System

Abstract

An object of a system according to an embodiment is to adjust desires of members and create a travel plan that satisfies all the members.SOLUTION: A system includes a desire collection part, an analysis part, a plan generation part, an adjustment part, a bookmark creation part, and a reservation substitution part. The desire collection unit collects desires of members. The analysis unit analyzes the preferences collected by the preference collection unit. The plan generation unit generates a travel plan based on the desire analyzed by the analysis unit. The arrangement unit arranges the travel plan generated by the plan generation unit. The bookmark creation unit creates a "travel bookmark" on the basis of the travel plan adjusted by the adjustment unit. The reservation vicarious execution unit collectively vicariously executes reservation on the basis of the "travel bookmark" created by the bookmark creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to coordinate the wishes of members when making travel plans and create a plan that would satisfy everyone.

[0005] The system according to the embodiment aims to adjust the wishes of members and create a travel plan that satisfies everyone. [Means for solving the problem]

[0006] The system according to the embodiment comprises a wish collection unit, an analysis unit, a plan generation unit, an adjustment unit, a bookmark creation unit, and a reservation agent unit. The wish collection unit collects the wishes of members. The analysis unit analyzes the wishes collected by the wish collection unit. The plan generation unit generates a travel plan based on the wishes analyzed by the analysis unit. The adjustment unit adjusts the travel plan generated by the plan generation unit. The bookmark creation unit creates a "travel bookmark" based on the travel plan adjusted by the adjustment unit. The reservation agent unit makes reservations in bulk based on the "travel bookmark" created by the bookmark creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can adjust the wishes of members and create a travel plan that satisfies everyone. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The travel planning system according to an embodiment of the present invention collects and analyzes the wishes of members, creates and adjusts the optimal travel plan, creates a "travel guide," and handles all reservations on behalf of the members. This allows the travel planning system to efficiently coordinate opinions among members and create a travel plan that satisfies everyone.

[0029] A travel planning system according to an embodiment includes a preference collection unit, an analysis unit, a plan generation unit, a coordination unit, a bookmark creation unit, and a reservation agent unit. The preference collection unit collects preferences from members. For example, the preference collection unit may collect preferences from members through a questionnaire. The preference collection unit may also collect preferences from members through interviews. The preference collection unit may also collect preferences from members through an online form. The analysis unit analyzes the preferences collected by the preference collection unit. For example, the analysis unit may analyze the preferences using data mining technology. The analysis unit may also analyze the preferences using statistical analysis technology. The analysis unit may also analyze the preferences using a machine learning algorithm. The plan generation unit generates a travel plan based on the preferences analyzed by the analysis unit. For example, the plan generation unit may generate a travel plan taking into account itineraries. The plan generation unit may also generate a travel plan taking into account places to be visited. The plan generation unit may also generate a travel plan taking into account activities. The coordination unit adjusts the travel plan generated by the plan generation unit. For example, the coordination unit may coordinate opinions among members. The adjustment unit can also adjust schedules. The adjustment unit can also adjust budgets. The bookmark creation unit creates a "travel guide" based on the travel plan adjusted by the adjustment unit. For example, the bookmark creation unit creates an itinerary. The bookmark creation unit can also include information about places to visit. The bookmark creation unit can also include notes. The reservation agent unit makes reservations in bulk based on the "travel guide" created by the bookmark creation unit. For example, the reservation agent unit makes reservations for transportation. The reservation agent unit can also make reservations for accommodations. The reservation agent unit can also make reservations for activities. As a result, the travel planning system according to the embodiment can collect and analyze member preferences, generate and adjust optimal travel plans, create a "travel guide," and make reservations in bulk.

[0030] The wish collection unit can analyze members' past travel history and social media posts to automatically extract latent wishes and preferences. For example, the wish collection unit analyzes each member's past travel history to automatically extract visited places, accommodations, and food preferences. For example, it suggests potential next travel destinations based on ratings of tourist spots and accommodations visited in the past. The wish collection unit also analyzes social media posts to extract members' latent wishes and preferences. For example, it identifies tourist spots and activities that members are interested in from the social media posts. The wish collection unit also uses text analysis technology to analyze members' emotions from the social media posts to extract latent wishes and preferences. For example, it suggests travel destinations that members prefer based on posts that contain a lot of positive emotions. In this way, it is possible to automatically extract members' latent wishes and preferences.

[0031] When collecting a member's wishes, the wish collection unit checks for consistency with the wishes of other members in real time, thereby preventing inconsistencies in advance. For example, when a member inputs a wish, the wish collection unit compares it with the wishes of other members in real time to check for any inconsistencies. For example, if a member requests to go to different places on the same day, the wish collection unit prompts the member to make adjustments. The wish collection unit also updates the database to check for consistency of wishes in real time. For example, the database is updated every time a wish is input, and consistency is checked based on the latest information. The wish collection unit also uses real-time communication technology to check for consistency between members' wishes. For example, when a member inputs a wish, it communicates with the wishes of other members in real time to check for any inconsistencies. This makes it possible to prevent inconsistencies in members' wishes in advance.

[0032] The wish collection unit collects wishes using voice input or image recognition, thereby providing a more intuitive interface. For example, when a member inputs a wish, the wish collection unit collects the wish using voice input. For example, using voice recognition technology, what the member says is converted into text and collected as a wish. Furthermore, when a member inputs a wish, the wish collection unit analyzes images and collects the wish using image recognition technology. For example, it analyzes images taken by the member and collects them as a wish. Furthermore, the wish collection unit combines voice input and image recognition to collect wishes. For example, it combines what the member says with images taken, analyzes them, and collects them as a wish. This allows a more intuitive interface to be provided.

[0033] The preference collection unit can collect preferences of members from different cultural areas and nationalities and generate an international travel plan. The preference collection unit, for example, collects preferences of members from different cultural areas and nationalities and generates an international travel plan. For example, it proposes a travel plan that takes into account the cultural background of each member. The preference collection unit also collects preferences of members who speak different languages ​​and generates an international travel plan. For example, it proposes a travel plan that corresponds to the language of each member. The preference collection unit also takes cultural considerations into account in collecting preferences of members from different cultural areas and nationalities. For example, it proposes a travel plan that takes into account the cultural customs and preferences of each member. This makes it possible to generate an international travel plan.

