system

Abstract

The system according to this embodiment aims to generate personalized travel plans, provide a communication environment, and make recommendations based on trend analysis. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a communication unit, a sharing unit, and an analysis unit. The reception unit inputs the user's travel information. The generation unit analyzes the information input by the reception unit and generates a personalized travel plan. The communication unit provides a communication environment based on the travel plan generated by the generation unit. The sharing unit shares information using the communication environment provided by the communication unit. The analysis unit performs trend analysis based on the information shared by the sharing 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, personalization of travel plans, provision of communication environments, and recommendations based on trend analysis have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a personalized travel plan, provide a communication environment, and make recommendations based on trend analysis.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a communication unit, a sharing unit, and an analysis unit. The reception unit inputs the user's travel information. The generation unit analyzes the information input by the reception unit and generates a personalized travel plan. The communication unit provides a communication environment based on the travel plan generated by the generation unit. The sharing unit shares information using the communication environment provided by the communication unit. The analysis unit performs trend analysis based on the information shared by the sharing unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate personalized travel plans, provide a communication environment, and make recommendations based on trend analysis. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 receiving 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 receiving 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 travel support system according to an embodiment of the present invention is a system that provides comprehensive travel support services by making maximum use of the synergies of the SoftBank Group. This travel support system integrates a travel content provision unit, a 5G communication network, a messaging platform, big data, communication technology, coworking spaces, and power saving technology to achieve highly accurate travel plan proposals, a seamless communication environment, an easy-to-use interface, and recommendations based on trend analysis. For example, the user inputs information such as travel destination, budget, and preferences. Next, AI analyzes past travel history and spending patterns and proposes a personalized travel plan. For example, it generates an optimal travel plan based on data of tourist destinations and accommodations the user has visited in the past. It also utilizes a 5G communication network to provide a real-time communication environment, enabling seamless communication even while traveling. Furthermore, it uses a messaging platform to facilitate information sharing and communication during travel. For example, users can share information with friends and family in real time during their trip and change or adjust their travel plan. It also utilizes big data to provide recommendations based on trend analysis, enabling it to propose tourist destinations and activities tailored to the user's preferences. Furthermore, it uses communication technology to provide a stable communication environment even in remote locations. This enables seamless communication even in areas with difficult connectivity, such as mountainous regions and remote islands. Furthermore, users can utilize co-working spaces to work while traveling, providing a convenient environment for business travelers. Finally, power-saving technology is employed to reduce device battery consumption, allowing users to use their devices without worrying about battery life, even during long trips. By maximizing the synergies of the SoftBank Group, comprehensive travel support services are provided, creating easier and more enjoyable travel experiences for users. This allows the travel support system to efficiently input, analyze, communicate, share, and analyze user travel information and trends.

[0029] The travel support system according to this embodiment comprises a reception unit, a generation unit, a communication unit, a sharing unit, and an analysis unit. The reception unit inputs the user's travel information. The user's travel information includes, but is not limited to, destination, budget, itinerary, accommodation, and means of transportation. The reception unit provides, for example, an interface for the user to input information such as travel destination, budget, and preferences. The generation unit uses AI to analyze the information input by the reception unit and generates a personalized travel plan. The generation unit analyzes, for example, past travel history and spending patterns and proposes a travel plan tailored to the user's preferences. For example, the generation unit uses AI to suggest tourist destinations and accommodations that the user would like based on past travel history. The generation unit can also use AI to analyze the user's spending patterns and propose a travel plan that fits the budget. The communication unit provides a communication environment based on the travel plan generated by the generation unit. The communication unit provides a real-time communication environment, for example, by utilizing a 5G communication network. The communication unit provides a high-speed and stable communication environment, for example, by using a 5G communication network. Furthermore, the communications unit can utilize communication technology to provide a stable communication environment even in remote locations. The sharing unit shares information using the communication environment provided by the communications unit. The sharing unit, for example, uses a messaging platform to share information and communicate during travel. The sharing unit, for example, uses a messaging platform to share information with friends and family in real time during travel. The sharing unit can also use a co-working space to work even while traveling. The analysis unit performs trend analysis based on the information shared by the sharing unit. The analysis unit, for example, uses big data to make recommendations based on trend analysis. The analysis unit, for example, uses big data to suggest tourist destinations and activities tailored to the user's preferences. As a result, the travel support system according to the embodiment can efficiently input, analyze, communicate, share, and perform trend analysis on the user's travel information.

[0030] The reception desk inputs the user's travel information. This information includes, but is not limited to, destination, budget, itinerary, accommodation, and transportation. The reception desk provides an interface for users to input information such as their travel destination, budget, and preferences. Specifically, the reception desk has an intuitive graphical user interface (GUI) that allows users to input information using dropdown menus, checkboxes, sliders, etc. Furthermore, it has a voice input function, allowing users to input travel information by voice. For example, if a user voice-inputs "I want to go to Tokyo next weekend," the system automatically converts that information into text and sets the destination and itinerary. The reception desk also remembers the user's past input history and can reuse previously entered information. This saves users the trouble of entering the same information every time. In addition, the reception desk provides real-time feedback based on the user's input, prompting them to check and correct their input. For example, if the budget exceeds the set range, the system displays a warning and proposes a revised plan within the budget. In this way, the reception desk supports users in efficiently and accurately inputting travel information.

[0031] The generation unit uses AI to analyze information entered by the reception unit and generate personalized travel plans. For example, the generation unit analyzes past travel history and spending patterns to propose travel plans tailored to the user's preferences. Specifically, the generation unit uses natural language processing (NLP) technology to analyze the user's input and understand their preferences and needs. For example, if a user enters "a relaxing hot spring trip," the generation unit extracts the keywords "relax" and "hot spring" and generates an appropriate travel plan based on them. The generation unit also uses machine learning algorithms to analyze the user's past travel history and spending patterns and proposes tourist destinations and accommodations that the user would like. For example, a user who has previously preferred luxury hotels will be suggested similar luxury hotels, and a user with budget constraints will be suggested cost-effective accommodations. Furthermore, the generation unit utilizes real-time updated data to provide travel plans based on the latest information. For example, it can consider current weather information and event information to suggest optimal tourist destinations and activities. In this way, the generation unit can provide optimal travel plans that meet the individual needs of each user, thereby increasing user satisfaction.

[0032] The communications department provides a communication environment based on the travel plan generated by the generation department. For example, the communications department utilizes 5G networks to provide a real-time communication environment. Specifically, it uses high-speed, low-latency 5G communication technology to enable users to quickly obtain necessary information during their trip. For instance, when a user searches for information about tourist attractions at their destination, the communications department provides high-speed data communication and displays the necessary information instantly. Furthermore, the communications department supports multiple communication protocols to provide a stable communication environment even in remote locations. For example, it combines Wi-Fi, LTE, and satellite communication to ensure stable communication in any environment. In addition, the communications department strengthens security measures to safely protect users' personal information and travel plan data. For example, it uses data encryption and authentication technologies to prevent unauthorized access and data leaks. This allows the communications department to provide a reliable communication environment that enables users to enjoy their trip with peace of mind.

[0033] The Shared Section shares information using the communication environment provided by the Communications Section. For example, the Shared Section uses a messaging platform to share information and communicate during travel. Specifically, the Shared Section enables users to share information with friends and family in real time while traveling. For example, they can instantly share photos and videos taken at their travel destination and communicate through comments and reactions. The Shared Section also provides a group chat function, allowing multiple users to share information simultaneously and plan and coordinate their trips. Furthermore, the Shared Section supports booking and checking the availability of coworking spaces, enabling users to work even while traveling. For example, if a user has an urgent work issue while traveling, they can search for and book a nearby coworking space through the Shared Section. In this way, the Shared Section provides an environment where users can efficiently share information and communicate even while traveling.

