system

Abstract

The system according to this embodiment aims to analyze user information and provide the optimal travel plan in real time. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an update unit, and a proposal unit. The reception unit receives user information. The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The update unit updates the travel plan generated by the generation unit in real time. The proposal unit proposes the travel plan updated by the update unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that it is difficult to provide a personalized travel plan in real time according to the preferences and budget of the user.

[0005] The system according to the embodiment aims to analyze user information and provide an optimal travel plan in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an update unit, and a proposal unit. The reception unit receives user information. The generation unit analyzes the information entered by the reception unit and generates an optimal travel plan. The update unit updates the travel plan generated by the generation unit in real time. The proposal unit proposes the travel plan updated by the update unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user information and provide an optimal travel plan in real time. [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 including 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 three or more matters are expressed by connecting them with "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) An AI concierge service according to an embodiment of the present invention is a system that provides personalized travel plans tailored to individual travelers in real time. This system provides an AI agent that analyzes the user's preferences, budget, and past travel history to design the optimal travel experience. First, the user inputs information such as their preferences, budget, and past travel history. This information is input into the generating AI. The generating AI analyzes the user's input information and generates an optimal travel plan based on the user's preferences and budget. For example, if the user likes nature and has a limited budget, the generating AI will suggest a travel plan that can be enjoyed in a nature-rich location within the budget. The generating AI also takes into account the user's past travel history. For example, based on places the user has visited and activities they have experienced in the past, it will suggest places and activities that the user has not yet visited but that might interest them. This allows the user to enjoy new experiences. Furthermore, the generating AI updates the user's travel plan in real time. For example, if the user wants to change their plans during the trip, the generating AI will regenerate the optimal plan based on the new information. This allows the user to flexibly respond to changes in circumstances during the trip. The generating AI also takes into account the user's psychological state. For example, if the user is feeling stressed, it will suggest a relaxing travel plan. This allows users to refresh themselves through travel. This AI concierge service has an intuitive interface, allowing users to easily input their preferences and requests. The generating AI understands the user's preferences through dialogue and instantly displays the most suitable suggestions. This makes it easy for users to obtain the perfect travel plan for themselves. This service employs a subscription model, and the premium plan offers custom travel plans. This allows users to choose a service that suits their needs. This AI concierge service also contributes to improving the efficiency of operations in the travel industry. For example, it is expected to reduce costs through improved operational efficiency in the travel industry, increase user satisfaction, and reduce the time spent on travel planning. This service is particularly targeted at active individual travelers in their 20s to 40s, and is ideal for adventurous individual travelers who are digital natives and seek new experiences.Furthermore, it is also suitable for professionals who seek high-quality travel experiences even while being busy with work. This AI concierge service is expected to be a turning point for the travel industry, as it responds to the increased demand for personalized travel experiences as travel demand recovers in the post-pandemic era. As a result, the AI ​​concierge service can generate and update optimal travel plans in real time based on the user's information.

[0029] The AI ​​concierge service according to this embodiment comprises a reception unit, a generation unit, an update unit, and a suggestion unit. The reception unit inputs user information. User information includes, but is not limited to, preferences, budget, and past travel history. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information entered by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan based, for example, the user's preferences and budget. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input information and generates an optimal travel plan. The generation AI also takes into account the user's past travel history. For example, the generation AI suggests places and activities that the user has not yet visited but might be interested in, based on places the user has visited and activities they have experienced in the past. The update unit updates the travel plan generated by the generation unit in real time. For example, if the user wants to change their plans during the trip, the update unit regenerates an optimal plan based on the new information. The update unit uses a generation AI to analyze the user's new information and regenerate the optimal travel plan. The suggestion unit proposes the travel plan updated by the update unit to the user. The suggestion unit, for example, displays the optimal travel plan to the user. The suggestion unit uses a generation AI to understand the user's preferences through dialogue and instantly displays the optimal suggestion. As a result, the AI ​​concierge service according to the embodiment can generate the optimal travel plan based on the user's information and update and propose it in real time. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's input information into the generation AI, and the generation AI generates the optimal travel plan. Some or all of the above processing in the update unit is performed using a generation AI. For example, the update unit inputs the user's new information into the generation AI, and the generation AI regenerates the optimal travel plan. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit displays the travel plan generated by the generation AI to the user.

[0030] The reception desk inputs user information. This information includes, but is not limited to, preferences, budget, and past travel history. The reception desk stores the user-entered information in a database and provides it to the generation desk. Specifically, information entered by users through web forms or applications is stored in a secure database. User preferences include places they want to visit, activities they are interested in, and food preferences. Budget information includes the overall travel budget, daily budget, and budget for accommodation. Past travel history includes details of places visited, hotels stayed at, and activities experienced. This information is centrally managed for each user and made accessible to the generation desk. The reception desk reflects the user-entered information in the database in real time, making it immediately available to the generation desk. Furthermore, if a user updates their information, the reception desk quickly updates the database and provides the latest information to the generation desk. In this way, the reception desk plays a role in accurately and quickly collecting detailed user information and providing it to the generation desk. In addition, the reception desk has a function to check the consistency of the user's input information and verify that there is no missing or inconsistent information. For example, if a user forgets to enter budget information or if there are inconsistencies in their past travel history, the reception desk will notify the user and prompt them to correct or add information. This allows the reception desk to provide accurate and complete information to the generation department, helping to create the optimal travel plan.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate the optimal travel plan. For example, the generation unit generates a travel plan based on the user's preferences and budget. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input information and generate the optimal travel plan. The generation AI also takes into account the user's past travel history. Specifically, the generation AI analyzes the user's preferences, budget, and past travel history in detail and suggests tourist destinations and activities that the user might be interested in. For example, if the user has previously enjoyed visiting beach resorts, the generation AI will suggest new beach resorts and related activities. Also, if the user is planning a trip within a specific budget, the generation AI will select the most suitable accommodations and transportation within that budget. The generation AI analyzes the user's input information using natural language processing technology to accurately understand the user's intentions and desires. Furthermore, the generation AI obtains the latest information from external travel information databases and review sites to provide the user with the optimal travel plan. For example, the generation AI obtains the latest tourist destination information and event information and reflects it in the user's travel plan. This allows the generation unit to provide customized travel plans tailored to the user's needs. Furthermore, the generation unit also has a function to check the consistency and feasibility of the generated travel plan before presenting it to the user. For example, it checks whether the travel plan proposed by the generation AI exceeds the user's budget and whether the suggested activities are actually feasible. This allows the generation unit to provide users with feasible and satisfying travel plans.

[0032] The update unit updates the travel plan generated by the generation unit in real time. For example, if a user wants to change their plans during their trip, the update unit will regenerate the optimal plan based on the new information. The update unit uses a generation AI to analyze the user's new information and regenerate the optimal travel plan. Specifically, if a user wants to change their plans during their trip, the update unit quickly receives that information and inputs it into the generation AI. The generation AI analyzes the user's new information and regenerates the optimal travel plan. For example, if a user suddenly wants to change their plans and visit a different tourist destination, the update unit will generate a new travel plan based on that information and propose it to the user. By receiving the user's new information in real time and inputting it into the generation AI, the update unit can update the travel plan quickly and accurately. The update unit also has a function to continuously improve the travel plan based on new information and feedback entered by the user during their trip. For example, if a user provides feedback on a specific tourist destination or activity, the update unit will adjust the travel plan based on that information to improve user satisfaction. Furthermore, the update unit also has a function to obtain the latest information from external sources and reflect it in the travel plan. For example, weather information, traffic information, and event information can be obtained in real time and reflected in the travel plan. This allows the update unit to continuously provide users with the most suitable travel plan.