[0034] The plan generation unit can learn from past examples of successful and unsuccessful travel plans and generate an optimal plan. For example, the plan generation unit uses a generation AI to learn from past examples of successful and unsuccessful travel plans and generate an optimal plan. For example, the plan generation unit proposes a travel plan with a high probability of success based on past data. The plan generation unit also uses a database to learn from past examples of successful and unsuccessful travel plans. For example, the database of past travel plans is analyzed to extract successful and unsuccessful examples. The plan generation unit also uses a machine learning algorithm to learn from past examples of successful and unsuccessful travel plans. For example, the machine learning algorithm is used to classify successful and unsuccessful examples and generate an optimal plan. In this way, past examples of successful and unsuccessful examples can be learned and an optimal plan can be generated.

[0035] The plan generation unit can take into account information about seasons, weather, and local events when generating a travel plan. For example, the plan generation unit takes into account information about seasons and weather when generating a travel plan. For example, a beach resort is suggested for a summer travel plan, and a ski resort is suggested for a winter travel plan. The plan generation unit also takes into account information about local events. For example, a travel plan is generated taking into account festivals and events held locally. The plan generation unit also uses weather data and an event calendar to take into account information about seasons, weather, and local events. For example, the optimal time for the travel plan is suggested based on weather data. The plan generation unit also suggests a travel plan that takes into account information about local events based on the event calendar. This makes it possible to generate a travel plan that takes into account information about seasons, weather, and local events.

[0036] The plan generation unit can simulate the generated travel plan in virtual reality (VR) to allow members to experience it in advance. The plan generation unit, for example, simulates the generated travel plan in virtual reality (VR) to allow members to experience it in advance. For example, tourist spots and accommodations are experienced in VR. The plan generation unit also simulates the travel plan using virtual reality (VR) technology. For example, a VR headset is used to virtually experience tourist spots at the travel destination. The plan generation unit also simulates the travel plan using simulation software. For example, simulation software is used to virtually experience tourist spots and accommodations at the travel destination. This allows members to experience the travel plan in advance.

[0037] The plan generation unit can generate multiple scenarios with different budgets and schedules, allowing members to select from them. For example, the plan generation unit uses a generation AI to generate multiple scenarios with different budgets and schedules, allowing members to select from them. For example, it proposes multiple travel plans based on budgets. The plan generation unit also uses a database to generate scenarios with different budgets and schedules. For example, it generates multiple travel plans based on budgets and schedules. The plan generation unit also uses a machine learning algorithm to generate scenarios with different budgets and schedules. For example, it uses a machine learning algorithm to generate multiple travel plans based on budgets and schedules. This allows multiple scenarios with different budgets and schedules to be generated, allowing members to select from them.

[0038] The coordination unit can automatically detect differences of opinion between members and propose solutions. For example, the coordination unit uses a generative AI to automatically detect differences of opinion between members and propose solutions. For example, it detects differences in budget or schedule and proposes a coordination proposal. The coordination unit also uses a database to detect differences of opinion. For example, it stores members' wishes in a database and detects differences of opinion. The coordination unit also uses a machine learning algorithm to detect differences of opinion. For example, it uses a machine learning algorithm to classify differences of opinion and propose solutions. This makes it possible to automatically detect differences of opinion between members and propose solutions.

[0039] The adjustment unit can take into account the priorities of the members when making adjustments and prioritize reflect requests with high importance. For example, the adjustment unit can take into account the priorities of the members when making adjustments and prioritize reflect requests with high importance. For example, requests that everyone agrees on are reflected as the highest priority. The adjustment unit also uses a survey to take into account the priorities of the members. For example, it conducts a survey of the members and sets the priorities. The adjustment unit also uses interviews to take into account the priorities of the members. For example, it interviews the members and sets the priorities. In this way, it is possible to take into account the priorities of the members and prioritize reflect requests with high importance.

[0040] The adjustment unit can perform the adjustment process in real time by coordinating with a chatbot or a video conferencing system. The adjustment unit, for example, can perform the adjustment process in real time by coordinating with a chatbot. For example, the adjustment unit collects opinions of members through a chatbot and makes adjustments. The adjustment unit can also perform the adjustment process in real time by coordinating with a video conferencing system. For example, the adjustment unit collects opinions of members through a video conferencing and makes adjustments. The adjustment unit can also perform the adjustment process in real time by combining a chatbot and a video conferencing system. For example, the adjustment unit collects opinions through a chatbot and makes adjustments through a video conferencing. This allows the adjustment process to be performed in real time.

[0041] The adjustment unit allows adjustments between members who speak different languages ​​to proceed smoothly by having the generation AI automatically translate. The adjustment unit, for example, allows adjustments between members who speak different languages ​​to proceed smoothly by having the generation AI automatically translate. For example, adjustments are made by automatically translating between English and Japanese. The adjustment unit also uses the generation AI to adjust between members who speak different languages. For example, the generation AI performs automatic translation in real time to proceed smoothly. The adjustment unit also uses a translation algorithm to adjust between members who speak different languages. For example, the translation algorithm is used to automatically translate opinions between members who speak different languages ​​and make adjustments. This allows adjustments between members who speak different languages ​​to proceed smoothly.

[0042] The bookmark creation unit can learn from past travel "travel guidebooks" and automatically generate the optimal format and content. For example, the bookmark creation unit uses a generation AI to learn from past travel "travel guidebooks" and automatically generate the optimal format and content. For example, it suggests the optimal "travel guidebook" based on past data. The bookmark creation unit also uses a database to learn from past "travel guidebooks." For example, it analyzes a database of past "travel guidebooks" and extracts the optimal format and content. The bookmark creation unit also uses a machine learning algorithm to learn from past "travel guidebooks." For example, it uses a machine learning algorithm to classify and automatically generate the optimal format and content. This makes it possible to automatically generate the optimal format and content.

[0043] The travel guide creation unit can enhance safety by including information on local emergency contacts and medical institutions in the "travel guide." The travel guide creation unit can enhance safety, for example, by including local emergency contacts in the "travel guide." For example, it can include contact information for local police and embassies. The travel guide creation unit can also enhance safety by including information on local medical institutions in the "travel guide." For example, it can include contact information for local hospitals and clinics. The travel guide creation unit can also enhance safety by including emergency response methods in the "travel guide." For example, it can include evacuation locations and contact methods in the event of an emergency. In this way, safety can be enhanced by including information on emergency contacts and medical institutions in the travel guide.

