system

Abstract

The system according to this embodiment aims to automatically generate an optimal travel plan based on a child's age and interests, and to handle bookings and arrangements. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, an arrangement unit, and an update unit. The reception unit receives information from the user, such as the child's age and interests. The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. The generation unit automatically generates a travel plan based on the experience proposed by the analysis unit. The arrangement unit makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that it is difficult to find an optimal travel plan based on the age and interests of children, and it takes time and effort to plan a family trip.

[0005] The system according to the embodiment aims to automatically generate an optimal travel plan based on the age and interests of children and make reservations and arrangements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a booking unit, and an update unit. The reception unit receives information from the user regarding the child's age and interests. The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. The generation unit automatically generates a travel plan based on the experience proposed by the analysis unit. The booking unit makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate an optimal travel plan based on the child's age and interests, and can also make reservations and arrangements. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent for enhancing children's travel experiences according to an embodiment of the present invention is a system that proposes travel plans including events and activities that children would not normally experience, based on their age and interests, and handles reservations and arrangements. The AI ​​agent for enhancing children's travel experiences proposes the optimal experiences based on the child's age and interests and automatically generates a travel plan. During the trip, it collects weather and local event information in real time and automatically updates and proposes the optimal plan. Furthermore, it provides a function to support information gathering and comparison during the travel planning stage and centralizes all reservations and arrangements. It also responds immediately to changes in weather and conditions during the trip and proposes alternative plans. First, during the travel planning stage, the user inputs the child's age and interests. At this time, the user only needs to input information about the child's interests and age. For example, if the child is interested in animals, that information is entered. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and proposes the optimal experiences based on the child's interests and age. Based on past data and evaluations from other users, the AI ​​agent proposes experiences that are valuable to children. For example, it proposes events and activities such as zoos and safari parks. Furthermore, the AI ​​agent automatically generates a travel plan based on the proposed experiences. The travel plan includes arrangements for accommodation, transportation, and meals. This allows users to plan their trip without hassle. During the trip, the AI ​​agent collects weather and local event information in real time and automatically updates and suggests the best plan. For example, if the weather deteriorates, it will suggest indoor activities. It also collects information on newly held local events and suggests them to the user. This system makes it easy to find travel plans that offer valuable experiences for children, and makes travel planning and arrangements hassle-free. Furthermore, it can respond immediately to changes in weather and conditions during the trip, allowing users to enjoy their trip with peace of mind. In this way, by using the AI ​​agent, it is possible to provide consistent support from planning to execution of family trips, reduce stress for families, and provide a richer travel experience.This allows the AI ​​agent for enhancing children's travel experiences to provide comprehensive support from planning to execution of family trips, reducing family stress and offering a richer travel experience.

[0029] The AI ​​agent for creating travel plans to enhance the value of children's experiences according to this embodiment comprises a reception unit, an analysis unit, a generation unit, an arrangement unit, and an update unit. The reception unit receives information from the user about the child's age and interests. The information received from the user includes, but is not limited to, the child's age, interests, and favorite activities. The reception unit stores the information entered by the user in a database, for example. The reception unit can also send the information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. The analysis unit proposes experiences that are valuable to children, for example, based on past data and evaluations from other users. The analysis unit proposes events and activities such as zoos and safari parks, for example. The analysis unit can also propose experiences that match the child's interests based on the user's input information. The generation unit automatically generates a travel plan based on the experiences proposed by the analysis unit. The generation unit automatically generates a travel plan that includes, for example, arrangements for accommodation, transportation, and meals. The generation unit generates the optimal travel plan, for example, based on the user's input information. Furthermore, the generation unit can customize the travel plan according to the user's wishes. The arrangement unit makes reservations and arrangements based on the travel plan generated by the generation unit. The arrangement unit makes reservations for accommodations, transportation, and meals, for example. The arrangement unit makes optimal reservations and arrangements based on the user's input information, for example. The arrangement unit can also customize reservations and arrangements according to the user's wishes. The update unit collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. For example, if the weather deteriorates, the update unit will suggest indoor activities that can be enjoyed. The update unit can also collect information on newly held local events and propose them to the user. As a result, the AI ​​agent for enhancing children's experience value travel plans according to the embodiment can propose the optimal travel plan based on the age and interests of the user's child, make reservations and arrangements, and respond to changes in circumstances during the trip.

[0030] The reception department receives information from users regarding the child's age and interests. This information may include, but is not limited to, the child's age, interests, and favorite activities. The reception department also stores the user-entered information in a database. Specifically, information entered by users through dedicated web forms or applications is stored in a secure database. This allows for the efficient collection of necessary information while protecting user privacy. Furthermore, the reception department can transmit the user-entered information to the analysis department. For example, as soon as a user completes the input, the data is sent to the analysis department in real time, and analysis begins immediately. This allows users to proceed smoothly to the next step without waiting. Additionally, the reception department has a feedback function to verify the accuracy of the information entered by users, and can send notifications prompting users to correct any errors. This allows the reception department to collect accurate and complete information, improving the precision of subsequent analysis and generation processes.

[0031] The analysis department analyzes the information received by the reception department and proposes the most suitable experience. For example, the analysis department suggests valuable experiences for children based on past data and reviews from other users. Specifically, an AI-powered data analysis algorithm analyzes past travel plans and user feedback to identify the activities best suited to the child's age and interests. For example, it may suggest events and activities such as zoos and safari parks. The analysis department can also suggest experiences that match the child's interests based on the user's input information. For example, it may suggest zoos or pet cafes for children who love animals, or science museums or experimental workshops for children interested in science. Furthermore, the analysis department can use AI and natural language processing technology to understand the user's input and provide more personalized suggestions. This allows the analysis department to quickly and accurately suggest the most suitable experience to meet the user's needs.

