system

Abstract

The system according to this embodiment aims to analyze the user's travel history and location information and provide a personalized and optimal travel plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a response unit, and a learning unit. The collection unit collects the user's travel history and location information. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The provision unit provides the travel plan generated by the generation unit. The response unit responds to problems during the trip. The learning unit learns the user's behavior patterns and improves its suggestions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is impossible to sufficiently utilize the user's travel history and location information to provide an individualized travel plan, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's travel history and location information and provide an individualized optimal travel plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a response unit, and a learning unit. The collection unit collects the user's travel history and location information. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The provision unit provides the travel plan generated by the generation unit. The response unit responds to problems that occur during the trip. The learning unit learns the user's behavior patterns and improves its suggestions. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's travel history and location information and provide a personalized and optimal travel plan. [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, etc. 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 expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal travel planner system according to an embodiment of the present invention is a system that creates an optimal travel plan tailored to an individual's hobbies and preferences. The personal travel planner system collects the user's travel history and location information, and the generating AI analyzes this data to propose a personalized travel plan and activities for each traveler. The personal travel planner system responds to all needs during, before, and after the trip, and makes increasingly refined suggestions over time. For example, the personal travel planner system collects the user's travel history and location information. For example, it collects data such as previously visited travel destinations, accommodations, and activity selection history. This data is input into the generating AI. Next, the generating AI analyzes the collected data and learns the user's hobbies and preferences. For example, it determines the user's level of interest in specific tourist destinations or activities and generates an optimal travel plan based on the results. The generated travel plan is provided to the user. For example, it suggests information such as tourist destinations, restaurants, and events at the travel destination. Furthermore, if an unexpected situation occurs during the trip (such as a change in weather or a delay in transportation), the generating AI in the personal travel planner system will propose the optimal action in real time. Furthermore, the generating AI learns the user's behavior patterns and preferences, making more refined suggestions over time. For example, it can suggest future travel plans with greater accuracy based on past travel history and location information. This system allows travelers to enjoy their trips efficiently and comfortably. For instance, it reduces the time and effort required for travel planning and allows users to find the best option from a vast amount of travel information. It can also flexibly respond to travel-related problems and offer suggestions tailored to individual needs. As a result, the dedicated travel planner system can utilize the user's travel history and location information to create optimal travel plans that match their personal tastes and preferences.

[0029] The dedicated travel planner system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a response unit, and a learning unit. The collection unit collects the user's travel history and location information. The collection unit collects data such as past travel destinations, accommodations, and activity selection history. The collection unit can, for example, store the user's travel history in a database and obtain location information from GPS data or Wi-Fi location information. The collection unit can, for example, collect information on past travel destinations and record the accommodation selection history. The collection unit can, for example, collect the history of activities selected by the user and store it in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit learns the user's hobbies and preferences using, for example, data mining techniques. The analysis unit can extract specific patterns from the user's travel history using, for example, statistical analysis. The analysis unit can predict the user's hobbies and preferences using, for example, machine learning algorithms. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The generation unit generates an optimal travel plan based on the user's hobbies and preferences, for example. The generation unit can generate a travel plan that includes tourist destinations and activities of interest to the user, for example. The generation unit can generate a travel plan that fits the user's budget and schedule, for example. The delivery unit provides the travel plan generated by the generation unit. The delivery unit presents the generated travel plan to the user, for example. The delivery unit can display the travel plan on the user's smartphone or personal computer, for example. The delivery unit can send the travel plan to the user via email or messaging app, for example. The response unit suggests the best course of action in the event of unexpected events during the trip. The response unit responds to, for example, changes in weather or delays in transportation. The response unit can suggest alternative means of transportation or accommodation, for example. The response unit can provide emergency contact information for problems during the trip, for example. The learning unit learns the user's behavior patterns and refines its suggestions over time. The learning unit suggests the next travel plan with greater accuracy based on, for example, the user's past travel history and location information.The learning unit can, for example, analyze user behavior patterns to improve the accuracy of suggestions. The learning unit can also, for example, improve suggestions based on user feedback. As a result, the dedicated travel planner system according to the embodiment can create an optimal travel plan tailored to the user's personal tastes and preferences by utilizing the user's travel history and location information.

[0030] The data collection unit collects user travel history and location information. For example, it collects data such as past travel destinations, accommodations, and activity selection history. Specifically, it can collect information on past travel destinations and record accommodation selection history. This includes information on hotels and accommodations booked by the user, length of stay, and ratings. It can also collect and store a history of activities selected by the user in a database. For example, it collects detailed information on tours and events the user participated in, tourist destinations visited, and activities experienced. Furthermore, the data collection unit can obtain user location information from GPS data and Wi-Fi location information. This allows the user's current location and travel route to be tracked in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit learns the user's hobbies and preferences using data mining techniques. Specifically, it can extract certain patterns from the user's travel history and understand the user's preferred travel destinations and activities. For example, it can use statistical analysis to analyze the characteristics and commonalities of travel destinations the user has visited in the past and predict the user's preferences. It can also use machine learning algorithms to predict the user's hobbies and preferences. For example, based on data of activities and accommodations the user has selected in the past, it can predict tourist destinations and activities that the user is likely to be interested in on their next trip. Furthermore, the analysis unit can analyze the collected location information to understand the user's movement patterns and behavioral tendencies. As a result, the analysis unit can gain a detailed understanding of the user's hobbies, preferences, and behavioral patterns, and provide the basic data to offer individually optimized travel plans.

[0032] The generation unit generates travel plans based on the analysis results obtained by the analysis unit. For example, the generation unit generates an optimal travel plan based on the user's hobbies and preferences. Specifically, it can generate travel plans that include tourist destinations and activities of interest to the user. For example, if the user likes nature, it will suggest a travel plan that includes nature parks and hiking trails. It can also generate travel plans that match the user's budget and schedule. For example, it will select the best accommodations and transportation within the user's budget and create a schedule that fits their dates. Furthermore, the generation unit can provide more personalized travel plans by considering the user's past travel history and location information. For example, it will suggest tourist destinations and activities that the user has never visited before, providing new experiences. In this way, the generation unit can generate and provide the user with an optimal travel plan that matches the user's hobbies, preferences, budget, and schedule.

[0033] The service provider provides the travel plan generated by the generation unit. The service provider, for example, presents the generated travel plan to the user. Specifically, the travel plan can be displayed on the user's smartphone or personal computer. For example, the user can check the details of the travel plan through a dedicated application or website. The travel plan can also be sent to the user via email or messaging app. This allows the user to check the travel plan anytime, anywhere, and make changes or adjustments as needed. Furthermore, the service provider can collect user feedback to improve the accuracy and satisfaction of the travel plans it provides. For example, users can provide ratings and comments on the travel plan, which can be used to improve future travel plans. This allows the service provider to provide the user with the best possible travel plan and increase user satisfaction.

