system

Abstract

The system according to this embodiment aims to facilitate travel planning in a simple and efficient manner. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and an arrangement unit. The reception unit receives input from the user regarding their travel preferences and conditions. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the most suitable destination based on the information analyzed by the analysis unit. The arrangement unit makes reservations for activities and arranges accommodations and transportation based on the destination proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of labor and time to make a travel plan, and it is difficult to proceed efficiently.

[0005] The system according to the embodiment aims to make a travel plan proceed simply and efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and an arrangement unit. The reception unit receives the user's travel preferences and conditions. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the most suitable destination based on the information analyzed by the analysis unit. The arrangement unit makes reservations for activities and arranges accommodations and transportation based on the destination proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for easy and efficient travel planning. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The TravelMate AI Planner, according to an embodiment of the present invention, is an AI-powered platform for easily and efficiently planning trips. This platform supports every step of a trip, including suggesting destinations based on the traveler's preferences and schedule, booking activities, and arranging accommodation and transportation. TravelMate AI Planner provides customized plans tailored to the traveler's preferences and schedule, enabling smooth travel planning. Furthermore, the use of AI reduces the stress of planning and supports efficient and enjoyable travel preparation. For example, when planning a family trip, TravelMate AI Planner considers the preferences and schedules of all family members and proposes a plan that everyone can enjoy. It supports every step, including arranging activities for children, family-friendly accommodations, and transportation. For adventurous travelers, it suggests activities such as trekking and skydiving, and handles activity bookings, accommodation arrangements, and transportation arrangements. This allows travelers to easily create the perfect plan for themselves and realize memorable trips. Through this mechanism, TravelMate AI Planner not only provides customized plans tailored to the traveler's needs, enabling smooth travel planning and a fulfilling experience. The use of AI reduces the stress of planning and supports efficient and enjoyable travel preparation. This allows TravelMate AI Planner to provide customized plans tailored to travelers' needs, not only making travel planning smoother but also enabling a more fulfilling experience. By utilizing AI, it can reduce the stress of planning and support efficient and enjoyable travel preparation.

[0029] The TravelMate AI Planner according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and an arrangement unit. The reception unit receives input from the user regarding their travel preferences and conditions. These preferences include, but are not limited to, a travel destination, budget, dates, and type of accommodation. The reception unit provides, for example, an interface for the user to select a travel destination and input their budget and dates. The reception unit also allows the user to input the type of accommodation and desired activities. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, uses AI to analyze the user's travel preferences and conditions and selects the optimal destination. The analysis unit suggests the optimal destination based, for example, the user's preferences, budget, and purpose of travel. The analysis unit can also select the optimal destination by considering the user's past travel history and current living situation. The suggestion unit suggests the optimal destination based on the information analyzed by the analysis unit. The suggestion unit suggests the optimal destination based on the user's preferences and schedule using AI. For example, if the user is planning a family trip, the suggestion unit suggests a destination that the whole family can enjoy. Furthermore, the suggestion unit can also suggest destinations where adventurous travelers can enjoy activities such as trekking and skydiving. The arrangement unit makes reservations for activities, accommodations, and transportation based on the destinations suggested by the suggestion unit. For example, the arrangement unit makes reservations for accommodations based on destinations suggested using AI. For example, the arrangement unit makes reservations for the most suitable accommodations based on the type of accommodation and budget desired by the user. The arrangement unit can also make arrangements for transportation based on the suggested destinations. For example, the arrangement unit makes arrangements for the most suitable transportations based on the mode of transportation and budget desired by the user. As a result, the TravelMate AI Planner according to this embodiment can efficiently input, analyze, suggest, and arrange the user's travel wishes and conditions.

[0030] The reception desk inputs the user's travel preferences and conditions. These preferences include, but are not limited to, destination, budget, dates, and type of accommodation. For example, the reception desk provides an interface for users to select a destination and input their budget and dates. Specifically, users can select from a list of potential destinations and input their budget and dates in a calendar format via a website or mobile app. The reception desk also allows users to input the type of accommodation and desired activities. For example, users can select accommodation types such as hotels, resorts, or guesthouses, and further specify detailed conditions such as rooms with pools, pet-friendly options, and breakfast included. Furthermore, the reception desk provides options for users to select desired activities. For example, users can select from categories such as sightseeing, shopping, outdoor activities, and cultural experiences, and list specific activities. This allows the reception desk to collect detailed information on the user's diverse needs and preferences and provide it as input data to the subsequent analysis department. The reception desk saves the information entered by users in real time, allowing for modifications and additions as needed. Furthermore, by remembering and reusing information previously entered by the user, the system can reduce the effort required for data entry. This allows the reception desk to enhance user convenience and support a smooth start to travel planning.

[0031] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze the user's travel preferences and conditions and select the optimal destination. Specifically, the AI ​​uses natural language processing technology to understand the user's input and narrow down the candidate travel destinations. For example, if a user enters "I want to relax at a beach resort," the AI ​​will list destinations related to beach resorts and select the best candidate based on budget and schedule. The analysis department can also select the optimal destination by considering the user's past travel history and current living situation. For example, it can suggest new destinations the user has not yet visited based on places they have visited in the past or their hobbies and preferences. Furthermore, the analysis department can refer to the user's social media posts and ratings on review sites to identify places that the user likes and that have high ratings. In this way, the analysis department comprehensively analyzes diverse user information and provides basic data for proposing the optimal travel plan. The analysis department processes data in real time and provides analysis results quickly according to the user's input. The analysis department can also utilize historical data and statistical information to make suggestions that take into account long-term trends and popular spots each season. This allows the analysis unit to quickly and accurately select the most suitable destination based on the user's travel preferences and conditions, and provide this data to the subsequent recommendation unit.

[0032] The suggestion department proposes the optimal destination based on the information analyzed by the analysis department. For example, the suggestion department uses AI to suggest the best destination based on the user's preferences and schedule. Specifically, the AI ​​generates multiple travel plans based on the user's input information and analysis results, and presents them to the user. For example, if the user is planning a family trip, it will suggest a destination that the whole family can enjoy. Family travel plans include activities for children, family-friendly accommodations, and sightseeing spots that the whole family can enjoy. The suggestion department can also suggest destinations where adventurous travelers can enjoy activities such as trekking or skydiving. Adventure travel plans include detailed activity schedules, necessary equipment, and local guide information. Furthermore, the suggestion department proposes the optimal plan according to the user's budget and schedule. For example, it can propose travel plans that allow users to enjoy themselves to the fullest with a limited budget, or plans that allow for efficient sightseeing in a short period of time. The suggestion department presents multiple plans simultaneously, clearly indicating the advantages and disadvantages of each, so that the user can compare and consider the proposed plans. The suggestion department can also revise its suggestions based on user feedback and re-propose a plan that is closer to the user's wishes. This allows the proposal department to provide optimal travel plans that meet the diverse needs of users, thereby increasing user satisfaction.