[0033] The suggestion unit proposes travel plans updated by the update unit to the user. For example, the suggestion unit displays the most suitable travel plan for the user. The suggestion unit uses generative AI to understand the user's preferences through dialogue and instantly displays the most suitable suggestion. Specifically, the suggestion unit displays the travel plan generated by the generative AI to the user in an easy-to-understand visual format. For example, it may display the details of the travel plan on a map or present it as an itinerary. The suggestion unit also has the function to understand the user's preferences and desires more deeply through dialogue with the user. For example, if the user shows interest in a particular tourist destination or activity, the suggestion unit adjusts the travel plan based on that information and makes the most suitable suggestion to the user. The suggestion unit uses generative AI to analyze the user's input information and dialogue content to accurately understand the user's preferences and desires. This allows the suggestion unit to provide the most suitable travel plan for the user. Furthermore, the suggestion unit also has the function to collect feedback from users and continuously improve the accuracy and effectiveness of the suggestions. For example, if the user provides feedback on the suggested travel plan, the suggestion unit adjusts the suggestion based on that information to improve user satisfaction. Furthermore, the proposal department can provide information to users using multiple communication methods. For example, it can propose travel plans to users through smartphone notifications, email, and in-app messages. This allows the proposal department to quickly and reliably provide users with the most suitable travel plans.

[0034] The psychological analysis department can take into account the user's psychological state. For example, it can measure the user's stress level and type of emotion. The psychological analysis department uses generative AI to analyze the user's psychological state and propose an optimal travel plan. For example, if the psychological analysis department is feeling stressed, it will propose a relaxing travel plan. The psychological analysis department can also estimate the user's emotions and adjust the travel plan based on the estimated emotions. For example, if the psychological analysis department is excited, it will generate a travel plan with visually stimulating effects. This allows the department to provide a travel plan that takes the user's psychological state into account. Some or all of the above processing in the psychological analysis department is performed using generative AI. For example, the psychological analysis department inputs the user's psychological state into the generative AI, which then generates an optimal travel plan.

[0035] The History Analysis Unit can analyze past travel history. For example, it obtains information such as the user's past travel destinations, purpose of travel, and duration of travel. Using a generative AI, the History Analysis Unit analyzes the user's past travel history and proposes an optimal travel plan. For example, based on places the user has visited and activities they have experienced, the History Analysis Unit suggests places and activities the user hasn't yet visited but might be interested in. The History Analysis Unit can also generate an optimal travel plan based on the user's preferences and budget, using their past travel history. This allows it to provide the best possible travel plan based on the user's past travel history. Some or all of the above processing in the History Analysis Unit is performed using a generative AI. For example, the History Analysis Unit inputs the user's past travel history into the generative AI, which then generates an optimal travel plan.

[0036] The dialogue unit can understand user preferences through dialogue. For example, it can acquire information such as the user's favorite activities, food, and type of accommodation. The dialogue unit uses generative AI to analyze the dialogue with the user and propose an optimal travel plan. For example, it can propose an optimal travel plan based on the user's favorite activities. The dialogue unit can also generate an optimal travel plan based on the user's preferences and budget through dialogue. This allows it to understand user preferences through dialogue and provide an optimal travel plan. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, the dialogue unit inputs the content of the dialogue with the user into the generative AI, which then generates an optimal travel plan.

[0037] The generation unit can generate an optimal travel plan based on the user's preferences and budget. For example, the generation unit obtains the user's preferences and budget range and generates a travel plan based on that. The generation unit uses a generation AI to analyze the user's preferences and budget and generate an optimal travel plan. For example, if the user likes nature and has a limited budget, the generation unit will suggest a travel plan that can be enjoyed in a nature-rich location within that budget. The generation unit can also generate an optimal travel plan for the user based on the user's preferences and budget. This allows the generation of an optimal travel plan based on the user's preferences and budget. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's preferences and budget into the generation AI, and the generation AI generates an optimal travel plan.

[0038] The suggestion unit can propose the most suitable travel plan to the user. For example, the suggestion unit displays a travel plan generated by the generation unit to the user. The suggestion unit uses generation AI to propose the most suitable travel plan to the user. For example, the suggestion unit displays a travel plan generated based on the user's preferences and budget to the user. By proposing the most suitable travel plan to the user, the suggestion unit makes it easy for the user to obtain the most suitable travel plan for them. This allows the suggestion unit to propose the most suitable travel plan to the user. Some or all of the above processing in the suggestion unit is performed using generation AI. For example, the suggestion unit displays a travel plan generated by generation AI to the user.

[0039] The reception desk can analyze the user's past information input history and select the optimal input method. For example, if the user has preferred using voice input in the past, the reception desk will prioritize suggesting voice input. If the user has preferred using text input in the past, the reception desk can also prioritize suggesting text input. The reception desk can also analyze the input methods the user has used in the past and suggest the most efficient method. This enables efficient information input by selecting the optimal input method based on the user's past information input history. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's past information input history into the AI, and the AI ​​selects the optimal input method.

[0040] The reception desk can filter information based on the user's current situation and areas of interest during input. For example, if the user is currently traveling, the reception desk will prioritize displaying travel-related information. If the user has a specific area of ​​interest, the reception desk can also prioritize displaying information related to that area. The reception desk can also filter and display highly relevant information based on the user's current situation. This allows the reception desk to provide highly relevant information by filtering information based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's current situation and areas of interest into the AI, and the AI ​​filters the information to determine the most relevant results.

[0041] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception desk will prioritize inputting information related to that region. If the user is traveling, the reception desk can also prioritize inputting information related to the travel destination. The reception desk can also prioritize inputting highly relevant information based on the user's current location. This allows for the provision of more appropriate information by prioritizing the input of highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location information into the AI, and the AI ​​prioritizes inputting highly relevant information.

[0042] The reception desk can analyze a user's social media activity and input relevant information when information is entered. For example, the reception desk can input relevant information based on information the user has shared on social media. The reception desk can also analyze the content of a user's social media posts and input relevant information. The reception desk can also input relevant information based on the user's social media activity history. This allows for the provision of more appropriate information by analyzing the user's social media activity and inputting relevant information. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​inputs relevant information.

[0043] The generation unit can adjust the level of detail in a travel plan based on the user's priorities when generating it. For example, the generation unit can generate a detailed travel plan based on elements that the user considers important. The generation unit can also generate a simplified travel plan based on elements that the user considers less important. The generation unit can also adjust the level of detail in the travel plan according to the user's priorities. This allows for the provision of more appropriate travel plans by adjusting the level of detail based on the user's priorities. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the user's priorities into the AI, and the AI ​​adjusts the level of detail in the travel plan.

[0044] The generation unit can apply different generation algorithms depending on the user's category when generating travel plans. For example, if the user is traveling with family, the generation unit applies a family-oriented generation algorithm. If the user is traveling alone, the generation unit can also apply a solo travel-oriented generation algorithm. If the user is traveling for business, the generation unit can also apply a business-oriented generation algorithm. This allows for the provision of more appropriate travel plans by applying different generation algorithms depending on the user's category. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the user's category into the AI, and the AI ​​applies a different generation algorithm.

[0045] The generation unit can determine the priority of travel plans based on when the user submits them. For example, if the user submits early, the generation unit will prioritize generating a detailed travel plan. If the user submits at the last minute, the generation unit can also prioritize generating a simplified travel plan. The generation unit can also adjust the priority of travel plans according to when the user submits them. This allows the system to provide more appropriate travel plans by prioritizing them based on when the user submits them. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs the user's submission date into the AI, and the AI ​​determines the priority of the travel plans.

[0046] The generation unit can adjust the order of travel plans based on user relevance when generating them. For example, the generation unit can prioritize placing activities that the user is interested in at the beginning of the plan. It can also place activities that the user is less interested in towards the end of the plan. The generation unit can also adjust the order of travel plans according to user relevance. This allows for the provision of more appropriate travel plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs user relevance into the AI, and the AI ​​adjusts the order of travel plans.

