system

Abstract

The system according to this embodiment aims to automatically generate an optimal travel plan based on the user's preferences and budget, and to support them through the booking process. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a suggestion unit, and a booking unit. The collection unit collects the user's travel history and social media data. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's preferences and budget. The suggestion unit proposes the most suitable flights and accommodations in real time based on the travel plan generated by the generation unit. The booking unit supports the booking of flights and accommodations proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 has not been fully carried out to automatically generate an optimal travel plan based on the user's preferences and budget and support it up to reservation, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate an optimal travel plan based on the user's preferences and budget and support it up to reservation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a suggestion unit, and a booking unit. The collection unit collects the user's travel history and social media data. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's preferences and budget. The suggestion unit proposes the most suitable flights and accommodations in real time based on the travel plan generated by the generation unit. The booking unit supports the booking of flights and accommodations proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate an optimal travel plan based on the user's preferences and budget, and can also support the booking process. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated travel plan generation system according to an embodiment of the present invention is a system that automatically generates an optimal travel plan based on the traveler's preferences and budget, and supports them through to booking. This system analyzes the user's travel history and social media data to provide a personalized travel experience. For example, the system collects and analyzes the user's travel history and social media data. In this process, the system inputs the traveler's past travel history, preferences, budget, and travel purpose (e.g., relaxation, adventure, cultural experience), and analyzes preferences from social media and past reviews. For example, it analyzes reviews of places and accommodations the user has visited in the past to identify the user's preferences. Next, based on the analysis results, it proposes destinations, activities, and accommodations that match the user's profile. For example, it proposes the optimal travel time considering the season and events from a large travel database. It also compares real-time prices for flights, hotels, and rental cars and presents the most advantageous plan. This allows the user to make a reservation with a single click. Furthermore, the system also provides support during the trip. For example, it supports booking local activities and restaurants, and provides real-time notifications of weather and traffic information. It also provides a community function where users can refer to reviews and experiences of other travelers. This allows users to travel with peace of mind. Technically, the system uses natural language processing to analyze user reviews and social media, and generates personalized suggestions using machine learning algorithms. It also utilizes APIs to obtain real-time pricing information. This allows the system to provide users with the most suitable travel plans. As a result, the automated travel plan generation system can automatically create optimal travel plans based on user preferences and budgets, and even support booking.

[0029] The automated travel plan generation system according to this embodiment comprises a collection unit, a generation unit, a proposal unit, and a booking unit. The collection unit collects the user's travel history and social media data. For example, the collection unit can collect data such as the user's past travel destinations, travel frequency, and travel purpose. The collection unit can also collect data such as the content of social media posts, the number of likes, and the number of followers. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's preferences and budget. For example, the generation unit can analyze the data using data mining or machine learning algorithms and propose tourist destinations and activities that suit the user's preferences. The generation unit can also generate a cost-effective travel plan based on the user's budget. The proposal unit proposes the optimal flights and accommodations in real time based on the travel plan generated by the generation unit. For example, the proposal unit can compare real-time prices of flights and accommodations and present the most advantageous plan. The proposal unit can also propose the optimal flights and accommodations considering the user's evaluation and convenience. The booking unit supports the booking of flights and accommodations proposed by the proposal unit. The reservation unit can, for example, provide an interface that enables one-click reservations. Furthermore, the reservation unit can refer to the user's past reservation history and suggest the optimal reservation method. As a result, the automated travel plan generation system according to this embodiment can automatically generate personalized travel plans based on the user's travel history and social media data, and support the reservation process.

[0030] The data collection unit collects user travel history and social media data. Specifically, it collects detailed information such as destinations visited in the past, frequency of travel, purpose of travel, length of stay, and information about travel companions. This allows for an accurate understanding of the user's travel patterns and preferences. The data collection unit also collects data such as the content of social media posts, the number of likes, the number of followers, and the content of comments. This allows for an understanding of what tourist destinations and activities the user is interested in. For example, tourist destinations and activities that a user frequently "likes" are important indicators of the user's interests. Furthermore, the data collection unit can also collect the user's search history and browsing history. This allows for an understanding of what kind of travel information the user has searched for in the past and what kind of travel plans they have been interested in. This data is stored on a cloud server and made accessible to the generation and suggestion units. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit analyzes data collected by the collection unit and generates travel plans based on the user's preferences and budget. Specifically, it uses data mining and machine learning algorithms to analyze the user's past travel history and social media data to identify the user's preferences and interests. For example, it analyzes data on tourist destinations and activities the user has visited in the past to understand the user's preferred destinations and activities. In addition, based on the user's budget, it collects price information on accommodations and transportation at the travel destination and proposes the optimal combination in order to generate cost-effective travel plans. The generation unit generates multiple travel plans based on the user's preferences and budget and proposes them to the user. This allows the user to select a travel plan that suits their preferences and budget. Furthermore, the generation unit can collect user feedback and continuously improve the generation algorithm. This allows the generation unit to provide the optimal travel plan based on the user's preferences and budget, thereby improving user satisfaction.

[0032] The suggestion unit proposes the most suitable flights and accommodations in real time based on the travel plan generated by the generation unit. Specifically, it compares real-time prices of flights and accommodations and presents the most cost-effective plan. The suggestion unit accesses databases of multiple travel booking sites, airlines, and accommodations to obtain the latest price and availability information. This allows it to propose the most cost-effective flights and accommodations to the user. The suggestion unit also proposes the most suitable flights and accommodations considering the user's ratings and convenience. For example, based on the user's past ratings and reviews, it prioritizes suggesting accommodations and airlines that the user has given high ratings to. Furthermore, the suggestion unit can also suggest the most suitable modes of transportation and tourist destinations in line with the user's travel plan. In this way, the suggestion unit can provide the user with the best possible travel plan and improve user satisfaction.