[0033] The booking department makes reservations for activities, accommodations, and transportation based on destinations suggested by the proposal department. For example, the booking department makes reservations for accommodations based on destinations suggested using AI. Specifically, the booking department reserves the most suitable accommodation based on the user's desired type of accommodation and budget. For example, if a user wants a resort hotel, the booking department checks the availability of resort hotels and reserves the most suitable room within the budget. The booking department can also make arrangements for transportation based on the suggested destination. For example, it reserves the most suitable transportation based on the user's desired mode of transport and budget. This includes booking flight tickets and rental cars, and arranging local transportation. Furthermore, the booking department also makes reservations for suggested activities. For example, this includes booking sightseeing tours and activities, and arranging local guides. The booking department can make all the necessary arrangements in one place so that users can enjoy their trip smoothly based on the proposed plan. The booking department also provides support such as confirming, changing, and canceling reservations. In this way, the booking department efficiently supports users' travel plans and allows users to enjoy their trip with peace of mind. Furthermore, the booking department can improve booking details and enhance services based on user feedback. This allows the booking department to increase user satisfaction and contribute to acquiring repeat customers.

[0034] The booking department includes a customization department that provides customized plans tailored to specific travel types, such as family trips and adventure trips. For example, in the case of a family trip, the booking department considers the preferences and schedules of all family members and proposes a plan that everyone can enjoy. The booking department arranges activities for children, family-friendly accommodations, and transportation. Furthermore, for adventure-loving travelers, the booking department can suggest activities such as trekking and skydiving, and can also book activities, accommodations, and transportation. This allows for customized plans tailored to specific travel types, enabling travelers to plan trips that meet their needs.

[0035] The suggestion section includes an activity suggestion section that proposes the most suitable activities based on the traveler's preferences and schedule. For example, the suggestion section uses AI to suggest the most suitable activities based on the traveler's preferences and schedule. If a traveler is planning a family trip, for example, the suggestion section will suggest activities that the whole family can enjoy. The suggestion section can also suggest activities such as trekking or skydiving to adventurous travelers. In this way, traveler satisfaction is improved by suggesting the most suitable activities based on the traveler's preferences and schedule.

[0036] The booking department makes accommodation reservations based on the suggested destination. For example, the booking department uses AI to make accommodation reservations based on the suggested destination. For example, the booking department can book the most suitable accommodation based on the type of accommodation and budget desired by the user. The booking department can also book the most suitable accommodation based on the type of accommodation and budget desired by the user. This allows travel planning to proceed smoothly by making accommodation reservations based on the suggested destination.

[0037] The booking department arranges transportation based on the proposed destination. For example, the booking department uses AI to arrange transportation based on the proposed destination. For example, the booking department arranges the most suitable transportation based on the user's desired transportation and budget. The booking department can also arrange the most suitable transportation based on the user's desired transportation and budget. This allows travel planning to proceed smoothly by arranging transportation based on the proposed destination.

[0038] The suggestion function proposes the best destination based on the traveler's preferences and schedule. For example, it uses AI to suggest the best destination based on the traveler's preferences and schedule. If a traveler is planning a family trip, the suggestion function will suggest a destination that the whole family can enjoy. It can also suggest destinations where adventurous travelers can enjoy activities such as trekking or skydiving. By suggesting the best destination based on the traveler's preferences and schedule, this improves traveler satisfaction.

[0039] The reception desk analyzes the user's past travel history and selects the optimal input method. For example, the reception desk uses AI to analyze the user's past travel history. For example, the reception desk automatically displays travel preferences and conditions that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests travel preferences and conditions to be used at a specific time of day based on the user's past travel history. In this way, by analyzing the user's past travel history, the optimal input method can be provided.

[0040] The reception desk filters the user's travel preferences and conditions based on their current living situation and areas of interest. For example, the reception desk uses AI to analyze the user's current living situation and areas of interest. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's current living situation. For example, the reception desk filters relevant travel preferences and conditions based on the user's areas of interest. For example, the reception desk suggests optimal travel preferences and conditions based on the user's current living situation and areas of interest. This allows users to input more appropriate travel preferences and conditions by filtering based on their current living situation and areas of interest.

[0041] The reception desk prioritizes inputting highly relevant information by considering the user's geographical location when they input their travel preferences and conditions. For example, the reception desk uses AI to analyze the user's geographical location. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's current location. For example, the reception desk filters relevant travel preferences and conditions based on the user's geographical location. For example, the reception desk suggests optimal travel preferences and conditions based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant information can be prioritized.

[0042] The reception desk analyzes the user's social media activity when they input their travel preferences and conditions, and inputs relevant information. For example, the reception desk uses AI to analyze the user's social media activity. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's social media activity. For example, the reception desk analyzes the user's social media activity and filters relevant travel preferences and conditions. For example, the reception desk suggests optimal travel preferences and conditions based on the user's social media activity. This allows for the input of relevant information by analyzing the user's social media activity.

[0043] The analysis unit adjusts the level of detail in the analysis based on the importance of travel preferences and conditions. For example, the analysis unit uses AI to evaluate the importance of travel preferences and conditions. For example, the analysis unit performs a detailed analysis on important travel preferences and conditions. For example, the analysis unit performs a simplified analysis on lower-priority travel preferences and conditions. The analysis unit adjusts the level of detail in the analysis based on the importance of travel preferences and conditions. This allows for more appropriate analysis by adjusting the level of detail in the analysis based on the importance of travel preferences and conditions.

[0044] The analysis unit applies different analysis algorithms depending on the travel category during the analysis. For example, the analysis unit uses AI to classify travel categories. For example, in the case of a family trip, the analysis unit applies an analysis algorithm that takes into account the preferences and schedules of all family members. For example, in the case of an adventure trip, the analysis unit applies an analysis algorithm that takes into account the risks and difficulty levels of the activities. For example, in the case of a business trip, the analysis unit applies an analysis algorithm that emphasizes efficiency and time management. By applying different analysis algorithms depending on the travel category, more appropriate analysis becomes possible.

[0045] The analysis unit determines the priority of analysis based on the submission timing of travel preferences and conditions. For example, the analysis unit uses AI to evaluate the submission timing of travel preferences and conditions. For example, the analysis unit prioritizes the analysis of travel preferences and conditions that have been submitted most recently. For example, the analysis unit postpones the analysis of travel preferences and conditions that have been submitted more recently. For example, the analysis unit adjusts the priority of analysis based on the submission timing. By determining the priority of analysis based on the submission timing of travel preferences and conditions, more appropriate analysis becomes possible.

[0046] The analysis unit adjusts the order of analysis based on the relevance of travel preferences and conditions during the analysis process. For example, the analysis unit uses AI to evaluate the relevance of travel preferences and conditions. For example, the analysis unit prioritizes analyzing highly relevant travel preferences and conditions. For example, the analysis unit postpones analyzing less relevant travel preferences and conditions. The analysis unit adjusts the order of analysis based on the relevance of travel preferences and conditions. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of travel preferences and conditions.

[0047] The proposal department adjusts the level of detail in its proposals based on the importance of the destination. For example, the proposal department uses AI to evaluate the importance of the destination. For example, the proposal department provides detailed proposals for important destinations. For example, the proposal department provides simplified proposals for lower-priority destinations. The proposal department adjusts the level of detail in its proposals based on the importance of the destination. This allows for more appropriate proposals by adjusting the level of detail in proposals based on the importance of the destination.

