system

Abstract

The system according to this embodiment aims to generate highly unique travel plans tailored to individual preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects data such as the user's past travel history, SNS posts, and search history. The analysis unit analyzes the data collected by the collection unit to understand the individual's preferences. Based on the analysis results obtained by the analysis unit, the generation unit combines the latest tourist information, seasonal events, and hidden gems provided by local governments and local businesses to generate a highly unique travel plan. The adjustment unit adjusts the details of the travel plan generated by the generation unit in a dialogue format with the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 efficiently generate a travel plan according to an individual's preference, and it is impossible to fully bring out the hidden charm of a local area.

[0005] The system according to the embodiment aims to generate a highly unique travel plan according to an individual's preference.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects data such as the user's past travel history, social media posts, and search history. The analysis unit analyzes the data collected by the collection unit to understand the user's preferences. Based on the analysis results obtained by the analysis unit, the generation unit combines the latest tourist information, seasonal events, and hidden gems provided by local governments and businesses to generate a highly unique travel plan. The adjustment unit adjusts the details of the travel plan generated by the generation unit in a dialogue format with the user. [Effects of the Invention]

[0007] The system according to this embodiment can generate highly unique travel plans tailored to individual preferences. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel plan suggestion system according to an embodiment of the present invention is a system that utilizes generation AI to uncover hidden attractions in rural areas tailored to an individual's interests and proposes a customized travel plan. This system aims to increase the number of tourists visiting rural areas and improve traveler satisfaction. The travel plan suggestion system analyzes data such as the user's past travel history, SNS posts, and search history to deeply understand the individual's preferences. Furthermore, it combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate a highly unique travel plan. The generated plan includes not only tourist spots but also off-the-beaten-path restaurants and experiential activities known only to locals, providing travelers with new discoveries and a deeper understanding of the region. The travel plan suggestion system also considers weather forecasts and congestion levels to propose the optimal schedule. Based on the proposed plan, the user can adjust the details with the generation AI in a dialogue format to create a perfect travel plan to their liking. This system maximizes the appeal of rural areas, leading to an increase in tourists and the revitalization of the local economy. In this way, the travel plan suggestion system can provide customized travel plans tailored to the user's preferences and maximize the appeal of rural areas.

[0029] The travel plan suggestion system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects data such as the user's past travel history, SNS posts, and search history. The collection unit collects data such as tourist destinations the user has visited in the past, photos posted, and keywords searched. This allows the collection unit to understand the user's preferences. The analysis unit analyzes the data collected by the collection unit to understand individual preferences. The analysis unit can, for example, identify users who have a high interest in a particular region or theme and generate an appropriate travel plan for that user. This allows the analysis unit to provide personalized travel plans tailored to the user's needs. Based on the analysis results obtained by the analysis unit, the generation unit combines data such as the latest tourist information, seasonal events, and hidden gems provided by local governments and local businesses to generate highly unique travel plans. The generation unit generates plans that include, for example, off-the-beaten-path restaurants and experiential activities known only to locals. This allows the generation unit to provide travelers with new discoveries and a deeper understanding of the region. The generation unit proposes an optimal schedule, taking into account factors such as weather forecasts and crowd levels. For example, the generation unit can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where crowds are expected. This allows the generation unit to ensure travelers can enjoy a comfortable trip. The adjustment unit then works with the user to refine the details of the travel plan generated by the generation unit. For example, the adjustment unit can add specific tourist spots or change the order in which they are visited. This allows the adjustment unit to enable users to create travel plans tailored to their preferences. As a result, the travel plan suggestion system according to this embodiment can provide customized travel plans that match the user's tastes and maximize the appeal of local areas.

[0030] The data collection unit collects data such as users' past travel history, social media posts, and search history. Specifically, it collects data such as tourist destinations visited in the past, photos posted, and keywords searched. This allows the data collection unit to understand users' preferences. For example, by obtaining a list of tourist destinations visited in the past and analyzing the length of stay and frequency of visits to those destinations, it is possible to identify regions and themes that users are particularly interested in. In addition, social media posts reveal what kinds of activities and tourist spots users are interested in. For example, by analyzing photos and comments posted by users, it is possible to analyze preferences such as natural landscapes, historical buildings, and gourmet food. Furthermore, search history reveals what kind of information users are seeking. For example, by analyzing search keywords related to specific tourist destinations, events, or accommodations, it is possible to identify users' interests. This allows the data collection unit to integrate diverse user data and create detailed preference profiles. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analytics department analyzes data collected by the data collection department to understand individual preferences. Specifically, it can identify users with a high interest in specific regions or themes and generate appropriate travel plans for them. For example, it can analyze user preferences in detail based on data such as tourist destinations visited in the past, photos posted, and keywords searched. Using AI, it can cluster user data and identify user groups with common preferences. This allows the analytics department to provide personalized travel plans tailored to user needs. Furthermore, the analytics department can understand what kind of travel style users prefer from their past travel history and social media posts. For example, it can suggest plans that include outdoor activities and sporting events to users who prefer active travel, and plans that include hot springs, spas, and resort facilities to users who prefer relaxing travel. In addition, the analytics department can understand current interests and trends from users' search history and provide plans that reflect the latest tourist information and events. This allows the analytics department to generate highly accurate travel plans based on user preferences and needs, providing users with a highly satisfying travel experience.