[0034] The support team proposes the best course of action in the event of unexpected situations during travel. Specifically, they respond to changes in weather and delays in transportation. For example, if the weather suddenly changes, the support team will suggest alternative tourist destinations or activities to ensure the user can continue their trip safely. In the event of transportation delays, the support team can suggest alternative transportation and accommodations. For example, if a flight is delayed or canceled, the support team will suggest the best alternative flight or accommodation to help the user continue their trip smoothly. Furthermore, the support team can provide emergency contact information for travel troubles. For example, if a user encounters trouble during their trip, the support team can provide emergency contact information and respond quickly. In this way, the support team can provide a swift and appropriate response to unexpected situations during travel, ensuring the safety and peace of mind of the user.

[0035] The learning unit learns user behavior patterns and refines its suggestions over time. Specifically, it uses the user's past travel history and location information to suggest more accurate travel plans for the next trip. For example, it analyzes data on destinations the user has visited and activities they have selected in the past to suggest tourist spots and activities that are likely to interest them on their next trip. It can also analyze user behavior patterns to improve the accuracy of its suggestions. For example, if a user tends to be interested in a particular season or event, it will suggest the most suitable travel plan based on that information. Furthermore, the learning unit can improve its suggestions based on user feedback. For example, users can provide ratings and comments on the travel plans offered, which can then be reflected in future suggestions. In this way, the learning unit can refine its suggestions over time based on user behavior patterns and feedback, providing more accurate travel plans.

[0036] The data collection unit can collect past travel destinations, accommodations, activity selection history, and other related data. For example, the data collection unit can collect information on travel destinations the user has visited in the past. For example, the data collection unit can record the user's accommodation selection history. For example, the data collection unit can collect the user's activity selection history. For example, the data collection unit can collect related data such as the user's interests and past reviews. This makes it easier to understand the user's tastes and preferences by collecting past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past travel history into a generative AI, which can then analyze the data and collect related data.

[0037] The analysis unit can analyze collected data and learn the user's hobbies and preferences. For example, the analysis unit can learn the user's hobbies and preferences using data mining techniques. For example, the analysis unit can extract specific patterns from the user's travel history using statistical analysis. For example, the analysis unit can predict the user's hobbies and preferences using machine learning algorithms. For example, the analysis unit can analyze data such as survey results and past selection history. By learning the user's hobbies and preferences, it is possible to provide more personalized travel plans. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input collected data into a generative AI, which can analyze the data and learn the user's hobbies and preferences.

[0038] The generation unit can generate an optimal travel plan based on the analysis results. For example, the generation unit can generate an optimal travel plan based on the user's hobbies and preferences. For example, the generation unit can generate a travel plan that includes tourist destinations and activities of interest to the user. For example, the generation unit can generate a travel plan that fits the user's budget and schedule. For example, the generation unit can generate an optimal travel plan based on criteria such as user satisfaction and cost performance. In this way, by generating a travel plan based on the analysis results, the user can be provided with the optimal travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI, and the generation AI can generate an optimal travel plan.

[0039] The service provider can provide the generated travel plan to the user. For example, the service provider can present the generated travel plan to the user. For example, the service provider can display the travel plan on the user's smartphone or personal computer. For example, the service provider can send the travel plan to the user via email or messaging app. By providing the user with the generated travel plan, it becomes easier for the user to review the travel plan. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the generated travel plan into a generation AI, and the generation AI can determine how to provide it to the user.

[0040] The response unit can suggest appropriate actions if unexpected events occur during travel. For example, it can respond to changes in weather or delays in transportation. For example, it can suggest alternative transportation or accommodation. For example, it can provide emergency contact information for travel troubles. For example, it can suggest appropriate actions such as suggesting alternative transportation or providing emergency contact information. This improves the user's travel experience by flexibly responding to travel troubles. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input data about travel troubles into a generative AI, which can then suggest the optimal action.

[0041] The learning unit can learn user behavior patterns and refine its suggestions over time. For example, the learning unit can suggest the next travel plan with greater accuracy based on the user's past travel history and location information. For example, the learning unit can analyze user behavior patterns to improve the accuracy of its suggestions. For example, the learning unit can improve its suggestions based on user feedback. In this way, the accuracy of suggestions improves by learning user behavior patterns. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user behavior pattern data into a generative AI, which can then analyze the data and refine its suggestions.

[0042] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the data collection unit can prioritize collecting important data based on the frequency of destinations the user has visited in the past. For example, the data collection unit can collect data related to specific seasons or events from the user's past travel history. For example, the data collection unit can analyze the user's past travel history and focus on collecting data related to specific regions or countries. This allows for the priority collection of important data by analyzing past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past travel history data into a generative AI, which can then analyze the data and select the optimal data collection method.

[0043] The data collection unit can filter travel history data based on the user's current lifestyle and areas of interest. For example, if the user is busy with their current lifestyle, the data collection unit can prioritize collecting data on travel destinations that can be visited in a short period of time. For example, if the user's area of ​​interest is nature, the data collection unit can collect data on nature-related travel destinations. For example, the data collection unit can filter and collect data related to specific activities based on the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data according to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's lifestyle and area of ​​interest data into a generative AI, which can then analyze and filter the data.

[0044] The data collection unit can prioritize the collection of highly relevant travel history by considering the user's geographical location information when collecting travel history. For example, the data collection unit can prioritize the collection of travel history for destinations close to the user's current location. For example, the data collection unit can prioritize the collection of travel history related to regions the user has visited in the past. For example, the data collection unit can prioritize the collection of travel history for destinations easily accessible from the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information data into a generative AI, which can then analyze the data and prioritize the collection of highly relevant travel history.

[0045] The data collection unit can analyze the user's social media activity and collect relevant history when collecting travel history. For example, the data collection unit can collect travel destinations shared by the user on social media. For example, the data collection unit can collect relevant travel history based on the user's interests on social media. For example, the data collection unit can collect relevant travel history based on the travel destinations of the user's friends on social media. This allows for the collection of highly relevant travel history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant history.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the travel data during the analysis. For example, the analysis unit can perform a detailed analysis on important travel data. For example, the analysis unit can perform a simplified analysis on less important travel data. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the travel data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the travel data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input travel data importance data into a generation AI, and the generation AI can analyze the data and adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of travel data during analysis. For example, the analysis unit can apply a destination-specific analysis algorithm to destination data. For example, the analysis unit can apply a destination-specific analysis algorithm to accommodation data. For example, the analysis unit can apply an activity-specific analysis algorithm to activity data. By applying analysis algorithms according to the category of travel data, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category data of the travel data into a generative AI, and the generative AI can analyze the data and apply different analysis algorithms.

[0048] The analysis unit can determine the priority of analysis based on the submission date of the travel data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent travel data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the travel data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the travel data submission date data into a generation AI, and the generation AI can analyze the data and determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the travel data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant travel data. For example, the analysis unit may postpone the analysis of less relevant travel data. For example, the analysis unit can adjust the order of analysis step by step based on the relevance of the travel data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the travel data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance data of the travel data into a generative AI, and the generative AI can analyze the data and adjust the order of analysis.

[0050] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, for a tourist destination plan, the generation unit can apply a tourist destination-specific generation algorithm. For example, for an accommodation plan, the generation unit can apply an accommodation-specific generation algorithm. For example, for an activity plan, the generation unit can apply an activity-specific generation algorithm. By applying a generation algorithm according to the category of the travel plan, it is possible to generate more accurate travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan category data into a generation AI, and the generation AI can analyze the data and apply a different generation algorithm.

