system

Abstract

The system according to this embodiment aims to provide an optimal travel plan based on the user's preferences and lifestyle. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an optimization unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit to determine the user's preferences and lifestyle. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The optimization unit optimizes the travel plan generated by the generation unit in real time.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, travel plans based on user preferences and lifestyles have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal travel plan based on user preferences and lifestyles.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an optimization unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit to determine the user's preferences and lifestyle. The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. The optimization unit optimizes the travel plan generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal travel plan based on the user's preferences and lifestyle. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel planning system according to an embodiment of the present invention is a system that collects user behavior data and provides highly personalized travel plans using generative AI. The travel planning system collects user behavior data such as search history, booking history, and reviews, and the generative AI analyzes this data to generate an optimal travel plan that matches the user's preferences, lifestyle, and even mood at the time. Furthermore, it flexibly optimizes the plan even during travel based on real-time weather and event information, and responds immediately to unforeseen circumstances. This system provides the optimal travel experience for users with different needs, such as busy business people, luxury-oriented travelers, and digitally native young people. For example, it provides busy business people with efficient and fulfilling trips, luxury-oriented travelers with unique and special experiences, and digitally native young people with unique trips incorporating the latest trends. In this way, by improving the quality of the travel experience using generative AI, the aim is to provide new value to users and elevate travel from mere transportation to an experience that enriches life. As a result, the travel planning system can provide individually customized travel plans based on the user's behavior data.

[0029] The travel planning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an optimization unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, search history, booking history, and reviews. For example, the collection unit collects website browsing history. The collection unit can also collect booking history. Furthermore, the collection unit can also collect review data. For example, the collection unit records the URLs and viewing times of web pages visited by the user. Booking history includes information on accommodations and transportation booked by the user. Review data includes reviews and ratings posted by the user. The analysis unit analyzes the data collected by the collection unit to determine the user's preferences and lifestyle. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the user's search history to identify the user's interests. The analysis unit can also analyze booking history to understand the user's travel patterns. Furthermore, the analysis unit can analyze review data to understand the user's evaluation trends. For example, the analysis unit extracts keywords that users frequently search for and identifies their interests. Analysis of booking history helps understand travel patterns based on information about places and accommodations users have visited in the past. Analysis of review data extracts the characteristics of facilities and services that users have given high ratings to. The generation unit generates travel plans based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to this example. For example, the generation unit generates travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. The optimization unit optimizes the travel plan generated by the generation unit in real time.Optimization is performed, for example, based on real-time weather information and event information, but is not limited to such examples. For example, the optimization unit can acquire real-time weather information and adjust the travel plan. The optimization unit can also acquire real-time event information and adjust the travel plan. Furthermore, the optimization unit can propose alternative plans to deal with unforeseen circumstances. For example, the optimization unit can suggest indoor activities if the weather deteriorates. When acquiring event information, the travel plan is adjusted based on events held in the vicinity. When proposing alternative plans to deal with unforeseen circumstances, alternative means of dealing with delays or cancellations of transportation are suggested. As a result, the travel planning system according to the embodiment can provide individually customized travel plans based on the user's behavioral data. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can input real-time weather information and event information into an AI model and output an optimized travel plan.

[0030] The data collection unit collects user behavior data. This data includes, but is not limited to, search history, booking history, and reviews. For example, the data collection unit collects website browsing history. It can also collect booking history. Furthermore, it can collect review data. For example, the data collection unit records the URLs and browsing time of web pages visited by the user. Booking history includes information on accommodations and transportation booked by the user. Review data includes reviews and ratings posted by the user. To efficiently collect this data, the data collection unit obtains information from multiple data sources. For example, it can collect the user's website browsing history in real time through browser extensions and mobile apps. For booking history, it integrates with travel booking sites, airlines, and hotel reservation systems to automatically obtain user booking information. Review data can be collected from social media and review sites using scraping techniques. This allows the data collection unit to comprehensively collect user behavior data and create detailed profiles. Furthermore, the data collection unit implements encryption technology and access control to ensure data privacy and security, protecting users' personal information. This allows the data collection unit to efficiently collect necessary data while gaining the trust of users.

[0031] The analysis unit analyzes data collected by the data collection unit to determine user preferences and lifestyles. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze a user's search history to identify their interests. It can also analyze booking history to understand a user's travel patterns. Furthermore, it can analyze review data to understand user evaluation trends. For example, the analysis unit can extract keywords frequently searched by users to identify their interests. Analysis of booking history helps understand travel patterns based on information about places and accommodations the user has visited in the past. Analysis of review data extracts the characteristics of facilities and services that users have given high ratings to. The analysis unit integrates this data to comprehensively evaluate user preferences and lifestyles. For example, machine learning algorithms can be used to extract patterns from user behavior data, and clustering techniques can be used to identify user groups with similar preferences. Furthermore, natural language processing technology can be used to perform sentiment analysis on review data to gain a detailed understanding of user evaluation trends. This allows the analysis unit to accurately determine the user's preferences and lifestyle, and use this information to generate individually customized travel plans.