[0047] The update unit can select the optimal update method when updating a travel plan by referring to the user's past update history. For example, if the user has updated frequently in the past, the update unit will select a method that updates frequently. If the user has not updated very often in the past, the update unit can also select a method that updates only as much as necessary. The update unit can also select the optimal update method based on the user's past update history. This enables efficient updates by selecting the optimal update method based on the user's past update history. Some or all of the above processing in the update unit is performed using AI. For example, the update unit inputs the user's past update history into the AI, and the AI ​​selects the optimal update method.

[0048] The update unit can customize the update method based on the user's current situation when updating travel plans. For example, if the user is traveling, the update unit will perform updates in real time. If the user is at home, the update unit can also perform updates in advance. The update unit can also customize the optimal update method based on the user's current situation. This allows for more appropriate updates by customizing the update method based on the user's current situation. Some or all of the above processes in the update unit are performed using AI. For example, the update unit inputs the user's current situation into the AI, and the AI ​​customizes the optimal update method.

[0049] The update unit can select the optimal update method when updating a travel plan, taking into account the user's geographical location. For example, if the user is in a specific region, the update unit will prioritize updating information related to that region. If the user is at their travel destination, the update unit can also prioritize updating information related to the travel destination. The update unit can also select the optimal update method based on the user's current location. This allows for more appropriate updates by selecting the optimal update method while considering the user's geographical location. Some or all of the above processing in the update unit is performed using AI. For example, the update unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal update method.

[0050] The update unit can analyze the user's social media activity and suggest update methods when updating travel plans. For example, the update unit updates relevant information based on information shared by the user on social media. The update unit can also analyze the content of the user's social media posts and update relevant information. The update unit can also update relevant information based on the user's social media activity history. This allows for more appropriate updates by analyzing the user's social media activity and suggesting update methods. Some or all of the above processes in the update unit are performed using AI. For example, the update unit inputs the user's social media activity into the AI, and the AI ​​suggests update methods.

[0051] The proposal function can adjust the level of detail of a proposal based on the user's priorities. For example, the proposal function can provide detailed proposals based on elements that the user considers important. It can also provide simplified proposals based on elements that the user considers less important. The proposal function can adjust the level of detail of a proposal according to the user's priorities. This allows for more appropriate proposals by adjusting the level of detail based on the user's priorities. Some or all of the above processes in the proposal function are performed using AI. For example, the proposal function inputs the user's priorities into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0052] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user is traveling with family, the suggestion unit will apply a suggestion algorithm for families. If the user is traveling alone, the suggestion unit can also apply a suggestion algorithm for solo travelers. If the user is traveling for business, the suggestion unit can also apply a suggestion algorithm for business travelers. By applying different suggestion algorithms depending on the user's category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit is performed using AI. For example, the suggestion unit inputs the user's category into the AI, and the AI ​​applies a different suggestion algorithm.

[0053] The proposal department can prioritize proposals based on when the user submits them. For example, if a user submits an early proposal, the department will prioritize detailed proposals. If a user submits a proposal at the last minute, the department may also prioritize simplified proposals. The proposal department can also adjust the priority of proposals according to when the user submits them. This allows for more appropriate proposals by prioritizing proposals based on when the user submits them. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs the user's submission date into the AI, and the AI ​​determines the priority of proposals.

[0054] The suggestion section can adjust the order of suggestions based on user relevance. For example, it can prioritize placing activities that the user is interested in at the beginning of the suggestions. It can also place activities that the user is less interested in towards the end of the suggestions. The suggestion section can also adjust the order of suggestions according to user relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on user relevance. Some or all of the above processing in the suggestion section is performed using AI. For example, the suggestion section inputs user relevance into the AI, and the AI ​​adjusts the order of suggestions.

[0055] The Psychological Analysis Department can select the optimal analysis method by referring to the user's past psychological state during the analysis. For example, if the user has experienced stress in the past, the Psychological Analysis Department will conduct the analysis using methods that promote relaxation. If the user has been relaxed in the past, the Psychological Analysis Department can also conduct a more detailed analysis. The Psychological Analysis Department can also select the optimal analysis method based on the user's past psychological state. This allows for efficient psychological analysis by selecting the optimal analysis method based on the user's past psychological state. Some or all of the above processes in the Psychological Analysis Department are performed using AI. For example, the Psychological Analysis Department inputs the user's past psychological state into the AI, and the AI ​​selects the optimal analysis method.

[0056] The Psychological Analysis Department can select the optimal analysis method during psychological analysis by considering the user's geographical location. For example, if the user is in a specific region, the Psychological Analysis Department will perform a psychological analysis related to that region. If the user is traveling, the Psychological Analysis Department can also perform a psychological analysis related to the travel destination. The Psychological Analysis Department can also select the optimal psychological analysis method based on the user's current location. This allows for more appropriate psychological analysis by selecting the optimal analysis method while considering the user's geographical location. Some or all of the above processes in the Psychological Analysis Department are performed using AI. For example, the Psychological Analysis Department inputs the user's geographical location information into the AI, and the AI ​​selects the optimal analysis method.

[0057] The history analysis unit can select the optimal analysis method by referring to the user's past history during history analysis. For example, the history analysis unit can perform the optimal history analysis based on places the user has visited in the past. The history analysis unit can also suggest places that the user might be interested in based on their past travel history. The history analysis unit can also select the optimal analysis method based on the user's past history. This enables efficient history analysis by selecting the optimal analysis method based on the user's past history. Some or all of the above processes in the history analysis unit are performed using AI. For example, the history analysis unit inputs the user's past history into the AI, and the AI ​​selects the optimal analysis method.

[0058] The history analysis unit can select the optimal analysis method by considering the user's geographical location information during history analysis. For example, if the user is in a specific region, the history analysis unit will perform history analysis related to that region. If the user is traveling, the history analysis unit can also perform history analysis related to the travel destination. The history analysis unit can also select the optimal history analysis method based on the user's current location. This allows for more appropriate history analysis by selecting the optimal analysis method while considering the user's geographical location information. Some or all of the above processes in the history analysis unit are performed using AI. For example, the history analysis unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal analysis method.

[0059] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can provide the optimal dialogue method based on the dialogue methods the user has preferred in the past. The dialogue unit can also suggest topics that the user might be interested in based on their past dialogue history. The dialogue unit can also select the optimal dialogue method based on the user's past dialogue history. This enables efficient dialogue by selecting the optimal dialogue method based on the user's past dialogue history. Some or all of the above processes in the dialogue unit are performed using AI. For example, the dialogue unit inputs the user's past dialogue history into the AI, and the AI ​​selects the optimal dialogue method.

[0060] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit will engage in conversations related to that region. If the user is traveling, the dialogue unit can also engage in conversations related to the travel destination. The dialogue unit can also select the optimal dialogue method based on the user's current location. This allows for more appropriate conversations by selecting the optimal dialogue method while considering the user's geographical location. Some or all of the above processing in the dialogue unit is performed using AI. For example, the dialogue unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal dialogue method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] AI concierge services can also suggest travel plans that take the user's health condition into consideration. For example, a health management department could be established to collect user health data and incorporate it into travel plans. If a user has allergies, the service can suggest restaurants that offer allergy-friendly meals. If a user has a chronic illness, the service can select travel destinations with nearby medical facilities. Furthermore, if a user prioritizes fitness, the service can suggest plans that include exercise facilities and activities at the travel destination. This allows for the provision of optimal travel plans tailored to the user's health condition.

[0063] AI concierge services can monitor users' travel activities in real time and dynamically adjust travel plans. For example, by incorporating a behavior monitoring unit, if a user finishes visiting a tourist spot earlier than planned, it can suggest the next tourist spot sooner. It can also provide information related to a place a user unexpectedly visits. Furthermore, if a user is tired, it can suggest resting places or cafes. This allows for the provision of flexible travel plans tailored to the user's behavior.