[0032] The generation unit, based on the analysis results obtained by the analysis unit, combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. Specifically, it generates plans that include off-the-beaten-path restaurants and experiential activities known only to locals. For example, it can collect the latest tourism information provided by local tourism associations and chambers of commerce and create customized plans tailored to the user's preferences. The generation unit also considers weather forecasts and crowd levels to propose the optimal schedule. For example, it can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where crowds are expected. This allows the generation unit to ensure travelers enjoy a comfortable trip. Furthermore, the generation unit can use AI to automatically generate optimal travel plans based on the user's preferences and needs. For example, if a user prefers natural landscapes, it can suggest a plan that includes nature parks and hiking trails; if interested in historical buildings, it can suggest a plan centered on historical tourist spots. The generation unit can also suggest the optimal plan according to the user's budget and travel duration. This allows the generation unit to provide users with new discoveries and a deeper understanding of the region, enabling travelers to enjoy a highly satisfying trip.

[0033] The adjustment unit interacts with the user to fine-tune the details based on the travel plan generated by the generation unit. Specifically, it can add specific tourist spots or change the order in which they are visited. For example, if a user wants to visit a particular tourist spot, it can add that spot to the plan and adjust the schedule to balance it with other plans. Also, if a user wants to visit a place at a specific time, it can adjust other plans to accommodate that time. In this way, the adjustment unit allows users to create travel plans tailored to their preferences. Furthermore, the adjustment unit can collect user feedback and make improvements to enhance the accuracy and satisfaction of the plan. For example, it can collect user impressions and evaluations after the trip and incorporate them into future plan creation. In addition, the adjustment unit can respond quickly to problems and changes during the trip through interaction with the user. For example, if unexpected events occur, such as sudden changes in weather or delays in transportation, it can propose a new plan on behalf of the user and support a smooth trip. In this way, the adjustment unit can provide users with flexible and prompt responses, increasing their satisfaction with their trip.

[0034] The data collection unit can collect data such as the user's past travel history, social media posts, and search history. For example, the data collection unit can collect data such as tourist destinations the user has visited in the past, photos posted, and keywords searched. This allows the data collection unit to understand the user's preferences. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's past travel history into a generation AI, which can then collect the data. This allows the data collection unit to understand the user's preferences by collecting data such as the user's past travel history, social media posts, and search history.

[0035] The analysis unit can analyze the collected data and understand the user's interests. For example, the analysis unit can identify users who have a high interest in a particular region or theme and generate appropriate travel plans for those users. This allows the analysis unit to provide personalized travel plans tailored to the user's needs. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data. This allows for a deeper understanding of the user's interests by analyzing the collected data.

[0036] The generation unit can generate highly unique travel plans by combining the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses. For example, the generation unit can generate plans that include off-the-beaten-path restaurants and experiential activities known only to locals. This allows the generation unit to provide travelers with new discoveries and a deeper understanding of the region. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not. For example, the generation unit can input the latest tourism information provided by local governments and businesses into a generation AI, which can then generate a travel plan. This allows for the generation of highly unique travel plans by combining the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses.

[0037] The generation unit can propose an optimal schedule considering weather forecasts and congestion levels. For example, the generation unit can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where congestion is expected. This allows travelers to enjoy a comfortable trip. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input weather forecast and congestion data into a generation AI, which can then propose an optimal schedule. This allows for the proposal of an optimal schedule by considering weather forecasts and congestion levels.

[0038] The adjustment unit can adjust the details of the travel plan interactively with the user. For example, the adjustment unit can add specific tourist spots or change the order in which they are visited. This allows the adjustment unit to create a travel plan tailored to the user's preferences. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the generated travel plan into a generation AI, which can then adjust the details interactively with the user. This allows for the creation of a travel plan tailored to the user's preferences by adjusting the details interactively with the user.

[0039] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on tourist destinations the user has visited in the past. The data collection unit can collect data on activities the user has enjoyed in the past. The data collection unit can exclude data on places the user has avoided in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past travel history into a generative AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest and lifestyle during data collection. For example, the data collection unit can prioritize collecting data related to themes the user is currently interested in. The data collection unit can collect highly relevant data based on the user's lifestyle (e.g., family structure, work situation). The data collection unit can collect data on appropriate activities based on the user's current health status. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's current areas of interest and lifestyle into a generative AI, which can then perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of tourist information around the user's current location. The data collection unit can collect data on relevant tourist spots based on the geographical information of the user's travel destination. The data collection unit can prioritize the collection of tourist information along the user's travel route. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on tourist destinations that the user has "liked" on social media. The data collection unit can collect relevant data based on event information that the user has shared on social media. The data collection unit can collect data on places visited by the user's social media followers. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's social media activity into a generative AI, and the generative AI can collect relevant data.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's past travel history during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on data of tourist destinations the user has visited in the past. The analysis unit can analyze the user's past travel patterns and propose highly accurate travel plans. The analysis unit can refer to the user's past travel history and perform analysis based on their interests. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past travel history. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis unit can input the user's past travel history into a generative AI, which can then improve the accuracy of its analysis.

[0044] The analysis unit can apply different analysis algorithms to data depending on the user's area of ​​interest. For example, if the user is interested in nature, the analysis unit can apply an algorithm that prioritizes analyzing nature-related data. If the user is interested in history, the analysis unit can apply an algorithm that prioritizes analyzing history-related data. If the user is interested in food, the analysis unit can apply an algorithm that prioritizes analyzing food-related data. In this way, the analysis unit can perform more appropriate data analysis by applying different analysis algorithms depending on the user's area of ​​interest. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input data on the user's area of ​​interest into a generative AI, and the generative AI can apply different analysis algorithms.