[0032] The generation unit generates travel plans based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit generates travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. The generation unit integrates multiple data sources to customize the travel plan based on the user's preferences and lifestyle. For example, it can refer to travel plans of other users with similar interests based on information about tourist destinations and accommodations the user has visited in the past. Furthermore, the generation unit can dynamically generate travel plans considering the user's current situation and real-time data. For example, it can suggest tourist destinations and events closest to the user's current location and optimize travel time and transportation methods. This allows the generation unit to provide users with the optimal travel plan and improve their travel satisfaction.

[0033] The optimization unit optimizes the travel plan generated by the generation unit in real time. Optimization is performed based on, for example, real-time weather and event information, but is not limited to such examples. For instance, the optimization unit can acquire real-time weather information and adjust the travel plan. It can also acquire real-time event information and adjust the travel plan. Furthermore, the optimization unit can propose alternative plans to cope with unforeseen circumstances. For example, it can suggest indoor activities if the weather deteriorates. When acquiring event information, it can adjust the travel plan based on events held nearby. When proposing alternative plans to cope with unforeseen circumstances, it can suggest alternative means of transportation to cope with delays or cancellations. The optimization unit utilizes AI technology to acquire this information in real time and dynamically adjust the travel plan. For example, it can input weather and event information into an AI model and output the optimal travel plan. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and satisfaction of the travel plan. For example, it can customize the next travel plan with greater accuracy based on feedback provided by the user during the trip. This allows the optimization unit to provide users with the best possible travel experience and maximize their travel satisfaction.

[0034] The data collection unit can collect data such as user search history, booking history, and reviews. For example, the data collection unit can collect user search history. For example, the data collection unit can record the keywords the user searched for and the date and time of the search. The data collection unit can also collect user booking history. For example, the data collection unit can record the accommodations the user booked and the date and time of the booking. Furthermore, the data collection unit can also collect user review data. For example, the data collection unit can record the rating and posting date of reviews posted by the user. This allows for more accurate analysis by collecting user behavior data from multiple perspectives. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user search history into an AI model and automatically collect relevant data.

[0035] The analysis unit can analyze collected data to determine user preferences and lifestyles. For example, the analysis unit can analyze collected data using statistical analysis or machine learning algorithms. For instance, the analysis unit can analyze a user's search history to identify their interests. It can also analyze booking history to understand a user's travel patterns. Furthermore, the analysis unit can analyze review data to understand user evaluation trends. For example, the analysis unit can extract keywords that users frequently search for to identify their interests. In the analysis of booking history, it can understand travel patterns based on information about places and accommodations the user has visited in the past. In the analysis of review data, it can extract the characteristics of facilities and services that users have given high ratings to. This makes it possible to provide travel plans based on user preferences and lifestyles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into an AI model to determine user preferences and lifestyles.

[0036] The generation unit can generate travel plans based on the analysis results. The generation unit can generate travel plans using, for example, a generation AI. For example, the generation unit can generate travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. This allows the generation of the optimal travel plan for the user based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the analysis results into an AI model to generate the optimal travel plan.

[0037] The optimization unit can optimize travel plans based on real-time weather and event information. For example, the optimization unit can acquire real-time weather information and adjust the travel plan. For example, the optimization unit can suggest indoor activities if the weather deteriorates. The optimization unit can also acquire real-time event information and adjust the travel plan. For example, the optimization unit can adjust the travel plan based on events held nearby. Furthermore, the optimization unit can also suggest alternative plans to deal with unforeseen circumstances. For example, the optimization unit can suggest alternative means to deal with delays or cancellations of public transportation. This allows for flexible optimization of travel plans based on real-time information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather and event information into an AI model and output an optimized travel plan.

[0038] The optimization unit can propose alternative plans to deal with unforeseen circumstances. For example, the optimization unit can propose alternative means to deal with delays or cancellations of transportation. For instance, if a flight is delayed, the optimization unit can propose alternative means of transportation. The optimization unit can also propose alternative accommodations if a hotel reservation is cancelled. Furthermore, the optimization unit can propose alternative activities to deal with sudden changes in weather. For example, if an outdoor activity is cancelled, the optimization unit can propose an alternative indoor activity. This makes it possible to provide travel plans that can flexibly respond to unforeseen circumstances. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input information about transportation delays into an AI model and output alternative means.