[0044] The bookmark creation unit can link the "travel bookmark" with a smartphone app or a wearable device, allowing it to be updated in real time. The bookmark creation unit, for example, links the "travel bookmark" with a smartphone app, allowing it to be updated in real time. For example, the latest information can be provided through the app. The bookmark creation unit can also link the "travel bookmark" with a wearable device, allowing it to be updated in real time. For example, the latest information can be provided through the wearable device. The bookmark creation unit can also combine a smartphone app and a wearable device, allowing the "travel bookmark" to be updated in real time. For example, the latest information can be provided through the app and the device. This makes it possible to update the travel bookmark in real time.

[0045] The bookmark creation unit can automatically translate the "travel guide" into different languages ​​to accommodate international travel. The bookmark creation unit, for example, can automatically translate the "travel guide" into different languages ​​to accommodate international travel. For example, it translates it into English or Chinese and provides it. The bookmark creation unit also uses a generation AI to automatically translate the "travel guide" into different languages. For example, the generation AI performs automatic translation in real time to accommodate different languages. The bookmark creation unit also uses a translation algorithm to automatically translate the "travel guide" into different languages. For example, it uses a translation algorithm to generate "travel guides" that accommodate different languages. This allows the travel guide to be automatically translated into different languages ​​to accommodate international travel.

[0046] The reservation agent unit predicts the optimal timing to make a reservation and can make the reservation at the most advantageous price. The reservation agent unit, for example, uses a generation AI to predict the optimal timing to make a reservation and makes the reservation at the most advantageous price. For example, it suggests the optimal reservation timing based on past data. The reservation agent unit also uses a database to predict the optimal timing. For example, it analyzes past price fluctuation data and predicts the optimal reservation timing. The reservation agent unit also uses a machine learning algorithm to predict the optimal timing. For example, it uses a machine learning algorithm to predict the optimal reservation timing. This allows reservations to be made at the optimal timing and at the most advantageous price.

[0047] The reservation agent unit can consider the member's past reservation history and preferences when making a reservation and suggest the optimal option. The reservation agent unit, for example, considers the member's past reservation history when making a reservation and suggests the optimal option. For example, suggestions are made based on accommodations and transportation methods used in the past. The reservation agent unit also uses surveys to consider the member's preferences. For example, it may conduct a survey of members and collect their preferences. The reservation agent unit also uses interviews to consider the member's preferences. For example, it may interview members and collect their preferences. This allows the reservation agent unit to consider the member's past reservation history and preferences and suggest the optimal option.

[0048] The reservation agent unit can provide optimal options by coordinating with multiple travel agencies and reservation sites to handle all reservations. The reservation agent unit, for example, can coordinating with multiple travel agencies to handle all reservations and provide optimal options. For example, the optimal reservation is made based on information from multiple agencies. The reservation agent unit also coordinating with reservation sites to provide optimal options. For example, the optimal reservation is made based on information from multiple reservation sites. The reservation agent unit also combines travel agencies and reservation sites to provide optimal options. For example, the optimal reservation is made based on information from the agency and the site. This allows the unit to coordinating with multiple travel agencies and reservation sites to provide optimal options.

[0049] The reservation agent can accommodate different currencies and payment methods when making reservations, and can also accommodate international travel. The reservation agent can, for example, accommodate different currencies when making reservations, and can also accommodate international travel. For example, it accepts payments in multiple currencies. The reservation agent can also accommodate different payment methods. For example, it can accept payments by credit card or electronic money. The reservation agent can also apply exchange rates to accommodate different currencies and payment methods. For example, it can accept payments in different currencies based on the exchange rate. This allows the reservation agent to accommodate different currencies and payment methods, and can also accommodate international travel.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The wish collection unit uses voice input and image recognition to collect the wishes of members, thereby providing a more intuitive interface. For example, when a member inputs their wishes, the wishes are collected using voice input. For example, voice recognition technology is used to convert what the member says into text and collect the text as their wishes. The wish collection unit also uses image recognition technology to analyze images when the member inputs their wishes and collect the wishes. For example, images taken by the member are analyzed and collected as their wishes. The wish collection unit also combines voice input and image recognition to collect the wishes. For example, what the member says and images taken are combined and analyzed and collected as their wishes. This allows a more intuitive interface to be provided.

[0052] The preference collection unit can collect preferences of members from different cultural areas and nationalities and generate an international travel plan. For example, the preferences of members from different cultural areas and nationalities are collected and an international travel plan is generated. For example, a travel plan that takes into account the cultural background of each member is proposed. The preference collection unit can also collect preferences of members who speak different languages ​​and generate an international travel plan. For example, a travel plan that corresponds to the language of each member is proposed. The preference collection unit can also take cultural considerations into account in order to collect preferences of members from different cultural areas and nationalities. For example, a travel plan that takes into account the cultural habits and preferences of each member is proposed. This makes it possible to generate an international travel plan.

[0053] The plan generation unit can learn from past examples of successful and unsuccessful travel plans and generate the optimal plan. For example, the generation AI learns from past examples of successful and unsuccessful travel plans and generates the optimal plan. For example, it proposes travel plans with a high probability of success based on past data. The plan generation unit also uses a database to learn from past examples of successful and unsuccessful travel plans. For example, it analyzes a database of past travel plans and extracts successful and unsuccessful examples. The plan generation unit also uses a machine learning algorithm to learn from past examples of successful and unsuccessful travel plans. For example, it uses a machine learning algorithm to classify successful and unsuccessful examples and generate the optimal plan. In this way, it is possible to learn from past examples of successful and unsuccessful examples and generate the optimal plan.

[0054] The plan generation unit can take into account information about the season, weather, and local events when generating a travel plan. For example, the season and weather are taken into account when generating a travel plan. For example, a beach resort is suggested for a summer travel plan, and a ski resort is suggested for a winter travel plan. The plan generation unit also takes into account information about local events. For example, a travel plan is generated taking into account festivals and events held locally. The plan generation unit also uses weather data and an event calendar to take into account information about the season, weather, and local events. For example, the optimal time for the travel plan is suggested based on weather data. The plan generation unit also suggests a travel plan that takes into account information about local events based on the event calendar. This makes it possible to generate a travel plan that takes into account information about the season, weather, and local events.