[0032] The generation unit automatically generates travel plans based on experiences suggested by the analysis unit. The generation unit automatically generates travel plans that include, for example, accommodation, transportation, and meal arrangements. Specifically, the AI ​​selects the optimal accommodation and transportation based on suggested activities and constructs a travel plan. For example, in a plan to visit a zoo, it automatically selects nearby accommodation and easily accessible transportation and incorporates them into the travel plan. Furthermore, the generation unit generates optimal travel plans based on user input information. For example, it proposes the best plan according to the user's desired budget and schedule. In addition, the generation unit can customize the travel plan according to the user's wishes. For example, specific activities can be added or the accommodation grade can be changed. This allows the generation unit to provide flexible travel plans tailored to the user's needs.

[0033] The booking unit makes reservations and arrangements based on the travel plan generated by the generation unit. For example, the booking unit handles accommodation reservations, transportation arrangements, and meal arrangements. Specifically, AI automatically checks accommodation availability and makes optimal reservations. It also selects the optimal route and time for transportation and makes reservations. For example, it automatically arranges plane and train tickets and notifies the user. Furthermore, the booking unit makes optimal reservations and arrangements based on the user's input information. For example, it selects the best accommodation and transportation options according to the user's desired budget and schedule. The booking unit can also customize reservations and arrangements according to the user's wishes. For example, it can modify meal arrangements or add special activities in response to specific requests. This allows the booking unit to provide flexible reservations and arrangements tailored to the user's needs.

[0034] The update function collects weather and local event information in real time during your trip and automatically updates and suggests the best plan for you. For example, if the weather deteriorates, the update function will suggest indoor activities. Specifically, the AI ​​monitors weather information in real time and automatically updates the travel plan according to changes in the weather. For example, if it rains, it will suggest indoor museums or amusement facilities. The update function can also collect information on newly held local events and suggest them to the user. For example, it will collect information on newly held festivals and special events in the area and notify the user. This allows users to enjoy the best plan based on the latest information during their trip. Furthermore, the update function can collect user feedback and use it to improve the plan. For example, by inputting evaluations of activities and services provided during the trip, the AI ​​analyzes this information and reflects it in future plan suggestions. This allows the update function to always provide highly accurate plans based on the latest information and improve user satisfaction.

[0035] The reception desk can analyze the user's past travel history and select the most suitable method for receiving information. For example, the reception desk can suggest a similar method of receiving information based on travel plans the user has used in the past. For example, the reception desk can analyze the user's preferred travel style from their past travel history and ask appropriate questions. For example, the reception desk can prioritize receiving relevant information based on places the user has visited in the past. This makes the system user-friendly by selecting the most suitable method of receiving information based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI select the most suitable method of receiving information.

[0036] The reception desk can filter information received, such as a child's age and interests, based on the user's current lifestyle and areas of interest. For example, the reception desk can prioritize receiving relevant information based on the user's current lifestyle (work, family, etc.). The reception desk can also present appropriate questions based on the user's areas of interest (sports, culture, etc.). The reception desk can also adjust the timing of information reception to match the user's daily rhythm. This allows for the reception of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0037] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about nearby events and activities based on the user's current location. The reception unit can also prioritize receiving relevant information by considering the geographical information of the user's travel destination. The reception unit can also prioritize receiving highly relevant information by referring to the user's travel history. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0038] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information by referring to the activities of the user's followers and friends, for example. The reception unit can also receive appropriate information based on the user's social media interests, for example. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's interests during the analysis. For example, if the child's interest is high, the analysis unit will provide detailed analysis results. For example, if the child's interest is low, the analysis unit can also provide concise analysis results. The analysis unit can also adjust the depth of the analysis according to the importance of the child's interests. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the child's category of interest during analysis. For example, if the child's interest is animals, the analysis unit will prioritize analyzing animal-related events and activities. If the child's interest is sports, the analysis unit can also prioritize analyzing sports-related events and activities. If the child's interest is science, the analysis unit can also prioritize analyzing science-related events and activities. By applying different analysis algorithms depending on the child's category of interest, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on when the child's interests were submitted. For example, the analysis unit may prioritize analysis if the child's interests were recently submitted. For example, the analysis unit may postpone analysis if the child's interests were submitted in the past. The analysis unit may also adjust the priority of analysis according to when the child's interests were submitted. This allows for more appropriate analysis results by determining the priority of analysis based on when the child's interests were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the children's interests during the analysis. For example, the analysis unit may prioritize analysis if the children are highly interested in that topic. For example, it may postpone analysis if the children are less interested in that topic. The analysis unit can also adjust the order of analysis according to the relevance of the children's interests. By adjusting the order of analysis based on the relevance of the children's interests, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the children's interest data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The generation unit can adjust the level of detail generated based on the importance of the child's interests when generating a travel plan. For example, if the child's interests are high, the generation unit will generate a detailed travel plan. For example, if the child's interests are low, the generation unit can also generate a concise travel plan. The generation unit can also adjust the depth of generation according to the importance of the child's interests. This allows for the provision of more appropriate travel plans by adjusting the level of detail based on the importance of the child's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0044] The generation unit can apply different generation algorithms depending on the child's interests when generating travel plans. For example, if the child's interests are animals, the generation unit will prioritize generating animal-related events and activities. If the child's interests are sports, the generation unit can also prioritize generating sports-related events and activities. If the child's interests are science, the generation unit can also prioritize generating science-related events and activities. By applying different generation algorithms depending on the child's interests, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0045] The generation unit can determine the generation priority based on when the child's interests are submitted when generating a travel plan. For example, the generation unit will prioritize generating plans if the child's interests have been submitted recently. The generation unit can also postpone generating plans if the child's interests have been submitted in the past. The generation unit can also adjust the generation priority according to when the child's interests are submitted. This allows for the provision of more appropriate travel plans by determining the generation priority based on when the child's interests are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on when the child's interests were submitted into a generation AI and have the generation AI determine the generation priority.