[0039] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on search keywords frequently used by the user in the past. For example, the data collection unit can analyze the user's past booking history and collect data with similar patterns. The data collection unit can also analyze the user's review posting trends and collect new data that might be of interest. For example, the data collection unit can collect data related to the user's next travel destination based on places the user has visited in the past. This allows for efficient data collection by selecting the optimal data collection method based on the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into an AI model and select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current travel plans and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the travel destination the user is currently planning. For example, the data collection unit can filter data based on the user's areas of interest (e.g., history, nature, food). The data collection unit can also collect data related to places the user has visited in the past to help with future travel planning. For example, the data collection unit can prioritize collecting tourist information related to the travel destination the user is currently planning. This allows for the collection of more relevant data by filtering data based on the user's current travel plans and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current travel plans and areas of interest into an AI model and perform 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 related to the user's current location. For example, the data collection unit can collect nearby event information based on the user's location information. The data collection unit can also collect information on nearby restaurants and accommodations based on the user's location information. For example, the data collection unit can prioritize the collection of tourist information related to the user's current location. By collecting data while considering the user's geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to travel destinations shared by the user on social media. For example, the data collection unit can collect data that the user's social media followers might be interested in. The data collection unit can also collect data related to posts that the user has "liked" on social media. For example, the data collection unit can collect data related to travel destinations shared by the user on social media. This allows for the efficient collection of 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 AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into an AI model and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data and provides it to the user. For example, the analysis unit performs a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on important data and provides it to the user. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into an AI model and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for travel destination information, the analysis unit can apply an algorithm that analyzes the popularity of the tourist destination. For example, for accommodation data, the analysis unit can apply an algorithm that analyzes the rating of the accommodation. The analysis unit can also apply an algorithm that analyzes the rating of the food for restaurant data. For example, for travel destination information, the analysis unit can apply an algorithm that analyzes the popularity of the tourist destination. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data category into an AI model and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and provide it to the user. For example, the analysis unit may perform analysis on older data as needed. The analysis unit can also determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data and provide it to the user. This allows for efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model to determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the user. For example, the analysis unit performs analysis on less relevant data as needed. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the user. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into an AI model and adjust the order of analysis.

[0047] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can suggest the next travel destination based on places the user has visited in the past. For example, the generation unit can generate a plan that includes the user's preferred activities from the user's past travel history. The generation unit can also analyze the user's past travel history and generate the most satisfying plan. For example, the generation unit can suggest the next travel destination based on places the user has visited in the past. In this way, by referring to the user's past travel history, it is possible to provide a more satisfying travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history into an AI model and generate the optimal plan.

[0048] The generation unit can customize travel plans based on the user's current lifestyle when generating them. For example, if the user is health-conscious, the generation unit will generate a plan that includes healthy activities. For example, if the user is luxury-oriented, the generation unit will generate a plan that includes high-end accommodations and restaurants. The generation unit can also generate a plan that includes adventurous activities if the user is adventure-oriented. For example, if the user is health-conscious, the generation unit will generate a plan that includes healthy activities. By customizing the plan based on the user's current lifestyle, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current lifestyle into an AI model to customize the plan.

[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 plan that includes nearby tourist attractions based on the user's current location. For example, the generation unit can generate a plan that includes the optimal mode of transportation based on the user's location information. The generation unit can also generate a plan that includes information on nearby events based on the user's location information. For example, the generation unit can generate a plan that includes nearby tourist attractions based on the user's current location. By generating a plan that considers the user's geographical location information, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into an AI model and generate an optimal plan.

[0050] The generation unit can analyze the user's social media activity and propose travel plans when generating them. For example, the generation unit can propose plans related to travel destinations that the user has shared on social media. For example, the generation unit can propose plans that the user's social media followers are likely to be interested in. The generation unit can also propose plans related to posts that the user has "liked" on social media. For example, the generation unit can propose plans related to travel destinations that the user has shared on social media. By analyzing the user's social media activity, it is possible to provide more relevant travel plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into an AI model and propose plans.

[0051] The optimization unit can adjust the plan by referring to real-time weather information during optimization. For example, in rainy weather, the optimization unit proposes a plan that prioritizes indoor activities. For example, in sunny weather, the optimization unit proposes a plan that prioritizes outdoor activities. The optimization unit can also propose a plan that includes less slippery routes on snowy days. For example, in rainy weather, the optimization unit proposes a plan that prioritizes indoor activities. By adjusting the plan by referring to real-time weather information, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather information into an AI model and adjust the plan.

[0052] The optimization unit can adjust the plan by referring to real-time event information during optimization. For example, the optimization unit proposes a plan that includes events based on information about events currently taking place. For example, the optimization unit proposes a plan that includes events based on information about events taking place in the vicinity. The optimization unit can also propose a plan that includes events based on event information related to the user's interests. For example, the optimization unit proposes a plan that includes events based on information about events currently taking place. This allows for the provision of more appropriate travel plans by adjusting the plan by referring to real-time event information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time event information into an AI model and adjust the plan.