[0051] The generation unit can determine the generation priority based on the submission date of the travel plan during generation. For example, the generation unit may prioritize the generation of the most recent travel plan. For example, the generation unit may postpone the generation of older plans. For example, the generation unit may adjust the generation priority in stages based on the submission date. This enables efficient generation by determining the generation priority based on the submission date of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan submission date data into a generation AI, and the generation AI can analyze the data to determine the generation priority.

[0052] The generation unit can adjust the generation order based on the relevance of the travel plans during generation. For example, the generation unit can prioritize the generation of highly relevant travel plans. For example, the generation unit can postpone the generation of less relevant travel plans. For example, the generation unit can adjust the generation order stepwise based on the relevance of the travel plans. This allows for efficient generation by adjusting the generation order based on the relevance of the travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan relevance data into a generation AI, and the generation AI can analyze the data and adjust the generation order.

[0053] The information provider can adjust the level of detail provided based on the importance of the travel plan at the time of provision. For example, the provider can provide detailed information for important travel plans. For example, the provider can provide simplified information for less important travel plans. The provider can adjust the level of detail provided in stages according to the importance of the travel plan. This allows for efficient information provision by adjusting the level of detail provided according to the importance of the travel plan. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input travel plan importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail provided.

[0054] The service provider can apply different service provision algorithms depending on the category of the travel plan at the time of provision. For example, for a tourist destination plan, the service provider can apply a service provision algorithm specific to the tourist destination. For example, for an accommodation plan, the service provider can apply an accommodation-specific service provision algorithm. For example, for an activity plan, the service provider can apply an activity-specific service provision algorithm. By applying a service provision algorithm according to the category of the travel plan, it becomes possible to provide more accurate information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input travel plan category data into a generative AI, and the generative AI can analyze the data and apply a different service provision algorithm.

[0055] The service provider can determine the priority of service provision based on the submission date of the travel plan. For example, the service provider may prioritize providing the most recent travel plan. For example, the service provider may postpone providing older plans. For example, the service provider may adjust the priority of service provision in stages based on the submission date. This enables efficient information provision by determining the priority of service provision based on the submission date of the travel plan. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input travel plan submission date data into a generating AI, and the generating AI can analyze the data to determine the priority of service provision.

[0056] The service provider can adjust the order of service based on the relevance of the travel plans at the time of service. For example, the service provider can prioritize providing highly relevant travel plans. For example, the service provider can postpone providing less relevant travel plans. For example, the service provider can adjust the order of service in stages based on the relevance of the travel plans. This allows for efficient information provision by adjusting the order of service based on the relevance of the travel plans. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input travel plan relevance data into a generative AI, and the generative AI can analyze the data and adjust the order of service.

[0057] The response unit can adjust the level of detail in its response based on the severity of the travel trouble. For example, it can provide a detailed response to a serious trouble, and a simplified response to a less serious trouble. The response unit can adjust the level of detail in its response in stages according to the severity of the trouble. This allows for efficient responses by adjusting the level of detail in response according to the severity of the travel trouble. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input data on the severity of travel troubles into a generating AI, and the generating AI can analyze the data and adjust the level of detail in response.

[0058] The response unit can apply different response algorithms depending on the category of the trouble during the response. For example, for traffic troubles, the response unit can apply a traffic-specific response algorithm. For example, for accommodation troubles, the response unit can apply an accommodation-specific response algorithm. For example, for activity troubles, the response unit can apply an activity-specific response algorithm. By applying a response algorithm according to the category of the trouble, a more accurate response becomes possible. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input trouble category data into a generative AI, and the generative AI can analyze the data and apply a different response algorithm.

[0059] The response unit can determine the priority of responses based on when the trouble occurred. For example, the response unit can prioritize responding to the most recent trouble. For example, the response unit can postpone responding to older troubles. For example, the response unit can adjust the priority of responses in stages based on when the trouble occurred. This enables efficient responses by determining the priority of responses based on when the trouble occurred. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the response unit can input trouble occurrence data into a generating AI, and the generating AI can analyze the data to determine the priority of responses.

[0060] The response unit can adjust the order of responses based on the relevance of the problems during the response process. For example, the response unit can prioritize responding to highly relevant problems. For example, the response unit can postpone responding to less relevant problems. For example, the response unit can adjust the order of responses in stages based on the relevance of the problems. This allows for efficient responses by adjusting the order of responses based on the relevance of the problems. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input problem relevance data into a generative AI, and the generative AI can analyze the data to adjust the order of responses.

[0061] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm from past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can analyze the data to optimize the learning algorithm.

[0062] The learning unit can adjust the level of detail of learning based on the user's behavior patterns during the learning process. For example, the learning unit can perform detailed learning based on the user's behavior patterns. For example, the learning unit can perform simplified learning based on the user's behavior patterns. For example, the learning unit can adjust the level of detail of learning in stages according to the user's behavior patterns. This enables efficient learning by adjusting the level of detail of learning based on the user's behavior patterns. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user behavior pattern data into a generative AI, and the generative AI can analyze the data to adjust the level of detail of learning.

[0063] The learning unit can weight the training data based on the submission date of travel history during training. For example, the learning unit can prioritize the most recent travel history during training. For example, the learning unit can reduce the weight of older travel history entries during training. For example, the learning unit can adjust the weighting of the training data in stages based on the submission date. This enables efficient training by weighting the training data based on the submission date of travel history. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input travel history submission date data into a generative AI, which can then analyze the data and weight the training data.

[0064] The learning unit can analyze the user's social media activity during training to supplement the training data. For example, the learning unit can reflect the user's interests on social media in the training data. For example, the learning unit can reflect the activities of the user's friends on social media in the training data. For example, the learning unit can reflect the content of the user's social media posts in the training data. This allows for more accurate learning by analyzing the user's social media activity and supplementing the training data. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI, which can then analyze the data to supplement the training data.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The dedicated travel planner system can also collect user health data and incorporate it into the travel plan. For example, the collection unit can collect health information such as heart rate, steps, and sleep data from the user's fitness tracker or smartwatch. The analysis unit can analyze the collected health data and adjust the travel plan based on the user's physical condition and health status. The generation unit can generate a travel plan that includes relaxing tourist destinations and activities according to the user's health status. The delivery unit can provide the generated health-conscious travel plan to the user. This allows the system to provide the user with an optimal travel plan tailored to their health status.

[0067] The dedicated travel planner system can further collect users' social media activity and incorporate it into travel plans. For example, the collection unit can collect data from users' social media accounts, such as the content of posts, posts they've "liked," and the travel destinations of their followers. The analysis unit can analyze the collected social media data to understand users' interests and trends. The generation unit can generate travel plans that include popular tourist destinations and events based on users' interests on social media. The delivery unit can provide users with the generated travel plans based on their social media activity. This allows the system to provide travel plans that are tailored to users' latest interests and trends.

[0068] The dedicated travel planner system can also collect users' past reviews and ratings and incorporate them into travel plans. For example, the collection unit can collect reviews and ratings of destinations, accommodations, and activities that users have visited in the past. The analysis unit can analyze the collected reviews and ratings to understand the user's preferences and satisfaction level. The generation unit can generate travel plans that include destinations and activities that the user has given high ratings to. The delivery unit can provide the user with the travel plan based on the generated reviews and ratings. This allows the system to provide the user with the most suitable travel plan based on their past evaluations.