[0046] The generation unit can adjust the order of travel plan generation based on the relevance of the child's interests. For example, the generation unit will prioritize generating travel plans that the child is highly interested in. For example, the generation unit can postpone generating travel plans that the child is less interested in. The generation unit can also adjust the order of generation according to the relevance of the child's interests. This allows for the provision of more appropriate travel plans by adjusting the order of generation based on the relevance of the child's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0047] The booking unit can adjust the level of detail in bookings and arrangements based on the importance of the child's interests. For example, if the child's interest is high, the booking unit will make detailed arrangements. If the child's interest is low, the booking unit can also make simple arrangements. The booking unit can also adjust the depth of arrangements according to the importance of the child's interests. This allows for more appropriate bookings and arrangements to be made by adjusting the level of detail in arrangements based on the importance of the child's interests. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in arrangements.

[0048] The booking unit can apply different booking algorithms depending on the child's interests when making reservations or bookings. For example, if a child is interested in animals, the booking unit will prioritize booking animal-related events and activities. If a child is interested in sports, the booking unit can also prioritize booking sports-related events and activities. If a child is interested in science, the booking unit can also prioritize booking science-related events and activities. By applying different booking algorithms depending on the child's interests, more appropriate reservations and bookings can be made. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI execute the application of different booking algorithms.

[0049] The booking unit can determine the priority of bookings based on when the child's interests were submitted. For example, the booking unit will prioritize bookings if the child's interests were recently submitted. For example, the booking unit may postpone bookings if the child's interests were submitted in the past. The booking unit can also adjust the priority of bookings according to when the child's interests were submitted. This allows for more appropriate bookings and arrangements by determining the priority of bookings based on when the child's interests were submitted. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of bookings.

[0050] The booking unit can adjust the order of bookings based on the relevance of the child's interests during the booking and arrangement process. For example, the booking unit can prioritize bookings that the child is highly interested in. For example, the booking unit can postpone bookings that the child is less interested in. The booking unit can also adjust the order of bookings according to the relevance of the child's interests. This allows for more appropriate bookings and arrangements to be made by adjusting the order of bookings based on the relevance of the child's interests. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the booking order.

[0051] The update unit can adjust the level of detail of the update based on the importance of the child's interests when updating the plan. For example, if the child's interest is high, the update unit will perform a detailed update. For example, if the child's interest is low, the update unit can also perform a concise update. The update unit can also adjust the depth of the update according to the importance of the child's interests. This allows for more appropriate plan updates by adjusting the level of detail of the update based on the importance of the child's interests. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the update.

[0052] The update unit can apply different update algorithms depending on the child's category of interest when updating the plan. For example, if the child's interest is animals, the update unit will prioritize updating animal-related events and activities. For example, if the child's interest is sports, the update unit can also prioritize updating sports-related events and activities. For example, if the child's interest is science, the update unit can also prioritize updating science-related events and activities. This allows for more appropriate plan updates by applying different update algorithms depending on the child's category of interest. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI execute the application of different update algorithms.

[0053] The update unit can determine the priority of updates based on when the child's interests are submitted when updating the plan. For example, the update unit will prioritize updates if the child's interests have been submitted recently. For example, the update unit may postpone updates if the child's interests have been submitted in the past. The update unit can also adjust the priority of updates according to when the child's interests are submitted. This allows for more appropriate plan updates by determining the priority of updates based on when the child's interests are submitted. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of updates.

[0054] The update unit can adjust the order of updates based on the relevance of the child's interests when updating the plan. For example, the update unit will prioritize updates that the child is highly interested in. For example, the update unit can postpone updates that the child is less interested in. The update unit can also adjust the order of updates according to the relevance of the child's interests. This allows for more appropriate plan updates by adjusting the order of updates based on the relevance of the child's interests. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the update order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze the user's past travel history and select the most suitable method for receiving information. For example, the reception desk can suggest a similar method of receiving information based on travel plans the user has used in the past. For example, the reception desk can analyze the user's preferred travel style from their past travel history and ask appropriate questions. For example, the reception desk can prioritize receiving relevant information based on places the user has visited in the past. This makes the system user-friendly by selecting the most suitable method of receiving information based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI select the most suitable method of receiving information.