[0053] The optimization unit can propose an optimal plan by considering the user's geographical location information during optimization. For example, the optimization unit can propose a plan that includes nearby tourist attractions based on the user's current location. For example, the optimization unit can propose a plan that includes the optimal mode of transportation based on the user's location information. The optimization unit can also propose a plan that includes information on nearby events based on the user's location information. For example, the optimization unit can propose a plan that includes nearby tourist attractions based on the user's current location. By considering the user's geographical location information when proposing a plan, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's geographical location information into an AI model and propose an optimal plan.

[0054] The optimization unit can analyze the user's social media activity and adjust the plan during the optimization process. For example, the optimization unit can suggest a plan related to a travel destination shared by the user on social media. For example, the optimization unit can suggest a plan that the user's social media followers are likely to be interested in. The optimization unit can also suggest a plan related to a post that the user has "liked" on social media. For example, the optimization unit can suggest a plan related to a travel destination shared by the user on social media. By analyzing the user's social media activity, it is possible to provide a more relevant travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's social media activity into an AI model and adjust the plan.

[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] A travel planning system can analyze a user's past travel history and suggest their next travel destination. For example, it can suggest a destination based on information about places and accommodations the user has visited in the past. It can also generate a plan that includes the user's preferred activities based on their past travel history. Furthermore, it can analyze the user's past travel history and generate the most satisfying plan. This allows the system to provide a more satisfying travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's past travel history into an AI model and generate the optimal plan.

[0057] The travel planning system can customize plans based on the user's current lifestyle. For example, if the user is health-conscious, it can generate a plan that includes healthy activities. If the user is luxury-oriented, it can generate a plan that includes high-end accommodations and restaurants. Furthermore, if the user is adventure-oriented, it can generate a plan that includes adventurous activities. By customizing the plan based on the user's current lifestyle, it can provide a more suitable travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's current lifestyle into an AI model to customize the plan.

[0058] A travel planning system can generate an optimal plan by considering the user's geographical location. For example, it can generate a plan that includes nearby tourist attractions based on the user's current location. It can also generate a plan that includes the optimal mode of transportation based on the user's location. Furthermore, it can generate a plan that includes information on nearby events based on the user's location. By generating a plan that considers the user's geographical location, it can provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location into an AI model to generate an optimal plan.

[0059] A travel planning system can analyze a user's social media activity and propose travel plans. For example, it can suggest plans related to destinations the user has shared on social media. It can also suggest plans that the user's social media followers might be interested in. Furthermore, it can suggest plans related to posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it can provide more relevant travel plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into an AI model and propose plans.

[0060] The travel planning system can adjust its plans by referring to real-time weather information during optimization. For example, in rainy weather, it can suggest a plan that prioritizes indoor activities. In sunny weather, it can suggest a plan that prioritizes outdoor activities. Furthermore, on snowy days, it can suggest a plan that includes less slippery routes. By adjusting the plan by referring to real-time weather information, it can provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather information into an AI model and adjust the plan.

[0061] The travel planning system can adjust its plans by referencing real-time event information during optimization. For example, it can suggest plans that include events based on currently ongoing events. It can also suggest plans that include events based on nearby events. Furthermore, it can suggest plans that include events based on events related to the user's interests. This allows the system to provide more appropriate travel plans by adjusting them by referencing real-time event information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time event information into an AI model and adjust the plan.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user behavior data. User behavior data includes, for example, search history, booking history, and reviews. The data collection unit collects website browsing history, booking history, and review data. For example, it records the URLs of web pages visited by the user and the time spent browsing them, and collects information on booked accommodations and transportation, as well as reviews and ratings posted. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the user's preferences and lifestyle. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the user's search history to identify their interests, analyzes their booking history to understand their travel patterns, and analyzes review data to understand their evaluation trends. Step 3: The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. Generation is performed using generation AI. For example, text generation AI (e.g., LLM) or multimodal generation AI is used to generate a travel plan tailored to the user's preferences and lifestyle. This allows the system to suggest tourist destinations and activities based on the user's interests. Step 4: The optimization unit optimizes the travel plan generated by the generation unit in real time. Optimization is performed based on real-time weather and event information. For example, it suggests indoor activities if the weather deteriorates, adjusts the travel plan based on nearby events, and suggests alternative plans to deal with unforeseen circumstances.

[0064] (Example of form 2) The travel planning system according to an embodiment of the present invention is a system that collects user behavior data and provides highly personalized travel plans using generative AI. The travel planning system collects user behavior data such as search history, booking history, and reviews, and the generative AI analyzes this data to generate an optimal travel plan that matches the user's preferences, lifestyle, and even mood at the time. Furthermore, it flexibly optimizes the plan even during travel based on real-time weather and event information, and responds immediately to unforeseen circumstances. This system provides the optimal travel experience for users with different needs, such as busy business people, luxury-oriented travelers, and digitally native young people. For example, it provides busy business people with efficient and fulfilling trips, luxury-oriented travelers with unique and special experiences, and digitally native young people with unique trips incorporating the latest trends. In this way, by improving the quality of the travel experience using generative AI, the aim is to provide new value to users and elevate travel from mere transportation to an experience that enriches life. As a result, the travel planning system can provide individually customized travel plans based on the user's behavior data.