[0045] The analysis department can prioritize analyses based on user submission timing during data analysis. For example, if a user is in a hurry, the analysis department can perform a rapid analysis and provide results. If a user has ample time, the analysis department can perform a detailed analysis and provide deeper insights. If a user has set a specific deadline, the analysis department can perform the analysis to meet that deadline. This allows the analysis department to perform analyses at a more appropriate time by prioritizing them based on user submission timing. Some or all of the above processes in the analysis department may be performed using generative AI, or not. For example, the analysis department can input user submission timing data into a generative AI, which can then determine the analysis priorities.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. The analysis unit can improve the accuracy of its analysis by referring to the latest literature related to the user's area of ​​interest. The analysis unit can improve the accuracy of its analysis based on literature provided by the user. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input data from the user's relevant literature into a generative AI, which can then improve the accuracy of its analysis.

[0047] The generation unit can improve the accuracy of travel plan generation by referring to the user's past travel history. For example, the generation unit can generate a highly accurate travel plan based on data of tourist destinations the user has visited in the past. The generation unit can analyze the user's past travel patterns and generate an optimal travel plan. The generation unit can refer to the user's past travel history and generate a travel plan based on their interests. In this way, the generation unit can improve the accuracy of generation by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then improve the accuracy of generation.

[0048] The generation unit can apply different generation algorithms depending on the user's areas of interest when generating travel plans. For example, if the user is interested in nature, the generation unit can generate a travel plan that prioritizes nature-related tourist spots. If the user is interested in history, the generation unit can generate a travel plan that prioritizes history-related tourist spots. If the user is interested in gourmet food, the generation unit can generate a travel plan that prioritizes gourmet food-related tourist spots. In this way, the generation unit can generate more appropriate travel plans by applying different generation algorithms depending on the user's areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's areas of interest into a generation AI, and the generation AI can apply different generation algorithms.

[0049] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit can generate a travel plan that prioritizes tourist spots around the user's current location. The generation unit can generate a travel plan that includes optimal tourist spots based on the geographical information of the user's travel destination. The generation unit can generate a travel plan that prioritizes tourist spots along the user's travel route. In this way, the generation unit can generate an optimal travel plan by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI, and the generation AI can generate an optimal plan.

[0050] The generation unit can generate relevant travel plans by analyzing the user's social media activity when generating travel plans. For example, the generation unit can generate travel plans that include tourist destinations that the user has "liked" on social media. The generation unit can generate relevant travel plans based on event information that the user has shared on social media. The generation unit can generate travel plans that include places visited by the user's social media followers. In this way, the generation unit can generate relevant travel plans by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI, and the generation AI can generate relevant plans.

[0051] The adjustment unit can select the optimal adjustment method when adjusting a travel plan by referring to the user's past travel history. For example, the adjustment unit may prioritize suggesting adjustment methods that the user has preferred in the past. The adjustment unit can provide optimal adjustment options based on the user's past travel history. The adjustment unit can suggest an appropriate adjustment method by referring to the user's past adjustment history. In this way, the adjustment unit can select the optimal adjustment method by referring to the user's past travel history. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the user's past travel history into a generation AI, and the generation AI can select the optimal adjustment method.

[0052] The adjustment unit can customize the means of adjustment based on the user's areas of interest when adjusting travel plans. For example, if the user is interested in nature, the adjustment unit can provide nature-related adjustment options. If the user is interested in history, the adjustment unit can provide history-related adjustment options. If the user is interested in food, the adjustment unit can provide food-related adjustment options. In this way, the adjustment unit can make more appropriate adjustments by customizing the means of adjustment based on the user's areas of interest. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input data on the user's areas of interest into a generative AI, and the generative AI can customize the means of adjustment.

[0053] The adjustment unit can select the optimal adjustment method when adjusting a travel plan, taking into account the user's geographical location information. For example, the adjustment unit can adjust a travel plan that prioritizes tourist spots around the user's current location. The adjustment unit can adjust a travel plan that includes the optimal tourist spots based on the geographical information of the user's travel destination. The adjustment unit can adjust a travel plan that prioritizes tourist spots along the user's travel route. In this way, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal adjustment method.

[0054] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting travel plans. For example, the adjustment unit can adjust travel plans to include tourist destinations that the user has "liked" on social media. The adjustment unit can adjust relevant travel plans based on event information that the user has shared on social media. The adjustment unit can adjust travel plans to include places visited by the user's social media followers. In this way, the adjustment unit can propose more appropriate adjustment methods by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input data on the user's social media activity into a generative AI, and the generative AI can propose adjustment methods.

[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 data collection unit can monitor the user's current health status and adjust the content of data collection based on that status. For example, if the user is tired, it can prioritize collecting data on relaxing tourist spots and activities. If the user is healthy and active, it can collect data on activities such as hiking and sports. Furthermore, if the user has a specific health problem, it can collect data on tourist spots and activities that address that problem. In this way, the data collection unit can collect appropriate data based on the user's health status.

[0057] The generation unit can create travel plans by referencing not only the user's past travel history but also the travel history of the user's friends and family. For example, it can generate plans that include tourist spots visited by the user's friends, or plans that include activities preferred by the user's family. It can also generate plans that exclude places avoided by the user's friends and family. In this way, the generation unit can create more personalized travel plans by taking into account the travel history of the user's friends and family.