[0055] The plan generation unit can simulate the generated travel plan in virtual reality (VR) to allow members to experience it in advance. For example, the generated travel plan can be simulated in virtual reality (VR) to allow members to experience it in advance. For example, tourist spots and accommodations can be experienced in VR. The plan generation unit also simulates the travel plan using virtual reality (VR) technology. For example, a VR headset can be used to virtually experience tourist spots at the travel destination. The plan generation unit also simulates the travel plan using simulation software. For example, simulation software can be used to virtually experience tourist spots and accommodations at the travel destination. This allows members to experience the travel plan in advance.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The preference collection department collects member preferences. For example, member preferences can be collected through questionnaires, interviews, or online forms. Step 2: The analysis unit analyzes the preferences collected by the preference collection unit. For example, the preferences can be analyzed using data mining techniques, statistical analysis techniques, or machine learning algorithms. Step 3: The plan generation unit generates a travel plan based on the desires analyzed by the analysis unit. For example, the travel plan can be generated taking into account the dates, places to visit, and activities. Step 4: The adjustment unit adjusts the travel plan generated by the plan generation unit. For example, it can adjust opinions among members, schedules, and budgets. Step 5: The travel guide creation unit creates a "travel guide" based on the travel plan adjusted by the adjustment unit. For example, it can include an itinerary, information about places to visit, and important points to note. Step 6: The reservation agent handles all reservations based on the "travel guidebook" created by the guidebook creation unit. For example, reservations can be made for transportation, accommodation, and activities.

[0058] (Example 2) The travel planning system according to an embodiment of the present invention collects and analyzes the wishes of members, creates and adjusts the optimal travel plan, creates a "travel guide," and handles all reservations on behalf of the members. This allows the travel planning system to efficiently coordinate opinions among members and create a travel plan that satisfies everyone.

[0059] A travel planning system according to an embodiment includes a preference collection unit, an analysis unit, a plan generation unit, a coordination unit, a bookmark creation unit, and a reservation agent unit. The preference collection unit collects preferences from members. For example, the preference collection unit may collect preferences from members through a questionnaire. The preference collection unit may also collect preferences from members through interviews. The preference collection unit may also collect preferences from members through an online form. The analysis unit analyzes the preferences collected by the preference collection unit. For example, the analysis unit may analyze the preferences using data mining technology. The analysis unit may also analyze the preferences using statistical analysis technology. The analysis unit may also analyze the preferences using a machine learning algorithm. The plan generation unit generates a travel plan based on the preferences analyzed by the analysis unit. For example, the plan generation unit may generate a travel plan taking into account itineraries. The plan generation unit may also generate a travel plan taking into account places to be visited. The plan generation unit may also generate a travel plan taking into account activities. The coordination unit adjusts the travel plan generated by the plan generation unit. For example, the coordination unit may coordinate opinions among members. The adjustment unit can also adjust schedules. The adjustment unit can also adjust budgets. The bookmark creation unit creates a "travel guide" based on the travel plan adjusted by the adjustment unit. For example, the bookmark creation unit creates an itinerary. The bookmark creation unit can also include information about places to visit. The bookmark creation unit can also include notes. The reservation agent unit makes reservations in bulk based on the "travel guide" created by the bookmark creation unit. For example, the reservation agent unit makes reservations for transportation. The reservation agent unit can also make reservations for accommodations. The reservation agent unit can also make reservations for activities. As a result, the travel planning system according to the embodiment can collect and analyze member preferences, generate and adjust optimal travel plans, create a "travel guide," and make reservations in bulk.

[0060] The wish collection unit can analyze members' past travel history and social media posts to automatically extract latent wishes and preferences. For example, the wish collection unit analyzes each member's past travel history to automatically extract visited places, accommodations, and food preferences. For example, it suggests potential next travel destinations based on ratings of tourist spots and accommodations visited in the past. The wish collection unit also analyzes social media posts to extract members' latent wishes and preferences. For example, it identifies tourist spots and activities that members are interested in from the social media posts. The wish collection unit also uses text analysis technology to analyze members' emotions from the social media posts to extract latent wishes and preferences. For example, it suggests travel destinations that members prefer based on posts that contain a lot of positive emotions. In this way, it is possible to automatically extract members' latent wishes and preferences.

[0061] When collecting a member's wishes, the wish collection unit checks for consistency with the wishes of other members in real time, thereby preventing inconsistencies in advance. For example, when a member inputs a wish, the wish collection unit compares it with the wishes of other members in real time to check for any inconsistencies. For example, if a member requests to go to different places on the same day, the wish collection unit prompts the member to make adjustments. The wish collection unit also updates the database to check for consistency of wishes in real time. For example, the database is updated every time a wish is input, and consistency is checked based on the latest information. The wish collection unit also uses real-time communication technology to check for consistency between members' wishes. For example, when a member inputs a wish, it communicates with the wishes of other members in real time to check for any inconsistencies. This makes it possible to prevent inconsistencies in members' wishes in advance.

[0062] The wish collection unit can use the emotion estimation function to analyze the emotions of members when they input their wishes, and automatically generate questions that elicit positive emotions. For example, the wish collection unit uses the emotion estimation function to analyze the emotions of members when they input their wishes, and automatically generate questions that elicit positive emotions. For example, questions that elicit positive emotions are displayed when a wish is input. The wish collection unit also uses the emotion estimation function to analyze members' emotions in real time, and generate questions that elicit positive emotions. For example, if a member is feeling stressed, questions that will help them relax are displayed. The wish collection unit also uses the emotion estimation function to analyze members' emotions, and provide feedback that elicits positive emotions. For example, positive feedback is displayed when a member inputs their wishes. This makes it possible to elicit positive emotions from members.

[0063] The wish collection unit collects wishes using voice input or image recognition, thereby providing a more intuitive interface. For example, when a member inputs a wish, the wish collection unit collects the wish using voice input. For example, using voice recognition technology, what the member says is converted into text and collected as a wish. Furthermore, when a member inputs a wish, the wish collection unit analyzes images and collects the wish using image recognition technology. For example, it analyzes images taken by the member and collects them as a wish. Furthermore, the wish collection unit combines voice input and image recognition to collect wishes. For example, it combines what the member says with images taken, analyzes them, and collects them as a wish. This allows a more intuitive interface to be provided.

[0064] The preference collection unit can collect preferences of members from different cultural areas and nationalities and generate an international travel plan. The preference collection unit, for example, collects preferences of members from different cultural areas and nationalities and generates an international travel plan. For example, it proposes a travel plan that takes into account the cultural background of each member. The preference collection unit also collects preferences of members who speak different languages ​​and generates an international travel plan. For example, it proposes a travel plan that corresponds to the language of each member. The preference collection unit also takes cultural considerations into account in collecting preferences of members from different cultural areas and nationalities. For example, it proposes a travel plan that takes into account the cultural customs and preferences of each member. This makes it possible to generate an international travel plan.