[0064] An AI concierge service can collect users' food preferences in real time during their travels and suggest the most suitable restaurants. For example, it can have a food preference data collection unit to gather data on the dishes users have eaten during their trip and use that information to suggest their next meal. If a user likes a particular dish, it can suggest restaurants that serve that dish. If a user wants to try something new, it can suggest restaurants that serve local specialties. Furthermore, if a user is health-conscious, it can suggest restaurants that offer healthy menus. In this way, it can provide the most suitable restaurants tailored to the user's food preferences.

[0065] AI concierge services can optimize a user's transportation during their trip. For example, a transportation optimization unit can collect user travel data and suggest the most suitable mode of transport. If a user wants to use public transport, it can provide the optimal route and timetable. If a user wants to rent a car, it can suggest the best rental car company and vehicle type. Furthermore, if a user wants to take a taxi, it can arrange for the nearest taxi. This allows the service to provide the most suitable transportation method for each user's travel needs.

[0066] AI concierge services can collect users' shopping preferences in real time during their travels and suggest optimal shopping spots. For example, a shopping preference collection department can gather data on items purchased by users during their trips and use this information to suggest future shopping destinations. If a user prefers a particular brand, the service can suggest stores that carry that brand's products. If a user wants to purchase local specialties, the service can suggest local markets and shops. Furthermore, if a user likes antiques or art, the service can suggest antique shops and galleries. This allows the service to provide users with the most suitable shopping spots tailored to their preferences.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk enters the user's information. This information includes preferences, budget, and past travel history. The reception desk saves the information entered by the user to the database and provides it to the generation department. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate the optimal travel plan. The generation unit generates a travel plan based on the user's preferences and budget, and also takes into account the user's past travel history. Step 3: The update unit updates the travel plan generated by the generation unit in real time. If the user wants to change their plans during the trip, the update unit regenerates the optimal plan based on the new information. Step 4: The suggestion unit proposes the travel plan updated by the update unit to the user. The suggestion unit displays the most suitable travel plan to the user, understands the user's preferences through dialogue using generation AI, and instantly displays the most suitable suggestion.

[0069] (Example of form 2) An AI concierge service according to an embodiment of the present invention is a system that provides personalized travel plans tailored to individual travelers in real time. This system provides an AI agent that analyzes the user's preferences, budget, and past travel history to design the optimal travel experience. First, the user inputs information such as their preferences, budget, and past travel history. This information is input into the generating AI. The generating AI analyzes the user's input information and generates an optimal travel plan based on the user's preferences and budget. For example, if the user likes nature and has a limited budget, the generating AI will suggest a travel plan that can be enjoyed in a nature-rich location within the budget. The generating AI also takes into account the user's past travel history. For example, based on places the user has visited and activities they have experienced in the past, it will suggest places and activities that the user has not yet visited but that might interest them. This allows the user to enjoy new experiences. Furthermore, the generating AI updates the user's travel plan in real time. For example, if the user wants to change their plans during the trip, the generating AI will regenerate the optimal plan based on the new information. This allows the user to flexibly respond to changes in circumstances during the trip. The generating AI also takes into account the user's psychological state. For example, if the user is feeling stressed, it will suggest a relaxing travel plan. This allows users to refresh themselves through travel. This AI concierge service has an intuitive interface, allowing users to easily input their preferences and requests. The generating AI understands the user's preferences through dialogue and instantly displays the most suitable suggestions. This makes it easy for users to obtain the perfect travel plan for themselves. This service employs a subscription model, and the premium plan offers custom travel plans. This allows users to choose a service that suits their needs. This AI concierge service also contributes to improving the efficiency of operations in the travel industry. For example, it is expected to reduce costs through improved operational efficiency in the travel industry, increase user satisfaction, and reduce the time spent on travel planning. This service is particularly targeted at active individual travelers in their 20s to 40s, and is ideal for adventurous individual travelers who are digital natives and seek new experiences.Furthermore, it is also suitable for professionals who seek high-quality travel experiences even while being busy with work. This AI concierge service is expected to be a turning point for the travel industry, as it responds to the increased demand for personalized travel experiences as travel demand recovers in the post-pandemic era. As a result, the AI ​​concierge service can generate and update optimal travel plans in real time based on the user's information.

[0070] The AI ​​concierge service according to this embodiment comprises a reception unit, a generation unit, an update unit, and a suggestion unit. The reception unit inputs user information. User information includes, but is not limited to, preferences, budget, and past travel history. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to analyze the information entered by the reception unit and generates an optimal travel plan. The generation unit generates a travel plan based, for example, the user's preferences and budget. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input information and generates an optimal travel plan. The generation AI also takes into account the user's past travel history. For example, the generation AI suggests places and activities that the user has not yet visited but might be interested in, based on places the user has visited and activities they have experienced in the past. The update unit updates the travel plan generated by the generation unit in real time. For example, if the user wants to change their plans during the trip, the update unit regenerates an optimal plan based on the new information. The update unit uses a generation AI to analyze the user's new information and regenerate the optimal travel plan. The suggestion unit proposes the travel plan updated by the update unit to the user. The suggestion unit, for example, displays the optimal travel plan to the user. The suggestion unit uses a generation AI to understand the user's preferences through dialogue and instantly displays the optimal suggestion. As a result, the AI ​​concierge service according to the embodiment can generate the optimal travel plan based on the user's information and update and propose it in real time. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's input information into the generation AI, and the generation AI generates the optimal travel plan. Some or all of the above processing in the update unit is performed using a generation AI. For example, the update unit inputs the user's new information into the generation AI, and the generation AI regenerates the optimal travel plan. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit displays the travel plan generated by the generation AI to the user.

[0071] The reception desk inputs user information. This information includes, but is not limited to, preferences, budget, and past travel history. The reception desk stores the user-entered information in a database and provides it to the generation desk. Specifically, information entered by users through web forms or applications is stored in a secure database. User preferences include places they want to visit, activities they are interested in, and food preferences. Budget information includes the overall travel budget, daily budget, and budget for accommodation. Past travel history includes details of places visited, hotels stayed at, and activities experienced. This information is centrally managed for each user and made accessible to the generation desk. The reception desk reflects the user-entered information in the database in real time, making it immediately available to the generation desk. Furthermore, if a user updates their information, the reception desk quickly updates the database and provides the latest information to the generation desk. In this way, the reception desk plays a role in accurately and quickly collecting detailed user information and providing it to the generation desk. In addition, the reception desk has a function to check the consistency of the user's input information and verify that there is no missing or inconsistent information. For example, if a user forgets to enter budget information or if there are inconsistencies in their past travel history, the reception desk will notify the user and prompt them to correct or add information. This allows the reception desk to provide accurate and complete information to the generation department, helping to create the optimal travel plan.

[0072] The generation unit uses a generation AI to analyze information entered by the reception unit and generate the optimal travel plan. For example, the generation unit generates a travel plan based on the user's preferences and budget. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input information and generate the optimal travel plan. The generation AI also takes into account the user's past travel history. Specifically, the generation AI analyzes the user's preferences, budget, and past travel history in detail and suggests tourist destinations and activities that the user might be interested in. For example, if the user has previously enjoyed visiting beach resorts, the generation AI will suggest new beach resorts and related activities. Also, if the user is planning a trip within a specific budget, the generation AI will select the most suitable accommodations and transportation within that budget. The generation AI analyzes the user's input information using natural language processing technology to accurately understand the user's intentions and desires. Furthermore, the generation AI obtains the latest information from external travel information databases and review sites to provide the user with the optimal travel plan. For example, the generation AI obtains the latest tourist destination information and event information and reflects it in the user's travel plan. This allows the generation unit to provide customized travel plans tailored to the user's needs. Furthermore, the generation unit also has a function to check the consistency and feasibility of the generated travel plan before presenting it to the user. For example, it checks whether the travel plan proposed by the generation AI exceeds the user's budget and whether the suggested activities are actually feasible. This allows the generation unit to provide users with feasible and satisfying travel plans.