[0069] The dedicated travel planner system can also collect users' food preferences and incorporate them into the travel plan. For example, the collection unit can collect the user's past dining history and restaurant selection history. The analysis unit can analyze the collected dining data to understand the user's food preferences and allergy information. The generation unit can generate a travel plan that includes restaurants and meal plans that match the user's preferences. The delivery unit can provide the user with the generated travel plan that takes food preferences into account. This allows the system to provide the optimal travel plan tailored to the user's food preferences.

[0070] The dedicated travel planner system can further collect user activity data during their trip and incorporate it into the travel plan. For example, the collection unit can collect data on activities and events the user participated in during their trip. The analysis unit can analyze the collected activity data to understand the user's preferences and interests. The generation unit can generate a travel plan that includes activities the user has enjoyed in the past. The delivery unit can provide the user with the travel plan based on the generated activities. This allows the system to provide the user with an optimal travel plan based on their activity data during their trip.

[0071] The dedicated travel planner system can further collect user spending data during their trip and incorporate it into the travel plan. For example, the collection unit can collect spending data from credit cards and electronic money used by the user during their trip. The analysis unit can analyze the collected spending data to understand the user's budget and spending patterns. The generation unit can generate travel plans tailored to the user's budget, or travel plans that include cost-effective activities. The delivery unit can provide the user with a travel plan based on the generated spending data. This allows the system to provide the user with an optimal travel plan based on their spending data.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects the user's travel history and location information. For example, it collects data such as previously visited travel destinations, accommodations, and activity selection history, and stores it in a database. Location information can be obtained from GPS data or Wi-Fi location information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses data mining techniques, statistical analysis, and machine learning algorithms to learn the user's hobbies and preferences and extract specific patterns from their travel history. Step 3: The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. For example, it generates an optimal travel plan based on the user's hobbies and preferences, creating a travel plan that includes tourist destinations and activities of interest. It can also generate a travel plan that matches the user's budget and schedule. Step 4: The providing unit provides the travel plan generated by the generating unit. For example, it presents the generated travel plan to the user and displays it on their smartphone or personal computer. The travel plan can also be sent to the user via email or messaging app. Step 5: The response team will suggest the best course of action in case of unexpected events during the trip. For example, they will respond to changes in weather or transportation delays and suggest alternative transportation or accommodation. They can also provide emergency contact information for any problems during the trip. Step 6: The learning unit learns the user's behavior patterns and refines its suggestions over time. For example, it can suggest a more accurate next travel plan based on the user's past travel history and location information. It analyzes the user's behavior patterns to improve the accuracy of its suggestions. It can also improve suggestions based on user feedback.

[0074] (Example of form 2) The personal travel planner system according to an embodiment of the present invention is a system that creates an optimal travel plan tailored to an individual's hobbies and preferences. The personal travel planner system collects the user's travel history and location information, and the generating AI analyzes this data to propose a personalized travel plan and activities for each traveler. The personal travel planner system responds to all needs during, before, and after the trip, and makes increasingly refined suggestions over time. For example, the personal travel planner system collects the user's travel history and location information. For example, it collects data such as previously visited travel destinations, accommodations, and activity selection history. This data is input into the generating AI. Next, the generating AI analyzes the collected data and learns the user's hobbies and preferences. For example, it determines the user's level of interest in specific tourist destinations or activities and generates an optimal travel plan based on the results. The generated travel plan is provided to the user. For example, it suggests information such as tourist destinations, restaurants, and events at the travel destination. Furthermore, if an unexpected situation occurs during the trip (such as a change in weather or a delay in transportation), the generating AI in the personal travel planner system will propose the optimal action in real time. Furthermore, the generating AI learns the user's behavior patterns and preferences, making more refined suggestions over time. For example, it can suggest future travel plans with greater accuracy based on past travel history and location information. This system allows travelers to enjoy their trips efficiently and comfortably. For instance, it reduces the time and effort required for travel planning and allows users to find the best option from a vast amount of travel information. It can also flexibly respond to travel-related problems and offer suggestions tailored to individual needs. As a result, the dedicated travel planner system can utilize the user's travel history and location information to create optimal travel plans that match their personal tastes and preferences.

[0075] The dedicated travel planner system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a response unit, and a learning unit. The collection unit collects the user's travel history and location information. The collection unit collects data such as past travel destinations, accommodations, and activity selection history. The collection unit can, for example, store the user's travel history in a database and obtain location information from GPS data or Wi-Fi location information. The collection unit can, for example, collect information on past travel destinations and record the accommodation selection history. The collection unit can, for example, collect the history of activities selected by the user and store it in a database. The analysis unit analyzes the data collected by the collection unit. The analysis unit learns the user's hobbies and preferences using, for example, data mining techniques. The analysis unit can extract specific patterns from the user's travel history using, for example, statistical analysis. The analysis unit can predict the user's hobbies and preferences using, for example, machine learning algorithms. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The generation unit generates an optimal travel plan based on the user's hobbies and preferences, for example. The generation unit can generate a travel plan that includes tourist destinations and activities of interest to the user, for example. The generation unit can generate a travel plan that fits the user's budget and schedule, for example. The delivery unit provides the travel plan generated by the generation unit. The delivery unit presents the generated travel plan to the user, for example. The delivery unit can display the travel plan on the user's smartphone or personal computer, for example. The delivery unit can send the travel plan to the user via email or messaging app, for example. The response unit suggests the best course of action in the event of unexpected events during the trip. The response unit responds to, for example, changes in weather or delays in transportation. The response unit can suggest alternative means of transportation or accommodation, for example. The response unit can provide emergency contact information for problems during the trip, for example. The learning unit learns the user's behavior patterns and refines its suggestions over time. The learning unit suggests the next travel plan with greater accuracy based on, for example, the user's past travel history and location information.The learning unit can, for example, analyze user behavior patterns to improve the accuracy of suggestions. The learning unit can also, for example, improve suggestions based on user feedback. As a result, the dedicated travel planner system according to the embodiment can create an optimal travel plan tailored to the user's personal tastes and preferences by utilizing the user's travel history and location information.

[0076] The data collection unit collects user travel history and location information. For example, it collects data such as past travel destinations, accommodations, and activity selection history. Specifically, it can collect information on past travel destinations and record accommodation selection history. This includes information on hotels and accommodations booked by the user, length of stay, and ratings. It can also collect and store a history of activities selected by the user in a database. For example, it collects detailed information on tours and events the user participated in, tourist destinations visited, and activities experienced. Furthermore, the data collection unit can obtain user location information from GPS data and Wi-Fi location information. This allows the user's current location and travel route to be tracked in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and generation units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit learns the user's hobbies and preferences using data mining techniques. Specifically, it can extract certain patterns from the user's travel history and understand the user's preferred travel destinations and activities. For example, it can use statistical analysis to analyze the characteristics and commonalities of travel destinations the user has visited in the past and predict the user's preferences. It can also use machine learning algorithms to predict the user's hobbies and preferences. For example, based on data of activities and accommodations the user has selected in the past, it can predict tourist destinations and activities that the user is likely to be interested in on their next trip. Furthermore, the analysis unit can analyze the collected location information to understand the user's movement patterns and behavioral tendencies. As a result, the analysis unit can gain a detailed understanding of the user's hobbies, preferences, and behavioral patterns, and provide the basic data to offer individually optimized travel plans.