[0048] The suggestion function applies different suggestion algorithms depending on the destination category when making suggestions. For example, the suggestion function uses AI to classify destination categories. For example, in the case of a family trip, the suggestion function applies an algorithm that suggests activities that the whole family can enjoy. For example, in the case of an adventure trip, the suggestion function applies an algorithm that suggests activities that take risk and difficulty into consideration. For example, in the case of a business trip, the suggestion function applies an algorithm that suggests activities that prioritize efficiency and time management. By applying different suggestion algorithms depending on the destination category, more appropriate suggestions can be made.

[0049] The proposal team determines the priority of proposals based on the submission timing of destinations. For example, the proposal team uses AI to evaluate the submission timing of destinations. For example, the proposal team prioritizes destinations that have been submitted recently. For example, the proposal team postpones destinations that have been submitted earlier. For example, the proposal team adjusts the priority of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the submission timing of destinations.

[0050] The suggestion function adjusts the order of suggestions based on the relevance of the destinations. For example, the suggestion function uses AI to evaluate the relevance of destinations. For example, the suggestion function prioritizes suggesting highly relevant destinations. For example, the suggestion function postpones suggesting less relevant destinations. For example, the suggestion function adjusts the order of suggestions based on the relevance of the destinations. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the destinations.

[0051] The booking department analyzes the user's past travel history to select the optimal booking method. For example, the booking department uses AI to analyze the user's past travel history. For example, the booking department proposes the optimal booking method based on the booking methods the user has used in the past. For example, the booking department proposes booking methods that avoid congestion based on the user's past travel history. For example, the booking department analyzes the user's past travel history to propose the most efficient booking method. In this way, by analyzing the user's past travel history, the optimal booking method can be provided.

[0052] The booking unit customizes the booking method based on the user's current living situation when booking. The booking unit analyzes the user's current living situation using, for example, AI. The booking unit proposes the optimal booking method based on the user's current living situation. The booking unit customizes the booking method based on the user's current living situation. The booking unit adjusts the booking method based on the user's current living situation. By customizing the booking method based on the user's current living situation, more appropriate booking becomes possible.

[0053] The booking unit selects the optimal booking method when booking, taking into account the user's geographical location information. The booking unit analyzes the user's geographical location information using, for example, AI. The booking unit proposes the optimal booking method based on the user's current location. The booking unit customizes the booking method based on the user's geographical location information. The booking unit adjusts the booking method based on the user's geographical location information. This allows the system to provide the optimal booking method by considering the user's geographical location information.

[0054] The arrangement department analyzes the user's social media activity and proposes arrangement methods during the arrangement process. For example, the arrangement department uses AI to analyze the user's social media activity. For example, the arrangement department proposes the optimal arrangement method based on the user's social media activity. For example, the arrangement department analyzes the user's social media activity and customizes the arrangement methods. For example, the arrangement department adjusts the arrangement methods based on the user's social media activity. This allows the system to provide the optimal arrangement method by analyzing the user's social media activity.

[0055] The customization department analyzes the user's past travel history to select the optimal customization method during the customization process. For example, the customization department uses AI to analyze the user's past travel history. For example, the customization department proposes the optimal customization method based on the user's past customization methods. For example, the customization department proposes a customization method that avoids congestion based on the user's past travel history. For example, the customization department analyzes the user's past travel history to propose the most efficient customization method. In this way, by analyzing the user's past travel history, the optimal customization method can be provided.

[0056] The customization unit selects the optimal customization method during customization, taking into account the user's geographical location information. For example, the customization unit analyzes the user's geographical location information using AI. For example, the customization unit proposes the optimal customization method based on the user's current location. For example, the customization unit adjusts the means of customization based on the user's geographical location information. For example, the customization unit customizes the means of customization based on the user's geographical location information. This allows the system to provide the optimal customization method by considering the user's geographical location information.

[0057] The activity suggestion unit provides optimal suggestions by referring to the user's past activity history when suggesting activities. For example, the activity suggestion unit uses AI to analyze the user's past activity history. For example, the activity suggestion unit suggests the optimal activity based on the activities the user has participated in in the past. For example, the activity suggestion unit suggests activities that avoid crowds based on the user's past activity history. For example, the activity suggestion unit analyzes the user's past activity history and suggests the most efficient activity. In this way, by referring to the user's past activity history, it can provide optimal suggestions.

[0058] The activity suggestion unit provides optimal suggestions by considering the user's geographical location information when suggesting activities. For example, the activity suggestion unit analyzes the user's geographical location information using AI. For example, the activity suggestion unit suggests the optimal activity based on the user's current location. For example, the activity suggestion unit adjusts the means of the activity based on the user's geographical location information. For example, the activity suggestion unit customizes the means of the activity based on the user's geographical location information. This makes it possible to suggest optimal activities by considering the user's geographical location information.

[0059] The activity suggestion unit filters activity suggestions based on the user's current lifestyle and areas of interest. For example, the activity suggestion unit uses AI to analyze the user's current lifestyle and areas of interest. For example, the activity suggestion unit prioritizes displaying relevant activities based on the user's current lifestyle. For example, the activity suggestion unit filters relevant activities based on the user's areas of interest. For example, the activity suggestion unit proposes the most suitable activity based on the user's current lifestyle and areas of interest. This allows for optimal activity suggestions by filtering based on the user's current lifestyle and areas of interest.

[0060] The activity suggestion unit provides optimal suggestions by taking into account the user's current weather information when suggesting activities. For example, the activity suggestion unit uses AI to analyze the user's current weather information. For example, when it is raining, the activity suggestion unit will prioritize suggesting indoor activities. For example, when it is sunny, the activity suggestion unit will suggest outdoor activities. For example, on a snowy day, the activity suggestion unit will suggest activities such as skiing or snowboarding. In this way, it is possible to suggest the most optimal activities by taking into account the user's current weather information.

[0061] The activity suggestion unit proposes the most suitable activity when suggesting activities, taking into account the user's health condition. For example, the activity suggestion unit analyzes the user's health condition using AI. For example, the activity suggestion unit prioritizes displaying relevant activities based on the user's health condition. For example, the activity suggestion unit filters relevant activities based on the user's health condition. For example, the activity suggestion unit proposes the most suitable activity based on the user's health condition. This makes it possible to suggest the most suitable activity by considering the user's health condition.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The suggestion function can analyze a user's past travel history and propose new destinations they have never visited before. For example, it can suggest new cities while avoiding cities the user has visited previously. It can also suggest activities the user has never participated in before. Furthermore, it can suggest accommodations and modes of transportation the user has never used. In this way, by analyzing a user's past travel history, it can provide new experiences.

[0064] The booking department can propose the most suitable booking method by considering the user's current lifestyle. For example, if the user is busy, the booking department can propose a quick booking method. If the user is relaxed, the booking department can propose a detailed booking method. Furthermore, if the user is planning a trip within a specific budget, the booking department can propose a booking method that fits that budget. In this way, by considering the user's current lifestyle, the booking department can provide the most suitable booking method.