[0034] The analytics department conducts trend analysis based on information shared by the sharing department. For example, the analytics department uses big data to make recommendations based on trend analysis. Specifically, the analytics department collects information and behavioral history shared by users and analyzes trends using machine learning algorithms. For example, if a particular tourist destination or activity is gaining popularity, it will recommend it to other users based on that information. The analytics department also analyzes users' preferences and behavioral patterns to provide individually customized recommendations. For example, it will suggest new adventure-type tourist destinations and activities to users who have previously enjoyed adventure-type activities. Furthermore, the analytics department utilizes real-time updated data to make recommendations based on the latest trends. For example, it can suggest the optimal plan by considering currently running events and seasonal activities. In this way, the analytics department supports users in always enjoying the best travel plans based on the latest information.

[0035] The reception desk allows users to input information such as their travel destination, budget, and preferences. For example, the reception desk may provide an interface for users to input their travel destination. The reception desk may also provide an interface for users to input their travel budget. The reception desk may also provide an interface for users to input their travel preferences (e.g., food preferences, activity preferences, accommodation preferences, etc.). This allows for the generation of personalized travel plans by inputting information such as the user's travel destination, budget, and preferences. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the travel information entered by the user into the AI, which can then analyze the information and generate an optimal travel plan.

[0036] The generation unit can use AI to analyze past travel history and spending patterns to generate personalized travel plans. For example, the generation unit can use AI to suggest tourist destinations and accommodations that the user would like based on their past travel history. The generation unit can also use AI to analyze the user's spending patterns and suggest travel plans that fit their budget. For example, the generation unit can use AI to generate the optimal travel plan based on the user's past travel history. In this way, the AI ​​can generate personalized travel plans by analyzing past travel history and spending patterns. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past travel history and spending patterns into a generation AI, which can then generate the optimal travel plan.

[0037] The communications department can provide a real-time communication environment by utilizing the 5G communication network. For example, the communications department can provide a high-speed and stable communication environment using the 5G communication network. For example, the communications department can perform real-time data communication using the 5G communication network. For example, the communications department can provide a seamless communication environment even while traveling using the 5G communication network. Thus, by utilizing the 5G communication network, a real-time communication environment can be provided. Some or all of the above processing in the communications department may be performed using AI, for example, or without AI. For example, the communications department can have AI perform the optimization of the communication environment using the 5G communication network.

[0038] The shared section can use a messaging platform to share information and communicate during travel. For example, the shared section can use the messaging platform to share information with friends and family in real time during travel. For example, the shared section can use the messaging platform to change or adjust travel plans. For example, the shared section can use the messaging platform to quickly share information during travel. In this way, by using the messaging platform, information sharing and communication during travel can be made smoother. Some or all of the above processes in the shared section may be performed using AI, for example, or not using AI. For example, the shared section can have AI perform the optimization of information sharing using the messaging platform.

[0039] The analytics department can use big data to make recommendations based on trend analysis. For example, the analytics department can use big data to suggest tourist destinations and activities tailored to the user's preferences. For example, the analytics department can use big data to analyze a user's past travel history and spending patterns and suggest the optimal travel plan. For example, the analytics department can use big data to make recommendations based on trend analysis. In this way, by utilizing big data, recommendations based on trend analysis can be made. Some or all of the above processes in the analytics department may be performed using AI, for example, or without AI. For example, the analytics department can have AI perform trend analysis using big data.

[0040] The communications department can provide a stable communication environment even in remote locations by utilizing communication technology. For example, the communications department can provide a stable communication environment even in mountainous areas or remote islands where communication is difficult, by utilizing communication technology. For example, the communications department can provide a seamless communication environment even in remote locations by utilizing communication technology. For example, the communications department can provide a high-speed and stable communication environment even in remote locations by utilizing communication technology. In this way, a stable communication environment can be provided even in remote locations by utilizing communication technology. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without using AI. For example, the communications department can have AI perform the optimization of the communication environment in remote locations.

[0041] The shared area allows users to work even while traveling by utilizing the coworking space. The shared area provides, for example, an environment for working while traveling by using the coworking space. The shared area allows users to use internet access and office facilities while traveling by using the coworking space. The shared area allows users to hold meetings and discussions while traveling by using the coworking space. In this way, by using the coworking space, users can work even while traveling. Some or all of the above processes in the shared area may be performed using, for example, AI, or not using AI. For example, the shared area can have AI analyze the usage status of the coworking space and suggest the optimal way to use it.

[0042] The communications unit can reduce the battery consumption of the device by utilizing power-saving technology. For example, the communications unit can minimize the battery consumption of the device by using power-saving technology. For example, the communications unit can use the device without worrying about the battery even during long trips by using power-saving technology. For example, the communications unit can extend the battery life of the device by using power-saving technology. In this way, the battery consumption of the device can be reduced by utilizing power-saving technology. Some or all of the above processing in the communications unit may be performed using AI, for example, or without using AI. For example, the communications unit can have AI perform the optimization of power-saving technology.

[0043] The reception desk can analyze the user's past travel information input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and budgets that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest travel destinations related to specific seasons or events based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the past travel information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have AI analyze the user's past input history and suggest the optimal input method.

[0044] The reception unit can automatically complete input content when users enter travel information, taking into account their current location and weather information. For example, when a user opens the app, the reception unit automatically obtains their current location and sets it as the departure point. For example, when a user enters a destination, the reception unit suggests the most suitable destination considering the distance from the current location and the weather. For example, when a user uses the app while traveling, the reception unit updates their current location and weather information in real time and automatically completes the input content. This allows for automatic completion of input content by considering the current location and weather information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have AI analyze the user's current location and weather information and automatically complete the input content.

[0045] The reception desk can analyze a user's social media activity when they enter travel information and automatically input relevant travel information. For example, the reception desk can automatically input travel destinations and activities that the user has shared on social media. For example, the reception desk can analyze a user's preferred travel style from their social media posts and suggest relevant information. For example, the reception desk can suggest travel destinations based on places visited by the user's social media friends. In this way, relevant travel information can be automatically input by analyzing social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can have AI analyze a user's social media activity and automatically input relevant travel information.

[0046] The reception desk can suggest relevant travel information by referring to the user's past purchase history when they input travel information. For example, the reception desk can suggest the next travel destination based on travel-related products (airline tickets, accommodations, etc.) that the user has purchased in the past. For example, the reception desk can analyze the user's preferred travel style (luxury resorts, backpacking, etc.) from their purchase history and suggest relevant information. For example, the reception desk can suggest travel destinations by referring to the contents of guidebooks and travel magazines that the user has purchased in the past. In this way, relevant travel information can be suggested by referring to past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have AI analyze the user's past purchase history and suggest relevant travel information.

[0047] The generation unit can generate an optimal travel plan by combining the user's past travel history with current trends. For example, the generation unit can suggest a plan that combines tourist destinations the user has visited in the past with currently popular spots. For example, the generation unit can suggest the best time and route to avoid crowds based on the user's past travel history. For example, the generation unit can suggest new tourist destinations and activities based on the user's past travel history and current trends. In this way, the optimal plan can be generated by combining past travel history and current trends. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history and current trend data into AI, and the AI ​​can generate an optimal travel plan.