[0065] The wish collection unit can use the emotion estimation function to analyze the emotions of members when they input their wishes in real time and make suggestions to reduce negative emotions. For example, when a member inputs their wishes, the wish collection unit can use the emotion estimation function to analyze the emotions in real time and make suggestions to reduce negative emotions. For example, if a negative emotion is detected, a positive suggestion is displayed. The wish collection unit can also use the emotion estimation function to analyze the emotions of members in real time and provide feedback to reduce negative emotions. For example, if a member is feeling stressed, a suggestion to help them relax is displayed. The wish collection unit can also use the emotion estimation function to analyze the emotions of members and automatically generate questions to reduce negative emotions. For example, if a member is feeling anxious, a question to help them feel reassured is displayed. This can reduce the member's negative emotions.

[0066] The plan generation unit can learn from past examples of successful and unsuccessful travel plans and generate an optimal plan. For example, the plan generation unit uses a generation AI to learn from past examples of successful and unsuccessful travel plans and generate an optimal plan. For example, the plan generation unit proposes a travel plan with a high probability of success based on past data. The plan generation unit also uses a database to learn from past examples of successful and unsuccessful travel plans. For example, the database of past travel plans is analyzed to extract successful and unsuccessful examples. The plan generation unit also uses a machine learning algorithm to learn from past examples of successful and unsuccessful travel plans. For example, the machine learning algorithm is used to classify successful and unsuccessful examples and generate an optimal plan. In this way, past examples of successful and unsuccessful examples can be learned and an optimal plan can be generated.

[0067] The plan generation unit can take into account information about seasons, weather, and local events when generating a travel plan. For example, the plan generation unit takes into account information about seasons and weather when generating a travel plan. For example, a beach resort is suggested for a summer travel plan, and a ski resort is suggested for a winter travel plan. The plan generation unit also takes into account information about local events. For example, a travel plan is generated taking into account festivals and events held locally. The plan generation unit also uses weather data and an event calendar to take into account information about seasons, weather, and local events. For example, the optimal time for the travel plan is suggested based on weather data. The plan generation unit also suggests a travel plan that takes into account information about local events based on the event calendar. This makes it possible to generate a travel plan that takes into account information about seasons, weather, and local events.

[0068] The plan generation unit can predict members' emotional reactions to plans generated using the emotion estimation function and prioritize plans that will elicit positive reactions. For example, when the generation AI summarizes, the plan generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The plan generation unit also uses the emotion estimation function to build a system that allows the generation AI to reflect the emotional elements of the answer in its evaluation. For example, it performs evaluation based on the emotion score. The plan generation unit also develops an algorithm that allows the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures emotional nuances, emotional elements can also be reflected in the evaluation.

[0069] The plan generation unit can simulate the generated travel plan in virtual reality (VR) to allow members to experience it in advance. The plan generation unit, for example, simulates the generated travel plan in virtual reality (VR) to allow members to experience it in advance. For example, tourist spots and accommodations are experienced in VR. The plan generation unit also simulates the travel plan using virtual reality (VR) technology. For example, a VR headset is used to virtually experience tourist spots at the travel destination. The plan generation unit also simulates the travel plan using simulation software. For example, simulation software is used to virtually experience tourist spots and accommodations at the travel destination. This allows members to experience the travel plan in advance.

[0070] The plan generation unit can generate multiple scenarios with different budgets and schedules, allowing members to select from them. For example, the plan generation unit uses a generation AI to generate multiple scenarios with different budgets and schedules, allowing members to select from them. For example, it proposes multiple travel plans based on budgets. The plan generation unit also uses a database to generate scenarios with different budgets and schedules. For example, it generates multiple travel plans based on budgets and schedules. The plan generation unit also uses a machine learning algorithm to generate scenarios with different budgets and schedules. For example, it uses a machine learning algorithm to generate multiple travel plans based on budgets and schedules. This allows multiple scenarios with different budgets and schedules to be generated, allowing members to select from them.

[0071] The plan generation unit can provide real-time feedback on the member's emotions regarding the plan generated using the emotion estimation function, and continuously adjust the optimal plan. The plan generation unit, for example, provides real-time feedback on the member's emotions regarding the generated travel plan, and continuously adjusts the optimal plan. For example, the plan can be adjusted based on an emotion score. The plan generation unit can also use the emotion estimation function to analyze the member's emotions in real time and provide feedback. For example, the plan can be adjusted if the member is dissatisfied. The plan generation unit can also use the emotion estimation function to analyze the member's emotions and provide feedback for continuously adjusting the optimal plan. For example, if the member is satisfied, the plan can be prioritized. In this way, the member's emotions can be provided in real time, and the optimal plan can be continuously adjusted.

[0072] The coordination unit can automatically detect differences of opinion between members and propose solutions. For example, the coordination unit uses a generative AI to automatically detect differences of opinion between members and propose solutions. For example, it detects differences in budget or schedule and proposes a coordination proposal. The coordination unit also uses a database to detect differences of opinion. For example, it stores members' wishes in a database and detects differences of opinion. The coordination unit also uses a machine learning algorithm to detect differences of opinion. For example, it uses a machine learning algorithm to classify differences of opinion and propose solutions. This makes it possible to automatically detect differences of opinion between members and propose solutions.

[0073] The adjustment unit can take into account the priorities of the members when making adjustments and prioritize reflect requests with high importance. For example, the adjustment unit can take into account the priorities of the members when making adjustments and prioritize reflect requests with high importance. For example, requests that everyone agrees on are reflected as the highest priority. The adjustment unit also uses a survey to take into account the priorities of the members. For example, it conducts a survey of the members and sets the priorities. The adjustment unit also uses interviews to take into account the priorities of the members. For example, it interviews the members and sets the priorities. In this way, it is possible to take into account the priorities of the members and prioritize reflect requests with high importance.

[0074] The adjustment unit can use the emotion estimation function to analyze the emotions of the member undergoing adjustment and make suggestions to reduce stress. The adjustment unit, for example, uses the emotion estimation function to analyze the emotions of the member undergoing adjustment and make suggestions to reduce stress. For example, if a negative emotion is detected, a suggestion to help the member relax is displayed. The adjustment unit also uses the emotion estimation function to analyze the emotions of the member in real time and provide feedback to help reduce stress. For example, if a member is feeling stressed, feedback to help the member relax is displayed. The adjustment unit also uses the emotion estimation function to analyze the emotions of the member and automatically generate questions to help reduce stress. For example, if a member is feeling anxious, a question to help the member relax is displayed. This makes it possible to make suggestions to help the member undergoing adjustment to reduce stress.