[0057] The reception desk can filter information received, such as a child's age and interests, based on the user's current lifestyle and areas of interest. For example, the reception desk can prioritize receiving relevant information based on the user's current lifestyle (work, family, etc.). The reception desk can also present appropriate questions based on the user's areas of interest (sports, culture, etc.). The reception desk can also adjust the timing of information reception to match the user's daily rhythm. This allows for the reception of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0058] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about nearby events and activities based on the user's current location. The reception unit can also prioritize receiving relevant information by considering the geographical information of the user's travel destination. The reception unit can also prioritize receiving highly relevant information by referring to the user's travel history. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0059] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information by referring to the activities of the user's followers and friends, for example. The reception unit can also receive appropriate information based on the user's social media interests, for example. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's interests during the analysis. For example, if the child's interest is high, the analysis unit will provide detailed analysis results. For example, if the child's interest is low, the analysis unit can also provide concise analysis results. The analysis unit can also adjust the depth of the analysis according to the importance of the child's interests. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception unit receives information from the user about the child's age and interests. This information includes, for example, the child's age, interests, and favorite activities. The reception unit can save the information entered by the user to a database and send it to the analysis unit. Step 2: The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. Based on past data and evaluations from other users, the analysis unit proposes experiences that are valuable to children. For example, it may suggest events and activities such as zoos and safari parks. It can also suggest experiences that match the child's interests based on the user's input information. Step 3: The generation unit automatically generates a travel plan based on the experience suggested by the analysis unit. The generation unit automatically generates a travel plan that includes accommodation, transportation, and meal arrangements. It can also generate the optimal travel plan based on the user's input information and customize it according to the user's preferences. Step 4: The arrangement unit makes reservations and arrangements based on the travel plan generated by the generation unit. The arrangement unit makes reservations for accommodations, transportation, meals, etc. Based on the user's input information, it makes the best reservations and arrangements and can also be customized according to the user's preferences. Step 5: The update function collects weather and local event information in real time during your trip and automatically updates and suggests the best plan. For example, if the weather deteriorates, it will suggest indoor activities. It can also collect information on newly held local events and suggest them to the user.

[0063] (Example of form 2) The AI ​​agent for enhancing children's travel experiences according to an embodiment of the present invention is a system that proposes travel plans including events and activities that children would not normally experience, based on their age and interests, and handles reservations and arrangements. The AI ​​agent for enhancing children's travel experiences proposes the optimal experiences based on the child's age and interests and automatically generates a travel plan. During the trip, it collects weather and local event information in real time and automatically updates and proposes the optimal plan. Furthermore, it provides a function to support information gathering and comparison during the travel planning stage and centralizes all reservations and arrangements. It also responds immediately to changes in weather and conditions during the trip and proposes alternative plans. First, during the travel planning stage, the user inputs the child's age and interests. At this time, the user only needs to input information about the child's interests and age. For example, if the child is interested in animals, that information is entered. This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and proposes the optimal experiences based on the child's interests and age. Based on past data and evaluations from other users, the AI ​​agent proposes experiences that are valuable to children. For example, it proposes events and activities such as zoos and safari parks. Furthermore, the AI ​​agent automatically generates a travel plan based on the proposed experiences. The travel plan includes arrangements for accommodation, transportation, and meals. This allows users to plan their trip without hassle. During the trip, the AI ​​agent collects weather and local event information in real time and automatically updates and suggests the best plan. For example, if the weather deteriorates, it will suggest indoor activities. It also collects information on newly held local events and suggests them to the user. This system makes it easy to find travel plans that offer valuable experiences for children, and makes travel planning and arrangements hassle-free. Furthermore, it can respond immediately to changes in weather and conditions during the trip, allowing users to enjoy their trip with peace of mind. In this way, by using the AI ​​agent, it is possible to provide consistent support from planning to execution of family trips, reduce stress for families, and provide a richer travel experience.This allows the AI ​​agent for enhancing children's travel experiences to provide comprehensive support from planning to execution of family trips, reducing family stress and offering a richer travel experience.

[0064] The AI ​​agent for creating travel plans to enhance the value of children's experiences according to this embodiment comprises a reception unit, an analysis unit, a generation unit, an arrangement unit, and an update unit. The reception unit receives information from the user about the child's age and interests. The information received from the user includes, but is not limited to, the child's age, interests, and favorite activities. The reception unit stores the information entered by the user in a database, for example. The reception unit can also send the information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. The analysis unit proposes experiences that are valuable to children, for example, based on past data and evaluations from other users. The analysis unit proposes events and activities such as zoos and safari parks, for example. The analysis unit can also propose experiences that match the child's interests based on the user's input information. The generation unit automatically generates a travel plan based on the experiences proposed by the analysis unit. The generation unit automatically generates a travel plan that includes, for example, arrangements for accommodation, transportation, and meals. The generation unit generates the optimal travel plan, for example, based on the user's input information. Furthermore, the generation unit can customize the travel plan according to the user's wishes. The arrangement unit makes reservations and arrangements based on the travel plan generated by the generation unit. The arrangement unit makes reservations for accommodations, transportation, and meals, for example. The arrangement unit makes optimal reservations and arrangements based on the user's input information, for example. The arrangement unit can also customize reservations and arrangements according to the user's wishes. The update unit collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. For example, if the weather deteriorates, the update unit will suggest indoor activities that can be enjoyed. The update unit can also collect information on newly held local events and propose them to the user. As a result, the AI ​​agent for enhancing children's experience value travel plans according to the embodiment can propose the optimal travel plan based on the age and interests of the user's child, make reservations and arrangements, and respond to changes in circumstances during the trip.