[0033] The Reservations Department supports booking flights and accommodations suggested by the Proposal Department. Specifically, it provides an interface that enables one-click booking. Users can select suggested flights and accommodations and complete the booking with a single click. The Reservations Department also refers to the user's past booking history to suggest the most suitable booking method. For example, it suggests the most user-friendly booking method based on information about airlines and accommodations the user has used in the past. Furthermore, the Reservations Department also provides support for booking confirmation, modification, and cancellation. Users can easily confirm, modify, or cancel their bookings. This allows the Reservations Department to provide users with a smooth booking experience and improve user satisfaction. In addition, the Reservations Department can collect user feedback and use it to improve the booking system. This allows the Reservations Department to provide optimal booking support tailored to user needs and improve the overall performance of the system.

[0034] The support department, powered by an AI chatbot, provides 24 / 7 support, including health management and security features. For example, it can monitor users' health status and offer health advice. It can also provide security features such as protecting personal information and handling emergencies. This 24 / 7 support from the AI ​​chatbot allows users to enjoy their trip with peace of mind.

[0035] The generation unit can analyze a user's travel history and social media data to provide a personalized travel experience. For example, it can analyze a user's past travel destinations and accommodation reviews to identify their preferences. It can also analyze social media posts and the number of likes they receive to understand the user's interests. In this way, the generation unit can provide a personalized travel experience by analyzing a user's travel history and social media data.

[0036] The proposal department can provide virtual travel guides using AR technology, environmentally friendly options, and event information notifications. For example, the proposal department can provide users with a virtual travel guide using an AR application. It can also propose environmentally friendly options such as eco-friendly accommodations and low-carbon transportation. Furthermore, the proposal department can provide users with event information through local event calendars and real-time notifications. In this way, the proposal department can offer users diverse travel experiences by providing virtual travel guides using AR technology, environmentally friendly options, and event information notifications.

[0037] The booking system can enable one-click booking. For example, it provides an interface that allows users to book flights and accommodations with a single click. It can also support quick booking by allowing users to pre-register their payment methods. This improves user convenience by enabling one-click booking.

[0038] The support department can assist with booking local activities and restaurants, and provide real-time notifications of weather and traffic information. For example, the support department can help users book sightseeing tours and sports activities locally. It can also assist users with making restaurant reservations. Furthermore, the support department can provide real-time notifications of weather forecasts and traffic congestion information. In this way, the support department enhances the user's travel experience by assisting with booking local activities and restaurants, and providing real-time notifications of weather and traffic information.

[0039] The data collection unit can analyze a user's past travel history and select the optimal data collection method. For example, the unit can prioritize collecting data for similar travel destinations based on data from places the user has visited in the past. The unit can also collect data related to specific seasons or events from the user's past travel history. Furthermore, the unit can collect relevant data based on data from transportation and accommodations the user has used in the past. In this way, the unit can select the optimal data collection method by analyzing the user's past travel history.

[0040] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if a user is busy with their current lifestyle, the unit will prioritize collecting data on travel destinations that can be enjoyed in a short period of time. If the user's area of ​​interest is nature, the unit can also collect data on nature-related travel destinations. Furthermore, if the user is interested in health, the unit can collect data on health-conscious travel destinations. This allows the unit to collect more relevant data by filtering it based on the user's current lifestyle and areas of interest.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, the unit can prioritize the collection of data for travel destinations close to the user's current location. Furthermore, if the user is interested in a particular region, the unit can prioritize the collection of data related to that region. In addition, the unit can collect relevant data based on data from regions the user has visited in the past. This allows the data collection unit to provide more relevant data by considering the user's geographical location during data collection.

[0042] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on travel destination data shared by users on social media. It can also collect relevant data based on travel-related accounts followed by users. Furthermore, the data collection unit can collect relevant data based on travel destination data that users have shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0043] The generation unit can adjust the level of detail in a travel plan based on the user's preferences and budget. For example, if the user has a limited budget, the generation unit will generate a cost-effective travel plan. It can also generate a luxurious travel plan if the user prefers high-end options. Furthermore, if the user prefers active travel, the generation unit can generate a travel plan with many activities. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the level of detail based on their preferences and budget.

[0044] The generation unit can apply different generation algorithms depending on the user's travel purpose when generating travel plans. For example, if the user's travel purpose is relaxation, the generation unit will apply an algorithm that generates a relaxing travel plan. If the user's travel purpose is adventure, the generation unit can also apply an algorithm that generates a travel plan with many adventurous activities. Furthermore, if the user's travel purpose is cultural experience, the generation unit can apply an algorithm that generates a travel plan with many cultural activities. In this way, the generation unit can provide the user with the most suitable travel plan by applying different generation algorithms according to the user's travel purpose.

[0045] The generation unit can determine the priority of travel plans based on the user's travel history when generating travel plans. For example, it can prioritize suggesting similar travel destinations based on data of places the user has visited in the past. It can also prioritize suggesting travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, it can prioritize suggesting relevant travel destinations based on data of transportation and accommodations the user has used in the past. In this way, the generation unit can provide the user with the most suitable travel plan by determining the priority of plans based on the user's travel history.

[0046] The generation unit can adjust the order of travel plans based on the user's areas of interest when generating them. For example, if the user's area of ​​interest is nature, the generation unit will prioritize suggesting nature-related activities. Similarly, if the user's area of ​​interest is culture, the generation unit can prioritize suggesting cultural activities. Furthermore, if the user's area of ​​interest is active activities, the generation unit can prioritize suggesting active activities. This allows the generation unit to provide the user with the most suitable travel plan by adjusting the order of plans based on the user's areas of interest.