[0048] The generation unit can optimize travel plans by considering the user's budget and time constraints. For example, the generation unit can propose the most cost-effective travel plan within the user's budget. For example, the generation unit can propose a plan that allows the user to efficiently visit tourist destinations, taking into account the user's time constraints. For example, the generation unit can propose the most suitable accommodations and transportation methods based on the user's budget and time constraints. In this way, the optimal plan can be generated by considering the budget and time constraints. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's budget and time constraints into AI, and the AI ​​can generate the optimal travel plan.

[0049] The generation unit can propose the optimal travel plan by considering the user's geographical location information when generating a travel plan. For example, the generation unit may prioritize suggesting the tourist destination closest to the user's current location. For example, the generation unit may suggest the optimal mode of transportation based on the user's geographical location information. For example, the generation unit may suggest a plan that minimizes travel time by considering the user's geographical location information. In this way, the optimal plan can be proposed by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can generate the optimal travel plan.

[0050] The generation unit can analyze the user's social media activity and suggest relevant plans when generating travel plans. For example, the generation unit can suggest relevant plans based on travel destinations and activities shared by the user on social media. For example, the generation unit can analyze the user's preferred travel style from the content of their social media posts and suggest relevant plans. For example, the generation unit can suggest travel plans by referring to places visited by the user's social media friends. In this way, relevant plans can be suggested by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the user's social media activity and suggest relevant travel plans.

[0051] The communications unit can provide an optimal communications environment by monitoring the user's current communications status in real time when providing a communications environment. For example, the communications unit can monitor the user's communications status in real time and automatically optimize if the communications speed decreases. For example, if the communications environment changes while the user is on the move, the communications unit can make the optimal communications settings in real time. For example, if the user is in a location with poor communications, the communications unit can suggest the optimal communications method. In this way, by monitoring communications status in real time, the communications unit can provide an optimal communications environment. Some or all of the above-described processes in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI monitor the user's communications status and provide an optimal communications environment.

[0052] The communications unit can optimize communication settings by considering the user's device information when providing a communication environment. For example, if the user is using a smartphone, the communications unit will configure communication settings to match the device's performance. For example, if the user is using a tablet, the communications unit will configure settings to prioritize the transfer of large amounts of data. For example, if the user is using a laptop computer, the communications unit will configure settings to provide a stable communication environment. In this way, by considering device information, the communications unit can provide optimal communication settings. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI analyze the user's device information and provide optimal communication settings.

[0053] The communications unit can provide an optimal communications environment by considering the user's geographical location information when providing a communications environment. For example, the communications unit may prioritize the use of the communications base station closest to the user's current location. For example, the communications unit may adjust communications settings based on geographical location information to provide an optimal communications environment while the user is on the move. For example, if the user is in an area with poor communications, the communications unit may suggest the optimal communications method. In this way, an optimal communications environment can be provided by considering geographical location information. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit may have AI analyze the user's geographical location information to provide an optimal communications environment.

[0054] The communications department can analyze the user's social media activity and suggest relevant communication settings when providing a communication environment. For example, the communications department can set settings to prioritize the upload speed of videos and images shared by the user on social media. For example, the communications department can analyze the user's frequency of social media use and suggest the optimal data plan. For example, the communications department can set settings to provide a stable communication environment when the user is live streaming on social media. In this way, relevant communication settings can be suggested by analyzing social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can have AI analyze the user's social media activity and suggest the optimal communication settings.

[0055] The sharing function can suggest the optimal sharing method when sharing information by referring to the user's past sharing history. For example, the sharing function may prioritize suggesting sharing methods that the user has frequently used in the past (email, messaging apps, etc.). For example, the sharing function may suggest the optimal sharing method for a specific recipient based on the user's past sharing history. For example, the sharing function may analyze the user's past sharing history and suggest the most efficient sharing method. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function may have AI analyze the user's past sharing history and suggest the optimal sharing method.

[0056] The sharing unit can optimize the shared content by considering the user's current communication status when sharing information. For example, the sharing unit can monitor the user's communication status in real time and compress the shared content if the communication speed decreases. For example, the sharing unit can adjust the sharing method in real time if the communication environment changes while the user is on the move. For example, the sharing unit can suggest the optimal sharing method if the user is in a location with a poor communication environment. In this way, the shared content can be optimized by considering the current communication status. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI monitor the user's communication status and suggest the optimal sharing method.

[0057] The sharing unit can suggest the optimal sharing method when sharing information, taking into account the user's geographical location. For example, the sharing unit prioritizes using the sharing method closest to the user's current location (Wi-Fi, mobile data, etc.). For example, the sharing unit adjusts sharing settings based on geographical location information to provide the optimal sharing method when the user is on the move. For example, the sharing unit suggests the optimal sharing method when the user is in an area with poor communication conditions. In this way, the sharing unit can suggest the optimal sharing method by considering geographical location information. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI analyze the user's geographical location information and suggest the optimal sharing method.

[0058] The sharing unit can analyze a user's social media activity and suggest relevant content to share when information is shared. For example, the sharing unit can suggest relevant content based on information the user has shared on social media. For example, the sharing unit can analyze a user's preferred sharing methods from their social media posts and suggest relevant information. For example, the sharing unit can suggest content to share by referring to information shared by the user's social media friends. In this way, relevant content can be suggested by analyzing social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI analyze a user's social media activity and suggest relevant content to share.

[0059] The analysis unit can predict current trends by referring to past trend data during trend analysis. For example, the analysis unit can predict current popular destinations based on past travel trend data. For example, the analysis unit can predict seasonal popular activities from past trend data. For example, the analysis unit can predict future travel trends by analyzing past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze past trend data to predict current trends.

[0060] The analysis unit can optimize analysis results by considering the user's travel history and spending patterns during trend analysis. For example, the analysis unit can suggest the optimal travel destination based on the user's past travel history. For example, the analysis unit can analyze the user's spending patterns and suggest a travel plan that fits the budget. For example, the analysis unit can suggest the most efficient travel plan by considering the user's travel history and spending patterns. In this way, the analysis results can be optimized by considering travel history and spending patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI analyze the user's travel history and spending patterns to provide optimal analysis results.

[0061] The analysis unit can optimize analysis results by considering the user's geographical location information during trend analysis. For example, the analysis unit can prioritize suggesting popular spots closest to the user's current location. For example, the analysis unit can suggest the optimal travel destination based on the user's geographical location information. For example, the analysis unit can suggest a travel plan that minimizes travel time, taking the user's geographical location information into consideration. In this way, the analysis results can be optimized by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the user's geographical location information to provide optimal analysis results.

[0062] The analysis unit can analyze users' social media activity and suggest relevant trends during trend analysis. For example, the analysis unit can suggest relevant trends based on information shared by users on social media. For example, the analysis unit can analyze users' preferred trends from the content of their social media posts and suggest relevant information. For example, the analysis unit can suggest trends by referring to information shared by users' friends on social media. In this way, relevant trends can be suggested by analyzing social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze users' social media activity and suggest relevant trends.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk can analyze the user's past travel information input history and suggest the optimal input method. For example, it can automatically display destinations and budgets that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest travel destinations related to specific seasons or events based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the past travel information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can have AI analyze the user's past input history and suggest the optimal input method.

[0065] The generation unit can generate an optimal travel plan by combining the user's past travel history with current trends. For example, it can suggest a plan that combines tourist destinations the user has visited in the past with currently popular spots. It can also suggest the best time and route to avoid crowds based on the user's past travel history. Furthermore, it can suggest new tourist destinations and activities based on the user's past travel history and current trends. In this way, the optimal plan can be generated by combining past travel history and current trends. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history and current trend data into AI, which can then generate an optimal travel plan.