[0058] The adjustment unit can adjust the travel plan considering the user's current schedule. For example, if the user is busy, it can adjust the plan to include sightseeing spots and activities that can be enjoyed in a short amount of time. If the user has more free time, it can adjust the plan to include sightseeing spots and activities that can be enjoyed at a leisurely pace. Furthermore, if the user has plans during a specific time period, the adjustment unit can adjust the plan to avoid that time. In this way, the adjustment unit can provide the optimal travel plan tailored to the user's schedule.

[0059] The analytics department can perform data analysis while considering the user's current living situation. For example, if a user is traveling with their family, the analysis can prioritize family-friendly tourist spots and activities. If a user is traveling for business, the analysis can prioritize business-oriented tourist spots and activities. Furthermore, if a user desires a trip tailored to their health condition, the analysis can prioritize tourist spots and activities that match that health condition. In this way, the analytics department can perform appropriate data analysis based on the user's living situation.

[0060] The adjustment unit can adjust travel plans by referring not only to the user's past travel history but also to the travel history of the user's friends and family. For example, it can adjust plans to include tourist spots visited by the user's friends, or to include activities preferred by the user's family. It can also adjust plans to exclude places avoided by the user's friends and family. In this way, the adjustment unit can create more personalized travel plans by taking into account the travel history of the user's friends and family.

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

[0062] Step 1: The data collection unit collects data such as the user's past travel history, social media posts, and search history. For example, it collects data such as tourist destinations the user has visited in the past, photos they have posted, and keywords they have searched for. This allows the data collection unit to understand the user's preferences. Step 2: The analytics department analyzes the data collected by the data collection department to understand individual preferences. For example, it can identify users who have a high interest in specific regions or themes and generate appropriate travel plans for those users. This allows the analytics department to provide personalized travel plans tailored to the user's needs. Step 3: Based on the analysis results obtained by the analysis unit, the generation unit combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. For example, it can generate plans that include off-the-beaten-path restaurants and experiential activities known only to locals. Furthermore, it takes into account weather forecasts and crowd conditions to propose the optimal schedule. This allows travelers to enjoy a comfortable trip. Step 4: The adjustment unit interacts with the user to fine-tune the details based on the travel plan generated by the generation unit. For example, specific tourist spots can be added or the order of visits can be changed. This allows the user to create a travel plan tailored to their preferences.

[0063] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that utilizes generation AI to uncover hidden attractions in rural areas tailored to an individual's interests and proposes a customized travel plan. This system aims to increase the number of tourists visiting rural areas and improve traveler satisfaction. The travel plan suggestion system analyzes data such as the user's past travel history, SNS posts, and search history to deeply understand the individual's preferences. Furthermore, it combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate a highly unique travel plan. The generated plan includes not only tourist spots but also off-the-beaten-path restaurants and experiential activities known only to locals, providing travelers with new discoveries and a deeper understanding of the region. The travel plan suggestion system also considers weather forecasts and congestion levels to propose the optimal schedule. Based on the proposed plan, the user can adjust the details with the generation AI in a dialogue format to create a perfect travel plan to their liking. This system maximizes the appeal of rural areas, leading to an increase in tourists and the revitalization of the local economy. In this way, the travel plan suggestion system can provide customized travel plans tailored to the user's preferences and maximize the appeal of rural areas.

[0064] The travel plan suggestion system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an adjustment unit. The collection unit collects data such as the user's past travel history, SNS posts, and search history. The collection unit collects data such as tourist destinations the user has visited in the past, photos posted, and keywords searched. This allows the collection unit to understand the user's preferences. The analysis unit analyzes the data collected by the collection unit to understand individual preferences. The analysis unit can, for example, identify users who have a high interest in a particular region or theme and generate an appropriate travel plan for that user. This allows the analysis unit to provide personalized travel plans tailored to the user's needs. Based on the analysis results obtained by the analysis unit, the generation unit combines data such as the latest tourist information, seasonal events, and hidden gems provided by local governments and local businesses to generate highly unique travel plans. The generation unit generates plans that include, for example, off-the-beaten-path restaurants and experiential activities known only to locals. This allows the generation unit to provide travelers with new discoveries and a deeper understanding of the region. The generation unit proposes an optimal schedule, taking into account factors such as weather forecasts and crowd levels. For example, the generation unit can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where crowds are expected. This allows the generation unit to ensure travelers can enjoy a comfortable trip. The adjustment unit then works with the user to refine the details of the travel plan generated by the generation unit. For example, the adjustment unit can add specific tourist spots or change the order in which they are visited. This allows the adjustment unit to enable users to create travel plans tailored to their preferences. As a result, the travel plan suggestion system according to this embodiment can provide customized travel plans that match the user's tastes and maximize the appeal of local areas.