[0047] The suggestion function can adjust the level of detail in its suggestions based on the importance of flights and accommodations. For example, if flights are important, the suggestion function will provide detailed flight information. Similarly, if accommodations are important, it can provide detailed accommodation information. Furthermore, if activities are important, it can provide detailed activity information. By adjusting the level of detail in suggestions based on the importance of flights and accommodations, the suggestion function can provide the most suitable suggestions for the user.

[0048] The suggestion function can apply different suggestion algorithms depending on the user's travel purpose. For example, if the user's travel purpose is relaxation, the suggestion function will apply an algorithm that provides relaxing suggestions. If the user's travel purpose is adventure, the suggestion function can also apply an algorithm that provides suggestions with many adventurous activities. Furthermore, if the user's travel purpose is cultural experience, the suggestion function can apply an algorithm that provides suggestions with many cultural activities. In this way, the suggestion function can provide the most suitable suggestions for the user by applying different suggestion algorithms according to the user's travel purpose.

[0049] The suggestion system can prioritize suggestions based on real-time prices for flights and accommodations. For example, if a flight has a low real-time price, the suggestion system will prioritize suggesting that flight. Similarly, if an accommodation has a low real-time price, the suggestion system can prioritize suggesting that accommodation. Furthermore, if an activity has a low real-time price, the suggestion system can prioritize suggesting that activity. In this way, the suggestion system can provide the user with the best possible suggestions by prioritizing suggestions based on real-time prices for flights and accommodations.

[0050] The suggestion function can adjust the order of suggestions based on the user's areas of interest. For example, if the user's area of ​​interest is nature, the suggestion function will prioritize nature-related suggestions. Similarly, if the user's area of ​​interest is culture, the suggestion function can prioritize cultural suggestions. Furthermore, if the user's area of ​​interest is active activities, the suggestion function can prioritize active activity suggestions. This allows the suggestion function to provide the most suitable suggestions for the user by adjusting the order of suggestions based on their areas of interest.

[0051] The reservation department can select the most suitable reservation method by referring to the user's past reservation history at the time of booking. For example, the reservation department can prioritize suggesting reservation methods that the user has used in the past. Furthermore, the reservation department can prioritize suggesting specific accommodations or flights based on the user's past reservation history. In addition, the reservation department can suggest the most suitable reservation method based on data from reservation sites the user has used in the past. As a result, the reservation department can provide the user with the most suitable reservation method by referring to the user's past reservation history.

[0052] The reservation department can customize the reservation process based on the user's current circumstances. For example, if the user is busy, the department can provide a simplified reservation process. Conversely, if the user is relaxed, the department can provide a more detailed reservation process. Furthermore, if the user is traveling, the department can provide a local reservation process. In this way, the reservation department can provide the most suitable reservation method for the user by customizing the reservation process based on the user's current circumstances.

[0053] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation system can prioritize booking accommodations close to the user's current location. Furthermore, if the user is interested in a particular region, the reservation system can prioritize booking accommodations in that region. In addition, the reservation system can prioritize booking relevant accommodations based on data from regions the user has visited in the past. This allows the reservation system to provide the user with the best possible reservation by selecting the most suitable reservation method based on their geographical location.

[0054] The booking department can analyze a user's social media activity during the booking process and suggest booking options. For example, it can suggest relevant accommodations based on data from accommodations shared by the user on social media. It can also suggest relevant accommodations based on data from travel-related accounts followed by the user. Furthermore, it can suggest relevant accommodations based on data from accommodations the user has shown interest in on social media. In this way, the booking department can provide the user with the most suitable booking method by analyzing their social media activity and suggesting booking options.

[0055] The support department can select the most suitable support method by referring to the user's past support history during support. For example, the support department can prioritize providing support methods that the user has used in the past. Furthermore, the support department can prioritize providing specific support methods based on the user's past support history. In addition, the support department can provide the most suitable support method based on data from support channels the user has used in the past. As a result, the support department can provide the best possible support to the user by selecting the most suitable support method by referring to the user's past support history.

[0056] The support department can customize the support provided based on the user's current living situation. For example, if the user is busy, the support department can provide simplified support. Conversely, if the user is relaxed, the support department can provide more detailed support. Furthermore, if the user is traveling, the support department can provide local support. In this way, the support department can provide the best possible support to the user by customizing the support based on the user's current living situation.

[0057] The support department can select the most appropriate support method by considering the user's geographical location during support. For example, the support department can prioritize providing support close to the user's current location. Furthermore, if the user is interested in a particular region, the support department can prioritize support for that region. In addition, the support department can prioritize relevant support based on data of regions the user has visited in the past. This allows the support department to provide the best possible support to the user by selecting the most appropriate support method considering the user's geographical location.

[0058] The support department can analyze a user's social media activity and suggest appropriate support measures during support sessions. For example, the support department can suggest relevant support based on data of support shared by the user on social media. It can also suggest relevant support based on data of support-related accounts followed by the user. Furthermore, it can suggest relevant support based on data of support the user has shown interest in on social media. This allows the support department to provide the most suitable support for the user by analyzing their social media activity and suggesting appropriate support measures.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can monitor the user's health status and propose health-conscious travel plans. For example, if a user has specific health conditions, it can suggest travel destinations and activities that are suitable for those conditions. The data collection unit can also provide information useful for health management during travel based on the user's health data. Furthermore, the data collection unit can provide advice on diet and exercise during travel according to the user's health status. In this way, the data collection unit proposes travel plans that take the user's health into consideration, allowing users to enjoy their trip with peace of mind.

[0061] The suggestion department can analyze a user's past travel history and suggest new travel destinations based on data from places the user has visited in the past. For example, it can suggest travel destinations similar to places the user has visited before. It can also suggest travel destinations that have received similar ratings based on reviews of places the user has visited in the past. Furthermore, it can suggest travel destinations related to the season or events of places the user has visited in the past. In this way, the suggestion department can suggest travel destinations that are highly relevant to the user by analyzing their past travel history.