[0066] The communications unit can monitor the user's current communication status in real time when providing a communication environment and provide the optimal communication environment. For example, it can monitor the user's communication status in real time and automatically optimize if the communication speed decreases. It can also make optimal communication settings in real time if the communication environment changes while the user is on the move. Furthermore, if the user is in a location with a poor communication environment, it can suggest the optimal communication method. In this way, by monitoring the communication status in real time, the optimal communication environment can be provided. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI monitor the user's communication status and provide the optimal communication environment.

[0067] The sharing function can suggest the optimal sharing method by referring to the user's past sharing history when sharing information. For example, it can prioritize suggesting sharing methods that the user has frequently used in the past (email, messaging apps, etc.). It can also suggest the optimal sharing method for a specific recipient based on the user's past sharing history. Furthermore, it can analyze the user's past sharing history and suggest the most efficient sharing method. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can have AI analyze the user's past sharing history and suggest the optimal sharing method.

[0068] The analysis unit can optimize analysis results by considering the user's travel history and spending patterns during trend analysis. For example, it can suggest the optimal travel destination based on the user's past travel history. It can also analyze the user's spending patterns and suggest a travel plan that fits their budget. Furthermore, it can suggest the most efficient travel plan by considering the user's travel history and spending patterns. In this way, the analysis results can be optimized by considering travel history and spending patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have AI analyze the user's travel history and spending patterns to provide optimal analysis results.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk enters the user's travel information. This information includes destination, budget, itinerary, accommodation, and transportation. The reception desk provides an interface for the user to enter information such as their travel destination, budget, and preferences. Step 2: The generation unit uses AI to analyze the information entered by the reception unit and generate a personalized travel plan. The generation unit analyzes past travel history and spending patterns to propose a travel plan that suits the user's preferences. For example, the AI ​​can suggest tourist destinations and accommodations that the user likes based on past travel history, and propose a travel plan that fits the budget. Step 3: The communications unit provides a communication environment based on the travel plan generated by the generation unit. The communications unit utilizes the 5G communication network to provide a real-time communication environment, ensuring high speed and stability. It can also provide a stable communication environment even in remote locations using communication technology. Step 4: The shared team shares information using the communication environment provided by the communications team. The shared team uses a messaging platform to share information and communicate while traveling. For example, they can share information with friends and family in real time while traveling and work even while traveling by using a coworking space. Step 5: The analysis department performs trend analysis based on the information shared by the sharing department. The analysis department uses big data to make recommendations based on the trend analysis. For example, it uses big data to suggest tourist destinations and activities tailored to the user's preferences.

[0071] (Example of form 2) The travel support system according to an embodiment of the present invention is a system that provides comprehensive travel support services by making maximum use of the synergies of the SoftBank Group. This travel support system integrates a travel content provision unit, a 5G communication network, a messaging platform, big data, communication technology, coworking spaces, and power saving technology to achieve highly accurate travel plan proposals, a seamless communication environment, an easy-to-use interface, and recommendations based on trend analysis. For example, the user inputs information such as travel destination, budget, and preferences. Next, AI analyzes past travel history and spending patterns and proposes a personalized travel plan. For example, it generates an optimal travel plan based on data of tourist destinations and accommodations the user has visited in the past. It also utilizes a 5G communication network to provide a real-time communication environment, enabling seamless communication even while traveling. Furthermore, it uses a messaging platform to facilitate information sharing and communication during travel. For example, users can share information with friends and family in real time during their trip and change or adjust their travel plan. It also utilizes big data to provide recommendations based on trend analysis, enabling it to propose tourist destinations and activities tailored to the user's preferences. Furthermore, it uses communication technology to provide a stable communication environment even in remote locations. This enables seamless communication even in areas with difficult connectivity, such as mountainous regions and remote islands. Furthermore, users can utilize co-working spaces to work while traveling, providing a convenient environment for business travelers. Finally, power-saving technology is employed to reduce device battery consumption, allowing users to use their devices without worrying about battery life, even during long trips. By maximizing the synergies of the SoftBank Group, comprehensive travel support services are provided, creating easier and more enjoyable travel experiences for users. This allows the travel support system to efficiently input, analyze, communicate, share, and analyze user travel information and trends.

[0072] The travel support system according to this embodiment comprises a reception unit, a generation unit, a communication unit, a sharing unit, and an analysis unit. The reception unit inputs the user's travel information. The user's travel information includes, but is not limited to, destination, budget, itinerary, accommodation, and means of transportation. The reception unit provides, for example, an interface for the user to input information such as travel destination, budget, and preferences. The generation unit uses AI to analyze the information input by the reception unit and generates a personalized travel plan. The generation unit analyzes, for example, past travel history and spending patterns and proposes a travel plan tailored to the user's preferences. For example, the generation unit uses AI to suggest tourist destinations and accommodations that the user would like based on past travel history. The generation unit can also use AI to analyze the user's spending patterns and propose a travel plan that fits the budget. The communication unit provides a communication environment based on the travel plan generated by the generation unit. The communication unit provides a real-time communication environment, for example, by utilizing a 5G communication network. The communication unit provides a high-speed and stable communication environment, for example, by using a 5G communication network. Furthermore, the communications unit can utilize communication technology to provide a stable communication environment even in remote locations. The sharing unit shares information using the communication environment provided by the communications unit. The sharing unit, for example, uses a messaging platform to share information and communicate during travel. The sharing unit, for example, uses a messaging platform to share information with friends and family in real time during travel. The sharing unit can also use a co-working space to work even while traveling. The analysis unit performs trend analysis based on the information shared by the sharing unit. The analysis unit, for example, uses big data to make recommendations based on trend analysis. The analysis unit, for example, uses big data to suggest tourist destinations and activities tailored to the user's preferences. As a result, the travel support system according to the embodiment can efficiently input, analyze, communicate, share, and perform trend analysis on the user's travel information.

[0073] The reception desk inputs the user's travel information. This information includes, but is not limited to, destination, budget, itinerary, accommodation, and transportation. The reception desk provides an interface for users to input information such as their travel destination, budget, and preferences. Specifically, the reception desk has an intuitive graphical user interface (GUI) that allows users to input information using dropdown menus, checkboxes, sliders, etc. Furthermore, it has a voice input function, allowing users to input travel information by voice. For example, if a user voice-inputs "I want to go to Tokyo next weekend," the system automatically converts that information into text and sets the destination and itinerary. The reception desk also remembers the user's past input history and can reuse previously entered information. This saves users the trouble of entering the same information every time. In addition, the reception desk provides real-time feedback based on the user's input, prompting them to check and correct their input. For example, if the budget exceeds the set range, the system displays a warning and proposes a revised plan within the budget. In this way, the reception desk supports users in efficiently and accurately inputting travel information.

[0074] The generation unit uses AI to analyze information entered by the reception unit and generate personalized travel plans. For example, the generation unit analyzes past travel history and spending patterns to propose travel plans tailored to the user's preferences. Specifically, the generation unit uses natural language processing (NLP) technology to analyze the user's input and understand their preferences and needs. For example, if a user enters "a relaxing hot spring trip," the generation unit extracts the keywords "relax" and "hot spring" and generates an appropriate travel plan based on them. The generation unit also uses machine learning algorithms to analyze the user's past travel history and spending patterns and proposes tourist destinations and accommodations that the user would like. For example, a user who has previously preferred luxury hotels will be suggested similar luxury hotels, and a user with budget constraints will be suggested cost-effective accommodations. Furthermore, the generation unit utilizes real-time updated data to provide travel plans based on the latest information. For example, it can consider current weather information and event information to suggest optimal tourist destinations and activities. In this way, the generation unit can provide optimal travel plans that meet the individual needs of each user, thereby increasing user satisfaction.