[0065] The analysis unit can apply different analysis algorithms depending on the travel category. For example, for family trips, it can apply an analysis algorithm that takes into account the preferences and schedules of all family members. For adventure trips, it can apply an analysis algorithm that takes into account the risks and difficulty levels of the activities. Furthermore, for business trips, it can apply an analysis algorithm that emphasizes efficiency and time management. By applying different analysis algorithms according to the travel category, more appropriate analysis becomes possible.

[0066] The suggestion function can provide optimal suggestions by taking into account the user's current weather information. For example, in rainy weather, it will prioritize suggesting indoor activities. In sunny weather, it can suggest outdoor activities. Furthermore, on snowy days, it can suggest activities such as skiing and snowboarding. This allows for optimal activity suggestions by considering the user's current weather information.

[0067] The booking unit can select the optimal booking method by considering the user's geographical location information. For example, it can propose the optimal booking method based on the user's current location. It can also customize the booking method based on the user's geographical location information. Furthermore, it can adjust the booking method based on the user's geographical location information. In this way, by considering the user's geographical location information, the optimal booking method can be provided.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk inputs the user's travel preferences and requirements. These preferences include, for example, destination, budget, dates, and type of accommodation. The reception desk provides an interface for the user to select a destination and input their budget and dates. The user can also input the type of accommodation and desired activities. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to analyze the user's travel preferences and conditions and select the optimal destination. The analysis unit suggests the best destination based on the user's preferences, budget, and purpose of travel. It can also select the best destination by considering the user's past travel history and current living situation. Step 3: The suggestion unit proposes the optimal destination based on the information analyzed by the analysis unit. The suggestion unit uses AI to propose the optimal destination based on the user's preferences and schedule. For example, if a family is planning a trip, it can suggest a destination that the whole family can enjoy, and for adventurous travelers, it can suggest a destination where they can enjoy activities such as trekking or skydiving. Step 4: The Arrangement Department makes activity reservations, accommodation bookings, and transportation arrangements based on the destinations suggested by the Proposal Department. The Arrangement Department uses AI to make accommodation bookings based on the suggested destinations, and books the most suitable accommodations based on the user's desired type of accommodation and budget. It also makes transportation arrangements based on the suggested destinations, and books the most suitable transportation options based on the user's desired mode of transport and budget.

[0070] (Example of form 2) The TravelMate AI Planner, according to an embodiment of the present invention, is an AI-powered platform for easily and efficiently planning trips. This platform supports every step of a trip, including suggesting destinations based on the traveler's preferences and schedule, booking activities, and arranging accommodation and transportation. TravelMate AI Planner provides customized plans tailored to the traveler's preferences and schedule, enabling smooth travel planning. Furthermore, the use of AI reduces the stress of planning and supports efficient and enjoyable travel preparation. For example, when planning a family trip, TravelMate AI Planner considers the preferences and schedules of all family members and proposes a plan that everyone can enjoy. It supports every step, including arranging activities for children, family-friendly accommodations, and transportation. For adventurous travelers, it suggests activities such as trekking and skydiving, and handles activity bookings, accommodation arrangements, and transportation arrangements. This allows travelers to easily create the perfect plan for themselves and realize memorable trips. Through this mechanism, TravelMate AI Planner not only provides customized plans tailored to the traveler's needs, enabling smooth travel planning and a fulfilling experience. The use of AI reduces the stress of planning and supports efficient and enjoyable travel preparation. This allows TravelMate AI Planner to provide customized plans tailored to travelers' needs, not only making travel planning smoother but also enabling a more fulfilling experience. By utilizing AI, it can reduce the stress of planning and support efficient and enjoyable travel preparation.

[0071] The TravelMate AI Planner according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, and an arrangement unit. The reception unit receives input from the user regarding their travel preferences and conditions. These preferences include, but are not limited to, a travel destination, budget, dates, and type of accommodation. The reception unit provides, for example, an interface for the user to select a travel destination and input their budget and dates. The reception unit also allows the user to input the type of accommodation and desired activities. The analysis unit analyzes the information input by the reception unit. The analysis unit, for example, uses AI to analyze the user's travel preferences and conditions and selects the optimal destination. The analysis unit suggests the optimal destination based, for example, the user's preferences, budget, and purpose of travel. The analysis unit can also select the optimal destination by considering the user's past travel history and current living situation. The suggestion unit suggests the optimal destination based on the information analyzed by the analysis unit. The suggestion unit suggests the optimal destination based on the user's preferences and schedule using AI. For example, if the user is planning a family trip, the suggestion unit suggests a destination that the whole family can enjoy. Furthermore, the suggestion unit can also suggest destinations where adventurous travelers can enjoy activities such as trekking and skydiving. The arrangement unit makes reservations for activities, accommodations, and transportation based on the destinations suggested by the suggestion unit. For example, the arrangement unit makes reservations for accommodations based on destinations suggested using AI. For example, the arrangement unit makes reservations for the most suitable accommodations based on the type of accommodation and budget desired by the user. The arrangement unit can also make arrangements for transportation based on the suggested destinations. For example, the arrangement unit makes arrangements for the most suitable transportations based on the mode of transportation and budget desired by the user. As a result, the TravelMate AI Planner according to this embodiment can efficiently input, analyze, suggest, and arrange the user's travel wishes and conditions.

[0072] The reception desk inputs the user's travel preferences and conditions. These preferences include, but are not limited to, destination, budget, dates, and type of accommodation. For example, the reception desk provides an interface for users to select a destination and input their budget and dates. Specifically, users can select from a list of potential destinations and input their budget and dates in a calendar format via a website or mobile app. The reception desk also allows users to input the type of accommodation and desired activities. For example, users can select accommodation types such as hotels, resorts, or guesthouses, and further specify detailed conditions such as rooms with pools, pet-friendly options, and breakfast included. Furthermore, the reception desk provides options for users to select desired activities. For example, users can select from categories such as sightseeing, shopping, outdoor activities, and cultural experiences, and list specific activities. This allows the reception desk to collect detailed information on the user's diverse needs and preferences and provide it as input data to the subsequent analysis department. The reception desk saves the information entered by users in real time, allowing for modifications and additions as needed. Furthermore, by remembering and reusing information previously entered by the user, the system can reduce the effort required for data entry. This allows the reception desk to enhance user convenience and support a smooth start to travel planning.

[0073] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze the user's travel preferences and conditions and select the optimal destination. Specifically, the AI ​​uses natural language processing technology to understand the user's input and narrow down the candidate travel destinations. For example, if a user enters "I want to relax at a beach resort," the AI ​​will list destinations related to beach resorts and select the best candidate based on budget and schedule. The analysis department can also select the optimal destination by considering the user's past travel history and current living situation. For example, it can suggest new destinations the user has not yet visited based on places they have visited in the past or their hobbies and preferences. Furthermore, the analysis department can refer to the user's social media posts and ratings on review sites to identify places that the user likes and that have high ratings. In this way, the analysis department comprehensively analyzes diverse user information and provides basic data for proposing the optimal travel plan. The analysis department processes data in real time and provides analysis results quickly according to the user's input. The analysis department can also utilize historical data and statistical information to make suggestions that take into account long-term trends and popular spots each season. This allows the analysis unit to quickly and accurately select the most suitable destination based on the user's travel preferences and conditions, and provide this data to the subsequent recommendation unit.