[0075] The adjustment unit can perform the adjustment process in real time by coordinating with a chatbot or a video conferencing system. The adjustment unit, for example, can perform the adjustment process in real time by coordinating with a chatbot. For example, the adjustment unit collects opinions of members through a chatbot and makes adjustments. The adjustment unit can also perform the adjustment process in real time by coordinating with a video conferencing system. For example, the adjustment unit collects opinions of members through a video conferencing and makes adjustments. The adjustment unit can also perform the adjustment process in real time by combining a chatbot and a video conferencing system. For example, the adjustment unit collects opinions through a chatbot and makes adjustments through a video conferencing. This allows the adjustment process to be performed in real time.

[0076] The adjustment unit allows adjustments between members who speak different languages ​​to proceed smoothly by having the generation AI automatically translate. The adjustment unit, for example, allows adjustments between members who speak different languages ​​to proceed smoothly by having the generation AI automatically translate. For example, adjustments are made by automatically translating between English and Japanese. The adjustment unit also uses the generation AI to adjust between members who speak different languages. For example, the generation AI performs automatic translation in real time to proceed smoothly. The adjustment unit also uses a translation algorithm to adjust between members who speak different languages. For example, the translation algorithm is used to automatically translate opinions between members who speak different languages ​​and make adjustments. This allows adjustments between members who speak different languages ​​to proceed smoothly.

[0077] The adjustment unit can use the emotion estimation function to monitor the emotions of the member during adjustment in real time and make suggestions to elicit positive emotions. The adjustment unit, for example, uses the emotion estimation function to monitor the emotions of the member during adjustment in real time and make suggestions to elicit positive emotions. For example, the adjustment unit displays suggestions to elicit positive emotions. The adjustment unit also uses the emotion estimation function to analyze the member's emotions in real time and provide feedback to elicit positive emotions. For example, if a member has positive emotions, the adjustment unit displays feedback that reinforces those emotions. The adjustment unit also uses the emotion estimation function to analyze the member's emotions and automatically generate questions to elicit positive emotions. For example, if a member is feeling happy, the adjustment unit displays questions that elicit those emotions. This makes it possible to make suggestions to elicit positive emotions from the member during adjustment.

[0078] The bookmark creation unit can learn from past travel "travel guidebooks" and automatically generate the optimal format and content. For example, the bookmark creation unit uses a generation AI to learn from past travel "travel guidebooks" and automatically generate the optimal format and content. For example, it suggests the optimal "travel guidebook" based on past data. The bookmark creation unit also uses a database to learn from past "travel guidebooks." For example, it analyzes a database of past "travel guidebooks" and extracts the optimal format and content. The bookmark creation unit also uses a machine learning algorithm to learn from past "travel guidebooks." For example, it uses a machine learning algorithm to classify and automatically generate the optimal format and content. This makes it possible to automatically generate the optimal format and content.

[0079] The travel guide creation unit can enhance safety by including information on local emergency contacts and medical institutions in the "travel guide." The travel guide creation unit can enhance safety, for example, by including local emergency contacts in the "travel guide." For example, it can include contact information for local police and embassies. The travel guide creation unit can also enhance safety by including information on local medical institutions in the "travel guide." For example, it can include contact information for local hospitals and clinics. The travel guide creation unit can also enhance safety by including emergency response methods in the "travel guide." For example, it can include evacuation locations and contact methods in the event of an emergency. In this way, safety can be enhanced by including information on emergency contacts and medical institutions in the travel guide.

[0080] The bookmark creation unit can use the emotion estimation function to predict the emotional reaction of members to the "travel guide" and prioritize content that will elicit a positive reaction. For example, the bookmark creation unit predicts the emotional reaction of members to the "travel guide" and prioritizes content that will elicit a positive reaction. For example, it writes down content with a high emotion score. The bookmark creation unit also uses the emotion estimation function to predict the emotional reaction of members and selects content that will elicit a positive reaction. For example, it writes down content that will make members happy. The bookmark creation unit also uses the emotion estimation function to analyze members' emotions and automatically generate content that will elicit a positive reaction. For example, it writes down content that will satisfy members. This makes it possible to prioritize content that will elicit a positive reaction from members.

[0081] The bookmark creation unit can link the "travel bookmark" with a smartphone app or a wearable device, allowing it to be updated in real time. The bookmark creation unit, for example, links the "travel bookmark" with a smartphone app, allowing it to be updated in real time. For example, the latest information can be provided through the app. The bookmark creation unit can also link the "travel bookmark" with a wearable device, allowing it to be updated in real time. For example, the latest information can be provided through the wearable device. The bookmark creation unit can also combine a smartphone app and a wearable device, allowing the "travel bookmark" to be updated in real time. For example, the latest information can be provided through the app and the device. This makes it possible to update the travel bookmark in real time.

[0082] The bookmark creation unit can automatically translate the "travel guide" into different languages ​​to accommodate international travel. The bookmark creation unit, for example, can automatically translate the "travel guide" into different languages ​​to accommodate international travel. For example, it translates it into English or Chinese and provides it. The bookmark creation unit also uses a generation AI to automatically translate the "travel guide" into different languages. For example, the generation AI performs automatic translation in real time to accommodate different languages. The bookmark creation unit also uses a translation algorithm to automatically translate the "travel guide" into different languages. For example, it uses a translation algorithm to generate "travel guides" that accommodate different languages. This allows the travel guide to be automatically translated into different languages ​​to accommodate international travel.

[0083] The bookmark creation unit uses the emotion estimation function to provide real-time feedback on members' emotions regarding the "travel guide" and continuously improve the content. The bookmark creation unit, for example, provides real-time feedback on members' emotions regarding the "travel guide" and continuously improves the content. For example, the content is adjusted based on the emotion score. The bookmark creation unit also uses the emotion estimation function to analyze members' emotions in real time and provide feedback. For example, if a member is dissatisfied, the content is improved. The bookmark creation unit also uses the emotion estimation function to analyze members' emotions and provide feedback to continuously improve the content. For example, if a member is satisfied, that content is prioritized. This allows the content of the travel guide to be continuously improved.