[0065] The travel planning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an optimization unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, search history, booking history, and reviews. For example, the collection unit collects website browsing history. The collection unit can also collect booking history. Furthermore, the collection unit can also collect review data. For example, the collection unit records the URLs and viewing times of web pages visited by the user. Booking history includes information on accommodations and transportation booked by the user. Review data includes reviews and ratings posted by the user. The analysis unit analyzes the data collected by the collection unit to determine the user's preferences and lifestyle. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the user's search history to identify the user's interests. The analysis unit can also analyze booking history to understand the user's travel patterns. Furthermore, the analysis unit can analyze review data to understand the user's evaluation trends. For example, the analysis unit extracts keywords that users frequently search for and identifies their interests. Analysis of booking history helps understand travel patterns based on information about places and accommodations users have visited in the past. Analysis of review data extracts the characteristics of facilities and services that users have given high ratings to. The generation unit generates travel plans based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to this example. For example, the generation unit generates travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. The optimization unit optimizes the travel plan generated by the generation unit in real time.Optimization is performed, for example, based on real-time weather information and event information, but is not limited to such examples. For example, the optimization unit can acquire real-time weather information and adjust the travel plan. The optimization unit can also acquire real-time event information and adjust the travel plan. Furthermore, the optimization unit can propose alternative plans to deal with unforeseen circumstances. For example, the optimization unit can suggest indoor activities if the weather deteriorates. When acquiring event information, the travel plan is adjusted based on events held in the vicinity. When proposing alternative plans to deal with unforeseen circumstances, alternative means of dealing with delays or cancellations of transportation are suggested. As a result, the travel planning system according to the embodiment can provide individually customized travel plans based on the user's behavioral data. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can input real-time weather information and event information into an AI model and output an optimized travel plan.

[0066] The data collection unit collects user behavior data. This data includes, but is not limited to, search history, booking history, and reviews. For example, the data collection unit collects website browsing history. It can also collect booking history. Furthermore, it can collect review data. For example, the data collection unit records the URLs and browsing time of web pages visited by the user. Booking history includes information on accommodations and transportation booked by the user. Review data includes reviews and ratings posted by the user. To efficiently collect this data, the data collection unit obtains information from multiple data sources. For example, it can collect the user's website browsing history in real time through browser extensions and mobile apps. For booking history, it integrates with travel booking sites, airlines, and hotel reservation systems to automatically obtain user booking information. Review data can be collected from social media and review sites using scraping techniques. This allows the data collection unit to comprehensively collect user behavior data and create detailed profiles. Furthermore, the data collection unit implements encryption technology and access control to ensure data privacy and security, protecting users' personal information. This allows the data collection unit to efficiently collect necessary data while gaining the trust of users.

[0067] The analysis unit analyzes data collected by the data collection unit to determine user preferences and lifestyles. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze a user's search history to identify their interests. It can also analyze booking history to understand a user's travel patterns. Furthermore, it can analyze review data to understand user evaluation trends. For example, the analysis unit can extract keywords frequently searched by users to identify their interests. Analysis of booking history helps understand travel patterns based on information about places and accommodations the user has visited in the past. Analysis of review data extracts the characteristics of facilities and services that users have given high ratings to. The analysis unit integrates this data to comprehensively evaluate user preferences and lifestyles. For example, machine learning algorithms can be used to extract patterns from user behavior data, and clustering techniques can be used to identify user groups with similar preferences. Furthermore, natural language processing technology can be used to perform sentiment analysis on review data to gain a detailed understanding of user evaluation trends. This allows the analysis unit to accurately determine the user's preferences and lifestyle, and use this information to generate individually customized travel plans.

[0068] The generation unit generates travel plans based on the analysis results obtained by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit generates travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. The generation unit integrates multiple data sources to customize the travel plan based on the user's preferences and lifestyle. For example, it can refer to travel plans of other users with similar interests based on information about tourist destinations and accommodations the user has visited in the past. Furthermore, the generation unit can dynamically generate travel plans considering the user's current situation and real-time data. For example, it can suggest tourist destinations and events closest to the user's current location and optimize travel time and transportation methods. This allows the generation unit to provide users with the optimal travel plan and improve their travel satisfaction.