[0078] The generation unit generates travel plans based on the analysis results obtained by the analysis unit. For example, the generation unit generates an optimal travel plan based on the user's hobbies and preferences. Specifically, it can generate travel plans that include tourist destinations and activities of interest to the user. For example, if the user likes nature, it will suggest a travel plan that includes nature parks and hiking trails. It can also generate travel plans that match the user's budget and schedule. For example, it will select the best accommodations and transportation within the user's budget and create a schedule that fits their dates. Furthermore, the generation unit can provide more personalized travel plans by considering the user's past travel history and location information. For example, it will suggest tourist destinations and activities that the user has never visited before, providing new experiences. In this way, the generation unit can generate and provide the user with an optimal travel plan that matches the user's hobbies, preferences, budget, and schedule.

[0079] The service provider provides the travel plan generated by the generation unit. The service provider, for example, presents the generated travel plan to the user. Specifically, the travel plan can be displayed on the user's smartphone or personal computer. For example, the user can check the details of the travel plan through a dedicated application or website. The travel plan can also be sent to the user via email or messaging app. This allows the user to check the travel plan anytime, anywhere, and make changes or adjustments as needed. Furthermore, the service provider can collect user feedback to improve the accuracy and satisfaction of the travel plans it provides. For example, users can provide ratings and comments on the travel plan, which can be used to improve future travel plans. This allows the service provider to provide the user with the best possible travel plan and increase user satisfaction.

[0080] The support team proposes the best course of action in the event of unexpected situations during travel. Specifically, they respond to changes in weather and delays in transportation. For example, if the weather suddenly changes, the support team will suggest alternative tourist destinations or activities to ensure the user can continue their trip safely. In the event of transportation delays, the support team can suggest alternative transportation and accommodations. For example, if a flight is delayed or canceled, the support team will suggest the best alternative flight or accommodation to help the user continue their trip smoothly. Furthermore, the support team can provide emergency contact information for travel troubles. For example, if a user encounters trouble during their trip, the support team can provide emergency contact information and respond quickly. In this way, the support team can provide a swift and appropriate response to unexpected situations during travel, ensuring the safety and peace of mind of the user.

[0081] The learning unit learns user behavior patterns and refines its suggestions over time. Specifically, it uses the user's past travel history and location information to suggest more accurate travel plans for the next trip. For example, it analyzes data on destinations the user has visited and activities they have selected in the past to suggest tourist spots and activities that are likely to interest them on their next trip. It can also analyze user behavior patterns to improve the accuracy of its suggestions. For example, if a user tends to be interested in a particular season or event, it will suggest the most suitable travel plan based on that information. Furthermore, the learning unit can improve its suggestions based on user feedback. For example, users can provide ratings and comments on the travel plans offered, which can then be reflected in future suggestions. In this way, the learning unit can refine its suggestions over time based on user behavior patterns and feedback, providing more accurate travel plans.

[0082] The data collection unit can collect past travel destinations, accommodations, activity selection history, and other related data. For example, the data collection unit can collect information on travel destinations the user has visited in the past. For example, the data collection unit can record the user's accommodation selection history. For example, the data collection unit can collect the user's activity selection history. For example, the data collection unit can collect related data such as the user's interests and past reviews. This makes it easier to understand the user's tastes and preferences by collecting past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past travel history into a generative AI, which can then analyze the data and collect related data.

[0083] The analysis unit can analyze collected data and learn the user's hobbies and preferences. For example, the analysis unit can learn the user's hobbies and preferences using data mining techniques. For example, the analysis unit can extract specific patterns from the user's travel history using statistical analysis. For example, the analysis unit can predict the user's hobbies and preferences using machine learning algorithms. For example, the analysis unit can analyze data such as survey results and past selection history. By learning the user's hobbies and preferences, it is possible to provide more personalized travel plans. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input collected data into a generative AI, which can analyze the data and learn the user's hobbies and preferences.

[0084] The generation unit can generate an optimal travel plan based on the analysis results. For example, the generation unit can generate an optimal travel plan based on the user's hobbies and preferences. For example, the generation unit can generate a travel plan that includes tourist destinations and activities of interest to the user. For example, the generation unit can generate a travel plan that fits the user's budget and schedule. For example, the generation unit can generate an optimal travel plan based on criteria such as user satisfaction and cost performance. In this way, by generating a travel plan based on the analysis results, the user can be provided with the optimal travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI, and the generation AI can generate an optimal travel plan.

[0085] The service provider can provide the generated travel plan to the user. For example, the service provider can present the generated travel plan to the user. For example, the service provider can display the travel plan on the user's smartphone or personal computer. For example, the service provider can send the travel plan to the user via email or messaging app. By providing the user with the generated travel plan, it becomes easier for the user to review the travel plan. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the generated travel plan into a generation AI, and the generation AI can determine how to provide it to the user.

[0086] The response unit can suggest appropriate actions if unexpected events occur during travel. For example, it can respond to changes in weather or delays in transportation. For example, it can suggest alternative transportation or accommodation. For example, it can provide emergency contact information for travel troubles. For example, it can suggest appropriate actions such as suggesting alternative transportation or providing emergency contact information. This improves the user's travel experience by flexibly responding to travel troubles. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input data about travel troubles into a generative AI, which can then suggest the optimal action.

[0087] The learning unit can learn user behavior patterns and refine its suggestions over time. For example, the learning unit can suggest the next travel plan with greater accuracy based on the user's past travel history and location information. For example, the learning unit can analyze user behavior patterns to improve the accuracy of its suggestions. For example, the learning unit can improve its suggestions based on user feedback. In this way, the accuracy of suggestions improves by learning user behavior patterns. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user behavior pattern data into a generative AI, which can then analyze the data and refine its suggestions.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of travel history collection based on the estimated emotions. For example, if the user is excited about the trip, the data collection unit can collect travel history frequently to reflect the latest information. For example, if the user is indifferent to the trip, the data collection unit can reduce the collection frequency and collect only the minimum necessary information. For example, if the user is stressed about the trip, the data collection unit can adjust the collection timing to reduce the user's burden. In this way, the user's burden is reduced by adjusting the collection timing 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using a generative AI, for example, or without a generative AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the data and adjust the collection timing.

[0089] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the data collection unit can prioritize collecting important data based on the frequency of destinations the user has visited in the past. For example, the data collection unit can collect data related to specific seasons or events from the user's past travel history. For example, the data collection unit can analyze the user's past travel history and focus on collecting data related to specific regions or countries. This allows for the priority collection of important data by analyzing past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past travel history data into a generative AI, which can then analyze the data and select the optimal data collection method.