[0084] The reservation agent unit predicts the optimal timing to make a reservation and can make the reservation at the most advantageous price. The reservation agent unit, for example, uses a generation AI to predict the optimal timing to make a reservation and makes the reservation at the most advantageous price. For example, it suggests the optimal reservation timing based on past data. The reservation agent unit also uses a database to predict the optimal timing. For example, it analyzes past price fluctuation data and predicts the optimal reservation timing. The reservation agent unit also uses a machine learning algorithm to predict the optimal timing. For example, it uses a machine learning algorithm to predict the optimal reservation timing. This allows reservations to be made at the optimal timing and at the most advantageous price.

[0085] The reservation agent unit can consider the member's past reservation history and preferences when making a reservation and suggest the optimal option. The reservation agent unit, for example, considers the member's past reservation history when making a reservation and suggests the optimal option. For example, suggestions are made based on accommodations and transportation methods used in the past. The reservation agent unit also uses surveys to consider the member's preferences. For example, it may conduct a survey of members and collect their preferences. The reservation agent unit also uses interviews to consider the member's preferences. For example, it may interview members and collect their preferences. This allows the reservation agent unit to consider the member's past reservation history and preferences and suggest the optimal option.

[0086] The reservation agent unit can use the emotion estimation function to make suggestions to reduce stress felt by members during the reservation process. The reservation agent unit, for example, uses the emotion estimation function to make suggestions to reduce stress felt by members during the reservation process. For example, if a negative emotion is detected, suggestions to help members relax are displayed. The reservation agent unit also uses the emotion estimation function to analyze members' emotions in real time and provide feedback to reduce stress. For example, if a member is feeling stressed, feedback to help members relax is displayed. The reservation agent unit also uses the emotion estimation function to analyze members' emotions and automatically generate questions to reduce stress. For example, if a member is feeling anxious, questions to help members feel reassured are displayed. This makes it possible to make suggestions to reduce stress felt by members during the reservation process.

[0087] The reservation agent unit can provide optimal options by coordinating with multiple travel agencies and reservation sites to handle all reservations. The reservation agent unit, for example, can coordinating with multiple travel agencies to handle all reservations and provide optimal options. For example, the optimal reservation is made based on information from multiple agencies. The reservation agent unit also coordinating with reservation sites to provide optimal options. For example, the optimal reservation is made based on information from multiple reservation sites. The reservation agent unit also combines travel agencies and reservation sites to provide optimal options. For example, the optimal reservation is made based on information from the agency and the site. This allows the unit to coordinating with multiple travel agencies and reservation sites to provide optimal options.

[0088] The reservation agent can accommodate different currencies and payment methods when making reservations, and can also accommodate international travel. The reservation agent can, for example, accommodate different currencies when making reservations, and can also accommodate international travel. For example, it accepts payments in multiple currencies. The reservation agent can also accommodate different payment methods. For example, it can accept payments by credit card or electronic money. The reservation agent can also apply exchange rates to accommodate different currencies and payment methods. For example, it can accept payments in different currencies based on the exchange rate. This allows the reservation agent to accommodate different currencies and payment methods, and can also accommodate international travel.

[0089] The reservation agent unit can use the emotion estimation function to monitor the member's emotions in real time during the reservation process and make suggestions to elicit positive emotions. The reservation agent unit, for example, uses the emotion estimation function to monitor the member's emotions in real time during the reservation process and make suggestions to elicit positive emotions. For example, it displays suggestions to elicit positive emotions. The reservation agent unit also uses the emotion estimation function to analyze the member's emotions in real time and provide feedback to elicit positive emotions. For example, if the member has positive emotions, it displays feedback that reinforces those emotions. The reservation agent unit also uses the emotion estimation function to analyze the member's emotions and automatically generate questions to elicit positive emotions. For example, if the member is feeling happy, it displays questions that elicit those emotions. This makes it possible to make suggestions to elicit positive emotions in the member during the reservation process.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The wish collection unit uses voice input and image recognition to collect the wishes of members, thereby providing a more intuitive interface. For example, when a member inputs their wishes, the wishes are collected using voice input. For example, voice recognition technology is used to convert what the member says into text and collect the text as their wishes. The wish collection unit also uses image recognition technology to analyze images when the member inputs their wishes and collect the wishes. For example, images taken by the member are analyzed and collected as their wishes. The wish collection unit also combines voice input and image recognition to collect the wishes. For example, what the member says and images taken are combined and analyzed and collected as their wishes. This allows a more intuitive interface to be provided.

[0092] The preference collection unit can collect preferences of members from different cultural areas and nationalities and generate an international travel plan. For example, the preferences of members from different cultural areas and nationalities are collected and an international travel plan is generated. For example, a travel plan that takes into account the cultural background of each member is proposed. The preference collection unit can also collect preferences of members who speak different languages ​​and generate an international travel plan. For example, a travel plan that corresponds to the language of each member is proposed. The preference collection unit can also take cultural considerations into account in order to collect preferences of members from different cultural areas and nationalities. For example, a travel plan that takes into account the cultural habits and preferences of each member is proposed. This makes it possible to generate an international travel plan.

[0093] The plan generation unit can learn from past examples of successful and unsuccessful travel plans and generate the optimal plan. For example, the generation AI learns from past examples of successful and unsuccessful travel plans and generates the optimal plan. For example, it proposes travel plans with a high probability of success based on past data. The plan generation unit also uses a database to learn from past examples of successful and unsuccessful travel plans. For example, it analyzes a database of past travel plans and extracts successful and unsuccessful examples. The plan generation unit also uses a machine learning algorithm to learn from past examples of successful and unsuccessful travel plans. For example, it uses a machine learning algorithm to classify successful and unsuccessful examples and generate the optimal plan. In this way, it is possible to learn from past examples of successful and unsuccessful examples and generate the optimal plan.

[0094] The plan generation unit can take into account information about the season, weather, and local events when generating a travel plan. For example, the season and weather are taken into account when generating a travel plan. For example, a beach resort is suggested for a summer travel plan, and a ski resort is suggested for a winter travel plan. The plan generation unit also takes into account information about local events. For example, a travel plan is generated taking into account festivals and events held locally. The plan generation unit also uses weather data and an event calendar to take into account information about the season, weather, and local events. For example, the optimal time for the travel plan is suggested based on weather data. The plan generation unit also suggests a travel plan that takes into account information about local events based on the event calendar. This makes it possible to generate a travel plan that takes into account information about the season, weather, and local events.