[0065] The data collection unit collects data such as users' past travel history, social media posts, and search history. Specifically, it collects data such as tourist destinations visited in the past, photos posted, and keywords searched. This allows the data collection unit to understand users' preferences. For example, by obtaining a list of tourist destinations visited in the past and analyzing the length of stay and frequency of visits to those destinations, it is possible to identify regions and themes that users are particularly interested in. In addition, social media posts reveal what kinds of activities and tourist spots users are interested in. For example, by analyzing photos and comments posted by users, it is possible to analyze preferences such as natural landscapes, historical buildings, and gourmet food. Furthermore, search history reveals what kind of information users are seeking. For example, by analyzing search keywords related to specific tourist destinations, events, or accommodations, it is possible to identify users' interests. This allows the data collection unit to integrate diverse user data and create detailed preference profiles. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analytics department analyzes data collected by the data collection department to understand individual preferences. Specifically, it can identify users with a high interest in specific regions or themes and generate appropriate travel plans for them. For example, it can analyze user preferences in detail based on data such as tourist destinations visited in the past, photos posted, and keywords searched. Using AI, it can cluster user data and identify user groups with common preferences. This allows the analytics department to provide personalized travel plans tailored to user needs. Furthermore, the analytics department can understand what kind of travel style users prefer from their past travel history and social media posts. For example, it can suggest plans that include outdoor activities and sporting events to users who prefer active travel, and plans that include hot springs, spas, and resort facilities to users who prefer relaxing travel. In addition, the analytics department can understand current interests and trends from users' search history and provide plans that reflect the latest tourist information and events. This allows the analytics department to generate highly accurate travel plans based on user preferences and needs, providing users with a highly satisfying travel experience.

[0067] The generation unit, based on the analysis results obtained by the analysis unit, combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. Specifically, it generates plans that include off-the-beaten-path restaurants and experiential activities known only to locals. For example, it can collect the latest tourism information provided by local tourism associations and chambers of commerce and create customized plans tailored to the user's preferences. The generation unit also considers weather forecasts and crowd levels to propose the optimal schedule. For example, it can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where crowds are expected. This allows the generation unit to ensure travelers enjoy a comfortable trip. Furthermore, the generation unit can use AI to automatically generate optimal travel plans based on the user's preferences and needs. For example, if a user prefers natural landscapes, it can suggest a plan that includes nature parks and hiking trails; if interested in historical buildings, it can suggest a plan centered on historical tourist spots. The generation unit can also suggest the optimal plan according to the user's budget and travel duration. This allows the generation unit to provide users with new discoveries and a deeper understanding of the region, enabling travelers to enjoy a highly satisfying trip.

[0068] The adjustment unit interacts with the user to fine-tune the details based on the travel plan generated by the generation unit. Specifically, it can add specific tourist spots or change the order in which they are visited. For example, if a user wants to visit a particular tourist spot, it can add that spot to the plan and adjust the schedule to balance it with other plans. Also, if a user wants to visit a place at a specific time, it can adjust other plans to accommodate that time. In this way, the adjustment unit allows users to create travel plans tailored to their preferences. Furthermore, the adjustment unit can collect user feedback and make improvements to enhance the accuracy and satisfaction of the plan. For example, it can collect user impressions and evaluations after the trip and incorporate them into future plan creation. In addition, the adjustment unit can respond quickly to problems and changes during the trip through interaction with the user. For example, if unexpected events occur, such as sudden changes in weather or delays in transportation, it can propose a new plan on behalf of the user and support a smooth trip. In this way, the adjustment unit can provide users with flexible and prompt responses, increasing their satisfaction with their trip.

[0069] The data collection unit can collect data such as the user's past travel history, social media posts, and search history. For example, the data collection unit can collect data such as tourist destinations the user has visited in the past, photos posted, and keywords searched. This allows the data collection unit to understand the user's preferences. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's past travel history into a generation AI, which can then collect the data. This allows the data collection unit to understand the user's preferences by collecting data such as the user's past travel history, social media posts, and search history.

[0070] The analysis unit can analyze the collected data and understand the user's interests. For example, the analysis unit can identify users who have a high interest in a particular region or theme and generate appropriate travel plans for those users. This allows the analysis unit to provide personalized travel plans tailored to the user's needs. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data. This allows for a deeper understanding of the user's interests by analyzing the collected data.

[0071] The generation unit can generate highly unique travel plans by combining the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses. For example, the generation unit can generate plans that include off-the-beaten-path restaurants and experiential activities known only to locals. This allows the generation unit to provide travelers with new discoveries and a deeper understanding of the region. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not. For example, the generation unit can input the latest tourism information provided by local governments and businesses into a generation AI, which can then generate a travel plan. This allows for the generation of highly unique travel plans by combining the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses.

[0072] The generation unit can propose an optimal schedule considering weather forecasts and congestion levels. For example, the generation unit can suggest indoor tourist spots on days with bad weather and create a schedule that avoids places where congestion is expected. This allows travelers to enjoy a comfortable trip. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input weather forecast and congestion data into a generation AI, which can then propose an optimal schedule. This allows for the proposal of an optimal schedule by considering weather forecasts and congestion levels.