[0090] The data collection unit can filter travel history data based on the user's current lifestyle and areas of interest. For example, if the user is busy with their current lifestyle, the data collection unit can prioritize collecting data on travel destinations that can be visited in a short period of time. For example, if the user's area of ​​interest is nature, the data collection unit can collect data on nature-related travel destinations. For example, the data collection unit can filter and collect data related to specific activities based on the user's lifestyle and areas of interest. This allows for the collection of more relevant data by filtering the data according to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's lifestyle and area of ​​interest data into a generative AI, which can then analyze and filter the data.

[0091] The data collection unit can estimate the user's emotions and determine the priority of travel history to collect based on the estimated emotions. For example, if the user is excited about the trip, the data collection unit may prioritize collecting the most recent travel history. If the user is indifferent to the trip, the data collection unit may prioritize collecting past travel history. If the user is stressed about the trip, the data collection unit may prioritize collecting only important travel history. This allows for the priority collection of important data by determining the priority of travel history to collect 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 data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then analyze the data to determine the priority of travel history to collect.

[0092] The data collection unit can prioritize the collection of highly relevant travel history by considering the user's geographical location information when collecting travel history. For example, the data collection unit can prioritize the collection of travel history for destinations close to the user's current location. For example, the data collection unit can prioritize the collection of travel history related to regions the user has visited in the past. For example, the data collection unit can prioritize the collection of travel history for destinations easily accessible from the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information data into a generative AI, which can then analyze the data and prioritize the collection of highly relevant travel history.

[0093] The data collection unit can analyze the user's social media activity and collect relevant history when collecting travel history. For example, the data collection unit can collect travel destinations shared by the user on social media. For example, the data collection unit can collect relevant travel history based on the user's interests on social media. For example, the data collection unit can collect relevant travel history based on the travel destinations of the user's friends on social media. This allows for the collection of highly relevant travel history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant history.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be made easier for the user to understand. 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 processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the presentation of the analysis.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the travel data during the analysis. For example, the analysis unit can perform a detailed analysis on important travel data. For example, the analysis unit can perform a simplified analysis on less important travel data. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the travel data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the travel data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input travel data importance data into a generation AI, and the generation AI can analyze the data and adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the category of travel data during analysis. For example, the analysis unit can apply a destination-specific analysis algorithm to destination data. For example, the analysis unit can apply a destination-specific analysis algorithm to accommodation data. For example, the analysis unit can apply an activity-specific analysis algorithm to activity data. By applying analysis algorithms according to the category of travel data, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category data of the travel data into a generative AI, and the generative AI can analyze the data and apply different analysis algorithms.

[0097] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the length of the analysis.

[0098] The analysis unit can determine the priority of analysis based on the submission date of the travel data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent travel data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the travel data. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the travel data submission date data into a generation AI, and the generation AI can analyze the data and determine the priority of analysis.

[0099] The analysis unit can adjust the order of analysis based on the relevance of the travel data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant travel data. For example, the analysis unit may postpone the analysis of less relevant travel data. For example, the analysis unit can adjust the order of analysis step by step based on the relevance of the travel data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the travel data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance data of the travel data into a generative AI, and the generative AI can analyze the data and adjust the order of analysis.

[0100] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, for a tourist destination plan, the generation unit can apply a tourist destination-specific generation algorithm. For example, for an accommodation plan, the generation unit can apply an accommodation-specific generation algorithm. For example, for an activity plan, the generation unit can apply an activity-specific generation algorithm. By applying a generation algorithm according to the category of the travel plan, it is possible to generate more accurate travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan category data into a generation AI, and the generation AI can analyze the data and apply a different generation algorithm.

[0101] 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. For example, if the user is relaxed, the generation unit can generate a longer travel plan with detailed explanations. For example, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. This allows the system to provide the user with an appropriate travel plan by adjusting the length of the travel plan according to their 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-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can analyze the data and adjust the length of the travel plan.

[0102] The generation unit can determine the generation priority based on the submission date of the travel plan during generation. For example, the generation unit may prioritize the generation of the most recent travel plan. For example, the generation unit may postpone the generation of older plans. For example, the generation unit may adjust the generation priority in stages based on the submission date. This enables efficient generation by determining the generation priority based on the submission date of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan submission date data into a generation AI, and the generation AI can analyze the data to determine the generation priority.

[0103] The generation unit can adjust the generation order based on the relevance of the travel plans during generation. For example, the generation unit can prioritize the generation of highly relevant travel plans. For example, the generation unit can postpone the generation of less relevant travel plans. For example, the generation unit can adjust the generation order stepwise based on the relevance of the travel plans. This allows for efficient generation by adjusting the generation order based on the relevance of the travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel plan relevance data into a generation AI, and the generation AI can analyze the data and adjust the generation order.

[0104] The service provider can estimate the user's emotions and adjust the presentation of the service based on the estimated emotions. For example, if the user is relaxed, the service provider may select a presentation method that includes detailed information. For example, if the user is in a hurry, the service provider may provide concise information. For example, if the user is excited, the service provider may select a visually appealing presentation method. By adjusting the presentation of the service according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the presentation of the service.

[0105] The information provider can adjust the level of detail provided based on the importance of the travel plan at the time of provision. For example, the provider can provide detailed information for important travel plans. For example, the provider can provide simplified information for less important travel plans. The provider can adjust the level of detail provided in stages according to the importance of the travel plan. This allows for efficient information provision by adjusting the level of detail provided according to the importance of the travel plan. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input travel plan importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail provided.

[0106] The service provider can apply different service provision algorithms depending on the category of the travel plan at the time of provision. For example, for a tourist destination plan, the service provider can apply a service provision algorithm specific to the tourist destination. For example, for an accommodation plan, the service provider can apply an accommodation-specific service provision algorithm. For example, for an activity plan, the service provider can apply an activity-specific service provision algorithm. By applying a service provision algorithm according to the category of the travel plan, it becomes possible to provide more accurate information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input travel plan category data into a generative AI, and the generative AI can analyze the data and apply a different service provision algorithm.

[0107] The service provider can estimate the user's emotions and adjust the length of the service based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise information. If the user is relaxed, the service provider can provide longer information with detailed explanations. If the user is excited, the service provider can provide information with visually stimulating effects. By adjusting the length of the service according to the user's emotions, the service provider can provide information that is appropriate for the user. 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 processing described above in the service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI, which can analyze the data and adjust the length of the service.

[0108] The service provider can determine the priority of service provision based on the submission date of the travel plan. For example, the service provider may prioritize providing the most recent travel plan. For example, the service provider may postpone providing older plans. For example, the service provider may adjust the priority of service provision in stages based on the submission date. This enables efficient information provision by determining the priority of service provision based on the submission date of the travel plan. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input travel plan submission date data into a generating AI, and the generating AI can analyze the data to determine the priority of service provision.

[0109] The service provider can adjust the order of service based on the relevance of the travel plans at the time of service. For example, the service provider can prioritize providing highly relevant travel plans. For example, the service provider can postpone providing less relevant travel plans. For example, the service provider can adjust the order of service in stages based on the relevance of the travel plans. This allows for efficient information provision by adjusting the order of service based on the relevance of the travel plans. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input travel plan relevance data into a generative AI, and the generative AI can analyze the data and adjust the order of service.