[0074] The suggestion department proposes the optimal destination based on the information analyzed by the analysis department. For example, the suggestion department uses AI to suggest the best destination based on the user's preferences and schedule. Specifically, the AI ​​generates multiple travel plans based on the user's input information and analysis results, and presents them to the user. For example, if the user is planning a family trip, it will suggest a destination that the whole family can enjoy. Family travel plans include activities for children, family-friendly accommodations, and sightseeing spots that the whole family can enjoy. The suggestion department can also suggest destinations where adventurous travelers can enjoy activities such as trekking or skydiving. Adventure travel plans include detailed activity schedules, necessary equipment, and local guide information. Furthermore, the suggestion department proposes the optimal plan according to the user's budget and schedule. For example, it can propose travel plans that allow users to enjoy themselves to the fullest with a limited budget, or plans that allow for efficient sightseeing in a short period of time. The suggestion department presents multiple plans simultaneously, clearly indicating the advantages and disadvantages of each, so that the user can compare and consider the proposed plans. The suggestion department can also revise its suggestions based on user feedback and re-propose a plan that is closer to the user's wishes. This allows the proposal department to provide optimal travel plans that meet the diverse needs of users, thereby increasing user satisfaction.

[0075] The booking department makes reservations for activities, accommodations, and transportation based on destinations suggested by the proposal department. For example, the booking department makes reservations for accommodations based on destinations suggested using AI. Specifically, the booking department reserves the most suitable accommodation based on the user's desired type of accommodation and budget. For example, if a user wants a resort hotel, the booking department checks the availability of resort hotels and reserves the most suitable room within the budget. The booking department can also make arrangements for transportation based on the suggested destination. For example, it reserves the most suitable transportation based on the user's desired mode of transport and budget. This includes booking flight tickets and rental cars, and arranging local transportation. Furthermore, the booking department also makes reservations for suggested activities. For example, this includes booking sightseeing tours and activities, and arranging local guides. The booking department can make all the necessary arrangements in one place so that users can enjoy their trip smoothly based on the proposed plan. The booking department also provides support such as confirming, changing, and canceling reservations. In this way, the booking department efficiently supports users' travel plans and allows users to enjoy their trip with peace of mind. Furthermore, the booking department can improve booking details and enhance services based on user feedback. This allows the booking department to increase user satisfaction and contribute to acquiring repeat customers.

[0076] The booking department includes a customization department that provides customized plans tailored to specific travel types, such as family trips and adventure trips. For example, in the case of a family trip, the booking department considers the preferences and schedules of all family members and proposes a plan that everyone can enjoy. The booking department arranges activities for children, family-friendly accommodations, and transportation. Furthermore, for adventure-loving travelers, the booking department can suggest activities such as trekking and skydiving, and can also book activities, accommodations, and transportation. This allows for customized plans tailored to specific travel types, enabling travelers to plan trips that meet their needs.

[0077] The suggestion section includes an activity suggestion section that proposes the most suitable activities based on the traveler's preferences and schedule. For example, the suggestion section uses AI to suggest the most suitable activities based on the traveler's preferences and schedule. If a traveler is planning a family trip, for example, the suggestion section will suggest activities that the whole family can enjoy. The suggestion section can also suggest activities such as trekking or skydiving to adventurous travelers. In this way, traveler satisfaction is improved by suggesting the most suitable activities based on the traveler's preferences and schedule.

[0078] The booking department makes accommodation reservations based on the suggested destination. For example, the booking department uses AI to make accommodation reservations based on the suggested destination. For example, the booking department can book the most suitable accommodation based on the type of accommodation and budget desired by the user. The booking department can also book the most suitable accommodation based on the type of accommodation and budget desired by the user. This allows travel planning to proceed smoothly by making accommodation reservations based on the suggested destination.

[0079] The booking department arranges transportation based on the proposed destination. For example, the booking department uses AI to arrange transportation based on the proposed destination. For example, the booking department arranges the most suitable transportation based on the user's desired transportation and budget. The booking department can also arrange the most suitable transportation based on the user's desired transportation and budget. This allows travel planning to proceed smoothly by arranging transportation based on the proposed destination.

[0080] The suggestion function proposes the best destination based on the traveler's preferences and schedule. For example, it uses AI to suggest the best destination based on the traveler's preferences and schedule. If a traveler is planning a family trip, the suggestion function will suggest a destination that the whole family can enjoy. It can also suggest destinations where adventurous travelers can enjoy activities such as trekking or skydiving. By suggesting the best destination based on the traveler's preferences and schedule, this improves traveler satisfaction.

[0081] The reception desk estimates the user's emotions and adjusts the input method for travel preferences and conditions based on the estimated emotions. The reception desk estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of travel preferences and conditions. This allows for more appropriate input of travel preferences and conditions by adjusting the input method according to the user's emotions.

[0082] The reception desk analyzes the user's past travel history and selects the optimal input method. For example, the reception desk uses AI to analyze the user's past travel history. For example, the reception desk automatically displays travel preferences and conditions that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests travel preferences and conditions to be used at a specific time of day based on the user's past travel history. In this way, by analyzing the user's past travel history, the optimal input method can be provided.

[0083] The reception desk filters the user's travel preferences and conditions based on their current living situation and areas of interest. For example, the reception desk uses AI to analyze the user's current living situation and areas of interest. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's current living situation. For example, the reception desk filters relevant travel preferences and conditions based on the user's areas of interest. For example, the reception desk suggests optimal travel preferences and conditions based on the user's current living situation and areas of interest. This allows users to input more appropriate travel preferences and conditions by filtering based on their current living situation and areas of interest.

[0084] The reception desk estimates the user's emotions and, based on the estimated emotions, determines the priority of the travel preferences and conditions to be entered. The reception desk estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is feeling stressed, the reception desk prioritizes the input of important travel preferences and conditions. For example, if the user is relaxed, the reception desk prioritizes the input of detailed travel preferences and conditions. For example, if the user is in a hurry, the reception desk prioritizes the input of the most important travel preferences and conditions. This allows for more appropriate travel planning by determining the priority of travel preferences and conditions to be entered according to the user's emotions.

[0085] The reception desk prioritizes inputting highly relevant information by considering the user's geographical location when they input their travel preferences and conditions. For example, the reception desk uses AI to analyze the user's geographical location. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's current location. For example, the reception desk filters relevant travel preferences and conditions based on the user's geographical location. For example, the reception desk suggests optimal travel preferences and conditions based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant information can be prioritized.