[0073] The adjustment unit can adjust the details of the travel plan interactively with the user. For example, the adjustment unit can add specific tourist spots or change the order in which they are visited. This allows the adjustment unit to create a travel plan tailored to the user's preferences. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the generated travel plan into a generation AI, which can then adjust the details interactively with the user. This allows for the creation of a travel plan tailored to the user's preferences by adjusting the details interactively with the user.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect data immediately and obtain detailed information. If the user is stressed, the data collection unit can postpone data collection and collect it when the user is calm. If the user is excited, the data collection unit can collect data quickly and obtain information in real time. This allows the data collection unit to collect data at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past travel history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on tourist destinations the user has visited in the past. The data collection unit can collect data on activities the user has enjoyed in the past. The data collection unit can exclude data on places the user has avoided in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past travel history into a generative AI, which can then select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current areas of interest and lifestyle during data collection. For example, the data collection unit can prioritize collecting data related to themes the user is currently interested in. The data collection unit can collect highly relevant data based on the user's lifestyle (e.g., family structure, work situation). The data collection unit can collect data on appropriate activities based on the user's current health status. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's current areas of interest and lifestyle into a generative AI, which can then perform the filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed tourist information. If the user is stressed, the data collection unit may prioritize collecting concise information. If the user is excited, the data collection unit may prioritize collecting the latest event information. This allows the data collection unit to collect more relevant data by prioritizing the data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the data priorities.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of tourist information around the user's current location. The data collection unit can collect data on relevant tourist spots based on the geographical information of the user's travel destination. The data collection unit can prioritize the collection of tourist information along the user's travel route. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI, which can then prioritize the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on tourist destinations that the user has "liked" on social media. The data collection unit can collect relevant data based on event information that the user has shared on social media. The data collection unit can collect data on places visited by the user's social media followers. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's social media activity into a generative AI, and the generative AI can collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis and provide deep insights. If the user is stressed, the analysis unit can perform a concise data analysis and provide concise information. If the user is agitated, the analysis unit can perform data analysis in real time and provide immediate feedback. This allows the analysis unit to perform more appropriate data analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the data analysis method.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the user's past travel history during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on data of tourist destinations the user has visited in the past. The analysis unit can analyze the user's past travel patterns and propose highly accurate travel plans. The analysis unit can refer to the user's past travel history and perform analysis based on their interests. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past travel history. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis unit can input the user's past travel history into a generative AI, which can then improve the accuracy of its analysis.

[0082] The analysis unit can apply different analysis algorithms to data depending on the user's area of ​​interest. For example, if the user is interested in nature, the analysis unit can apply an algorithm that prioritizes analyzing nature-related data. If the user is interested in history, the analysis unit can apply an algorithm that prioritizes analyzing history-related data. If the user is interested in food, the analysis unit can apply an algorithm that prioritizes analyzing food-related data. In this way, the analysis unit can perform more appropriate data analysis by applying different analysis algorithms depending on the user's area of ​​interest. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input data on the user's area of ​​interest into a generative AI, and the generative AI can apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, the analysis unit can display concise analysis results. If the user is excited, the analysis unit can display visually appealing analysis results. In this way, the analysis unit can provide a more appropriate display method by adjusting how the analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust how the analysis results are displayed.

[0084] The analysis department can prioritize analyses based on user submission timing during data analysis. For example, if a user is in a hurry, the analysis department can perform a rapid analysis and provide results. If a user has ample time, the analysis department can perform a detailed analysis and provide deeper insights. If a user has set a specific deadline, the analysis department can perform the analysis to meet that deadline. This allows the analysis department to perform analyses at a more appropriate time by prioritizing them based on user submission timing. Some or all of the above processes in the analysis department may be performed using generative AI, or not. For example, the analysis department can input user submission timing data into a generative AI, which can then determine the analysis priorities.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on literature the user has previously referenced. The analysis unit can improve the accuracy of its analysis by referring to the latest literature related to the user's area of ​​interest. The analysis unit can improve the accuracy of its analysis based on literature provided by the user. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input data from the user's relevant literature into a generative AI, which can then improve the accuracy of its analysis.

[0086] The generation unit can estimate the user's emotions and adjust the method of generating the travel plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed travel plan. If the user is stressed, the generation unit can generate a concise travel plan. If the user is excited, the generation unit can generate a visually appealing travel plan. In this way, the generation unit can generate a more appropriate travel plan by adjusting the method of generating the travel plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the method of generating the travel plan.

[0087] The generation unit can improve the accuracy of travel plan generation by referring to the user's past travel history. For example, the generation unit can generate a highly accurate travel plan based on data of tourist destinations the user has visited in the past. The generation unit can analyze the user's past travel patterns and generate an optimal travel plan. The generation unit can refer to the user's past travel history and generate a travel plan based on their interests. In this way, the generation unit can improve the accuracy of generation by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then improve the accuracy of generation.

[0088] The generation unit can apply different generation algorithms depending on the user's areas of interest when generating travel plans. For example, if the user is interested in nature, the generation unit can generate a travel plan that prioritizes nature-related tourist spots. If the user is interested in history, the generation unit can generate a travel plan that prioritizes history-related tourist spots. If the user is interested in gourmet food, the generation unit can generate a travel plan that prioritizes gourmet food-related tourist spots. In this way, the generation unit can generate more appropriate travel plans by applying different generation algorithms depending on the user's areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's areas of interest into a generation AI, and the generation AI can apply different generation algorithms.

[0089] 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 relaxed, the generation unit can generate a travel plan that prioritizes detailed sightseeing spots. If the user is stressed, the generation unit can generate a travel plan that prioritizes concise sightseeing spots. If the user is excited, the generation unit can generate a travel plan that prioritizes the latest event information. In this way, the generation unit can generate a more appropriate travel plan by determining the priority of the travel plan to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can then determine the priority of the travel plan.

[0090] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit can generate a travel plan that prioritizes tourist spots around the user's current location. The generation unit can generate a travel plan that includes optimal tourist spots based on the geographical information of the user's travel destination. The generation unit can generate a travel plan that prioritizes tourist spots along the user's travel route. In this way, the generation unit can generate an optimal travel plan by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI, and the generation AI can generate an optimal plan.

[0091] The generation unit can generate relevant travel plans by analyzing the user's social media activity when generating travel plans. For example, the generation unit can generate travel plans that include tourist destinations that the user has "liked" on social media. The generation unit can generate relevant travel plans based on event information that the user has shared on social media. The generation unit can generate travel plans that include places visited by the user's social media followers. In this way, the generation unit can generate relevant travel plans by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI, and the generation AI can generate relevant plans.