[0065] The reception department receives information from users regarding the child's age and interests. This information may include, but is not limited to, the child's age, interests, and favorite activities. The reception department also stores the user-entered information in a database. Specifically, information entered by users through dedicated web forms or applications is stored in a secure database. This allows for the efficient collection of necessary information while protecting user privacy. Furthermore, the reception department can transmit the user-entered information to the analysis department. For example, as soon as a user completes the input, the data is sent to the analysis department in real time, and analysis begins immediately. This allows users to proceed smoothly to the next step without waiting. Additionally, the reception department has a feedback function to verify the accuracy of the information entered by users, and can send notifications prompting users to correct any errors. This allows the reception department to collect accurate and complete information, improving the precision of subsequent analysis and generation processes.

[0066] The analysis department analyzes the information received by the reception department and proposes the most suitable experience. For example, the analysis department suggests valuable experiences for children based on past data and reviews from other users. Specifically, an AI-powered data analysis algorithm analyzes past travel plans and user feedback to identify the activities best suited to the child's age and interests. For example, it may suggest events and activities such as zoos and safari parks. The analysis department can also suggest experiences that match the child's interests based on the user's input information. For example, it may suggest zoos or pet cafes for children who love animals, or science museums or experimental workshops for children interested in science. Furthermore, the analysis department can use AI and natural language processing technology to understand the user's input and provide more personalized suggestions. This allows the analysis department to quickly and accurately suggest the most suitable experience to meet the user's needs.

[0067] The generation unit automatically generates travel plans based on experiences suggested by the analysis unit. The generation unit automatically generates travel plans that include, for example, accommodation, transportation, and meal arrangements. Specifically, the AI ​​selects the optimal accommodation and transportation based on suggested activities and constructs a travel plan. For example, in a plan to visit a zoo, it automatically selects nearby accommodation and easily accessible transportation and incorporates them into the travel plan. Furthermore, the generation unit generates optimal travel plans based on user input information. For example, it proposes the best plan according to the user's desired budget and schedule. In addition, the generation unit can customize the travel plan according to the user's wishes. For example, specific activities can be added or the accommodation grade can be changed. This allows the generation unit to provide flexible travel plans tailored to the user's needs.

[0068] The booking unit makes reservations and arrangements based on the travel plan generated by the generation unit. For example, the booking unit handles accommodation reservations, transportation arrangements, and meal arrangements. Specifically, AI automatically checks accommodation availability and makes optimal reservations. It also selects the optimal route and time for transportation and makes reservations. For example, it automatically arranges plane and train tickets and notifies the user. Furthermore, the booking unit makes optimal reservations and arrangements based on the user's input information. For example, it selects the best accommodation and transportation options according to the user's desired budget and schedule. The booking unit can also customize reservations and arrangements according to the user's wishes. For example, it can modify meal arrangements or add special activities in response to specific requests. This allows the booking unit to provide flexible reservations and arrangements tailored to the user's needs.

[0069] The update function collects weather and local event information in real time during your trip and automatically updates and suggests the best plan for you. For example, if the weather deteriorates, the update function will suggest indoor activities. Specifically, the AI ​​monitors weather information in real time and automatically updates the travel plan according to changes in the weather. For example, if it rains, it will suggest indoor museums or amusement facilities. The update function can also collect information on newly held local events and suggest them to the user. For example, it will collect information on newly held festivals and special events in the area and notify the user. This allows users to enjoy the best plan based on the latest information during their trip. Furthermore, the update function can collect user feedback and use it to improve the plan. For example, by inputting evaluations of activities and services provided during the trip, the AI ​​analyzes this information and reflects it in future plan suggestions. This allows the update function to always provide highly accurate plans based on the latest information and improve user satisfaction.

[0070] The reception desk can estimate the user's emotions and adjust the timing of receiving information about the child's age and interests based on the estimated emotions. For example, if the user is stressed, the reception desk may receive information in the form of simple questions. For example, if the user is relaxed, the reception desk may also offer the option to enter more detailed information. For example, if the user is in a hurry, the reception desk may prioritize voice input to receive information quickly. This allows for the reception of more appropriate information by adjusting the timing of information reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The reception desk can analyze the user's past travel history and select the most suitable method for receiving information. For example, the reception desk can suggest a similar method of receiving information based on travel plans the user has used in the past. For example, the reception desk can analyze the user's preferred travel style from their past travel history and ask appropriate questions. For example, the reception desk can prioritize receiving relevant information based on places the user has visited in the past. This makes the system user-friendly by selecting the most suitable method of receiving information based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI select the most suitable method of receiving information.