[0075] The communications department provides a communication environment based on the travel plan generated by the generation department. For example, the communications department utilizes 5G networks to provide a real-time communication environment. Specifically, it uses high-speed, low-latency 5G communication technology to enable users to quickly obtain necessary information during their trip. For instance, when a user searches for information about tourist attractions at their destination, the communications department provides high-speed data communication and displays the necessary information instantly. Furthermore, the communications department supports multiple communication protocols to provide a stable communication environment even in remote locations. For example, it combines Wi-Fi, LTE, and satellite communication to ensure stable communication in any environment. In addition, the communications department strengthens security measures to safely protect users' personal information and travel plan data. For example, it uses data encryption and authentication technologies to prevent unauthorized access and data leaks. This allows the communications department to provide a reliable communication environment that enables users to enjoy their trip with peace of mind.

[0076] The Shared Section shares information using the communication environment provided by the Communications Section. For example, the Shared Section uses a messaging platform to share information and communicate during travel. Specifically, the Shared Section enables users to share information with friends and family in real time while traveling. For example, they can instantly share photos and videos taken at their travel destination and communicate through comments and reactions. The Shared Section also provides a group chat function, allowing multiple users to share information simultaneously and plan and coordinate their trips. Furthermore, the Shared Section supports booking and checking the availability of coworking spaces, enabling users to work even while traveling. For example, if a user has an urgent work issue while traveling, they can search for and book a nearby coworking space through the Shared Section. In this way, the Shared Section provides an environment where users can efficiently share information and communicate even while traveling.

[0077] The analytics department conducts trend analysis based on information shared by the sharing department. For example, the analytics department uses big data to make recommendations based on trend analysis. Specifically, the analytics department collects information and behavioral history shared by users and analyzes trends using machine learning algorithms. For example, if a particular tourist destination or activity is gaining popularity, it will recommend it to other users based on that information. The analytics department also analyzes users' preferences and behavioral patterns to provide individually customized recommendations. For example, it will suggest new adventure-type tourist destinations and activities to users who have previously enjoyed adventure-type activities. Furthermore, the analytics department utilizes real-time updated data to make recommendations based on the latest trends. For example, it can suggest the optimal plan by considering currently running events and seasonal activities. In this way, the analytics department supports users in always enjoying the best travel plans based on the latest information.

[0078] The reception desk allows users to input information such as their travel destination, budget, and preferences. For example, the reception desk may provide an interface for users to input their travel destination. The reception desk may also provide an interface for users to input their travel budget. The reception desk may also provide an interface for users to input their travel preferences (e.g., food preferences, activity preferences, accommodation preferences, etc.). This allows for the generation of personalized travel plans by inputting information such as the user's travel destination, budget, and preferences. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the travel information entered by the user into the AI, which can then analyze the information and generate an optimal travel plan.

[0079] The generation unit can use AI to analyze past travel history and spending patterns to generate personalized travel plans. For example, the generation unit can use AI to suggest tourist destinations and accommodations that the user would like based on their past travel history. The generation unit can also use AI to analyze the user's spending patterns and suggest travel plans that fit their budget. For example, the generation unit can use AI to generate the optimal travel plan based on the user's past travel history. In this way, the AI ​​can generate personalized travel plans by analyzing past travel history and spending patterns. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past travel history and spending patterns into a generation AI, which can then generate the optimal travel plan.

[0080] The communications department can provide a real-time communication environment by utilizing the 5G communication network. For example, the communications department can provide a high-speed and stable communication environment using the 5G communication network. For example, the communications department can perform real-time data communication using the 5G communication network. For example, the communications department can provide a seamless communication environment even while traveling using the 5G communication network. Thus, by utilizing the 5G communication network, a real-time communication environment can be provided. Some or all of the above processing in the communications department may be performed using AI, for example, or without AI. For example, the communications department can have AI perform the optimization of the communication environment using the 5G communication network.

[0081] The shared section can use a messaging platform to share information and communicate during travel. For example, the shared section can use the messaging platform to share information with friends and family in real time during travel. For example, the shared section can use the messaging platform to change or adjust travel plans. For example, the shared section can use the messaging platform to quickly share information during travel. In this way, by using the messaging platform, information sharing and communication during travel can be made smoother. Some or all of the above processes in the shared section may be performed using AI, for example, or not using AI. For example, the shared section can have AI perform the optimization of information sharing using the messaging platform.

[0082] The analytics department can use big data to make recommendations based on trend analysis. For example, the analytics department can use big data to suggest tourist destinations and activities tailored to the user's preferences. For example, the analytics department can use big data to analyze a user's past travel history and spending patterns and suggest the optimal travel plan. For example, the analytics department can use big data to make recommendations based on trend analysis. In this way, by utilizing big data, recommendations based on trend analysis can be made. Some or all of the above processes in the analytics department may be performed using AI, for example, or without AI. For example, the analytics department can have AI perform trend analysis using big data.

[0083] The communications department can provide a stable communication environment even in remote locations by utilizing communication technology. For example, the communications department can provide a stable communication environment even in mountainous areas or remote islands where communication is difficult, by utilizing communication technology. For example, the communications department can provide a seamless communication environment even in remote locations by utilizing communication technology. For example, the communications department can provide a high-speed and stable communication environment even in remote locations by utilizing communication technology. In this way, a stable communication environment can be provided even in remote locations by utilizing communication technology. Some or all of the above-described processes in the communications department may be performed using AI, for example, or without using AI. For example, the communications department can have AI perform the optimization of the communication environment in remote locations.

[0084] The shared area allows users to work even while traveling by utilizing the coworking space. The shared area provides, for example, an environment for working while traveling by using the coworking space. The shared area allows users to use internet access and office facilities while traveling by using the coworking space. The shared area allows users to hold meetings and discussions while traveling by using the coworking space. In this way, by using the coworking space, users can work even while traveling. Some or all of the above processes in the shared area may be performed using, for example, AI, or not using AI. For example, the shared area can have AI analyze the usage status of the coworking space and suggest the optimal way to use it.

[0085] The communications unit can reduce the battery consumption of the device by utilizing power-saving technology. For example, the communications unit can minimize the battery consumption of the device by using power-saving technology. For example, the communications unit can use the device without worrying about the battery even during long trips by using power-saving technology. For example, the communications unit can extend the battery life of the device by using power-saving technology. In this way, the battery consumption of the device can be reduced by utilizing power-saving technology. Some or all of the above processing in the communications unit may be performed using AI, for example, or without using AI. For example, the communications unit can have AI perform the optimization of power-saving technology.

[0086] The reception desk can estimate the user's emotions and customize the travel information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel information. This allows for a more user-friendly interface by customizing the input interface based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then customize the interface based on the emotions.

[0087] The reception desk can analyze the user's past travel information input history and suggest the optimal input method. For example, the reception desk can automatically display destinations and budgets that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest travel destinations related to specific seasons or events based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the past travel information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have AI analyze the user's past input history and suggest the optimal input method.

[0088] The reception unit can automatically complete input content when users enter travel information, taking into account their current location and weather information. For example, when a user opens the app, the reception unit automatically obtains their current location and sets it as the departure point. For example, when a user enters a destination, the reception unit suggests the most suitable destination considering the distance from the current location and the weather. For example, when a user uses the app while traveling, the reception unit updates their current location and weather information in real time and automatically completes the input content. This allows for automatic completion of input content by considering the current location and weather information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can have AI analyze the user's current location and weather information and automatically complete the input content.