[0069] The optimization unit optimizes the travel plan generated by the generation unit in real time. Optimization is performed based on, for example, real-time weather and event information, but is not limited to such examples. For instance, the optimization unit can acquire real-time weather information and adjust the travel plan. It can also acquire real-time event information and adjust the travel plan. Furthermore, the optimization unit can propose alternative plans to cope with unforeseen circumstances. For example, it can suggest indoor activities if the weather deteriorates. When acquiring event information, it can adjust the travel plan based on events held nearby. When proposing alternative plans to cope with unforeseen circumstances, it can suggest alternative means of transportation to cope with delays or cancellations. The optimization unit utilizes AI technology to acquire this information in real time and dynamically adjust the travel plan. For example, it can input weather and event information into an AI model and output the optimal travel plan. Furthermore, the optimization unit can collect user feedback and continuously improve the accuracy and satisfaction of the travel plan. For example, it can customize the next travel plan with greater accuracy based on feedback provided by the user during the trip. This allows the optimization unit to provide users with the best possible travel experience and maximize their travel satisfaction.

[0070] The data collection unit can collect data such as user search history, booking history, and reviews. For example, the data collection unit can collect user search history. For example, the data collection unit can record the keywords the user searched for and the date and time of the search. The data collection unit can also collect user booking history. For example, the data collection unit can record the accommodations the user booked and the date and time of the booking. Furthermore, the data collection unit can also collect user review data. For example, the data collection unit can record the rating and posting date of reviews posted by the user. This allows for more accurate analysis by collecting user behavior data from multiple perspectives. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user search history into an AI model and automatically collect relevant data.

[0071] The analysis unit can analyze collected data to determine user preferences and lifestyles. For example, the analysis unit can analyze collected data using statistical analysis or machine learning algorithms. For instance, the analysis unit can analyze a user's search history to identify their interests. It can also analyze booking history to understand a user's travel patterns. Furthermore, the analysis unit can analyze review data to understand user evaluation trends. For example, the analysis unit can extract keywords that users frequently search for to identify their interests. In the analysis of booking history, it can understand travel patterns based on information about places and accommodations the user has visited in the past. In the analysis of review data, it can extract the characteristics of facilities and services that users have given high ratings to. This makes it possible to provide travel plans based on user preferences and lifestyles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into an AI model to determine user preferences and lifestyles.

[0072] The generation unit can generate travel plans based on the analysis results. The generation unit can generate travel plans using, for example, a generation AI. For example, the generation unit can generate travel plans using a text generation AI (e.g., LLM). The generation unit can also generate travel plans using a multimodal generation AI. Furthermore, the generation unit can use a generation AI to generate travel plans tailored to the user's preferences and lifestyle. For example, the generation unit suggests tourist destinations and activities based on the user's interests. A multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. The generation AI generates the optimal travel plan based on the user's preferences and lifestyle. This allows the generation of the optimal travel plan for the user based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the analysis results into an AI model to generate the optimal travel plan.

[0073] The optimization unit can optimize travel plans based on real-time weather and event information. For example, the optimization unit can acquire real-time weather information and adjust the travel plan. For example, the optimization unit can suggest indoor activities if the weather deteriorates. The optimization unit can also acquire real-time event information and adjust the travel plan. For example, the optimization unit can adjust the travel plan based on events held nearby. Furthermore, the optimization unit can also suggest alternative plans to deal with unforeseen circumstances. For example, the optimization unit can suggest alternative means to deal with delays or cancellations of public transportation. This allows for flexible optimization of travel plans based on real-time information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather and event information into an AI model and output an optimized travel plan.

[0074] The optimization unit can propose alternative plans to deal with unforeseen circumstances. For example, the optimization unit can propose alternative means to deal with delays or cancellations of transportation. For instance, if a flight is delayed, the optimization unit can propose alternative means of transportation. The optimization unit can also propose alternative accommodations if a hotel reservation is cancelled. Furthermore, the optimization unit can propose alternative activities to deal with sudden changes in weather. For example, if an outdoor activity is cancelled, the optimization unit can propose an alternative indoor activity. This makes it possible to provide travel plans that can flexibly respond to unforeseen circumstances. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input information about transportation delays into an AI model and output alternative means.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can collect detailed data. The data collection unit can also collect data immediately if the user is excited, prioritizing real-time reactions. Furthermore, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. For example, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. By adjusting the timing of data collection according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI model and adjust the timing of data collection.

[0076] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant data based on search keywords frequently used by the user in the past. For example, the data collection unit can analyze the user's past booking history and collect data with similar patterns. The data collection unit can also analyze the user's review posting trends and collect new data that might be of interest. For example, the data collection unit can collect data related to the user's next travel destination based on places the user has visited in the past. This allows for efficient data collection by selecting the optimal data collection method based on the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into an AI model and select the optimal data collection method.

[0077] The data collection unit can filter data based on the user's current travel plans and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the travel destination the user is currently planning. For example, the data collection unit can filter data based on the user's areas of interest (e.g., history, nature, food). The data collection unit can also collect data related to places the user has visited in the past to help with future travel planning. For example, the data collection unit can prioritize collecting tourist information related to the travel destination the user is currently planning. This allows for the collection of more relevant data by filtering data based on the user's current travel plans and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current travel plans and areas of interest into an AI model and perform filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting important data. The data collection unit can also prioritize collecting interesting data if the user is excited. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. This allows for the collection of more appropriate data by prioritizing the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI model to determine the priority of data to collect.