[0073] The update unit updates the travel plan generated by the generation unit in real time. For example, if a user wants to change their plans during their trip, the update unit will regenerate the optimal plan based on the new information. The update unit uses a generation AI to analyze the user's new information and regenerate the optimal travel plan. Specifically, if a user wants to change their plans during their trip, the update unit quickly receives that information and inputs it into the generation AI. The generation AI analyzes the user's new information and regenerates the optimal travel plan. For example, if a user suddenly wants to change their plans and visit a different tourist destination, the update unit will generate a new travel plan based on that information and propose it to the user. By receiving the user's new information in real time and inputting it into the generation AI, the update unit can update the travel plan quickly and accurately. The update unit also has a function to continuously improve the travel plan based on new information and feedback entered by the user during their trip. For example, if a user provides feedback on a specific tourist destination or activity, the update unit will adjust the travel plan based on that information to improve user satisfaction. Furthermore, the update unit also has a function to obtain the latest information from external sources and reflect it in the travel plan. For example, weather information, traffic information, and event information can be obtained in real time and reflected in the travel plan. This allows the update unit to continuously provide users with the most suitable travel plan.

[0074] The suggestion unit proposes travel plans updated by the update unit to the user. For example, the suggestion unit displays the most suitable travel plan for the user. The suggestion unit uses generative AI to understand the user's preferences through dialogue and instantly displays the most suitable suggestion. Specifically, the suggestion unit displays the travel plan generated by the generative AI to the user in an easy-to-understand visual format. For example, it may display the details of the travel plan on a map or present it as an itinerary. The suggestion unit also has the function to understand the user's preferences and desires more deeply through dialogue with the user. For example, if the user shows interest in a particular tourist destination or activity, the suggestion unit adjusts the travel plan based on that information and makes the most suitable suggestion to the user. The suggestion unit uses generative AI to analyze the user's input information and dialogue content to accurately understand the user's preferences and desires. This allows the suggestion unit to provide the most suitable travel plan for the user. Furthermore, the suggestion unit also has the function to collect feedback from users and continuously improve the accuracy and effectiveness of the suggestions. For example, if the user provides feedback on the suggested travel plan, the suggestion unit adjusts the suggestion based on that information to improve user satisfaction. Furthermore, the proposal department can provide information to users using multiple communication methods. For example, it can propose travel plans to users through smartphone notifications, email, and in-app messages. This allows the proposal department to quickly and reliably provide users with the most suitable travel plans.

[0075] The psychological analysis department can take into account the user's psychological state. For example, it can measure the user's stress level and type of emotion. The psychological analysis department uses generative AI to analyze the user's psychological state and propose an optimal travel plan. For example, if the psychological analysis department is feeling stressed, it will propose a relaxing travel plan. The psychological analysis department can also estimate the user's emotions and adjust the travel plan based on the estimated emotions. For example, if the psychological analysis department is excited, it will generate a travel plan with visually stimulating effects. This allows the department to provide a travel plan that takes the user's psychological state into account. Some or all of the above processing in the psychological analysis department is performed using generative AI. For example, the psychological analysis department inputs the user's psychological state into the generative AI, which then generates an optimal travel plan.

[0076] The History Analysis Unit can analyze past travel history. For example, it obtains information such as the user's past travel destinations, purpose of travel, and duration of travel. Using a generative AI, the History Analysis Unit analyzes the user's past travel history and proposes an optimal travel plan. For example, based on places the user has visited and activities they have experienced, the History Analysis Unit suggests places and activities the user hasn't yet visited but might be interested in. The History Analysis Unit can also generate an optimal travel plan based on the user's preferences and budget, using their past travel history. This allows it to provide the best possible travel plan based on the user's past travel history. Some or all of the above processing in the History Analysis Unit is performed using a generative AI. For example, the History Analysis Unit inputs the user's past travel history into the generative AI, which then generates an optimal travel plan.

[0077] The dialogue unit can understand user preferences through dialogue. For example, it can acquire information such as the user's favorite activities, food, and type of accommodation. The dialogue unit uses generative AI to analyze the dialogue with the user and propose an optimal travel plan. For example, it can propose an optimal travel plan based on the user's favorite activities. The dialogue unit can also generate an optimal travel plan based on the user's preferences and budget through dialogue. This allows it to understand user preferences through dialogue and provide an optimal travel plan. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, the dialogue unit inputs the content of the dialogue with the user into the generative AI, which then generates an optimal travel plan.

[0078] The generation unit can generate an optimal travel plan based on the user's preferences and budget. For example, the generation unit obtains the user's preferences and budget range and generates a travel plan based on that. The generation unit uses a generation AI to analyze the user's preferences and budget and generate an optimal travel plan. For example, if the user likes nature and has a limited budget, the generation unit will suggest a travel plan that can be enjoyed in a nature-rich location within that budget. The generation unit can also generate an optimal travel plan for the user based on the user's preferences and budget. This allows the generation of an optimal travel plan based on the user's preferences and budget. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's preferences and budget into the generation AI, and the generation AI generates an optimal travel plan.

[0079] The suggestion unit can propose the most suitable travel plan to the user. For example, the suggestion unit displays a travel plan generated by the generation unit to the user. The suggestion unit uses generation AI to propose the most suitable travel plan to the user. For example, the suggestion unit displays a travel plan generated based on the user's preferences and budget to the user. By proposing the most suitable travel plan to the user, the suggestion unit makes it easy for the user to obtain the most suitable travel plan for them. This allows the suggestion unit to propose the most suitable travel plan to the user. Some or all of the above processing in the suggestion unit is performed using generation AI. For example, the suggestion unit displays a travel plan generated by generation AI to the user.

[0080] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception desk may prompt the user to input information during a time when they can relax. If the user is excited, the reception desk may prompt them to input information immediately, completing the input before their excitement subsides. If the user is tired, the reception desk may prompt them to input information after a break. By adjusting the timing of information input according to the user's emotions, more appropriate information input becomes possible. 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 reception desk is performed using generative AI. For example, the reception desk inputs the user's emotions into the generative AI, and the generative AI adjusts the timing of information input.

[0081] The reception desk can analyze the user's past information input history and select the optimal input method. For example, if the user has preferred using voice input in the past, the reception desk will prioritize suggesting voice input. If the user has preferred using text input in the past, the reception desk can also prioritize suggesting text input. The reception desk can also analyze the input methods the user has used in the past and suggest the most efficient method. This enables efficient information input by selecting the optimal input method based on the user's past information input history. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's past information input history into the AI, and the AI ​​selects the optimal input method.

[0082] The reception desk can filter information based on the user's current situation and areas of interest during input. For example, if the user is currently traveling, the reception desk will prioritize displaying travel-related information. If the user has a specific area of ​​interest, the reception desk can also prioritize displaying information related to that area. The reception desk can also filter and display highly relevant information based on the user's current situation. This allows the reception desk to provide highly relevant information by filtering information based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's current situation and areas of interest into the AI, and the AI ​​filters the information to determine the most relevant results.

[0083] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize the input of important information. If the user is relaxed, the reception desk may also allow the input of detailed information. If the user is in a hurry, the reception desk may also allow the input of only the most important information. This ensures that important information is prioritized by determining the priority of the information to be entered according to 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 reception desk is performed using generative AI. For example, the reception desk inputs the user's emotions into the generative AI, and the generative AI determines the priority of the information to be entered.

[0084] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception desk will prioritize inputting information related to that region. If the user is traveling, the reception desk can also prioritize inputting information related to the travel destination. The reception desk can also prioritize inputting highly relevant information based on the user's current location. This allows for the provision of more appropriate information by prioritizing the input of highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location information into the AI, and the AI ​​prioritizes inputting highly relevant information.