[0072] The reception desk can filter information received, such as a child's age and interests, based on the user's current lifestyle and areas of interest. For example, the reception desk can prioritize receiving relevant information based on the user's current lifestyle (work, family, etc.). The reception desk can also present appropriate questions based on the user's areas of interest (sports, culture, etc.). The reception desk can also adjust the timing of information reception to match the user's daily rhythm. This allows for the reception of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0073] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For example, if the user is relaxed, the reception desk may also prioritize receiving detailed information. For example, if the user is in a hurry, the reception desk may also prioritize receiving only the essential information. This allows for the priority of receiving important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about nearby events and activities based on the user's current location. The reception unit can also prioritize receiving relevant information by considering the geographical information of the user's travel destination. The reception unit can also prioritize receiving highly relevant information by referring to the user's travel history. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0075] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information by referring to the activities of the user's followers and friends, for example. The reception unit can also receive appropriate information based on the user's social media interests, for example. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's interests during the analysis. For example, if the child's interest is high, the analysis unit will provide detailed analysis results. For example, if the child's interest is low, the analysis unit can also provide concise analysis results. The analysis unit can also adjust the depth of the analysis according to the importance of the child's interests. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the child's category of interest during analysis. For example, if the child's interest is animals, the analysis unit will prioritize analyzing animal-related events and activities. If the child's interest is sports, the analysis unit can also prioritize analyzing sports-related events and activities. If the child's interest is science, the analysis unit can also prioritize analyzing science-related events and activities. By applying different analysis algorithms depending on the child's category of interest, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a brief analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's voice data into the generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on when the child's interests were submitted. For example, the analysis unit may prioritize analysis if the child's interests were recently submitted. For example, the analysis unit may postpone analysis if the child's interests were submitted in the past. The analysis unit may also adjust the priority of analysis according to when the child's interests were submitted. This allows for more appropriate analysis results by determining the priority of analysis based on when the child's interests were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the children's interests during the analysis. For example, the analysis unit may prioritize analysis if the children are highly interested in that topic. For example, it may postpone analysis if the children are less interested in that topic. The analysis unit can also adjust the order of analysis according to the relevance of the children's interests. By adjusting the order of analysis based on the relevance of the children's interests, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the children's interest data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0082] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-understand travel plan. For example, if the user is relaxed, the generation unit can also generate a detailed travel plan. For example, if the user is in a hurry, the generation unit can generate a concise travel plan. In this way, by adjusting the travel plan generation method according to the user's emotions, a more appropriate travel plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI adjust the travel plan generation method.

[0083] The generation unit can adjust the level of detail generated based on the importance of the child's interests when generating a travel plan. For example, if the child's interests are high, the generation unit will generate a detailed travel plan. For example, if the child's interests are low, the generation unit can also generate a concise travel plan. The generation unit can also adjust the depth of generation according to the importance of the child's interests. This allows for the provision of more appropriate travel plans by adjusting the level of detail based on the importance of the child's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generation.

[0084] The generation unit can apply different generation algorithms depending on the child's interests when generating travel plans. For example, if the child's interests are animals, the generation unit will prioritize generating animal-related events and activities. If the child's interests are sports, the generation unit can also prioritize generating sports-related events and activities. If the child's interests are science, the generation unit can also prioritize generating science-related events and activities. By applying different generation algorithms depending on the child's interests, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI execute the application of different generation algorithms.

[0085] The generation unit can estimate the user's emotions and determine the priority of the travel plan to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important travel plans. For example, if the user is relaxed, the generation unit can also generate detailed travel plans. For example, if the user is in a hurry, the generation unit can also prioritize generating minimal travel plans. This allows for the provision of more appropriate travel plans by prioritizing travel plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's voice data into the generation AI and have the generation AI determine the priority of the travel plan.

[0086] The generation unit can determine the generation priority based on when the child's interests are submitted when generating a travel plan. For example, the generation unit will prioritize generating plans if the child's interests have been submitted recently. The generation unit can also postpone generating plans if the child's interests have been submitted in the past. The generation unit can also adjust the generation priority according to when the child's interests are submitted. This allows for the provision of more appropriate travel plans by determining the generation priority based on when the child's interests are submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on when the child's interests were submitted into a generation AI and have the generation AI determine the generation priority.

[0087] The generation unit can adjust the order of travel plan generation based on the relevance of the child's interests. For example, the generation unit will prioritize generating travel plans that the child is highly interested in. For example, the generation unit can postpone generating travel plans that the child is less interested in. The generation unit can also adjust the order of generation according to the relevance of the child's interests. This allows for the provision of more appropriate travel plans by adjusting the order of generation based on the relevance of the child's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0088] The booking unit can estimate the user's emotions and adjust the booking and arrangement methods based on the estimated emotions. For example, if the user is stressed, the booking unit can provide a simple and quick booking method. For example, if the user is relaxed, the booking unit can also provide detailed booking options. For example, if the user is in a hurry, the booking unit can prioritize voice input to make a booking quickly. This allows for more appropriate bookings and arrangements by adjusting the booking and arrangement methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not. For example, the booking unit can input user facial expression data into a generative AI and have the generative AI adjust the booking and arrangement methods.

[0089] The booking unit can adjust the level of detail in bookings and arrangements based on the importance of the child's interests. For example, if the child's interest is high, the booking unit will make detailed arrangements. If the child's interest is low, the booking unit can also make simple arrangements. The booking unit can also adjust the depth of arrangements according to the importance of the child's interests. This allows for more appropriate bookings and arrangements to be made by adjusting the level of detail in arrangements based on the importance of the child's interests. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in arrangements.

[0090] The booking unit can apply different booking algorithms depending on the child's interests when making reservations or bookings. For example, if a child is interested in animals, the booking unit will prioritize booking animal-related events and activities. If a child is interested in sports, the booking unit can also prioritize booking sports-related events and activities. If a child is interested in science, the booking unit can also prioritize booking science-related events and activities. By applying different booking algorithms depending on the child's interests, more appropriate reservations and bookings can be made. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI execute the application of different booking algorithms.

[0091] The booking unit can estimate the user's emotions and determine booking priorities based on those emotions. For example, if the user is stressed, the booking unit will prioritize important bookings. If the user is relaxed, the booking unit can also prioritize detailed bookings. If the user is in a hurry, the booking unit can also prioritize bookings that are only essential. This allows for more appropriate reservations and bookings by prioritizing bookings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not. For example, the booking unit can input user voice data into a generative AI and have the generative AI determine the booking priorities.