[0089] The reception desk can estimate the user's emotions and prioritize the travel information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize inputting important information (such as departure point, destination, and budget). If the user is relaxed, the reception desk will prioritize inputting detailed information (such as preferred activities and accommodation type). If the user is in a hurry, the reception desk will set priorities to generate a travel plan with minimal information. This allows important information to be entered preferentially by prioritizing the travel information to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI, which can then prioritize the input information based on the emotions.

[0090] The reception desk can analyze a user's social media activity when they enter travel information and automatically input relevant travel information. For example, the reception desk can automatically input travel destinations and activities that the user has shared on social media. For example, the reception desk can analyze a user's preferred travel style from their social media posts and suggest relevant information. For example, the reception desk can suggest travel destinations based on places visited by the user's social media friends. In this way, relevant travel information can be automatically input by analyzing social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can have AI analyze a user's social media activity and automatically input relevant travel information.

[0091] The reception desk can suggest relevant travel information by referring to the user's past purchase history when they input travel information. For example, the reception desk can suggest the next travel destination based on travel-related products (airline tickets, accommodations, etc.) that the user has purchased in the past. For example, the reception desk can analyze the user's preferred travel style (luxury resorts, backpacking, etc.) from their purchase history and suggest relevant information. For example, the reception desk can suggest travel destinations by referring to the contents of guidebooks and travel magazines that the user has purchased in the past. In this way, relevant travel information can be suggested by referring to past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have AI analyze the user's past purchase history and suggest relevant travel information.

[0092] The generation unit can estimate the user's emotions and adjust the suggested travel plan based on those emotions. For example, if the user is relaxed, the generation unit will suggest a travel plan that can be enjoyed at a leisurely pace. If the user is in a hurry, the generation unit will suggest a plan that allows for efficient sightseeing. If the user is excited, the generation unit will suggest a plan that includes active activities. By adjusting the suggested travel plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the suggested travel plan based on the emotion.

[0093] The generation unit can generate an optimal travel plan by combining the user's past travel history with current trends. For example, the generation unit can suggest a plan that combines tourist destinations the user has visited in the past with currently popular spots. For example, the generation unit can suggest the best time and route to avoid crowds based on the user's past travel history. For example, the generation unit can suggest new tourist destinations and activities based on the user's past travel history and current trends. In this way, the optimal plan can be generated by combining past travel history and current trends. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history and current trend data into AI, and the AI ​​can generate an optimal travel plan.

[0094] The generation unit can optimize travel plans by considering the user's budget and time constraints. For example, the generation unit can propose the most cost-effective travel plan within the user's budget. For example, the generation unit can propose a plan that allows the user to efficiently visit tourist destinations, taking into account the user's time constraints. For example, the generation unit can propose the most suitable accommodations and transportation methods based on the user's budget and time constraints. In this way, the optimal plan can be generated by considering the budget and time constraints. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's budget and time constraints into AI, and the AI ​​can generate the optimal travel plan.

[0095] The generation unit can estimate the user's emotions and prioritize travel plans based on those emotions. For example, if the user is stressed, the generation unit will prioritize suggesting relaxing activities. If the user is relaxed, the generation unit will provide detailed information about tourist attractions and activities. If the user is in a hurry, the generation unit will prioritize suggesting plans that allow for efficient sightseeing. This allows for the provision of more appropriate plans by prioritizing travel plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI, which can then prioritize travel plans based on those emotions.

[0096] The generation unit can propose the optimal travel plan by considering the user's geographical location information when generating a travel plan. For example, the generation unit may prioritize suggesting the tourist destination closest to the user's current location. For example, the generation unit may suggest the optimal mode of transportation based on the user's geographical location information. For example, the generation unit may suggest a plan that minimizes travel time by considering the user's geographical location information. In this way, the optimal plan can be proposed by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can generate the optimal travel plan.

[0097] The generation unit can analyze the user's social media activity and suggest relevant plans when generating travel plans. For example, the generation unit can suggest relevant plans based on travel destinations and activities shared by the user on social media. For example, the generation unit can analyze the user's preferred travel style from the content of their social media posts and suggest relevant plans. For example, the generation unit can suggest travel plans by referring to places visited by the user's social media friends. In this way, relevant plans can be suggested by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have AI analyze the user's social media activity and suggest relevant travel plans.

[0098] The communication unit can estimate the user's emotions and adjust the communication environment settings based on the estimated emotions. For example, if the user is stressed, the communication unit will prioritize communication speed to provide a smooth communication environment. For example, if the user is relaxed, the communication unit will prioritize communication stability. For example, if the user is in a hurry, the communication unit will prioritize the shortest possible data transfer time. In this way, a more appropriate communication environment can be provided by adjusting the communication environment settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can adjust the communication environment settings based on the emotions.

[0099] The communications unit can provide an optimal communications environment by monitoring the user's current communications status in real time when providing a communications environment. For example, the communications unit can monitor the user's communications status in real time and automatically optimize if the communications speed decreases. For example, if the communications environment changes while the user is on the move, the communications unit can make the optimal communications settings in real time. For example, if the user is in a location with poor communications, the communications unit can suggest the optimal communications method. In this way, by monitoring communications status in real time, the communications unit can provide an optimal communications environment. Some or all of the above-described processes in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI monitor the user's communications status and provide an optimal communications environment.

[0100] The communications unit can optimize communication settings by considering the user's device information when providing a communication environment. For example, if the user is using a smartphone, the communications unit will configure communication settings to match the device's performance. For example, if the user is using a tablet, the communications unit will configure settings to prioritize the transfer of large amounts of data. For example, if the user is using a laptop computer, the communications unit will configure settings to provide a stable communication environment. In this way, by considering device information, the communications unit can provide optimal communication settings. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI analyze the user's device information and provide optimal communication settings.

[0101] The communication unit can estimate the user's emotions and determine the priority of the communication environment based on the estimated emotions. For example, if the user is stressed, the communication unit will prioritize communication speed to provide a smooth communication environment. For example, if the user is relaxed, the communication unit will prioritize communication stability. For example, if the user is in a hurry, the communication unit will prioritize the shortest possible data transfer time. In this way, a more appropriate communication environment can be provided by determining the priority of the communication environment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can determine the priority of the communication environment based on the emotions.

[0102] The communications unit can provide an optimal communications environment by considering the user's geographical location information when providing a communications environment. For example, the communications unit may prioritize the use of the communications base station closest to the user's current location. For example, the communications unit may adjust communications settings based on geographical location information to provide an optimal communications environment while the user is on the move. For example, if the user is in an area with poor communications, the communications unit may suggest the optimal communications method. In this way, an optimal communications environment can be provided by considering geographical location information. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit may have AI analyze the user's geographical location information to provide an optimal communications environment.

[0103] The communications department can analyze the user's social media activity and suggest relevant communication settings when providing a communication environment. For example, the communications department can set settings to prioritize the upload speed of videos and images shared by the user on social media. For example, the communications department can analyze the user's frequency of social media use and suggest the optimal data plan. For example, the communications department can set settings to provide a stable communication environment when the user is live streaming on social media. In this way, relevant communication settings can be suggested by analyzing social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can have AI analyze the user's social media activity and suggest the optimal communication settings.

[0104] The sharing unit can estimate the user's emotions and adjust the method of information sharing based on the estimated emotions. For example, if the user is stressed, the sharing unit provides a simple and intuitive method of information sharing. For example, if the user is relaxed, the sharing unit provides detailed information sharing options. For example, if the user is in a hurry, the sharing unit provides a way to share information quickly. This allows for more appropriate information sharing by adjusting the method of information sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can then adjust the method of information sharing based on the emotions.