[0085] The reception desk can analyze a user's social media activity and input relevant information when information is entered. For example, the reception desk can input relevant information based on information the user has shared on social media. The reception desk can also analyze the content of a user's social media posts and input relevant information. The reception desk can also input relevant information based on the user's social media activity history. This allows for the provision of more appropriate information by analyzing the user's social media activity and inputting relevant information. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​inputs relevant information.

[0086] The generation unit can estimate the user's emotions and adjust how the travel plan is presented based on those emotions. For example, if the user is relaxed, the generation unit will generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit can also generate a travel plan with visually stimulating effects. This allows for the provision of a more appropriate travel plan by adjusting how the travel plan is presented according to 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generative AI. For example, the generation unit inputs the user's emotions into the generative AI, and the generative AI adjusts how the travel plan is presented.

[0087] The generation unit can adjust the level of detail in a travel plan based on the user's priorities when generating it. For example, the generation unit can generate a detailed travel plan based on elements that the user considers important. The generation unit can also generate a simplified travel plan based on elements that the user considers less important. The generation unit can also adjust the level of detail in the travel plan according to the user's priorities. This allows for the provision of more appropriate travel plans by adjusting the level of detail based on the user's priorities. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the user's priorities into the AI, and the AI ​​adjusts the level of detail in the travel plan.

[0088] The generation unit can apply different generation algorithms depending on the user's category when generating travel plans. For example, if the user is traveling with family, the generation unit applies a family-oriented generation algorithm. If the user is traveling alone, the generation unit can also apply a solo travel-oriented generation algorithm. If the user is traveling for business, the generation unit can also apply a business-oriented generation algorithm. This allows for the provision of more appropriate travel plans by applying different generation algorithms depending on the user's category. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs the user's category into the AI, and the AI ​​applies a different generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise travel plan. If the user is relaxed, the generation unit can also generate a longer travel plan with detailed explanations. If the user is excited, the generation unit can also generate a travel plan with visually stimulating effects. This allows for the provision of a more appropriate travel plan by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, and the generation AI adjusts the length of the travel plan.

[0090] The generation unit can determine the priority of travel plans based on when the user submits them. For example, if the user submits early, the generation unit will prioritize generating a detailed travel plan. If the user submits at the last minute, the generation unit can also prioritize generating a simplified travel plan. The generation unit can also adjust the priority of travel plans according to when the user submits them. This allows the system to provide more appropriate travel plans by prioritizing them based on when the user submits them. Some or all of the above processes in the generation unit are performed using AI. For example, the generation unit inputs the user's submission date into the AI, and the AI ​​determines the priority of the travel plans.

[0091] The generation unit can adjust the order of travel plans based on user relevance when generating them. For example, the generation unit can prioritize placing activities that the user is interested in at the beginning of the plan. It can also place activities that the user is less interested in towards the end of the plan. The generation unit can also adjust the order of travel plans according to user relevance. This allows for the provision of more appropriate travel plans by adjusting the order of plans based on user relevance. Some or all of the above processing in the generation unit is performed using AI. For example, the generation unit inputs user relevance into the AI, and the AI ​​adjusts the order of travel plans.

[0092] The update unit can estimate the user's emotions and adjust the frequency of travel plan updates based on the estimated emotions. For example, if the user is stressed, the update unit will update frequently to provide reassurance. If the user is relaxed, the update unit can update only as often as necessary. If the user is in a hurry, the update unit can update quickly to provide the latest information. This allows for more appropriate updates by adjusting the travel plan update frequency according to 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 update unit is performed using generative AI. For example, the update unit inputs the user's emotions into the generative AI, which then adjusts the frequency of travel plan updates.

[0093] The update unit can select the optimal update method when updating a travel plan by referring to the user's past update history. For example, if the user has updated frequently in the past, the update unit will select a method that updates frequently. If the user has not updated very often in the past, the update unit can also select a method that updates only as much as necessary. The update unit can also select the optimal update method based on the user's past update history. This enables efficient updates by selecting the optimal update method based on the user's past update history. Some or all of the above processing in the update unit is performed using AI. For example, the update unit inputs the user's past update history into the AI, and the AI ​​selects the optimal update method.

[0094] The update unit can customize the update method based on the user's current situation when updating travel plans. For example, if the user is traveling, the update unit will perform updates in real time. If the user is at home, the update unit can also perform updates in advance. The update unit can also customize the optimal update method based on the user's current situation. This allows for more appropriate updates by customizing the update method based on the user's current situation. Some or all of the above processes in the update unit are performed using AI. For example, the update unit inputs the user's current situation into the AI, and the AI ​​customizes the optimal update method.

[0095] The update unit can estimate the user's emotions and determine the priority of the plans to update based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating important plans. If the user is relaxed, the update unit may also prioritize updating detailed plans. If the user is in a hurry, the update unit may also update only the most important plans. This allows for prioritizing important plans by determining the priority of the plans to update according to 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 update unit is performed using generative AI. For example, the update unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of the plans to update.

[0096] The update unit can select the optimal update method when updating a travel plan, taking into account the user's geographical location. For example, if the user is in a specific region, the update unit will prioritize updating information related to that region. If the user is at their travel destination, the update unit can also prioritize updating information related to the travel destination. The update unit can also select the optimal update method based on the user's current location. This allows for more appropriate updates by selecting the optimal update method while considering the user's geographical location. Some or all of the above processing in the update unit is performed using AI. For example, the update unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal update method.

[0097] The update unit can analyze the user's social media activity and suggest update methods when updating travel plans. For example, the update unit updates relevant information based on information shared by the user on social media. The update unit can also analyze the content of the user's social media posts and update relevant information. The update unit can also update relevant information based on the user's social media activity history. This allows for more appropriate updates by analyzing the user's social media activity and suggesting update methods. Some or all of the above processes in the update unit are performed using AI. For example, the update unit inputs the user's social media activity into the AI, and the AI ​​suggests update methods.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions at a leisurely pace. If the user is in a hurry, the suggestion unit may also present suggestions that emphasize the shortest route. If the user is excited, the suggestion unit may also present suggestions with visually stimulating effects. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotions into the generative AI, which then adjusts the presentation of the suggestions.

[0099] The proposal function can adjust the level of detail of a proposal based on the user's priorities. For example, the proposal function can provide detailed proposals based on elements that the user considers important. It can also provide simplified proposals based on elements that the user considers less important. The proposal function can adjust the level of detail of a proposal according to the user's priorities. This allows for more appropriate proposals by adjusting the level of detail based on the user's priorities. Some or all of the above processes in the proposal function are performed using AI. For example, the proposal function inputs the user's priorities into the AI, and the AI ​​adjusts the level of detail of the proposal.

[0100] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user is traveling with family, the suggestion unit will apply a suggestion algorithm for families. If the user is traveling alone, the suggestion unit can also apply a suggestion algorithm for solo travelers. If the user is traveling for business, the suggestion unit can also apply a suggestion algorithm for business travelers. By applying different suggestion algorithms depending on the user's category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit is performed using AI. For example, the suggestion unit inputs the user's category into the AI, and the AI ​​applies a different suggestion algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit may provide longer suggestions with more detailed explanations. If the user is excited, the suggestion unit may also provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotions into the generative AI, which then adjusts the length of the suggestions.

[0102] The proposal department can prioritize proposals based on when the user submits them. For example, if a user submits an early proposal, the department will prioritize detailed proposals. If a user submits a proposal at the last minute, the department may also prioritize simplified proposals. The proposal department can also adjust the priority of proposals according to when the user submits them. This allows for more appropriate proposals by prioritizing proposals based on when the user submits them. Some or all of the above processes in the proposal department are performed using AI. For example, the proposal department inputs the user's submission date into the AI, and the AI ​​determines the priority of proposals.