[0062] The support department can customize the means of support based on the user's current living situation. For example, if the user is busy, simplified support can be provided. Conversely, if the user is relaxed, more detailed support can be offered. Furthermore, if the user is traveling, local support can be provided. In this way, the support department can provide the optimal support for the user by customizing the means of support based on the user's current living situation.

[0063] The data collection unit can analyze users' social media activity and collect relevant data. For example, it can collect relevant data based on travel destinations shared by users on social media. It can also collect relevant data based on travel-related accounts that users follow. Furthermore, it can collect relevant data based on travel destinations that users have shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0064] The suggestion function can adjust the order of suggestions based on the user's areas of interest. For example, if the user's area of ​​interest is nature, suggestions related to nature will be prioritized. Similarly, if the user's area of ​​interest is culture, cultural suggestions can be prioritized. Furthermore, if the user's area of ​​interest is active activities, active suggestions can be prioritized. In this way, the suggestion function can provide the user with the most suitable suggestions by adjusting the order of suggestions based on the user's areas of interest.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects the user's travel history and social media data. For example, it can collect data such as the user's past travel destinations, travel frequency, and travel purpose. It can also collect data such as the content of social media posts, the number of likes, and the number of followers. Step 2: The generation unit analyzes the data collected by the collection unit and generates travel plans based on the user's preferences and budget. For example, it analyzes the data using data mining and machine learning algorithms to suggest tourist destinations and activities that suit the user's preferences. It can also generate cost-effective travel plans based on the user's budget. Step 3: The suggestion unit proposes the best flights and accommodations in real time based on the travel plan generated by the generation unit. For example, it compares real-time prices of flights and accommodations and presents the best deal. It can also propose the best flights and accommodations considering user ratings and convenience. Step 4: The booking unit supports booking flights and accommodations suggested by the suggestion unit. For example, it provides an interface that enables one-click booking. It can also refer to the user's past booking history to suggest the best booking method.

[0067] (Example of form 2) The automated travel plan generation system according to an embodiment of the present invention is a system that automatically generates an optimal travel plan based on the traveler's preferences and budget, and supports them through to booking. This system analyzes the user's travel history and social media data to provide a personalized travel experience. For example, the system collects and analyzes the user's travel history and social media data. In this process, the system inputs the traveler's past travel history, preferences, budget, and travel purpose (e.g., relaxation, adventure, cultural experience), and analyzes preferences from social media and past reviews. For example, it analyzes reviews of places and accommodations the user has visited in the past to identify the user's preferences. Next, based on the analysis results, it proposes destinations, activities, and accommodations that match the user's profile. For example, it proposes the optimal travel time considering the season and events from a large travel database. It also compares real-time prices for flights, hotels, and rental cars and presents the most advantageous plan. This allows the user to make a reservation with a single click. Furthermore, the system also provides support during the trip. For example, it supports booking local activities and restaurants, and provides real-time notifications of weather and traffic information. It also provides a community function where users can refer to reviews and experiences of other travelers. This allows users to travel with peace of mind. Technically, the system uses natural language processing to analyze user reviews and social media, and generates personalized suggestions using machine learning algorithms. It also utilizes APIs to obtain real-time pricing information. This allows the system to provide users with the most suitable travel plans. As a result, the automated travel plan generation system can automatically create optimal travel plans based on user preferences and budgets, and even support booking.

[0068] The automated travel plan generation system according to this embodiment comprises a collection unit, a generation unit, a proposal unit, and a booking unit. The collection unit collects the user's travel history and social media data. For example, the collection unit can collect data such as the user's past travel destinations, travel frequency, and travel purpose. The collection unit can also collect data such as the content of social media posts, the number of likes, and the number of followers. The generation unit analyzes the data collected by the collection unit and generates a travel plan based on the user's preferences and budget. For example, the generation unit can analyze the data using data mining or machine learning algorithms and propose tourist destinations and activities that suit the user's preferences. The generation unit can also generate a cost-effective travel plan based on the user's budget. The proposal unit proposes the optimal flights and accommodations in real time based on the travel plan generated by the generation unit. For example, the proposal unit can compare real-time prices of flights and accommodations and present the most advantageous plan. The proposal unit can also propose the optimal flights and accommodations considering the user's evaluation and convenience. The booking unit supports the booking of flights and accommodations proposed by the proposal unit. The reservation unit can, for example, provide an interface that enables one-click reservations. Furthermore, the reservation unit can refer to the user's past reservation history and suggest the optimal reservation method. As a result, the automated travel plan generation system according to this embodiment can automatically generate personalized travel plans based on the user's travel history and social media data, and support the reservation process.

[0069] The data collection unit collects user travel history and social media data. Specifically, it collects detailed information such as destinations visited in the past, frequency of travel, purpose of travel, length of stay, and information about travel companions. This allows for an accurate understanding of the user's travel patterns and preferences. The data collection unit also collects data such as the content of social media posts, the number of likes, the number of followers, and the content of comments. This allows for an understanding of what tourist destinations and activities the user is interested in. For example, tourist destinations and activities that a user frequently "likes" are important indicators of the user's interests. Furthermore, the data collection unit can also collect the user's search history and browsing history. This allows for an understanding of what kind of travel information the user has searched for in the past and what kind of travel plans they have been interested in. This data is stored on a cloud server and made accessible to the generation and suggestion units. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The generation unit analyzes data collected by the collection unit and generates travel plans based on the user's preferences and budget. Specifically, it uses data mining and machine learning algorithms to analyze the user's past travel history and social media data to identify the user's preferences and interests. For example, it analyzes data on tourist destinations and activities the user has visited in the past to understand the user's preferred destinations and activities. In addition, based on the user's budget, it collects price information on accommodations and transportation at the travel destination and proposes the optimal combination in order to generate cost-effective travel plans. The generation unit generates multiple travel plans based on the user's preferences and budget and proposes them to the user. This allows the user to select a travel plan that suits their preferences and budget. Furthermore, the generation unit can collect user feedback and continuously improve the generation algorithm. This allows the generation unit to provide the optimal travel plan based on the user's preferences and budget, thereby improving user satisfaction.