[0092] The booking unit can determine the priority of bookings based on when the child's interests were submitted. For example, the booking unit will prioritize bookings if the child's interests were recently submitted. For example, the booking unit may postpone bookings if the child's interests were submitted in the past. The booking unit can also adjust the priority of bookings according to when the child's interests were submitted. This allows for more appropriate bookings and arrangements by determining the priority of bookings based on when the child's interests were submitted. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of bookings.

[0093] The booking unit can adjust the order of bookings based on the relevance of the child's interests during the booking and arrangement process. For example, the booking unit can prioritize bookings that the child is highly interested in. For example, the booking unit can postpone bookings that the child is less interested in. The booking unit can also adjust the order of bookings according to the relevance of the child's interests. This allows for more appropriate bookings and arrangements to be made by adjusting the order of bookings based on the relevance of the child's interests. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the booking order.

[0094] The update unit can estimate the user's emotions and adjust the plan update method based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple and quick update method. For example, if the user is relaxed, the update unit can also provide detailed update options. For example, if the user is in a hurry, the update unit can prioritize voice input and perform a quick update. This allows for more appropriate plan updates by adjusting the plan update method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user facial expression data into the generative AI and have the generative AI adjust the plan update method.

[0095] The update unit can adjust the level of detail of the update based on the importance of the child's interests when updating the plan. For example, if the child's interest is high, the update unit will perform a detailed update. For example, if the child's interest is low, the update unit can also perform a concise update. The update unit can also adjust the depth of the update according to the importance of the child's interests. This allows for more appropriate plan updates by adjusting the level of detail of the update based on the importance of the child's interests. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the update.

[0096] The update unit can apply different update algorithms depending on the child's category of interest when updating the plan. For example, if the child's interest is animals, the update unit will prioritize updating animal-related events and activities. For example, if the child's interest is sports, the update unit can also prioritize updating sports-related events and activities. For example, if the child's interest is science, the update unit can also prioritize updating science-related events and activities. This allows for more appropriate plan updates by applying different update algorithms depending on the child's category of interest. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI execute the application of different update algorithms.

[0097] The update unit can estimate the user's emotions and determine the priority of the plans to update based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating important plans. For example, if the user is relaxed, the update unit may also update detailed plans. For example, if the user is in a hurry, the update unit may also prioritize updating only the essential plans. This allows for more appropriate plan updates by determining the priority of plans to update according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user voice data into a generative AI and have the generative AI determine the priority of plans.

[0098] The update unit can determine the priority of updates based on when the child's interests are submitted when updating the plan. For example, the update unit will prioritize updates if the child's interests have been submitted recently. For example, the update unit may postpone updates if the child's interests have been submitted in the past. The update unit can also adjust the priority of updates according to when the child's interests are submitted. This allows for more appropriate plan updates by determining the priority of updates based on when the child's interests are submitted. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input data on when the child's interests were submitted into a generating AI and have the generating AI determine the priority of updates.

[0099] The update unit can adjust the order of updates based on the relevance of the child's interests when updating the plan. For example, the update unit will prioritize updates that the child is highly interested in. For example, the update unit can postpone updates that the child is less interested in. The update unit can also adjust the order of updates according to the relevance of the child's interests. This allows for more appropriate plan updates by adjusting the order of updates based on the relevance of the child's interests. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the update order.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0102] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-understand travel plan. For example, if the user is relaxed, the generation unit can also generate a detailed travel plan. For example, if the user is in a hurry, the generation unit can generate a concise travel plan. In this way, by adjusting the travel plan generation method according to the user's emotions, a more appropriate travel plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI adjust the travel plan generation method.

[0103] The booking unit can estimate the user's emotions and adjust the booking and arrangement methods based on the estimated emotions. For example, if the user is stressed, the booking unit can provide a simple and quick booking method. For example, if the user is relaxed, the booking unit can also provide detailed booking options. For example, if the user is in a hurry, the booking unit can prioritize voice input to make a booking quickly. This allows for more appropriate bookings and arrangements by adjusting the booking and arrangement methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not. For example, the booking unit can input user facial expression data into a generative AI and have the generative AI adjust the booking and arrangement methods.

[0104] The update unit can estimate the user's emotions and adjust the plan update method based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple and quick update method. For example, if the user is relaxed, the update unit can also provide detailed update options. For example, if the user is in a hurry, the update unit can prioritize voice input and perform a quick update. This allows for more appropriate plan updates by adjusting the plan update method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user facial expression data into the generative AI and have the generative AI adjust the plan update method.

[0105] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For example, if the user is relaxed, the reception desk may also prioritize receiving detailed information. For example, if the user is in a hurry, the reception desk may also prioritize receiving only the essential information. This allows for the priority of receiving important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0106] The reception desk can analyze the user's past travel history and select the most suitable method for receiving information. For example, the reception desk can suggest a similar method of receiving information based on travel plans the user has used in the past. For example, the reception desk can analyze the user's preferred travel style from their past travel history and ask appropriate questions. For example, the reception desk can prioritize receiving relevant information based on places the user has visited in the past. This makes the system user-friendly by selecting the most suitable method of receiving information based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI select the most suitable method of receiving information.