[0103] The suggestion section can adjust the order of suggestions based on user relevance. For example, it can prioritize placing activities that the user is interested in at the beginning of the suggestions. It can also place activities that the user is less interested in towards the end of the suggestions. The suggestion section can also adjust the order of suggestions according to user relevance. This allows for more appropriate suggestions by adjusting the order of suggestions based on user relevance. Some or all of the above processing in the suggestion section is performed using AI. For example, the suggestion section inputs user relevance into the AI, and the AI ​​adjusts the order of suggestions.

[0104] The psychological analysis unit can estimate the user's emotions and adjust the method of psychological analysis based on the estimated emotions. For example, if the user is stressed, the psychological analysis unit will perform the analysis in a relaxing manner. If the user is relaxed, the psychological analysis unit can also perform a detailed psychological analysis. If the user is excited, the psychological analysis unit can also perform the analysis in a visually stimulating manner. This allows for a more appropriate psychological analysis by adjusting the method of psychological analysis according to 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-described processes in the psychological analysis unit are performed using generative AI. For example, the psychological analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the method of psychological analysis.

[0105] The Psychological Analysis Department can select the optimal analysis method by referring to the user's past psychological state during the analysis. For example, if the user has experienced stress in the past, the Psychological Analysis Department will conduct the analysis using methods that promote relaxation. If the user has been relaxed in the past, the Psychological Analysis Department can also conduct a more detailed analysis. The Psychological Analysis Department can also select the optimal analysis method based on the user's past psychological state. This allows for efficient psychological analysis by selecting the optimal analysis method based on the user's past psychological state. Some or all of the above processes in the Psychological Analysis Department are performed using AI. For example, the Psychological Analysis Department inputs the user's past psychological state into the AI, and the AI ​​selects the optimal analysis method.

[0106] The psychological analysis unit can estimate the user's emotions and determine the priority of psychological analysis based on the estimated emotions. For example, if the user is stressed, the psychological analysis unit will prioritize important psychological analyses. If the user is relaxed, the psychological analysis unit may also prioritize detailed psychological analyses. If the user is in a hurry, the psychological analysis unit may also perform only the most important psychological analyses. This allows for prioritizing important psychological analyses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the psychological analysis unit is performed using generative AI. For example, the psychological analysis unit inputs the user's emotions into the generative AI, which then determines the priority of psychological analysis.

[0107] The Psychological Analysis Department can select the optimal analysis method during psychological analysis by considering the user's geographical location. For example, if the user is in a specific region, the Psychological Analysis Department will perform a psychological analysis related to that region. If the user is traveling, the Psychological Analysis Department can also perform a psychological analysis related to the travel destination. The Psychological Analysis Department can also select the optimal psychological analysis method based on the user's current location. This allows for more appropriate psychological analysis by selecting the optimal analysis method while considering the user's geographical location. Some or all of the above processes in the Psychological Analysis Department are performed using AI. For example, the Psychological Analysis Department inputs the user's geographical location information into the AI, and the AI ​​selects the optimal analysis method.

[0108] The history analysis unit can estimate the user's emotions and adjust the history analysis method based on the estimated emotions. For example, if the user is stressed, the history analysis unit will perform the history analysis in a relaxing manner. If the user is relaxed, the history analysis unit can also perform a detailed history analysis. If the user is excited, the history analysis unit can also perform the history analysis in a visually stimulating manner. This allows for more appropriate history analysis by adjusting the history analysis method according to 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 history analysis unit is performed using generative AI. For example, the history analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the history analysis method.

[0109] The history analysis unit can select the optimal analysis method by referring to the user's past history during history analysis. For example, the history analysis unit can perform the optimal history analysis based on places the user has visited in the past. The history analysis unit can also suggest places that the user might be interested in based on their past travel history. The history analysis unit can also select the optimal analysis method based on the user's past history. This enables efficient history analysis by selecting the optimal analysis method based on the user's past history. Some or all of the above processes in the history analysis unit are performed using AI. For example, the history analysis unit inputs the user's past history into the AI, and the AI ​​selects the optimal analysis method.

[0110] The history analysis unit can estimate the user's emotions and determine the priority of the history analysis based on the estimated emotions. For example, if the user is stressed, the history analysis unit will prioritize important history analyses. If the user is relaxed, the history analysis unit may also prioritize detailed history analyses. If the user is in a hurry, the history analysis unit may also perform only the most important history analyses. This allows for prioritizing important history analyses by determining the priority of the history analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 history analysis unit is performed using generative AI. For example, the history analysis unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of the history analysis.

[0111] The history analysis unit can select the optimal analysis method by considering the user's geographical location information during history analysis. For example, if the user is in a specific region, the history analysis unit will perform history analysis related to that region. If the user is traveling, the history analysis unit can also perform history analysis related to the travel destination. The history analysis unit can also select the optimal history analysis method based on the user's current location. This allows for more appropriate history analysis by selecting the optimal analysis method while considering the user's geographical location information. Some or all of the above processes in the history analysis unit are performed using AI. For example, the history analysis unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal analysis method.

[0112] The dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is stressed, the dialogue unit can provide a relaxing dialogue method. If the user is relaxed, the dialogue unit can also provide a detailed dialogue. If the user is excited, the dialogue unit can also provide a visually stimulating dialogue method. This allows for more appropriate dialogue by adjusting the dialogue method according to 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 dialogue unit is performed using generative AI. For example, the dialogue unit inputs the user's emotions into the generative AI, and the generative AI adjusts the dialogue method.

[0113] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can provide the optimal dialogue method based on the dialogue methods the user has preferred in the past. The dialogue unit can also suggest topics that the user might be interested in based on their past dialogue history. The dialogue unit can also select the optimal dialogue method based on the user's past dialogue history. This enables efficient dialogue by selecting the optimal dialogue method based on the user's past dialogue history. Some or all of the above processes in the dialogue unit are performed using AI. For example, the dialogue unit inputs the user's past dialogue history into the AI, and the AI ​​selects the optimal dialogue method.

[0114] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize important dialogues. If the user is relaxed, the dialogue unit may also prioritize detailed dialogues. If the user is in a hurry, the dialogue unit may only perform the most important dialogues. This allows important dialogues to be prioritized by determining the priority of dialogues according to 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 dialogue unit is performed using generative AI. For example, the dialogue unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of the dialogue.

[0115] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user is in a specific region, the dialogue unit will engage in conversations related to that region. If the user is traveling, the dialogue unit can also engage in conversations related to the travel destination. The dialogue unit can also select the optimal dialogue method based on the user's current location. This allows for more appropriate conversations by selecting the optimal dialogue method while considering the user's geographical location. Some or all of the above processing in the dialogue unit is performed using AI. For example, the dialogue unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal dialogue method.

[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] AI concierge services can also suggest travel plans that take the user's health condition into consideration. For example, a health management department could be established to collect user health data and incorporate it into travel plans. If a user has allergies, the service can suggest restaurants that offer allergy-friendly meals. If a user has a chronic illness, the service can select travel destinations with nearby medical facilities. Furthermore, if a user prioritizes fitness, the service can suggest plans that include exercise facilities and activities at the travel destination. This allows for the provision of optimal travel plans tailored to the user's health condition.

[0118] AI concierge services can estimate a user's emotions and select travel destinations based on those estimates. For example, by incorporating an emotion estimation unit, if a user is feeling relaxed, it can suggest a quiet beach resort. If the user is seeking adventure, it can suggest a mountainous area with plenty of activities. Furthermore, if the user is looking for a cultural experience, it can suggest historical cities or regions with many museums. This allows for the selection of the optimal travel destination tailored to the user's emotions.