[0071] The suggestion unit proposes the most suitable flights and accommodations in real time based on the travel plan generated by the generation unit. Specifically, it compares real-time prices of flights and accommodations and presents the most cost-effective plan. The suggestion unit accesses databases of multiple travel booking sites, airlines, and accommodations to obtain the latest price and availability information. This allows it to propose the most cost-effective flights and accommodations to the user. The suggestion unit also proposes the most suitable flights and accommodations considering the user's ratings and convenience. For example, based on the user's past ratings and reviews, it prioritizes suggesting accommodations and airlines that the user has given high ratings to. Furthermore, the suggestion unit can also suggest the most suitable modes of transportation and tourist destinations in line with the user's travel plan. In this way, the suggestion unit can provide the user with the best possible travel plan and improve user satisfaction.

[0072] The Reservations Department supports booking flights and accommodations suggested by the Proposal Department. Specifically, it provides an interface that enables one-click booking. Users can select suggested flights and accommodations and complete the booking with a single click. The Reservations Department also refers to the user's past booking history to suggest the most suitable booking method. For example, it suggests the most user-friendly booking method based on information about airlines and accommodations the user has used in the past. Furthermore, the Reservations Department also provides support for booking confirmation, modification, and cancellation. Users can easily confirm, modify, or cancel their bookings. This allows the Reservations Department to provide users with a smooth booking experience and improve user satisfaction. In addition, the Reservations Department can collect user feedback and use it to improve the booking system. This allows the Reservations Department to provide optimal booking support tailored to user needs and improve the overall performance of the system.

[0073] The support department, powered by an AI chatbot, provides 24 / 7 support, including health management and security features. For example, it can monitor users' health status and offer health advice. It can also provide security features such as protecting personal information and handling emergencies. This 24 / 7 support from the AI ​​chatbot allows users to enjoy their trip with peace of mind.

[0074] The generation unit can analyze a user's travel history and social media data to provide a personalized travel experience. For example, it can analyze a user's past travel destinations and accommodation reviews to identify their preferences. It can also analyze social media posts and the number of likes they receive to understand the user's interests. In this way, the generation unit can provide a personalized travel experience by analyzing a user's travel history and social media data.

[0075] The proposal department can provide virtual travel guides using AR technology, environmentally friendly options, and event information notifications. For example, the proposal department can provide users with a virtual travel guide using an AR application. It can also propose environmentally friendly options such as eco-friendly accommodations and low-carbon transportation. Furthermore, the proposal department can provide users with event information through local event calendars and real-time notifications. In this way, the proposal department can offer users diverse travel experiences by providing virtual travel guides using AR technology, environmentally friendly options, and event information notifications.

[0076] The booking system can enable one-click booking. For example, it provides an interface that allows users to book flights and accommodations with a single click. It can also support quick booking by allowing users to pre-register their payment methods. This improves user convenience by enabling one-click booking.

[0077] The support department can assist with booking local activities and restaurants, and provide real-time notifications of weather and traffic information. For example, the support department can help users book sightseeing tours and sports activities locally. It can also assist users with making restaurant reservations. Furthermore, the support department can provide real-time notifications of weather forecasts and traffic congestion information. In this way, the support department enhances the user's travel experience by assisting with booking local activities and restaurants, and providing real-time notifications of weather and traffic information.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect data in real time and analyze it immediately. Additionally, if the user is tired, the data collection unit can temporarily stop data collection and resume it after the user has rested. This allows the data collection unit to reduce the user's burden by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The data collection unit can analyze a user's past travel history and select the optimal data collection method. For example, the unit can prioritize collecting data for similar travel destinations based on data from places the user has visited in the past. The unit can also collect data related to specific seasons or events from the user's past travel history. Furthermore, the unit can collect relevant data based on data from transportation and accommodations the user has used in the past. In this way, the unit can select the optimal data collection method by analyzing the user's past travel history.

[0080] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if a user is busy with their current lifestyle, the unit will prioritize collecting data on travel destinations that can be enjoyed in a short period of time. If the user's area of ​​interest is nature, the unit can also collect data on nature-related travel destinations. Furthermore, if the user is interested in health, the unit can collect data on health-conscious travel destinations. This allows the unit to collect more relevant data by filtering it based on the user's current lifestyle and areas of interest.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is relaxed, the unit will prioritize collecting data on travel destinations that promote relaxation. If the user is excited, the unit can prioritize collecting data on travel destinations that offer active activities. Furthermore, if the user is tired, the unit can prioritize collecting data on travel destinations that promote refreshment. In this way, the data collection unit can collect data that meets the user's needs by prioritizing the data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, the unit can prioritize the collection of data for travel destinations close to the user's current location. Furthermore, if the user is interested in a particular region, the unit can prioritize the collection of data related to that region. In addition, the unit can collect relevant data based on data from regions the user has visited in the past. This allows the data collection unit to provide more relevant data by considering the user's geographical location during data collection.