[0092] The adjustment unit can estimate the user's emotions and adjust the travel plan adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can provide detailed adjustment options, allowing for detailed customization. If the user is stressed, the adjustment unit can provide concise adjustment options, allowing for quick adjustments. If the user is excited, the adjustment unit can provide visually appealing adjustment options, allowing for enjoyable adjustments. This allows the adjustment unit to make more appropriate adjustments by adjusting the travel plan adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using or without a generative AI. For example, the adjustment unit can input user emotion data into a generative AI, which can then adjust the adjustment method.

[0093] The adjustment unit can select the optimal adjustment method when adjusting a travel plan by referring to the user's past travel history. For example, the adjustment unit may prioritize suggesting adjustment methods that the user has preferred in the past. The adjustment unit can provide optimal adjustment options based on the user's past travel history. The adjustment unit can suggest an appropriate adjustment method by referring to the user's past adjustment history. In this way, the adjustment unit can select the optimal adjustment method by referring to the user's past travel history. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the user's past travel history into a generation AI, and the generation AI can select the optimal adjustment method.

[0094] The adjustment unit can customize the means of adjustment based on the user's areas of interest when adjusting travel plans. For example, if the user is interested in nature, the adjustment unit can provide nature-related adjustment options. If the user is interested in history, the adjustment unit can provide history-related adjustment options. If the user is interested in food, the adjustment unit can provide food-related adjustment options. In this way, the adjustment unit can make more appropriate adjustments by customizing the means of adjustment based on the user's areas of interest. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input data on the user's areas of interest into a generative AI, and the generative AI can customize the means of adjustment.

[0095] The adjustment unit can estimate the user's emotions and determine the priority of the travel plan to adjust based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can adjust the travel plan to prioritize detailed sightseeing spots. If the user is stressed, the adjustment unit can adjust the travel plan to prioritize concise sightseeing spots. If the user is excited, the adjustment unit can adjust the travel plan to prioritize the latest event information. This allows the adjustment unit to make more appropriate adjustments by determining the priority of the travel plan to adjust based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using or without a generative AI. For example, the adjustment unit can input user emotion data into a generative AI, which can then determine the priority of the travel plan.

[0096] The adjustment unit can select the optimal adjustment method when adjusting a travel plan, taking into account the user's geographical location information. For example, the adjustment unit can adjust a travel plan that prioritizes tourist spots around the user's current location. The adjustment unit can adjust a travel plan that includes the optimal tourist spots based on the geographical information of the user's travel destination. The adjustment unit can adjust a travel plan that prioritizes tourist spots along the user's travel route. In this way, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the adjustment unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal adjustment method.

[0097] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting travel plans. For example, the adjustment unit can adjust travel plans to include tourist destinations that the user has "liked" on social media. The adjustment unit can adjust relevant travel plans based on event information that the user has shared on social media. The adjustment unit can adjust travel plans to include places visited by the user's social media followers. In this way, the adjustment unit can propose more appropriate adjustment methods by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the adjustment unit can input data on the user's social media activity into a generative AI, and the generative AI can propose adjustment methods.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can monitor the user's current health status and adjust the content of data collection based on that status. For example, if the user is tired, it can prioritize collecting data on relaxing tourist spots and activities. If the user is healthy and active, it can collect data on activities such as hiking and sports. Furthermore, if the user has a specific health problem, it can collect data on tourist spots and activities that address that problem. In this way, the data collection unit can collect appropriate data based on the user's health status.

[0100] The analytics department can estimate the user's emotions and prioritize analysis results based on those emotions. For example, if the user is relaxed, detailed analysis results can be prioritized. If the user is stressed, concise analysis results can be prioritized. If the user is excited, the latest event information can be prioritized. In this way, the analytics department can provide more relevant information by prioritizing analysis results based on the user's emotions.

[0101] The generation unit can create travel plans by referencing not only the user's past travel history but also the travel history of the user's friends and family. For example, it can generate plans that include tourist spots visited by the user's friends, or plans that include activities preferred by the user's family. It can also generate plans that exclude places avoided by the user's friends and family. In this way, the generation unit can create more personalized travel plans by taking into account the travel history of the user's friends and family.

[0102] The generation unit can estimate the user's emotions and adjust the travel plan content based on those emotions. For example, if the user is relaxed, it can generate a plan that includes relaxing sightseeing spots and activities. If the user is stressed, it can generate a plan that includes sightseeing spots and activities that help relieve stress. If the user is excited, it can generate a plan that includes adventure and exciting activities. In this way, the generation unit can provide a more appropriate travel plan by adjusting the content based on the user's emotions.

[0103] The adjustment unit can adjust the travel plan considering the user's current schedule. For example, if the user is busy, it can adjust the plan to include sightseeing spots and activities that can be enjoyed in a short amount of time. If the user has more free time, it can adjust the plan to include sightseeing spots and activities that can be enjoyed at a leisurely pace. Furthermore, if the user has plans during a specific time period, the adjustment unit can adjust the plan to avoid that time. In this way, the adjustment unit can provide the optimal travel plan tailored to the user's schedule.

[0104] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is relaxed, detailed data collection can be performed to gain deeper insights. If the user is stressed, concise data collection can be performed to obtain key information. If the user is excited, data collection can be performed in real time to obtain immediate feedback. In this way, the data collection unit can collect more relevant data by adjusting the data collection method based on the user's emotions.