[0110] The response unit can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is nervous, the response unit can provide a calm response method. For example, if the user is relaxed, the response unit can provide a detailed response method. For example, if the user is in a hurry, the response unit can provide a quick response method. In this way, by adjusting the response method according to the user's emotions, an appropriate response can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response unit may be performed using a generative AI, or not using a generative AI. For example, the response unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust its response method.

[0111] The response unit can adjust the level of detail in its response based on the severity of the travel trouble. For example, it can provide a detailed response to a serious trouble, and a simplified response to a less serious trouble. The response unit can adjust the level of detail in its response in stages according to the severity of the trouble. This allows for efficient responses by adjusting the level of detail in response according to the severity of the travel trouble. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input data on the severity of travel troubles into a generating AI, and the generating AI can analyze the data and adjust the level of detail in response.

[0112] The response unit can apply different response algorithms depending on the category of the trouble during the response. For example, for traffic troubles, the response unit can apply a traffic-specific response algorithm. For example, for accommodation troubles, the response unit can apply an accommodation-specific response algorithm. For example, for activity troubles, the response unit can apply an activity-specific response algorithm. By applying a response algorithm according to the category of the trouble, a more accurate response becomes possible. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input trouble category data into a generative AI, and the generative AI can analyze the data and apply a different response algorithm.

[0113] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is tense, the response unit will prioritize important responses. For example, if the user is relaxed, the response unit can provide detailed responses. For example, if the user is in a hurry, the response unit can prioritize quick responses. In this way, by determining the priority of responses according to the user's emotions, important responses can be prioritized. 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 response unit may be performed using a generative AI, or not using a generative AI. For example, the response unit can input user emotion data into a generative AI, and the generative AI can analyze the data to determine the priority of responses.

[0114] The response unit can determine the priority of responses based on when the trouble occurred. For example, the response unit can prioritize responding to the most recent trouble. For example, the response unit can postpone responding to older troubles. For example, the response unit can adjust the priority of responses in stages based on when the trouble occurred. This enables efficient responses by determining the priority of responses based on when the trouble occurred. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the response unit can input trouble occurrence data into a generating AI, and the generating AI can analyze the data to determine the priority of responses.

[0115] The response unit can adjust the order of responses based on the relevance of the problems during the response process. For example, the response unit can prioritize responding to highly relevant problems. For example, the response unit can postpone responding to less relevant problems. For example, the response unit can adjust the order of responses in stages based on the relevance of the problems. This allows for efficient responses by adjusting the order of responses based on the relevance of the problems. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input problem relevance data into a generative AI, and the generative AI can analyze the data to adjust the order of responses.

[0116] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. For example, if the user is in a hurry, the learning unit can select concise training data. For example, if the user is excited, the learning unit can select visually appealing training data. This allows for appropriate learning for the user by selecting training data 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 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 learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI, which can then analyze the data and select training data.

[0117] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm from past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm can be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can analyze the data to optimize the learning algorithm.

[0118] The learning unit can adjust the level of detail of learning based on the user's behavior patterns during the learning process. For example, the learning unit can perform detailed learning based on the user's behavior patterns. For example, the learning unit can perform simplified learning based on the user's behavior patterns. For example, the learning unit can adjust the level of detail of learning in stages according to the user's behavior patterns. This enables efficient learning by adjusting the level of detail of learning based on the user's behavior patterns. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user behavior pattern data into a generative AI, and the generative AI can analyze the data to adjust the level of detail of learning.

[0119] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit will learn more frequently if the user is relaxed. For example, the learning unit can reduce the learning frequency if the user is in a hurry. For example, the learning unit can adjust the learning frequency if the user is excited. This allows for appropriate learning for the user by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the learning frequency.

[0120] The learning unit can weight the training data based on the submission date of travel history during training. For example, the learning unit can prioritize the most recent travel history during training. For example, the learning unit can reduce the weight of older travel history entries during training. For example, the learning unit can adjust the weighting of the training data in stages based on the submission date. This enables efficient training by weighting the training data based on the submission date of travel history. Some or all of the above processing in the learning unit may be performed using a generative AI, for example, or without a generative AI. For example, the learning unit can input travel history submission date data into a generative AI, which can then analyze the data and weight the training data.

[0121] The learning unit can analyze the user's social media activity during training to supplement the training data. For example, the learning unit can reflect the user's interests on social media in the training data. For example, the learning unit can reflect the activities of the user's friends on social media in the training data. For example, the learning unit can reflect the content of the user's social media posts in the training data. This allows for more accurate learning by analyzing the user's social media activity and supplementing the training data. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI, which can then analyze the data to supplement the training data.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The dedicated travel planner system can also collect user health data and incorporate it into the travel plan. For example, the collection unit can collect health information such as heart rate, steps, and sleep data from the user's fitness tracker or smartwatch. The analysis unit can analyze the collected health data and adjust the travel plan based on the user's physical condition and health status. The generation unit can generate a travel plan that includes relaxing tourist destinations and activities according to the user's health status. The delivery unit can provide the generated health-conscious travel plan to the user. This allows the system to provide the user with an optimal travel plan tailored to their health status.

[0124] The dedicated travel planner system can further collect users' social media activity and incorporate it into travel plans. For example, the collection unit can collect data from users' social media accounts, such as the content of posts, posts they've "liked," and the travel destinations of their followers. The analysis unit can analyze the collected social media data to understand users' interests and trends. The generation unit can generate travel plans that include popular tourist destinations and events based on users' interests on social media. The delivery unit can provide users with the generated travel plans based on their social media activity. This allows the system to provide travel plans that are tailored to users' latest interests and trends.

[0125] The dedicated travel planner system can further estimate the user's emotions and propose travel plans based on those estimated emotions. For example, the analysis unit can estimate emotions from the user's past travel history, location information, and social media posts. The generation unit can generate travel plans that allow the user to relax or travel plans that include exciting activities based on the estimated emotions. The delivery unit can provide the user with the travel plan based on the generated emotions. This allows the system to provide the optimal travel plan tailored to the user's emotions.

[0126] The dedicated travel planner system can also collect users' past reviews and ratings and incorporate them into travel plans. For example, the collection unit can collect reviews and ratings of destinations, accommodations, and activities that users have visited in the past. The analysis unit can analyze the collected reviews and ratings to understand the user's preferences and satisfaction level. The generation unit can generate travel plans that include destinations and activities that the user has given high ratings to. The delivery unit can provide the user with the travel plan based on the generated reviews and ratings. This allows the system to provide the user with the most suitable travel plan based on their past evaluations.

[0127] The dedicated travel planner system can further estimate the user's emotions and provide support during the trip based on those estimates. For example, if the user is feeling stressed during the trip, the system can suggest relaxing activities and sightseeing spots. If the user is feeling excited, the system can suggest active activities and events. If the user is feeling tired, the system can suggest places to rest and relaxation facilities. This allows the system to provide support during the trip that is tailored to the user's emotions.

[0128] The dedicated travel planner system can also collect users' food preferences and incorporate them into the travel plan. For example, the collection unit can collect the user's past dining history and restaurant selection history. The analysis unit can analyze the collected dining data to understand the user's food preferences and allergy information. The generation unit can generate a travel plan that includes restaurants and meal plans that match the user's preferences. The delivery unit can provide the user with the generated travel plan that takes food preferences into account. This allows the system to provide the optimal travel plan tailored to the user's food preferences.