[0086] The reception desk analyzes the user's social media activity when they input their travel preferences and conditions, and inputs relevant information. For example, the reception desk uses AI to analyze the user's social media activity. For example, the reception desk prioritizes displaying relevant travel preferences and conditions based on the user's social media activity. For example, the reception desk analyzes the user's social media activity and filters relevant travel preferences and conditions. For example, the reception desk suggests optimal travel preferences and conditions based on the user's social media activity. This allows for the input of relevant information by analyzing the user's social media activity.

[0087] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. The analysis unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit performs a simplified analysis. For example, if the user is stressed, the analysis unit performs a simple analysis. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible.

[0088] The analysis unit adjusts the level of detail in the analysis based on the importance of travel preferences and conditions. For example, the analysis unit uses AI to evaluate the importance of travel preferences and conditions. For example, the analysis unit performs a detailed analysis on important travel preferences and conditions. For example, the analysis unit performs a simplified analysis on lower-priority travel preferences and conditions. The analysis unit adjusts the level of detail in the analysis based on the importance of travel preferences and conditions. This allows for more appropriate analysis by adjusting the level of detail in the analysis based on the importance of travel preferences and conditions.

[0089] The analysis unit applies different analysis algorithms depending on the travel category during the analysis. For example, the analysis unit uses AI to classify travel categories. For example, in the case of a family trip, the analysis unit applies an analysis algorithm that takes into account the preferences and schedules of all family members. For example, in the case of an adventure trip, the analysis unit applies an analysis algorithm that takes into account the risks and difficulty levels of the activities. For example, in the case of a business trip, the analysis unit applies an analysis algorithm that emphasizes efficiency and time management. By applying different analysis algorithms depending on the travel category, more appropriate analysis becomes possible.

[0090] The analysis unit estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The analysis unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is stressed, the analysis unit prioritizes important analyses. For example, if the user is relaxed, the analysis unit prioritizes detailed analyses. For example, if the user is in a hurry, the analysis unit prioritizes the most important analyses. By determining the priority of analyses according to the user's emotions, more appropriate analyses become possible.

[0091] The analysis unit determines the priority of analysis based on the submission timing of travel preferences and conditions. For example, the analysis unit uses AI to evaluate the submission timing of travel preferences and conditions. For example, the analysis unit prioritizes the analysis of travel preferences and conditions that have been submitted most recently. For example, the analysis unit postpones the analysis of travel preferences and conditions that have been submitted more recently. For example, the analysis unit adjusts the priority of analysis based on the submission timing. By determining the priority of analysis based on the submission timing of travel preferences and conditions, more appropriate analysis becomes possible.

[0092] The analysis unit adjusts the order of analysis based on the relevance of travel preferences and conditions during the analysis process. For example, the analysis unit uses AI to evaluate the relevance of travel preferences and conditions. For example, the analysis unit prioritizes analyzing highly relevant travel preferences and conditions. For example, the analysis unit postpones analyzing less relevant travel preferences and conditions. The analysis unit adjusts the order of analysis based on the relevance of travel preferences and conditions. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of travel preferences and conditions.

[0093] The suggestion function estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, the suggestion function uses an emotion estimation feature, such as an emotion engine or generative AI, to estimate the user's emotions. For instance, if the user is relaxed, the suggestion function provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is stressed, it provides simple suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions become possible.

[0094] The suggestion function estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, the suggestion function uses an emotion estimation feature, such as an emotion engine or generative AI, to estimate the user's emotions. For instance, if the user is relaxed, the suggestion function provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is stressed, it provides simple suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions become possible.

[0095] The proposal department adjusts the level of detail in its proposals based on the importance of the destination. For example, the proposal department uses AI to evaluate the importance of the destination. For example, the proposal department provides detailed proposals for important destinations. For example, the proposal department provides simplified proposals for lower-priority destinations. The proposal department adjusts the level of detail in its proposals based on the importance of the destination. This allows for more appropriate proposals by adjusting the level of detail in proposals based on the importance of the destination.

[0096] The suggestion function applies different suggestion algorithms depending on the destination category when making suggestions. For example, the suggestion function uses AI to classify destination categories. For example, in the case of a family trip, the suggestion function applies an algorithm that suggests activities that the whole family can enjoy. For example, in the case of an adventure trip, the suggestion function applies an algorithm that suggests activities that take risk and difficulty into consideration. For example, in the case of a business trip, the suggestion function applies an algorithm that suggests activities that prioritize efficiency and time management. By applying different suggestion algorithms depending on the destination category, more appropriate suggestions can be made.

[0097] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on those emotions. The suggestion unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, the suggestion unit provides concise suggestions. If the user is stressed, the suggestion unit provides simple suggestions. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions become possible.

[0098] The proposal team determines the priority of proposals based on the submission timing of destinations. For example, the proposal team uses AI to evaluate the submission timing of destinations. For example, the proposal team prioritizes destinations that have been submitted recently. For example, the proposal team postpones destinations that have been submitted earlier. For example, the proposal team adjusts the priority of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the submission timing of destinations.

[0099] The suggestion function adjusts the order of suggestions based on the relevance of the destinations. For example, the suggestion function uses AI to evaluate the relevance of destinations. For example, the suggestion function prioritizes suggesting highly relevant destinations. For example, the suggestion function postpones suggesting less relevant destinations. For example, the suggestion function adjusts the order of suggestions based on the relevance of the destinations. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the destinations.

[0100] The ordering unit estimates the user's emotions and adjusts the ordering method based on the estimated emotions. The ordering unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, the ordering unit will make detailed ordering. For example, if the user is in a hurry, the ordering unit will make concise ordering. For example, if the user is stressed, the ordering unit will make simple ordering. By adjusting the ordering method according to the user's emotions, more appropriate ordering becomes possible.

[0101] The booking department analyzes the user's past travel history to select the optimal booking method. For example, the booking department uses AI to analyze the user's past travel history. For example, the booking department proposes the optimal booking method based on the booking methods the user has used in the past. For example, the booking department proposes booking methods that avoid congestion based on the user's past travel history. For example, the booking department analyzes the user's past travel history to propose the most efficient booking method. In this way, by analyzing the user's past travel history, the optimal booking method can be provided.

[0102] The booking unit customizes the booking method based on the user's current living situation when booking. The booking unit analyzes the user's current living situation using, for example, AI. The booking unit proposes the optimal booking method based on the user's current living situation. The booking unit customizes the booking method based on the user's current living situation. The booking unit adjusts the booking method based on the user's current living situation. By customizing the booking method based on the user's current living situation, more appropriate booking becomes possible.

[0103] The scheduling unit estimates the user's emotions and determines the priority of arrangements based on the estimated emotions. The scheduling unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is feeling stressed, the scheduling unit will prioritize important arrangements. For example, if the user is relaxed, the scheduling unit will prioritize detailed arrangements. For example, if the user is in a hurry, the scheduling unit will prioritize the most important arrangements. This allows for more appropriate arrangements by determining the priority of arrangements according to the user's emotions.

[0104] The booking unit selects the optimal booking method when booking, taking into account the user's geographical location information. The booking unit analyzes the user's geographical location information using, for example, AI. The booking unit proposes the optimal booking method based on the user's current location. The booking unit customizes the booking method based on the user's geographical location information. The booking unit adjusts the booking method based on the user's geographical location information. This allows the system to provide the optimal booking method by considering the user's geographical location information.