[0095] The plan generation unit can simulate the generated travel plan in virtual reality (VR) to allow members to experience it in advance. For example, the generated travel plan can be simulated in virtual reality (VR) to allow members to experience it in advance. For example, tourist spots and accommodations can be experienced in VR. The plan generation unit also simulates the travel plan using virtual reality (VR) technology. For example, a VR headset can be used to virtually experience tourist spots at the travel destination. The plan generation unit also simulates the travel plan using simulation software. For example, simulation software can be used to virtually experience tourist spots and accommodations at the travel destination. This allows members to experience the travel plan in advance.

[0096] The wish collection unit can use the emotion estimation function to analyze the emotions of members when they input their wishes, and automatically generate questions that elicit positive emotions. For example, when a member inputs their wishes, the emotion estimation function is used to analyze their emotions and automatically generate questions that elicit positive emotions. For example, when a member inputs their wishes, questions that elicit positive emotions are displayed. The wish collection unit also uses the emotion estimation function to analyze the emotions of members in real time, and generate questions that elicit positive emotions. For example, if a member is feeling stressed, questions that will help them relax are displayed. The wish collection unit also uses the emotion estimation function to analyze the emotions of members, and provide feedback that elicits positive emotions. For example, positive feedback is displayed when a member inputs their wishes. This makes it possible to elicit positive emotions from members.

[0097] The wish collection unit can use the emotion estimation function to analyze the emotions of members in real time when they input their wishes and make suggestions to reduce negative emotions. For example, when a member inputs their wishes, the emotion estimation function can be used to analyze their emotions in real time and make suggestions to reduce negative emotions. For example, if a negative emotion is detected, a positive suggestion can be displayed. The wish collection unit can also use the emotion estimation function to analyze the emotions of members in real time and provide feedback to reduce negative emotions. For example, if a member is feeling stressed, a suggestion to help them relax can be displayed. The wish collection unit can also use the emotion estimation function to analyze the emotions of members and automatically generate questions to reduce negative emotions. For example, if a member is feeling anxious, a question to help them feel reassured can be displayed. This can reduce the member's negative emotions.

[0098] The plan generation unit can predict members' emotional reactions to plans generated using the emotion estimation function and prioritize plans that will elicit a positive reaction. For example, when the generation AI summarizes, it uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The plan generation unit also uses the emotion estimation function to build a system that allows the generation AI to reflect the emotional elements of the answer in its evaluation. For example, it performs evaluation based on the emotion score. The plan generation unit also develops an algorithm that allows the generation AI to use the emotion estimation function to generate summaries that capture the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects this in the evaluation. In this way, by generating a summary that captures emotional nuances, emotional elements can also be reflected in the evaluation.

[0099] The plan generation unit can provide real-time feedback on the member's emotions regarding the plan generated using the emotion estimation function, and continuously adjust the optimal plan. For example, the plan can be adjusted based on an emotion score by providing real-time feedback on the member's emotions regarding the generated travel plan. The plan generation unit can also use the emotion estimation function to analyze the member's emotions in real time and provide feedback. For example, the plan can be adjusted if the member is dissatisfied. The plan generation unit can also use the emotion estimation function to analyze the member's emotions and provide feedback for continuously adjusting the optimal plan. For example, the plan can be prioritized if the member is satisfied. This allows the plan to be adjusted based on real-time feedback on the member's emotions.

[0100] The adjustment unit can use the emotion estimation function to monitor the emotions of the member during adjustment in real time and make suggestions to elicit positive emotions. For example, the emotion estimation function can be used to monitor the emotions of the member during adjustment in real time and make suggestions to elicit positive emotions. For example, suggestions to elicit positive emotions can be displayed. The adjustment unit also uses the emotion estimation function to analyze the member's emotions in real time and provide feedback to elicit positive emotions. For example, if a member has positive emotions, feedback that reinforces those emotions can be displayed. The adjustment unit also uses the emotion estimation function to analyze the member's emotions and automatically generate questions to elicit positive emotions. For example, if a member is feeling happy, a question that elicits that emotion can be displayed. This makes it possible to make suggestions to elicit positive emotions from the member during adjustment.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The preference collection department collects member preferences. For example, member preferences can be collected through questionnaires, interviews, or online forms. Step 2: The analysis unit analyzes the preferences collected by the preference collection unit. For example, the preferences can be analyzed using data mining techniques, statistical analysis techniques, or machine learning algorithms. Step 3: The plan generation unit generates a travel plan based on the desires analyzed by the analysis unit. For example, the travel plan can be generated taking into account the dates, places to visit, and activities. Step 4: The adjustment unit adjusts the travel plan generated by the plan generation unit. For example, it can adjust opinions among members, schedules, and budgets. Step 5: The travel guide creation unit creates a "travel guide" based on the travel plan adjusted by the adjustment unit. For example, it can include an itinerary, information about places to visit, and important points to note. Step 6: The reservation agent handles all reservations based on the "travel guidebook" created by the guidebook creation unit. For example, reservations can be made for transportation, accommodation, and activities.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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 wish collection department that collects member wishes; an analysis unit that analyzes the wishes collected by the wish collection unit; a plan generation unit that generates a travel plan based on the desires analyzed by the analysis unit; an adjustment unit that adjusts the travel plan generated by the plan generation unit; a bookmark creation unit that creates a "travel guide" based on the travel plan adjusted by the adjustment unit; a reservation agent unit that collectively handles reservations based on the "travel guidebook" created by the guidebook creation unit; A system characterized by:

2. The request collection unit Analyze the member's past travel history and the content of posts on the SNS, and automatically extract their latent desires and preferences.

2. The system of claim 1.

3. The plan generation unit Learn from past examples of success and failure of the travel plan and generate the optimal plan 2. The system of claim 1.

4. The adjustment unit Automatically detect disagreements between said members and propose solutions 2. The system of claim 1.

5. The bookmark creation unit Learn from past travel itineraries and automatically generate the optimal format and content 2. The system of claim 1.

6. The reservation agent unit Predicting the best timing for making the reservation and making the reservation at the most advantageous price 2. The system of claim 1.

7. The request collection unit Analyzing the emotions of the member when inputting their wishes and automatically generating questions that elicit positive emotions 2. The system of claim 1.

8. The plan generation unit Predicting the emotional reactions of the members to the generated plans and prioritizing the plans that will generate positive reactions.

2. The system of claim 1.

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