[0105] The sharing function can suggest the optimal sharing method when sharing information by referring to the user's past sharing history. For example, the sharing function may prioritize suggesting sharing methods that the user has frequently used in the past (email, messaging apps, etc.). For example, the sharing function may suggest the optimal sharing method for a specific recipient based on the user's past sharing history. For example, the sharing function may analyze the user's past sharing history and suggest the most efficient sharing method. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function may have AI analyze the user's past sharing history and suggest the optimal sharing method.

[0106] The sharing unit can optimize the shared content by considering the user's current communication status when sharing information. For example, the sharing unit can monitor the user's communication status in real time and compress the shared content if the communication speed decreases. For example, the sharing unit can adjust the sharing method in real time if the communication environment changes while the user is on the move. For example, the sharing unit can suggest the optimal sharing method if the user is in a location with a poor communication environment. In this way, the shared content can be optimized by considering the current communication status. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI monitor the user's communication status and suggest the optimal sharing method.

[0107] The sharing unit can estimate the user's emotions and determine the priority of information sharing based on the estimated emotions. For example, if the user is stressed, the sharing unit will prioritize sharing important information. For example, if the user is relaxed, the sharing unit will share detailed information. For example, if the user is in a hurry, the sharing unit will prioritize providing information that can be shared quickly. This allows for more appropriate information sharing by determining the priority of information sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can then determine the priority of information sharing based on the emotions.

[0108] The sharing unit can suggest the optimal sharing method when sharing information, taking into account the user's geographical location. For example, the sharing unit prioritizes using the sharing method closest to the user's current location (Wi-Fi, mobile data, etc.). For example, the sharing unit adjusts sharing settings based on geographical location information to provide the optimal sharing method when the user is on the move. For example, the sharing unit suggests the optimal sharing method when the user is in an area with poor communication conditions. In this way, the sharing unit can suggest the optimal sharing method by considering geographical location information. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI analyze the user's geographical location information and suggest the optimal sharing method.

[0109] The sharing unit can analyze a user's social media activity and suggest relevant content to share when information is shared. For example, the sharing unit can suggest relevant content based on information the user has shared on social media. For example, the sharing unit can analyze a user's preferred sharing methods from their social media posts and suggest relevant information. For example, the sharing unit can suggest content to share by referring to information shared by the user's social media friends. In this way, relevant content can be suggested by analyzing social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can have AI analyze a user's social media activity and suggest relevant content to share.

[0110] The analysis unit can estimate the user's emotions and adjust how the trend analysis results are displayed based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method based on the user's emotions, more appropriate trend analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust how the trend analysis results are displayed based on the emotions.

[0111] The analysis unit can predict current trends by referring to past trend data during trend analysis. For example, the analysis unit can predict current popular destinations based on past travel trend data. For example, the analysis unit can predict seasonal popular activities from past trend data. For example, the analysis unit can predict future travel trends by analyzing past trend data. In this way, current trends can be predicted by referring to past trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze past trend data to predict current trends.

[0112] The analysis unit can optimize analysis results by considering the user's travel history and spending patterns during trend analysis. For example, the analysis unit can suggest the optimal travel destination based on the user's past travel history. For example, the analysis unit can analyze the user's spending patterns and suggest a travel plan that fits the budget. For example, the analysis unit can suggest the most efficient travel plan by considering the user's travel history and spending patterns. In this way, the analysis results can be optimized by considering travel history and spending patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI analyze the user's travel history and spending patterns to provide optimal analysis results.

[0113] The analysis unit can estimate the user's emotions and determine the priority of trend analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize suggesting relaxing travel destinations. For example, if the user is relaxed, the analysis unit will provide detailed trend information. For example, if the user is in a hurry, the analysis unit will provide concise trend information. This allows for more appropriate analysis results by determining the priority of trend analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then determine the priority of trend analysis based on the emotions.

[0114] The analysis unit can optimize analysis results by considering the user's geographical location information during trend analysis. For example, the analysis unit can prioritize suggesting popular spots closest to the user's current location. For example, the analysis unit can suggest the optimal travel destination based on the user's geographical location information. For example, the analysis unit can suggest a travel plan that minimizes travel time, taking the user's geographical location information into consideration. In this way, the analysis results can be optimized by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze the user's geographical location information to provide optimal analysis results.

[0115] The analysis unit can analyze users' social media activity and suggest relevant trends during trend analysis. For example, the analysis unit can suggest relevant trends based on information shared by users on social media. For example, the analysis unit can analyze users' preferred trends from the content of their social media posts and suggest relevant information. For example, the analysis unit can suggest trends by referring to information shared by users' friends on social media. In this way, relevant trends can be suggested by analyzing social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI analyze users' social media activity and suggest relevant trends.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception desk can estimate the user's emotions and customize the travel information input interface based on the estimated emotions. For example, if the user is stressed, a simple and intuitive interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of travel information. This allows for a more user-friendly interface by customizing the input interface based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then customize the interface based on the emotions.

[0118] The generation unit can estimate the user's emotions and adjust the suggested travel plan based on those emotions. For example, if the user is relaxed, it can suggest a travel plan that can be enjoyed at a leisurely pace. If the user is in a hurry, it can suggest a plan that allows them to efficiently visit tourist spots. Furthermore, if the user is excited, it can suggest a plan that includes active activities. In this way, by adjusting the suggested travel plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI, and the generative AI can adjust the suggested travel plan based on the emotion.

[0119] The communication unit can estimate the user's emotions and adjust the communication environment settings based on the estimated emotions. For example, if the user is stressed, the communication speed can be prioritized to provide a smooth communication environment. If the user is relaxed, the settings can be prioritized to ensure communication stability. Furthermore, if the user is in a hurry, the settings can be prioritized to ensure the shortest possible data transfer time. In this way, a more appropriate communication environment can be provided by adjusting the communication environment settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, or not using AI. For example, the communication unit can input user emotion data into a generative AI, and the generative AI can adjust the communication environment settings based on the emotions.

[0120] The sharing unit can estimate the user's emotions and adjust the method of information sharing based on the estimated emotions. For example, if the user is stressed, a simple and intuitive method of information sharing can be provided. If the user is relaxed, more detailed information sharing options can be provided. Furthermore, if the user is in a hurry, a method for quickly sharing information can be provided. This allows for more appropriate information sharing by adjusting the method of information sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not using AI. For example, the sharing unit can input user emotion data into a generative AI, which can then adjust the method of information sharing based on the emotions.

[0121] The analysis unit can estimate the user's emotions and adjust how the trend analysis results are displayed based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the display method based on the user's emotions, more appropriate trend analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust how the trend analysis results are displayed based on the emotions.

[0122] The reception desk can analyze the user's past travel information input history and suggest the optimal input method. For example, it can automatically display destinations and budgets that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest travel destinations related to specific seasons or events based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the past travel information input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can have AI analyze the user's past input history and suggest the optimal input method.

[0123] The generation unit can generate an optimal travel plan by combining the user's past travel history with current trends. For example, it can suggest a plan that combines tourist destinations the user has visited in the past with currently popular spots. It can also suggest the best time and route to avoid crowds based on the user's past travel history. Furthermore, it can suggest new tourist destinations and activities based on the user's past travel history and current trends. In this way, the optimal plan can be generated by combining past travel history and current trends. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history and current trend data into AI, which can then generate an optimal travel plan.

