the problem into consideration. This makes it possible to provide an optimal travel plan based on the user's health condition.

[0112] The interlocking unit can select optimal tickets and accommodations taking into account the user's budget. For example, if the user has a low budget, the interlocking unit can suggest cost-effective options. Alternatively, if the user has a high budget, the interlocking unit can suggest more luxurious options. Furthermore, the interlocking unit can apply specific discounts and promotions depending on the user's budget. This allows the user to be provided with an optimal travel plan that suits their budget.

[0113] The reception unit can customize the input interface based on the user's language setting. For example, if the user selects English, the reception unit can provide an English interface. Also, if the user selects Japanese, the reception unit can provide a Japanese interface. Furthermore, if the user uses multiple languages, the reception unit can support input in multiple languages. This makes it possible to provide an optimal input interface according to the user's language setting.

[0114] The suggestion unit can customize the itinerary and schedule based on the user's travel purpose. For example, if the user's travel purpose is sightseeing, the suggestion unit can suggest an itinerary that includes many tourist spots. If the user's travel purpose is business, the suggestion unit can also suggest an itinerary that takes business meetings and conferences into consideration. Furthermore, if the user's travel purpose is relaxation, the suggestion unit can also suggest an itinerary that includes many relaxing places and activities. This makes it possible to provide an optimal travel plan that matches the user's travel purpose.

[0115] The interlocking unit can suggest optimal options taking into consideration the user's preferred brands and services. For example, if the user prefers a particular hotel chain, the interlocking unit can preferentially suggest accommodations from that hotel chain. Also, if the user prefers a particular airline, the interlocking unit can preferentially suggest tickets from that airline. Furthermore, if the user prefers a particular rental car company, the interlocking unit can preferentially suggest vehicles from that rental car company. This makes it possible to provide an optimal travel plan based on the user's preferences.

[0116] The suggestion unit can estimate the user's emotions and adjust the method of suggesting an itinerary or schedule based on the estimated user's emotions. For example, the suggestion unit can suggest a leisurely itinerary if the user is relaxed. The suggestion unit can also suggest an efficient schedule if the user is in a hurry. Furthermore, the suggestion unit can also suggest an active itinerary if the user is excited. This makes it possible to provide an optimal travel plan according to the user's emotions.

[0117] The reception unit can estimate the user's emotion and adjust the input interface based on the estimated user's emotion. For example, the reception unit can provide a simple interface when the user is feeling stressed. The reception unit can also provide detailed input options when the user is relaxed. Furthermore, the reception unit can prioritize voice input when the user is in a hurry. This makes it possible to provide an optimal input interface according to the user's emotion.

[0118] The linking unit can estimate the user's emotions and select a linked website based on the estimated user's emotions. For example, if the user is relaxed, the linking unit can select a website with a relaxed atmosphere. If the user is in a hurry, the linking unit can select a website that allows quick reservation completion. Furthermore, if the user is excited, the linking unit can select a visually stimulating website. This makes it possible to provide the optimal website according to the user's emotions.

[0119] The reservation unit can estimate the user's emotions and adjust the ticket reservation method based on the estimated user's emotions. For example, if the user is relaxed, the reservation unit can reserve the ticket at a leisurely pace. If the user is in a hurry, the reservation unit can also reserve the ticket quickly. Furthermore, if the user is excited, the reservation unit can provide a ticket reservation method that adds visually stimulating effects. This makes it possible to provide the optimal ticket reservation method according to the user's emotions.

[0120] The reservation unit (accommodation) can estimate the user's emotions and adjust the hotel reservation method based on the estimated user's emotions. For example, if the user is relaxed, the reservation unit (accommodation) can make a hotel reservation at a leisurely pace. If the user is in a hurry, the reservation unit (accommodation) can also make a hotel reservation quickly. Furthermore, if the user is excited, the reservation unit (accommodation) can provide a hotel reservation method that adds visually stimulating effects. This makes it possible to provide an optimal hotel reservation method that corresponds to the user's emotions.

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

[0122] Step 1: The reception unit receives input of a destination from the user. For example, the user can input the destination in text format. The reception unit can also receive voice input. For example, when the user speaks the destination, it can be converted into text using voice recognition technology. Step 2: The suggestion unit proposes an itinerary or schedule based on the information received by the reception unit. For example, the suggestion unit calculates the optimal means of transportation and travel time based on the user's input and determines the order of visits. The suggestion unit can also propose a customized itinerary based on the user's preferences. Step 3: The interlocking unit secures tickets and makes accommodation reservations by interlocking with the websites of each transportation company and hotel based on the itinerary proposed by the proposal unit. For example, the interlocking unit uses API integration to connect with the reservation sites of each transportation company and hotel, automatically securing tickets and making accommodation reservations. The interlocking unit can also use scraping technology to obtain necessary information from the reservation sites and perform manual operations.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0194] [Explanation of symbols]

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

Claims

1. a reception unit that receives input of a desired destination from a user; a proposal unit that proposes an itinerary or schedule based on the information received by the reception unit; and a linking unit that links with the website of each transportation facility or hotel to secure tickets or make accommodation reservations based on the itinerary proposed by the suggestion unit. A system characterized by:

2. The interlocking portion is Equipped with a ticket locking section for securing transportation tickets 2. The system of claim 1.

3. The interlocking portion is Equipped with a reservation department for making hotel reservations 2. The system of claim 1.

4. The interlocking portion is Equipped with a reservation department for car rental reservations 2. The system of claim 1.

5. The proposal unit Have a department to organize drinking parties 2. The system of claim 1.

6. The proposal unit Equipped with a department for organizing golf 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way to input the destination based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

9. The reception unit When typing a destination, suggestions are provided based on the user's current interests.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

Images