[0105] The arrangement department analyzes the user's social media activity and proposes arrangement methods during the arrangement process. For example, the arrangement department uses AI to analyze the user's social media activity. For example, the arrangement department proposes the optimal arrangement method based on the user's social media activity. For example, the arrangement department analyzes the user's social media activity and customizes the arrangement methods. For example, the arrangement department adjusts the arrangement methods based on the user's social media activity. This allows the system to provide the optimal arrangement method by analyzing the user's social media activity.

[0106] The customization unit estimates the user's emotions and adjusts the customization method based on the estimated emotions. The customization unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is relaxed, the customization unit performs detailed customization. If the user is in a hurry, the customization unit performs concise customization. If the user is stressed, the customization unit performs simple customization. This allows for more appropriate customization by adjusting the customization method according to the user's emotions.

[0107] The customization department analyzes the user's past travel history to select the optimal customization method during the customization process. For example, the customization department uses AI to analyze the user's past travel history. For example, the customization department proposes the optimal customization method based on the user's past customization methods. For example, the customization department proposes a customization method that avoids congestion based on the user's past travel history. For example, the customization department analyzes the user's past travel history to propose the most efficient customization method. In this way, by analyzing the user's past travel history, the optimal customization method can be provided.

[0108] The customization unit estimates the user's emotions and determines the priority of customizations based on those emotions. The customization unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is stressed, the customization unit prioritizes important customizations. If the user is relaxed, the customization unit prioritizes detailed customizations. If the user is in a hurry, the customization unit prioritizes the most important customizations. This allows for more appropriate customizations by determining the priority of customizations according to the user's emotions.

[0109] The customization unit selects the optimal customization method during customization, taking into account the user's geographical location information. For example, the customization unit analyzes the user's geographical location information using AI. For example, the customization unit proposes the optimal customization method based on the user's current location. For example, the customization unit adjusts the means of customization based on the user's geographical location information. For example, the customization unit customizes the means of customization based on the user's geographical location information. This allows the system to provide the optimal customization method by considering the user's geographical location information.

[0110] The activity suggestion unit estimates the user's emotions and adjusts the activity suggestion method based on the estimated emotions. The activity suggestion unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is relaxed, the activity suggestion unit will provide detailed activity suggestions. For example, if the user is in a hurry, the activity suggestion unit will provide concise activity suggestions. For example, if the user is stressed, the activity suggestion unit will provide simple activity suggestions. By adjusting the activity suggestion method according to the user's emotions, more appropriate suggestions can be made.

[0111] The activity suggestion unit provides optimal suggestions by referring to the user's past activity history when suggesting activities. For example, the activity suggestion unit uses AI to analyze the user's past activity history. For example, the activity suggestion unit suggests the optimal activity based on the activities the user has participated in in the past. For example, the activity suggestion unit suggests activities that avoid crowds based on the user's past activity history. For example, the activity suggestion unit analyzes the user's past activity history and suggests the most efficient activity. In this way, by referring to the user's past activity history, it can provide optimal suggestions.

[0112] The activity suggestion unit estimates the user's emotions and determines the priority of activities based on the estimated emotions. The activity suggestion unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is feeling stressed, the activity suggestion unit will prioritize suggesting important activities. For example, if the user is relaxed, the activity suggestion unit will prioritize suggesting detailed activities. For example, if the user is in a hurry, the activity suggestion unit will prioritize suggesting the most important activities. By determining the priority of activities according to the user's emotions, more appropriate suggestions can be made.

[0113] The activity suggestion unit provides optimal suggestions by considering the user's geographical location information when suggesting activities. For example, the activity suggestion unit analyzes the user's geographical location information using AI. For example, the activity suggestion unit suggests the optimal activity based on the user's current location. For example, the activity suggestion unit adjusts the means of the activity based on the user's geographical location information. For example, the activity suggestion unit customizes the means of the activity based on the user's geographical location information. This makes it possible to suggest optimal activities by considering the user's geographical location information.

[0114] The activity suggestion unit filters activity suggestions based on the user's current lifestyle and areas of interest. For example, the activity suggestion unit uses AI to analyze the user's current lifestyle and areas of interest. For example, the activity suggestion unit prioritizes displaying relevant activities based on the user's current lifestyle. For example, the activity suggestion unit filters relevant activities based on the user's areas of interest. For example, the activity suggestion unit proposes the most suitable activity based on the user's current lifestyle and areas of interest. This allows for optimal activity suggestions by filtering based on the user's current lifestyle and areas of interest.

[0115] The activity suggestion unit provides optimal suggestions by taking into account the user's current weather information when suggesting activities. For example, the activity suggestion unit uses AI to analyze the user's current weather information. For example, when it is raining, the activity suggestion unit will prioritize suggesting indoor activities. For example, when it is sunny, the activity suggestion unit will suggest outdoor activities. For example, on a snowy day, the activity suggestion unit will suggest activities such as skiing or snowboarding. In this way, it is possible to suggest the most optimal activities by taking into account the user's current weather information.

[0116] The activity suggestion unit proposes the most suitable activity when suggesting activities, taking into account the user's health condition. For example, the activity suggestion unit analyzes the user's health condition using AI. For example, the activity suggestion unit prioritizes displaying relevant activities based on the user's health condition. For example, the activity suggestion unit filters relevant activities based on the user's health condition. For example, the activity suggestion unit proposes the most suitable activity based on the user's health condition. This makes it possible to suggest the most suitable activity by considering the user's health condition.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function can delay the suggestion to give the user time to relax. If the user is in a hurry, the suggestion function can provide suggestions quickly to save the user time. Furthermore, if the user is relaxed, the suggestion function can provide detailed suggestions, giving the user time to consider them thoroughly. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0119] The booking system can also estimate the user's emotions and adjust the booking method based on those emotions. For example, if the user is stressed, the booking system can provide a simple booking method to reduce the user's burden. If the user is relaxed, the booking system can provide a detailed booking method to allow the user to carefully consider their options. Furthermore, if the user is in a hurry, the booking system can provide a quick booking method to save the user's time. In this way, adjusting the booking method according to the user's emotions makes it possible to make more appropriate bookings.

[0120] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is stressed, the analysis unit can provide a simpler analysis method to reduce the user's burden. If the user is relaxed, the analysis unit can provide a more detailed analysis method to ensure the user fully understands the results. Furthermore, if the user is in a hurry, the analysis unit can provide a rapid analysis method to save the user's time. By adjusting the analysis method according to the user's emotions, more appropriate analysis becomes possible.

[0121] The suggestion function can also estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion function can prioritize suggesting relaxing activities. If the user is relaxed, the suggestion function can suggest adventurous activities. Furthermore, if the user is in a hurry, the suggestion function can suggest activities that can be enjoyed in a short amount of time. By adjusting the content of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0122] The scheduling system can also estimate the user's emotions and prioritize arrangements based on those emotions. For example, if the user is stressed, the scheduling system can prioritize important arrangements to reduce the user's burden. If the user is relaxed, the scheduling system can prioritize detailed arrangements to allow the user ample time to consider their options. Furthermore, if the user is in a hurry, the scheduling system can prioritize quick arrangements to save the user time. By prioritizing arrangements according to the user's emotions, more appropriate arrangements can be made.