[0079] 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 related to the user's current location. For example, the data collection unit can collect nearby event information based on the user's location information. The data collection unit can also collect information on nearby restaurants and accommodations based on the user's location information. For example, the data collection unit can prioritize the collection of tourist information related to the user's current location. By collecting data while considering the user's geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to travel destinations shared by the user on social media. For example, the data collection unit can collect data that the user's social media followers might be interested in. The data collection unit can also collect data related to posts that the user has "liked" on social media. For example, the data collection unit can collect data related to travel destinations shared by the user on social media. This allows for the efficient collection of 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 AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into an AI model and collect relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. The analysis unit can also provide visually appealing analysis results if the user is excited. For example, if the user is relaxed, the analysis unit provides detailed analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI model and adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data and provides it to the user. For example, the analysis unit performs a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on important data and provides it to the user. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into an AI model and adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for travel destination information, the analysis unit can apply an algorithm that analyzes the popularity of the tourist destination. For example, for accommodation data, the analysis unit can apply an algorithm that analyzes the rating of the accommodation. The analysis unit can also apply an algorithm that analyzes the rating of the food for restaurant data. For example, for travel destination information, the analysis unit can apply an algorithm that analyzes the popularity of the tourist destination. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data category into an AI model and apply an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI model and adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and provide it to the user. For example, the analysis unit may perform analysis on older data as needed. The analysis unit can also determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data and provide it to the user. This allows for efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model to determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the user. For example, the analysis unit performs analysis on less relevant data as needed. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data and provides it to the user. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into an AI model and adjust the order of analysis.

[0087] The generation unit can estimate the user's emotions and adjust the way the travel plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed travel plan. For example, if the user is in a hurry, the generation unit can provide a concise travel plan that gets straight to the point. The generation unit can also provide a visually appealing travel plan if the user is excited. For example, if the user is relaxed, the generation unit can provide a detailed travel plan. This allows for the provision of a more appropriate travel plan by adjusting the way the travel plan is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI model and adjust the way the travel plan is presented.

[0088] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can suggest the next travel destination based on places the user has visited in the past. For example, the generation unit can generate a plan that includes the user's preferred activities from the user's past travel history. The generation unit can also analyze the user's past travel history and generate the most satisfying plan. For example, the generation unit can suggest the next travel destination based on places the user has visited in the past. In this way, by referring to the user's past travel history, it is possible to provide a more satisfying travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history into an AI model and generate the optimal plan.

[0089] The generation unit can customize travel plans based on the user's current lifestyle when generating them. For example, if the user is health-conscious, the generation unit will generate a plan that includes healthy activities. For example, if the user is luxury-oriented, the generation unit will generate a plan that includes high-end accommodations and restaurants. The generation unit can also generate a plan that includes adventurous activities if the user is adventure-oriented. For example, if the user is health-conscious, the generation unit will generate a plan that includes healthy activities. By customizing the plan based on the user's current lifestyle, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current lifestyle into an AI model to customize the plan.

[0090] The generation unit can estimate the user's emotions and prioritize travel plans based on those emotions. For example, if the user is relaxed, the generation unit will prioritize providing a detailed travel plan. For example, if the user is in a hurry, the generation unit will prioritize providing a concise travel plan that gets straight to the point. The generation unit can also prioritize providing a visually appealing travel plan if the user is excited. For example, if the user is relaxed, the generation unit will prioritize providing a detailed travel plan. This allows for the provision of more appropriate travel plans by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI model to determine the priority of travel plans.

[0091] 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 plan that includes nearby tourist attractions based on the user's current location. For example, the generation unit can generate a plan that includes the optimal mode of transportation based on the user's location information. The generation unit can also generate a plan that includes information on nearby events based on the user's location information. For example, the generation unit can generate a plan that includes nearby tourist attractions based on the user's current location. By generating a plan that considers the user's geographical location information, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into an AI model and generate an optimal plan.

[0092] The generation unit can analyze the user's social media activity and propose travel plans when generating them. For example, the generation unit can propose plans related to travel destinations that the user has shared on social media. For example, the generation unit can propose plans that the user's social media followers are likely to be interested in. The generation unit can also propose plans related to posts that the user has "liked" on social media. For example, the generation unit can propose plans related to travel destinations that the user has shared on social media. By analyzing the user's social media activity, it is possible to provide more relevant travel plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into an AI model and propose plans.