[0107] The reception desk can filter information received, such as a child's age and interests, based on the user's current lifestyle and areas of interest. For example, the reception desk can prioritize receiving relevant information based on the user's current lifestyle (work, family, etc.). The reception desk can also present appropriate questions based on the user's areas of interest (sports, culture, etc.). The reception desk can also adjust the timing of information reception to match the user's daily rhythm. This allows for the reception of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0108] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception unit can prioritize receiving information about nearby events and activities based on the user's current location. The reception unit can also prioritize receiving relevant information by considering the geographical information of the user's travel destination. The reception unit can also prioritize receiving highly relevant information by referring to the user's travel history. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0109] The reception unit can analyze the user's social media activity and receive relevant information when information is received. For example, the reception unit can analyze the content of the user's social media posts and receive relevant information. The reception unit can also receive relevant information by referring to the activities of the user's followers and friends, for example. The reception unit can also receive appropriate information based on the user's social media interests, for example. In this way, relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0110] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's interests during the analysis. For example, if the child's interest is high, the analysis unit will provide detailed analysis results. For example, if the child's interest is low, the analysis unit can also provide concise analysis results. The analysis unit can also adjust the depth of the analysis according to the importance of the child's interests. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the child's interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child interest data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception unit receives information from the user about the child's age and interests. This information includes, for example, the child's age, interests, and favorite activities. The reception unit can save the information entered by the user to a database and send it to the analysis unit. Step 2: The analysis unit analyzes the information received by the reception unit and proposes the optimal experience. Based on past data and evaluations from other users, the analysis unit proposes experiences that are valuable to children. For example, it may suggest events and activities such as zoos and safari parks. It can also suggest experiences that match the child's interests based on the user's input information. Step 3: The generation unit automatically generates a travel plan based on the experience suggested by the analysis unit. The generation unit automatically generates a travel plan that includes accommodation, transportation, and meal arrangements. It can also generate the optimal travel plan based on the user's input information and customize it according to the user's preferences. Step 4: The arrangement unit makes reservations and arrangements based on the travel plan generated by the generation unit. The arrangement unit makes reservations for accommodations, transportation, meals, etc. Based on the user's input information, it makes the best reservations and arrangements and can also be customized according to the user's preferences. Step 5: The update function collects weather and local event information in real time during your trip and automatically updates and suggests the best plan. For example, if the weather deteriorates, it will suggest indoor activities. It can also collect information on newly held local events and suggest them to the user.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, arrangement unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information from the user about the child's age and interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit and proposes the optimal experience. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a travel plan based on the experience proposed by the analysis unit. The arrangement unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit is implemented by the control unit 46A of the smart device 14 and collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, arrangement unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information from the user about the child's age and interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit and proposes the optimal experience. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a travel plan based on the experience proposed by the analysis unit. The arrangement unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit is implemented by the control unit 46A of the smart glasses 214 and collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the 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.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, arrangement unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information from the user about the child's age and interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit and proposes the optimal experience. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a travel plan based on the experience proposed by the analysis unit. The arrangement unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit is implemented by the control unit 46A of the headset terminal 314 and collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] 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.

[0157] 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.

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

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

[0160] In 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.

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

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, arrangement unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information from the user about the child's age and interests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit and proposes the optimal experience. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a travel plan based on the experience proposed by the analysis unit. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes reservations and arrangements based on the travel plan generated by the generation unit. The update unit is implemented by, for example, the control unit 46A of the robot 414 and collects weather and local event information in real time during the trip and automatically updates and proposes the optimal plan. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk that receives information from users about their child's age and interests, The analysis unit analyzes the information received by the reception unit and proposes the optimal experience, A generation unit that automatically generates a travel plan based on the experience proposed by the analysis unit, A booking unit that makes reservations and arrangements based on the travel plan generated by the generation unit, It includes an update unit that collects weather and local event information in real time during travel and automatically updates and suggests the optimal plan. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving information about the child's age and interests based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past travel history to select the most suitable method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving information about a child's age and interests, the system filters the information based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the children's interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the child's category of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the children submitted their interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the children's interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a travel plan, adjust the level of detail based on the importance of the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating travel plans, different generation algorithms are applied depending on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and determines the priority of travel plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, prioritize the creation process based on when children submit their interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating travel plans, adjust the order of generation based on the relevance of the children's interests. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned ordering unit, It estimates the user's emotions and adjusts the booking and arrangement methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned ordering unit, When making reservations or arrangements, adjust the level of detail based on the importance of the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned ordering unit, When making reservations or arrangements, different arrangement algorithms are applied depending on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned ordering unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ordering unit, When making reservations or arrangements, we prioritize arrangements based on when the child submits their interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering unit, When making reservations or arrangements, we adjust the order of arrangements based on the relevance of the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is It estimates the user's emotions and adjusts how the plan is updated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is When renewing the plan, adjust the level of detail in the update based on the importance of the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is When renewing a plan, a different renewal algorithm is applied depending on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is It estimates user sentiment and determines the priority of update plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is When renewing the plan, prioritize renewals based on when the child submits their interests. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is When renewing the plan, the order of renewals will be adjusted based on the relevance of the child's interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users about their child's age and interests, The analysis unit analyzes the information received by the reception unit and proposes the optimal experience, A generation unit that automatically generates a travel plan based on the experience proposed by the analysis unit, A booking unit that makes reservations and arrangements based on the travel plan generated by the generation unit, It includes an update unit that collects weather and local event information in real time during travel and automatically updates and suggests the optimal plan. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving information about the child's age and interests based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past travel history to select the most suitable method for receiving information. The system according to feature 1.

4. The aforementioned reception unit is When receiving information about a child's age and interests, the system filters the information based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to accept based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the children's interests. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the child's category of interest. The system according to feature 1.

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