[0083] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on travel destination data shared by users on social media. It can also collect relevant data based on travel-related accounts followed by users. Furthermore, the data collection unit can collect relevant data based on travel destination data that users have shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0084] The generation unit can estimate the user's emotions and adjust how the travel plan is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that can be enjoyed in a short period of time. Furthermore, if the user is excited, the generation unit can generate a travel plan with many active activities. In this way, the generation unit can provide the user with the optimal travel plan by adjusting how the travel plan is presented based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The generation unit can adjust the level of detail in a travel plan based on the user's preferences and budget. For example, if the user has a limited budget, the generation unit will generate a cost-effective travel plan. It can also generate a luxurious travel plan if the user prefers high-end options. Furthermore, if the user prefers active travel, the generation unit can generate a travel plan with many activities. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the level of detail based on their preferences and budget.

[0086] The generation unit can apply different generation algorithms depending on the user's travel purpose when generating travel plans. For example, if the user's travel purpose is relaxation, the generation unit will apply an algorithm that generates a relaxing travel plan. If the user's travel purpose is adventure, the generation unit can also apply an algorithm that generates a travel plan with many adventurous activities. Furthermore, if the user's travel purpose is cultural experience, the generation unit can apply an algorithm that generates a travel plan with many cultural activities. In this way, the generation unit can provide the user with the most suitable travel plan by applying different generation algorithms according to the user's travel purpose.

[0087] The generation unit can estimate the user's emotions and adjust the length of the travel plan based on those emotions. For example, if the user is relaxed, the generation unit can generate a longer travel plan. If the user is in a hurry, the generation unit can generate a shorter, more enjoyable travel plan. Furthermore, if the user is excited, the generation unit can generate a travel plan with more activities. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the length of the travel plan based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The generation unit can determine the priority of travel plans based on the user's travel history when generating travel plans. For example, it can prioritize suggesting similar travel destinations based on data of places the user has visited in the past. It can also prioritize suggesting travel destinations related to specific seasons or events based on the user's past travel history. Furthermore, it can prioritize suggesting relevant travel destinations based on data of transportation and accommodations the user has used in the past. In this way, the generation unit can provide the user with the most suitable travel plan by determining the priority of plans based on the user's travel history.

[0089] The generation unit can adjust the order of travel plans based on the user's areas of interest when generating them. For example, if the user's area of ​​interest is nature, the generation unit will prioritize suggesting nature-related activities. Similarly, if the user's area of ​​interest is culture, the generation unit can prioritize suggesting cultural activities. Furthermore, if the user's area of ​​interest is active activities, the generation unit can prioritize suggesting active activities. This allows the generation unit to provide the user with the most suitable travel plan by adjusting the order of plans based on the user's areas of interest.

[0090] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will present suggestions that proceed at a leisurely pace. If the user is in a hurry, it can present suggestions that can be enjoyed in a short amount of time. Furthermore, if the user is excited, it can present suggestions with more active activities. In this way, the suggestion function can provide the optimal suggestions for the user by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The suggestion function can adjust the level of detail in its suggestions based on the importance of flights and accommodations. For example, if flights are important, the suggestion function will provide detailed flight information. Similarly, if accommodations are important, it can provide detailed accommodation information. Furthermore, if activities are important, it can provide detailed activity information. By adjusting the level of detail in suggestions based on the importance of flights and accommodations, the suggestion function can provide the most suitable suggestions for the user.

[0092] The suggestion function can apply different suggestion algorithms depending on the user's travel purpose. For example, if the user's travel purpose is relaxation, the suggestion function will apply an algorithm that provides relaxing suggestions. If the user's travel purpose is adventure, the suggestion function can also apply an algorithm that provides suggestions with many adventurous activities. Furthermore, if the user's travel purpose is cultural experience, the suggestion function can apply an algorithm that provides suggestions with many cultural activities. In this way, the suggestion function can provide the most suitable suggestions for the user by applying different suggestion algorithms according to the user's travel purpose.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will offer longer suggestions. If the user is in a hurry, it can offer suggestions that can be enjoyed in a shorter time. Furthermore, if the user is excited, it can offer suggestions with more activity. In this way, the suggestion unit can provide the optimal suggestions for the user by adjusting the length of suggestions based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The suggestion system can prioritize suggestions based on real-time prices for flights and accommodations. For example, if a flight has a low real-time price, the suggestion system will prioritize suggesting that flight. Similarly, if an accommodation has a low real-time price, the suggestion system can prioritize suggesting that accommodation. Furthermore, if an activity has a low real-time price, the suggestion system can prioritize suggesting that activity. In this way, the suggestion system can provide the user with the best possible suggestions by prioritizing suggestions based on real-time prices for flights and accommodations.

[0095] The suggestion function can adjust the order of suggestions based on the user's areas of interest. For example, if the user's area of ​​interest is nature, the suggestion function will prioritize nature-related suggestions. Similarly, if the user's area of ​​interest is culture, the suggestion function can prioritize cultural suggestions. Furthermore, if the user's area of ​​interest is active activities, the suggestion function can prioritize active activity suggestions. This allows the suggestion function to provide the most suitable suggestions for the user by adjusting the order of suggestions based on their areas of interest.

[0096] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is relaxed, the system can provide detailed reservation instructions. If the user is in a hurry, it can provide simplified instructions. Furthermore, if the user is feeling anxious, the system can display supportive messages while guiding them through the reservation process. In this way, the reservation system can provide the optimal reservation process for the user by adjusting the procedure based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The reservation department can select the most suitable reservation method by referring to the user's past reservation history at the time of booking. For example, the reservation department can prioritize suggesting reservation methods that the user has used in the past. Furthermore, the reservation department can prioritize suggesting specific accommodations or flights based on the user's past reservation history. In addition, the reservation department can suggest the most suitable reservation method based on data from reservation sites the user has used in the past. As a result, the reservation department can provide the user with the most suitable reservation method by referring to the user's past reservation history.