[0124] The communications unit can monitor the user's current communication status in real time when providing a communication environment and provide the optimal communication environment. For example, it can monitor the user's communication status in real time and automatically optimize if the communication speed decreases. It can also make optimal communication settings in real time if the communication environment changes while the user is on the move. Furthermore, if the user is in a location with a poor communication environment, it can suggest the optimal communication method. In this way, by monitoring the communication status in real time, the optimal communication environment can be provided. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can have AI monitor the user's communication status and provide the optimal communication environment.

[0125] The sharing function can suggest the optimal sharing method by referring to the user's past sharing history when sharing information. For example, it can prioritize suggesting sharing methods that the user has frequently used in the past (email, messaging apps, etc.). It can also suggest the optimal sharing method for a specific recipient based on the user's past sharing history. Furthermore, it can analyze the user's past sharing history and suggest the most efficient sharing method. In this way, the optimal sharing method can be suggested by referring to past sharing history. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can have AI analyze the user's past sharing history and suggest the optimal sharing method.

[0126] The analysis unit can optimize analysis results by considering the user's travel history and spending patterns during trend analysis. For example, it can suggest the optimal travel destination based on the user's past travel history. It can also analyze the user's spending patterns and suggest a travel plan that fits their budget. Furthermore, it can suggest the most efficient travel plan by considering the user's travel history and spending patterns. In this way, the analysis results can be optimized by considering travel history and spending patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can have AI analyze the user's travel history and spending patterns to provide optimal analysis results.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk enters the user's travel information. This information includes destination, budget, itinerary, accommodation, and transportation. The reception desk provides an interface for the user to enter information such as their travel destination, budget, and preferences. Step 2: The generation unit uses AI to analyze the information entered by the reception unit and generate a personalized travel plan. The generation unit analyzes past travel history and spending patterns to propose a travel plan that suits the user's preferences. For example, the AI ​​can suggest tourist destinations and accommodations that the user likes based on past travel history, and propose a travel plan that fits the budget. Step 3: The communications unit provides a communication environment based on the travel plan generated by the generation unit. The communications unit utilizes the 5G communication network to provide a real-time communication environment, ensuring high speed and stability. It can also provide a stable communication environment even in remote locations using communication technology. Step 4: The shared team shares information using the communication environment provided by the communications team. The shared team uses a messaging platform to share information and communicate while traveling. For example, they can share information with friends and family in real time while traveling and work even while traveling by using a coworking space. Step 5: The analysis department performs trend analysis based on the information shared by the sharing department. The analysis department uses big data to make recommendations based on the trend analysis. For example, it uses big data to suggest tourist destinations and activities tailored to the user's preferences.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, communication unit, sharing unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for inputting the user's travel information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized travel plan using AI. The communication unit is implemented by the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing unit 12 and provides a real-time communication environment utilizing a 5G communication network. The sharing unit is implemented by the control unit 46A of the smart device 14 and shares information using a messaging platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs trend analysis utilizing big data. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, communication unit, sharing unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for inputting the user's travel information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized travel plan using AI. The communication unit is implemented by the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing unit 12 and provides a real-time communication environment utilizing a 5G communication network. The sharing unit is implemented by the control unit 46A of the smart glasses 214 and shares information using a messaging platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs trend analysis utilizing big data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, communication unit, sharing unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for inputting the user's travel information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized travel plan using AI. The communication unit is implemented by the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12 and provides a real-time communication environment utilizing a 5G communication network. The sharing unit is implemented by the control unit 46A of the headset terminal 314 and shares information using a messaging platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs trend analysis utilizing big data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, generation unit, communication unit, sharing unit, and analysis 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 microphone 238 of the robot 414 and provides an interface for inputting user travel information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates personalized travel plans using AI. The communication unit is implemented by the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing unit 12 and provides a real-time communication environment utilizing a 5G communication network. The sharing unit is implemented by the control unit 46A of the robot 414 and shares information using a messaging platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs trend analysis utilizing big data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A reception area where users enter their travel information, A generation unit analyzes the information entered by the reception unit and generates a personalized travel plan, A communication unit provides a communication environment based on the travel plan generated by the generation unit, A sharing unit that shares information using the communication environment provided by the aforementioned communication unit, The system includes an analysis unit that performs trend analysis based on information shared by the aforementioned sharing unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter information such as the user's travel destination, budget, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is AI analyzes past travel history and spending patterns to generate personalized travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communications unit is We will provide a real-time communication environment utilizing the 5G communication network. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, Use messaging platforms to share information and communicate while traveling. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Using big data to make recommendations based on trend analysis The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned communications unit is Using communication technology, we provide a stable communication environment even in remote locations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned shared portion is, I use coworking spaces to work even while traveling. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned communications unit is Utilizing power-saving technologies reduces the battery consumption of devices. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and customizes the travel information input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is We analyze the user's past travel information input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering travel information, the system automatically completes the input by taking into account the user's current location and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of travel information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users enter travel information, the system analyzes their social media activity and automatically fills in relevant travel information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When users enter travel information, the system suggests relevant travel information by referencing their past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the suggested travel plans based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating travel plans, the system combines the user's past travel history with current trends to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, the plan is optimized considering the user's budget and time constraints. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and prioritizes travel plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating travel plans, the system analyzes the user's social media activity and suggests relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned communications unit is It estimates the user's emotions and adjusts the communication environment settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned communications unit is When providing a communication environment, we monitor the user's current communication status in real time and provide the optimal communication environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned communications unit is When providing a communication environment, we optimize communication settings while taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communications unit is It estimates the user's emotions and determines the priority of the communication environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications unit is When providing a communication environment, we will provide the optimal communication environment by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications unit is When providing a communication environment, we analyze the user's social media activity and suggest relevant communication settings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, It estimates user emotions and adjusts the way information is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing information, the system refers to the user's past sharing history to suggest the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing information, the content shared is optimized considering the user's current network status. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shared portion is, It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, When sharing information, we propose the optimal sharing method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, When sharing information, the system analyzes users' social media activity and suggests relevant content to share. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is Adjusting how we estimate user sentiment and display trend analysis results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is When analyzing trends, we refer to past trend data to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is When performing trend analysis, we optimize the analysis results by taking into account the user's travel history and spending patterns. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit is We estimate user sentiment and prioritize trend analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit is When performing trend analysis, optimize the analysis results by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned analysis unit is When analyzing trends, we analyze users' social media activity and suggest relevant trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 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 area where users enter their travel information, A generation unit analyzes the information entered by the reception unit and generates a personalized travel plan, A communication unit provides a communication environment based on the travel plan generated by the generation unit, A sharing unit that shares information using the communication environment provided by the aforementioned communication unit, The system includes an analysis unit that performs trend analysis based on information shared by the aforementioned sharing unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter information such as the user's travel destination, budget, and preferences. The system according to feature 1.

3. The generating unit is AI analyzes past travel history and spending patterns to generate personalized travel plans. The system according to feature 1.

4. The aforementioned communications unit is We will provide a real-time communication environment utilizing the 5G communication network. The system according to feature 1.

5. The aforementioned shared portion is, Use messaging platforms to share information and communicate while traveling. The system according to feature 1.

6. The aforementioned analysis unit is Using big data to make recommendations based on trend analysis The system according to feature 1.

7. The aforementioned communications unit is Using communication technology, we provide a stable communication environment even in remote locations. The system according to feature 1.

8. The aforementioned shared portion is, I use coworking spaces to work even while traveling. The system according to feature 1.

Images