[0105] The analytics department can perform data analysis while considering the user's current living situation. For example, if a user is traveling with their family, the analysis can prioritize family-friendly tourist spots and activities. If a user is traveling for business, the analysis can prioritize business-oriented tourist spots and activities. Furthermore, if a user desires a trip tailored to their health condition, the analysis can prioritize tourist spots and activities that match that health condition. In this way, the analytics department can perform appropriate data analysis based on the user's living situation.

[0106] The generation unit can estimate the user's emotions and adjust how travel plans are suggested based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions, allowing the user to carefully consider their options. If the user is stressed, it can provide concise suggestions, enabling them to make a quick decision. If the user is excited, it can provide visually appealing suggestions, allowing them to consider their options while having fun. In this way, the generation unit can provide more appropriate suggestions by adjusting its suggestion method based on the user's emotions.

[0107] The adjustment unit can adjust travel plans by referring not only to the user's past travel history but also to the travel history of the user's friends and family. For example, it can adjust plans to include tourist spots visited by the user's friends, or to include activities preferred by the user's family. It can also adjust plans to exclude places avoided by the user's friends and family. In this way, the adjustment unit can create more personalized travel plans by taking into account the travel history of the user's friends and family.

[0108] The adjustment unit can estimate the user's emotions and determine the content of the travel plan to adjust based on those emotions. For example, if the user is relaxed, the plan can be adjusted to include relaxing sightseeing spots and activities. If the user is stressed, the plan can be adjusted to include sightseeing spots and activities that help relieve stress. If the user is excited, the plan can be adjusted to include adventure and exciting activities. In this way, the adjustment unit can provide a more appropriate travel plan by determining the content of the travel plan to adjust based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data such as the user's past travel history, social media posts, and search history. For example, it collects data such as tourist destinations the user has visited in the past, photos they have posted, and keywords they have searched for. This allows the data collection unit to understand the user's preferences. Step 2: The analytics department analyzes the data collected by the data collection department to understand individual preferences. For example, it can identify users who have a high interest in specific regions or themes and generate appropriate travel plans for those users. This allows the analytics department to provide personalized travel plans tailored to the user's needs. Step 3: Based on the analysis results obtained by the analysis unit, the generation unit combines the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. For example, it can generate plans that include off-the-beaten-path restaurants and experiential activities known only to locals. Furthermore, it takes into account weather forecasts and crowd conditions to propose the optimal schedule. This allows travelers to enjoy a comfortable trip. Step 4: The adjustment unit interacts with the user to fine-tune the details based on the travel plan generated by the generation unit. For example, specific tourist spots can be added or the order of visits can be changed. This allows the user to create a travel plan tailored to their preferences.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a travel plan based on the analysis results. The adjustment unit is implemented in the specific processing unit 46A of the smart device 14 and adjusts the details of the travel plan in an interactive manner with the user. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The generation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and generates a travel plan based on the analysis results. The adjustment unit is implemented, for example, in the control unit 46A of the smart glasses 214 and adjusts the details of the travel plan in an interactive manner with the user. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a travel plan based on the analysis results. The adjustment unit is implemented in the specific processing unit 46A of the headset terminal 314 and adjusts the details of the travel plan in an interactive manner with the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and adjustment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a travel plan based on the analysis results. The adjustment unit is implemented, for example, by the control unit 46A of the robot 414 and adjusts the details of the travel plan in an interactive manner with the user. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects data such as the user's past travel history, social media posts, and search history, The data collected by the aforementioned collection unit is analyzed by an analysis unit that understands individual preferences, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit combines data such as the latest tourism information, seasonal events, and hidden gems provided by local governments and local businesses to generate highly unique travel plans. The system includes an adjustment unit that allows the user to adjust details in an interactive manner based on the travel plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as the user's past travel history, social media posts, and search history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the collected data to understand user interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We combine the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We will suggest the optimal schedule taking into account the weather forecast and congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, The user can interact with the system to fine-tune the details of their travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past travel history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing data, referencing the user's past travel history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing data, different analytical algorithms are applied depending on the user's area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing data, prioritize the analysis based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing data, referencing relevant user literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The generating unit is When generating travel plans, we improve the accuracy of the generation process by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating travel plans, different generation algorithms are applied depending on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating travel plans, the system analyzes the user's social media activity to generate relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, The system estimates the user's emotions and adjusts the travel plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, When adjusting travel plans, the system selects the optimal adjustment method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When adjusting travel plans, customize the adjustment methods based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and prioritizes travel plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, When adjusting travel plans, the system selects the optimal adjustment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When adjusting travel plans, we analyze users' social media activity and suggest ways to make adjustments. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as the user's past travel history, social media posts, and search history, The data collected by the aforementioned collection unit is analyzed by an analysis unit that understands individual preferences, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit combines data such as the latest tourism information, seasonal events, and hidden gems provided by local governments and local businesses to generate highly unique travel plans. The system includes an adjustment unit that allows the user to adjust details in an interactive manner based on the travel plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as the user's past travel history, social media posts, and search history. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the collected data to understand user interests. The system according to feature 1.

4. The generating unit is We combine the latest tourism information, seasonal events, and hidden gems provided by local governments and businesses to generate highly unique travel plans. The system according to feature 1.

5. The generating unit is We will suggest the optimal schedule taking into account the weather forecast and congestion levels. The system according to feature 1.

6. The adjustment unit is, The user can interact with the system to fine-tune the details of their travel plan. The system according to feature 1.

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

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

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