[0098] The reservation department can customize the reservation process based on the user's current circumstances. For example, if the user is busy, the department can provide a simplified reservation process. Conversely, if the user is relaxed, the department can provide a more detailed reservation process. Furthermore, if the user is traveling, the department can provide a local reservation process. In this way, the reservation department can provide the most suitable reservation method for the user by customizing the reservation process based on the user's current circumstances.

[0099] The booking system can estimate a user's emotions and prioritize bookings based on those emotions. For example, if a user is relaxed, the system will prioritize booking relaxing accommodations. If a user is in a hurry, the system can also prioritize booking flights that can be booked quickly. Furthermore, if a user is excited, the system can prioritize booking accommodations that offer active activities. This allows the system to provide users with the best possible bookings by prioritizing them based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The reservation system can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation system can prioritize booking accommodations close to the user's current location. Furthermore, if the user is interested in a particular region, the reservation system can prioritize booking accommodations in that region. In addition, the reservation system can prioritize booking relevant accommodations based on data from regions the user has visited in the past. This allows the reservation system to provide the user with the best possible reservation by selecting the most suitable reservation method based on their geographical location.

[0101] The booking department can analyze a user's social media activity during the booking process and suggest booking options. For example, it can suggest relevant accommodations based on data from accommodations shared by the user on social media. It can also suggest relevant accommodations based on data from travel-related accounts followed by the user. Furthermore, it can suggest relevant accommodations based on data from accommodations the user has shown interest in on social media. In this way, the booking department can provide the user with the most suitable booking method by analyzing their social media activity and suggesting booking options.

[0102] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is relaxed, the support unit will provide support at a relaxed pace. If the user is in a hurry, the support unit can provide support quickly. Furthermore, if the user is feeling anxious, the support unit can provide reassuring support. In this way, the support unit can provide optimal support to the user by adjusting its support methods based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The support department can select the most suitable support method by referring to the user's past support history during support. For example, the support department can prioritize providing support methods that the user has used in the past. Furthermore, the support department can prioritize providing specific support methods based on the user's past support history. In addition, the support department can provide the most suitable support method based on data from support channels the user has used in the past. As a result, the support department can provide the best possible support to the user by selecting the most suitable support method by referring to the user's past support history.

[0104] The support department can customize the support provided based on the user's current living situation. For example, if the user is busy, the support department can provide simplified support. Conversely, if the user is relaxed, the support department can provide more detailed support. Furthermore, if the user is traveling, the support department can provide local support. In this way, the support department can provide the best possible support to the user by customizing the support based on the user's current living situation.

[0105] The support unit can estimate the user's emotions and prioritize support based on those emotions. For example, if the user is relaxed, the support unit will prioritize providing relaxing support. It can also provide rapid support if the user is in a hurry. Furthermore, if the user is feeling anxious, the support unit can prioritize providing reassuring support. In this way, the support unit can provide optimal support to the user by prioritizing support based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The support department can select the most appropriate support method by considering the user's geographical location during support. For example, the support department can prioritize providing support close to the user's current location. Furthermore, if the user is interested in a particular region, the support department can prioritize support for that region. In addition, the support department can prioritize relevant support based on data of regions the user has visited in the past. This allows the support department to provide the best possible support to the user by selecting the most appropriate support method considering the user's geographical location.

[0107] The support department can analyze a user's social media activity and suggest appropriate support measures during support sessions. For example, the support department can suggest relevant support based on data of support shared by the user on social media. It can also suggest relevant support based on data of support-related accounts followed by the user. Furthermore, it can suggest relevant support based on data of support the user has shown interest in on social media. This allows the support department to provide the most suitable support for the user by analyzing their social media activity and suggesting appropriate support measures.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data collection unit can monitor the user's health status and propose health-conscious travel plans. For example, if a user has specific health conditions, it can suggest travel destinations and activities that are suitable for those conditions. The data collection unit can also provide information useful for health management during travel based on the user's health data. Furthermore, the data collection unit can provide advice on diet and exercise during travel according to the user's health status. In this way, the data collection unit proposes travel plans that take the user's health into consideration, allowing users to enjoy their trip with peace of mind.

[0110] The generation unit can estimate the user's emotions and adjust the travel plan content based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing destinations and activities. If the user is excited, it can suggest a travel plan with more active activities. Furthermore, if the user is feeling anxious, it can suggest destinations and activities that provide a sense of security. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the content based on the user's emotions.

[0111] The suggestion department can analyze a user's past travel history and suggest new travel destinations based on data from places the user has visited in the past. For example, it can suggest travel destinations similar to places the user has visited before. It can also suggest travel destinations that have received similar ratings based on reviews of places the user has visited in the past. Furthermore, it can suggest travel destinations related to the season or events of places the user has visited in the past. In this way, the suggestion department can suggest travel destinations that are highly relevant to the user by analyzing their past travel history.

[0112] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is relaxed, it can provide detailed reservation instructions. If the user is in a hurry, it can provide simplified instructions. Furthermore, if the user is feeling anxious, it can display support messages while guiding them through the reservation process. In this way, the reservation system can provide the optimal reservation process for the user by adjusting the procedure based on their emotions.

[0113] The support department can customize the means of support based on the user's current living situation. For example, if the user is busy, simplified support can be provided. Conversely, if the user is relaxed, more detailed support can be offered. Furthermore, if the user is traveling, local support can be provided. In this way, the support department can provide the optimal support for the user by customizing the means of support based on the user's current living situation.