[0129] The dedicated travel planner system can further estimate the user's emotions and collect feedback on the travel plan based on those estimated emotions. For example, the learning unit can collect positive feedback if the user is satisfied with the travel plan. The learning unit can collect negative feedback if the user is dissatisfied with the travel plan. The learning unit can analyze the feedback based on the user's emotions and use it to improve the next travel plan. This allows for the collection of feedback tailored to the user's emotions, thereby improving the accuracy of the travel plan.

[0130] The dedicated travel planner system can further collect user activity data during their trip and incorporate it into the travel plan. For example, the collection unit can collect data on activities and events the user participated in during their trip. The analysis unit can analyze the collected activity data to understand the user's preferences and interests. The generation unit can generate a travel plan that includes activities the user has enjoyed in the past. The delivery unit can provide the user with the travel plan based on the generated activities. This allows the system to provide the user with an optimal travel plan based on their activity data during their trip.

[0131] The dedicated travel planner system can further estimate the user's emotions and adjust how travel plans are suggested based on those emotions. For example, if the user is relaxed, the system can provide a travel plan with detailed information. If the user is in a hurry, the system can provide a travel plan with concise information. If the user is excited, the system can provide a travel plan with visually appealing effects. This allows the system to provide the most suitable travel plan suggestions based on the user's emotions.

[0132] The dedicated travel planner system can further collect user spending data during their trip and incorporate it into the travel plan. For example, the collection unit can collect spending data from credit cards and electronic money used by the user during their trip. The analysis unit can analyze the collected spending data to understand the user's budget and spending patterns. The generation unit can generate travel plans tailored to the user's budget, or travel plans that include cost-effective activities. The delivery unit can provide the user with a travel plan based on the generated spending data. This allows the system to provide the user with an optimal travel plan based on their spending data.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The data collection unit collects the user's travel history and location information. For example, it collects data such as previously visited travel destinations, accommodations, and activity selection history, and stores it in a database. Location information can be obtained from GPS data or Wi-Fi location information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses data mining techniques, statistical analysis, and machine learning algorithms to learn the user's hobbies and preferences and extract specific patterns from their travel history. Step 3: The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. For example, it generates an optimal travel plan based on the user's hobbies and preferences, creating a travel plan that includes tourist destinations and activities of interest. It can also generate a travel plan that matches the user's budget and schedule. Step 4: The providing unit provides the travel plan generated by the generating unit. For example, it presents the generated travel plan to the user and displays it on their smartphone or personal computer. The travel plan can also be sent to the user via email or messaging app. Step 5: The response team will suggest the best course of action in case of unexpected events during the trip. For example, they will respond to changes in weather or transportation delays and suggest alternative transportation or accommodation. They can also provide emergency contact information for any problems during the trip. Step 6: The learning unit learns the user's behavior patterns and refines its suggestions over time. For example, it can suggest a more accurate next travel plan based on the user's past travel history and location information. It analyzes the user's behavior patterns to improve the accuracy of its suggestions. It can also improve suggestions based on user feedback.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, response unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's travel history and location information using the camera 42 and GPS data of the smart device 14. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and learns the user's hobbies and preferences. The generation unit generates an optimal travel plan based on the analysis results, and the provision unit provides the generated travel plan to the user through the display 40A and speaker 40B of the smart device 14. The response unit proposes the optimal action for unexpected situations during the trip, and the learning unit learns the user's behavior patterns to refine the proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, response unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's travel history and location information using the camera 42 and GPS data of the smart glasses 214. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and learns the user's hobbies and preferences. The generation unit generates an optimal travel plan based on the analysis results, and the provision unit provides the generated travel plan to the user through the speaker 240 of the smart glasses 214. The response unit proposes the best action for unexpected situations during the trip, and the learning unit learns the user's behavior patterns to refine the proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, response unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's travel history and location information using the camera 42 and GPS data of the headset terminal 314. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and learns the user's hobbies and preferences. The generation unit generates an optimal travel plan based on the analysis results, and the provision unit provides the generated travel plan to the user through the display 343 and speaker 240 of the headset terminal 314. The response unit proposes the optimal action for unexpected situations during the trip, and the learning unit learns the user's behavior patterns to refine the proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, response unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's travel history and location information using the camera 42 and GPS data of the robot 414. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 and learns the user's hobbies and preferences. The generation unit generates an optimal travel plan based on the analysis results, and the provision unit provides the generated travel plan to the user through the speaker 240 and display of the robot 414. The response unit proposes the best action for unexpected situations during the trip, and the learning unit learns the user's behavior patterns to refine the proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A collection unit that collects the user's travel history and location information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a travel plan based on the analysis results obtained by the analysis unit, A providing unit that provides the travel plan generated by the generation unit, A support department to handle problems during travel, It includes a learning unit that learns user behavior patterns and improves its suggestions. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect your past travel destinations, accommodations, activity choices, and other relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to learn the user's hobbies and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate the optimal travel plan based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated travel plan to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The corresponding part is, Suggesting appropriate actions in case of unexpected events during your trip. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Learn user behavior patterns and refine suggestions over time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of travel history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past travel history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting travel history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of travel history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting travel history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting travel history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the travel data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of travel data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the travel data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the travel data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The generating unit is During generation, the priority of generation is determined based on when the travel plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the order of generation is adjusted based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we will adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a travel plan, a different provisioning algorithm is applied depending on the travel plan category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing services, we will determine the priority of service provision based on when the travel plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the services, we will adjust the order of delivery based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The corresponding part is, It estimates the user's emotions and adjusts its response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The corresponding part is, When responding, we adjust the level of detail in our response based on the severity of the travel-related problem. The system described in Appendix 1, characterized by the features described herein. (Note 32) The corresponding part is, When addressing a problem, different troubleshooting algorithms are applied depending on the category of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 33) The corresponding part is, It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The corresponding part is, When responding to an issue, prioritize the response based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 35) The corresponding part is, When addressing an issue, adjust the order of responses based on the relevance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, During training, the level of detail of the learning process is adjusted based on the user's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned learning unit, During training, the training data is weighted based on when the travel history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned learning unit, During training, the system analyzes users' social media activity to supplement the training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 collection unit that collects the user's travel history and location information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a travel plan based on the analysis results obtained by the analysis unit, A providing unit that provides the travel plan generated by the generation unit, A support department to handle problems during travel, It includes a learning unit that learns user behavior patterns and improves its suggestions. A system characterized by the following features.

2. The aforementioned collection unit is We collect your past travel destinations, accommodations, activity choices, and other relevant data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to learn the user's hobbies and preferences. The system according to feature 1.

4. The generating unit is Generate the optimal travel plan based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated travel plan to the user. The system according to feature 1.

6. The corresponding part is, Suggesting appropriate actions in case of unexpected events during your trip. The system according to feature 1.

7. The aforementioned learning unit, Learn user behavior patterns and refine suggestions over time. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of travel history collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past travel history and select the appropriate data collection method. The system according to feature 1.

Images