[0123] The suggestion function can analyze a user's past travel history and propose new destinations they have never visited before. For example, it can suggest new cities while avoiding cities the user has visited previously. It can also suggest activities the user has never participated in before. Furthermore, it can suggest accommodations and modes of transportation the user has never used. In this way, by analyzing a user's past travel history, it can provide new experiences.

[0124] The booking department can propose the most suitable booking method by considering the user's current lifestyle. For example, if the user is busy, the booking department can propose a quick booking method. If the user is relaxed, the booking department can propose a detailed booking method. Furthermore, if the user is planning a trip within a specific budget, the booking department can propose a booking method that fits that budget. In this way, by considering the user's current lifestyle, the booking department can provide the most suitable booking method.

[0125] The analysis unit can apply different analysis algorithms depending on the travel category. For example, for family trips, it can apply an analysis algorithm that takes into account the preferences and schedules of all family members. For adventure trips, it can apply an analysis algorithm that takes into account the risks and difficulty levels of the activities. Furthermore, for business trips, it can apply an analysis algorithm that emphasizes efficiency and time management. By applying different analysis algorithms according to the travel category, more appropriate analysis becomes possible.

[0126] The suggestion function can provide optimal suggestions by taking into account the user's current weather information. For example, in rainy weather, it will prioritize suggesting indoor activities. In sunny weather, it can suggest outdoor activities. Furthermore, on snowy days, it can suggest activities such as skiing and snowboarding. This allows for optimal activity suggestions by considering the user's current weather information.

[0127] The booking unit can select the optimal booking method by considering the user's geographical location information. For example, it can propose the optimal booking method based on the user's current location. It can also customize the booking method based on the user's geographical location information. Furthermore, it can adjust the booking method based on the user's geographical location information. In this way, by considering the user's geographical location information, the optimal booking method can be provided.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk inputs the user's travel preferences and requirements. These preferences include, for example, destination, budget, dates, and type of accommodation. The reception desk provides an interface for the user to select a destination and input their budget and dates. The user can also input the type of accommodation and desired activities. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to analyze the user's travel preferences and conditions and select the optimal destination. The analysis unit suggests the best destination based on the user's preferences, budget, and purpose of travel. It can also select the best destination by considering the user's past travel history and current living situation. Step 3: The suggestion unit proposes the optimal destination based on the information analyzed by the analysis unit. The suggestion unit uses AI to propose the optimal destination based on the user's preferences and schedule. For example, if a family is planning a trip, it can suggest a destination that the whole family can enjoy, and for adventurous travelers, it can suggest a destination where they can enjoy activities such as trekking or skydiving. Step 4: The Arrangement Department makes activity reservations, accommodation bookings, and transportation arrangements based on the destinations suggested by the Proposal Department. The Arrangement Department uses AI to make accommodation bookings based on the suggested destinations, and books the most suitable accommodations based on the user's desired type of accommodation and budget. It also makes transportation arrangements based on the suggested destinations, and books the most suitable transportation options based on the user's desired mode of transport and budget.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and arrangement unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input their travel preferences and conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to select the optimal destination. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal destination based on the analysis results. The arrangement unit is implemented by, for example, the control unit 46A of the smart device 14 and arranges accommodation and transportation based on the proposed destination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and arrangement unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their travel preferences and conditions. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the user's input information to select the optimal destination. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes the optimal destination based on the analysis results. The arrangement unit is implemented by, for example, the control unit 46A of the smart glasses 214 and arranges accommodation and transportation based on the proposed destination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and arrangement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their travel preferences and conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to select the optimal destination. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal destination based on the analysis results. The arrangement unit is implemented by, for example, the control unit 46A of the headset terminal 314 and arranges accommodation and transportation based on the proposed destination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and arrangement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting the user's travel preferences and conditions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to select the optimal destination. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal destination based on the analysis results. The arrangement unit is implemented by, for example, the control unit 46A of the robot 414 and arranges accommodation and transportation based on the proposed destination. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) A reception desk where users enter their travel preferences and requirements, An analysis unit analyzes the information input by the reception unit, A proposal unit that suggests the optimal destination based on the information analyzed by the aforementioned analysis unit, The system includes a booking unit that makes reservations for activities and arranges accommodations and transportation based on the destination proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned ordering unit, It has a customization department that provides customized plans tailored to specific travel types, such as family trips and adventure trips. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It has an activity suggestion department that proposes the most suitable activities based on the traveler's preferences and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned ordering unit, Book accommodation based on the suggested destination. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering unit, We will arrange transportation based on the proposed destination. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest the best destination based on the traveler's preferences and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how travel preferences and conditions are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past travel history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their travel preferences and requirements, the system filters them based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of their travel preferences and conditions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their travel preferences and requirements, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their travel preferences and requirements, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of travel preferences and conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the travel category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when travel preferences and conditions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of travel preferences and conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the destination. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the destination category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission timing of the destination. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the destinations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering unit, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering unit, During the booking process, the system analyzes the user's past travel history to select the most suitable booking method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering unit, When making arrangements, the method of arrangement is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering unit, When making arrangements, the optimal arrangement method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned ordering unit, When making arrangements, we analyze the user's social media activity and suggest arrangement methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customization unit is During customization, the system analyzes the user's past travel history to select the optimal customization method. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned activity proposal unit, It estimates the user's emotions and adjusts how it suggests activities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned activity proposal unit, When suggesting activities, the system provides optimal suggestions by referencing the user's past activity history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned activity proposal unit, It estimates the user's emotions and prioritizes activities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned activity proposal unit, When suggesting activities, we provide optimal suggestions by taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned activity proposal unit, When suggesting activities, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned activity proposal unit, When suggesting activities, the system takes into account the user's current weather information to provide the most suitable suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned activity proposal unit, When suggesting activities, we take the user's health condition into consideration to suggest the most suitable activity. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users enter their travel preferences and requirements, An analysis unit analyzes the information input by the reception unit, A proposal unit that proposes the optimal destination based on the information analyzed by the aforementioned analysis unit, The system includes a booking unit that makes reservations for activities and arranges accommodations and transportation based on the destination proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned procurement unit, It has a customization department that provides customized plans tailored to specific travel types, such as family trips and adventure trips. The system according to feature 1.

3. The aforementioned proposal section is, It has an activity suggestion department that proposes the most suitable activities based on the traveler's preferences and schedule. The system according to feature 1.

4. The aforementioned procurement unit, Book accommodation based on the suggested destination. The system according to feature 1.

5. The aforementioned procurement unit, We will arrange transportation based on the proposed destination. The system according to feature 1.

6. The aforementioned proposal section is, We suggest the best destination based on the traveler's preferences and schedule. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how travel preferences and conditions are entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past travel history and select the optimal input method. The system according to feature 1.

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