[0119] AI concierge services can monitor users' travel activities in real time and dynamically adjust travel plans. For example, by incorporating a behavior monitoring unit, if a user finishes visiting a tourist spot earlier than planned, it can suggest the next tourist spot sooner. It can also provide information related to a place a user unexpectedly visits. Furthermore, if a user is tired, it can suggest resting places or cafes. This allows for the provision of flexible travel plans tailored to the user's behavior.

[0120] AI concierge services can estimate a user's emotions and suggest entertainment options during their trip based on those estimates. For example, by incorporating an emotion estimation unit, if the user is feeling relaxed, it can suggest relaxing music or movies. If the user is excited, it can suggest action movies or exciting games. Furthermore, if the user is seeking emotional experiences, it can suggest emotionally moving movies or documentaries. This allows for the provision of optimal entertainment tailored to the user's emotions.

[0121] An AI concierge service can collect users' food preferences in real time during their travels and suggest the most suitable restaurants. For example, it can have a food preference data collection unit to gather data on the dishes users have eaten during their trip and use that information to suggest their next meal. If a user likes a particular dish, it can suggest restaurants that serve that dish. If a user wants to try something new, it can suggest restaurants that serve local specialties. Furthermore, if a user is health-conscious, it can suggest restaurants that offer healthy menus. In this way, it can provide the most suitable restaurants tailored to the user's food preferences.

[0122] AI concierge services can estimate a user's emotions and suggest travel activities based on those estimates. For example, by incorporating an emotion estimation unit, if a user is feeling relaxed, it can suggest relaxing activities such as yoga or a spa treatment. If the user is excited, it can suggest exciting activities such as skydiving or jet skiing. Furthermore, if the user is seeking a cultural experience, it can suggest visits to museums or art galleries. This allows for the provision of optimal activities tailored to the user's emotions.

[0123] AI concierge services can optimize a user's transportation during their trip. For example, a transportation optimization unit can collect user travel data and suggest the most suitable mode of transport. If a user wants to use public transport, it can provide the optimal route and timetable. If a user wants to rent a car, it can suggest the best rental car company and vehicle type. Furthermore, if a user wants to take a taxi, it can arrange for the nearest taxi. This allows the service to provide the most suitable transportation method for each user's travel needs.

[0124] AI concierge services can estimate a user's emotions and suggest accommodations based on those emotions. For example, by including an emotion estimation unit, if a user is feeling relaxed, it can suggest a quiet and peaceful accommodation. If the user is excited, it can suggest a resort hotel with plenty of activities. Furthermore, if the user is seeking a cultural experience, it can suggest accommodations in historical buildings. This allows for the provision of optimal accommodations tailored to the user's emotions.

[0125] AI concierge services can collect users' shopping preferences in real time during their travels and suggest optimal shopping spots. For example, a shopping preference collection department can gather data on items purchased by users during their trips and use this information to suggest future shopping destinations. If a user prefers a particular brand, the service can suggest stores that carry that brand's products. If a user wants to purchase local specialties, the service can suggest local markets and shops. Furthermore, if a user likes antiques or art, the service can suggest antique shops and galleries. This allows the service to provide users with the most suitable shopping spots tailored to their preferences.

[0126] AI concierge services can estimate a user's emotions and suggest communication methods during their trip based on those estimates. For example, by incorporating an emotion estimation unit, if the user wants to relax, it can suggest communication in a calm tone. If the user is excited, it can suggest communication in an energetic tone. Furthermore, if the user is seeking inspiration, it can suggest sharing an inspiring story. This allows for the provision of the most appropriate communication method tailored to the user's emotions.

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

[0128] Step 1: The reception desk enters the user's information. This information includes preferences, budget, and past travel history. The reception desk saves the information entered by the user to the database and provides it to the generation department. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate the optimal travel plan. The generation unit generates a travel plan based on the user's preferences and budget, and also takes into account the user's past travel history. Step 3: The update unit updates the travel plan generated by the generation unit in real time. If the user wants to change their plans during the trip, the update unit regenerates the optimal plan based on the new information. Step 4: The suggestion unit proposes the travel plan updated by the update unit to the user. The suggestion unit displays the most suitable travel plan to the user, understands the user's preferences through dialogue using generation AI, and instantly displays the most suitable suggestion.

[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, update unit, proposal unit, psychological analysis unit, history analysis unit, and dialogue 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 control unit 46A of the smart device 14 and takes user information as input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the travel plan in real time. The proposal unit is implemented by the control unit 46A of the smart device 14 and displays the optimal travel plan to the user. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's psychological state. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past travel history. The dialogue unit is implemented by the control unit 46A of the smart device 14 and understands the user's preferences through dialogue with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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, update unit, proposal unit, psychological analysis unit, history analysis unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes user information as input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the travel plan in real time. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and displays the optimal travel plan to the user. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's psychological state. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past travel history. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and understands the user's preferences through dialogue with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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, update unit, proposal unit, psychological analysis unit, history analysis unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and takes user information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the travel plan in real time. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and displays the optimal travel plan to the user. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's psychological state. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past travel history. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and understands the user's preferences through dialogue with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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, update unit, proposal unit, psychological analysis unit, history analysis unit, and dialogue unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes user information as input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the travel plan in real time. The proposal unit is implemented by the control unit 46A of the robot 414 and displays the optimal travel plan to the user. The psychological analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's psychological state. The history analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's past travel history. The dialogue unit is implemented by the control unit 46A of the robot 414 and understands the user's preferences through dialogue with the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[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 user information is entered, A generation unit analyzes the information entered by the reception unit and generates an optimal travel plan, An update unit updates the travel plan generated by the generation unit in real time, The system includes a proposal unit that proposes the travel plan updated by the update unit to the user. A system characterized by the following features. (Note 2) It includes a psychological analysis unit that takes into account the user's psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a history analysis department that analyzes past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a dialogue unit that understands user preferences through interaction. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generates the optimal travel plan based on the user's preferences and budget. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal travel plan for the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past information input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, filtering is performed based on the user's current situation and areas of interest. 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 prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating travel plans, adjust the level of detail based on the user's priorities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating travel plans, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the travel plan 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 priority of the plans is determined based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, the order of the plans is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is The system estimates the user's emotions and adjusts the frequency of travel plan updates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is When updating a travel plan, the system will refer to the user's past update history to select the most suitable update method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is When updating travel plans, customize the update method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is It estimates user sentiment and determines the priority of update plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is When updating travel plans, the system selects the optimal update method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When updating travel plans, we analyze the user's social media activity and suggest ways to update them. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting a proposal, we prioritize the proposals based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Psychological Analysis Department We estimate the user's emotions and adjust the psychological analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned Psychological Analysis Department During psychological analysis, the optimal analysis method is selected by referring to the user's past psychological state. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned Psychological Analysis Department It estimates the user's emotions and determines the priority of psychological analysis based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned Psychological Analysis Department When conducting psychological analysis, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned history analysis unit, We estimate the user's emotions and adjust the historical analysis method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned history analysis unit, During historical analysis, the system selects the optimal analysis method by referring to the user's past history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned history analysis unit, It estimates the user's emotions and determines the priority of historical analysis based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned history analysis unit, When performing historical analysis, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way it interacts based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 4, 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 user information is entered, A generation unit analyzes the information entered by the reception unit and generates an optimal travel plan, An update unit updates the travel plan generated by the generation unit in real time, The system includes a proposal unit that proposes the travel plan updated by the update unit to the user. A system characterized by the following features.

2. It includes a psychological analysis unit that takes into account the user's psychological state. The system according to feature 1.

3. It has a history analysis department that analyzes past travel history. The system according to feature 1.

4. It features a dialogue unit that understands user preferences through interaction. The system according to feature 1.

5. The generating unit is Generates the optimal travel plan based on the user's preferences and budget. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal travel plan for the user. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

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

9. The aforementioned reception unit is When entering information, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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