[0114] The data collection unit can analyze users' social media activity and collect relevant data. For example, it can collect relevant data based on travel destinations shared by users on social media. It can also collect relevant data based on travel-related accounts that users follow. Furthermore, it can collect relevant data based on travel destinations that users have shown interest in on social media. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0115] The generation unit can estimate the user's emotions and adjust how the travel plan is presented based on those emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a travel plan that can be enjoyed in a short period of time. Furthermore, if the user is excited, it can generate a travel plan with many active activities. In this way, the generation unit can provide the user with the optimal travel plan by adjusting how the travel plan is presented based on the user's emotions.

[0116] The suggestion function can adjust the order of suggestions based on the user's areas of interest. For example, if the user's area of ​​interest is nature, suggestions related to nature will be prioritized. Similarly, if the user's area of ​​interest is culture, cultural suggestions can be prioritized. Furthermore, if the user's area of ​​interest is active activities, active suggestions can be prioritized. In this way, the suggestion function can provide the user with the most suitable suggestions by adjusting the order of suggestions based on the user's areas of interest.

[0117] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is relaxed, it can prioritize booking relaxing accommodations. If the user is in a hurry, it can prioritize booking flights that can be booked quickly. Furthermore, if the user is excited, it can prioritize booking accommodations that offer active activities. In this way, the booking system can provide users with the best possible bookings by prioritizing bookings based on their emotions.

[0118] The support team can estimate the user's emotions and adjust their support methods based on those estimates. For example, if the user is relaxed, they can provide support at a leisurely pace. If the user is in a hurry, they can provide support quickly. Furthermore, if the user is feeling anxious, they can provide reassuring support. In this way, the support team can provide the best possible support to the user by adjusting their support methods based on the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The data collection unit collects the user's travel history and social media data. For example, it can collect data such as the user's past travel destinations, travel frequency, and travel purpose. It can also collect data such as the content of social media posts, the number of likes, and the number of followers. Step 2: The generation unit analyzes the data collected by the collection unit and generates travel plans based on the user's preferences and budget. For example, it analyzes the data using data mining and machine learning algorithms to suggest tourist destinations and activities that suit the user's preferences. It can also generate cost-effective travel plans based on the user's budget. Step 3: The suggestion unit proposes the best flights and accommodations in real time based on the travel plan generated by the generation unit. For example, it compares real-time prices of flights and accommodations and presents the best deal. It can also propose the best flights and accommodations considering user ratings and convenience. Step 4: The booking unit supports booking flights and accommodations suggested by the suggestion unit. For example, it provides an interface that enables one-click booking. It can also refer to the user's past booking history to suggest the best booking method.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, reservation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's travel history and social media data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan by analyzing the collected data. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes the optimal flights and accommodations based on the generated travel plan. The reservation unit is implemented by the control unit 46A of the smart device 14 and supports the reservation of the proposed flights and accommodations. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides 24-hour support from an AI chatbot, as well as health management and security functions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, reservation unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's travel history and social media data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan by analyzing the collected data. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes the optimal flights and accommodations based on the generated travel plan. The reservation unit is implemented by the control unit 46A of the smart glasses 214 and supports the reservation of the proposed flights and accommodations. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides 24-hour support from an AI chatbot, offering health management and security functions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, reservation unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's travel history and social media data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan by analyzing the collected data. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes the optimal flights and accommodations based on the generated travel plan. The reservation unit is implemented by the control unit 46A of the headset terminal 314 and supports the reservation of the proposed flights and accommodations. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides 24-hour support from an AI chatbot, as well as health management and security functions. 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.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, generation unit, proposal unit, reservation unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's travel history and social media data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan by analyzing the collected data. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes the optimal flights and accommodations based on the generated travel plan. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and supports the reservation of the proposed flights and accommodations. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides 24-hour support from an AI chatbot, as well as health management and security functions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A data collection unit that collects users' travel history and social media data, A generation unit analyzes the data collected by the aforementioned collection unit and generates a travel plan based on the user's preferences and budget. Based on the travel plan generated by the generation unit, the proposal unit suggests the most suitable flights and accommodations in real time. The system includes a reservation unit that supports booking flights and accommodations proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The AI ​​chatbot provides 24 / 7 support, and the support department further enhances the service by offering health management and security features. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is We analyze users' travel history and social media data to provide personalized travel experiences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, This service provides virtual travel guides using AR technology, environmentally friendly options, and event information notifications. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reservation section is, Enabling one-click booking The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We provide support for booking local activities and restaurants, and offer real-time notifications of weather and traffic information. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past travel history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a travel plan, adjust the level of detail based on the user's preferences and budget. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating travel plans, different generation algorithms are applied depending on the user's travel purpose. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the travel plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating travel plans, the system prioritizes plans based on the user's travel history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a travel plan, the order of the plan will be adjusted based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of flights and accommodations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's travel purpose. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on real-time prices for flights and accommodations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, When a reservation is made, the system will refer to the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, When making a reservation, the reservation method will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, the system will refer to the user's past support history to select the most appropriate support method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0193] 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 data collection unit that collects users' travel history and social media data, A generation unit analyzes the data collected by the aforementioned collection unit and generates a travel plan based on the user's preferences and budget. Based on the travel plan generated by the generation unit, the proposal unit suggests the most suitable flights and accommodations in real time. The system includes a reservation unit that supports booking flights and accommodations proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The AI ​​chatbot provides 24 / 7 support, and the support department further enhances the system by offering health management and security features. The system according to feature 1.

3. The generating unit is We analyze users' travel history and social media data to provide personalized travel experiences. The system according to feature 1.

4. The aforementioned proposal section is, This service provides virtual travel guides using AR technology, environmentally friendly options, and event information notifications. The system according to feature 1.

5. The aforementioned reservation section is, Enables one-click booking The system according to feature 1.

6. The aforementioned support unit is We provide support for booking local activities and restaurants, and offer real-time notifications of weather and traffic information. The system according to feature 2.

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

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

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images