[0093] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is relaxed, the optimization unit performs detailed optimization. For example, if the user is in a hurry, the optimization unit performs concise optimization that gets straight to the point. The optimization unit can also perform visually appealing optimization if the user is excited. For example, if the user is relaxed, the optimization unit performs detailed optimization. By adjusting the optimization method according to the user's emotions, a more appropriate travel plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into an AI model and adjust the optimization method.

[0094] The optimization unit can adjust the plan by referring to real-time weather information during optimization. For example, in rainy weather, the optimization unit proposes a plan that prioritizes indoor activities. For example, in sunny weather, the optimization unit proposes a plan that prioritizes outdoor activities. The optimization unit can also propose a plan that includes less slippery routes on snowy days. For example, in rainy weather, the optimization unit proposes a plan that prioritizes indoor activities. By adjusting the plan by referring to real-time weather information, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather information into an AI model and adjust the plan.

[0095] The optimization unit can adjust the plan by referring to real-time event information during optimization. For example, the optimization unit proposes a plan that includes events based on information about events currently taking place. For example, the optimization unit proposes a plan that includes events based on information about events taking place in the vicinity. The optimization unit can also propose a plan that includes events based on event information related to the user's interests. For example, the optimization unit proposes a plan that includes events based on information about events currently taking place. This allows for the provision of more appropriate travel plans by adjusting the plan by referring to real-time event information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time event information into an AI model and adjust the plan.

[0096] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is relaxed, the optimization unit may prioritize detailed optimization. For example, if the user is in a hurry, the optimization unit may prioritize concise optimization that gets straight to the point. The optimization unit may also prioritize visually appealing optimization if the user is excited. For example, if the user is relaxed, the optimization unit may prioritize detailed optimization. By determining optimization priorities according to the user's emotions, a more appropriate travel plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into an AI model to determine optimization priorities.

[0097] The optimization unit can propose an optimal plan by considering the user's geographical location information during optimization. For example, the optimization unit can propose a plan that includes nearby tourist attractions based on the user's current location. For example, the optimization unit can propose a plan that includes the optimal mode of transportation based on the user's location information. The optimization unit can also propose a plan that includes information on nearby events based on the user's location information. For example, the optimization unit can propose a plan that includes nearby tourist attractions based on the user's current location. By considering the user's geographical location information when proposing a plan, it is possible to provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's geographical location information into an AI model and propose an optimal plan.

[0098] The optimization unit can analyze the user's social media activity and adjust the plan during the optimization process. For example, the optimization unit can suggest a plan related to a travel destination shared by the user on social media. For example, the optimization unit can suggest a plan that the user's social media followers are likely to be interested in. The optimization unit can also suggest a plan related to a post that the user has "liked" on social media. For example, the optimization unit can suggest a plan related to a travel destination shared by the user on social media. By analyzing the user's social media activity, it is possible to provide a more relevant travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the user's social media activity into an AI model and adjust the plan.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] A travel planning system can estimate a user's emotions and adjust travel plan suggestions based on those emotions. For example, if a user is stressed, it can suggest relaxing destinations and activities. If a user is excited, it can suggest adventurous activities and exciting events. Furthermore, if a user is tired, it can suggest plans that include relaxing accommodations and spas. This allows the system to provide the optimal travel plan tailored to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into an AI model and adjust travel plan suggestions.

[0101] A travel planning system can analyze a user's past travel history and suggest their next travel destination. For example, it can suggest a destination based on information about places and accommodations the user has visited in the past. It can also generate a plan that includes the user's preferred activities based on their past travel history. Furthermore, it can analyze the user's past travel history and generate the most satisfying plan. This allows the system to provide a more satisfying travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's past travel history into an AI model and generate the optimal plan.

[0102] The travel planning system can customize plans based on the user's current lifestyle. For example, if the user is health-conscious, it can generate a plan that includes healthy activities. If the user is luxury-oriented, it can generate a plan that includes high-end accommodations and restaurants. Furthermore, if the user is adventure-oriented, it can generate a plan that includes adventurous activities. By customizing the plan based on the user's current lifestyle, it can provide a more suitable travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's current lifestyle into an AI model to customize the plan.

[0103] A travel planning system can estimate a user's emotions and prioritize travel plans based on those emotions. For example, if the user is relaxed, a detailed travel plan may be prioritized. If the user is in a hurry, a concise travel plan focusing on the essentials may be prioritized. Furthermore, if the user is excited, a visually appealing travel plan may be prioritized. This allows for the provision of more appropriate travel plans by prioritizing them according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI model to determine the priority of travel plans.

[0104] A travel planning system can generate an optimal plan by considering the user's geographical location. For example, it can generate a plan that includes nearby tourist attractions based on the user's current location. It can also generate a plan that includes the optimal mode of transportation based on the user's location. Furthermore, it can generate a plan that includes information on nearby events based on the user's location. By generating a plan that considers the user's geographical location, it can provide a more appropriate travel plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location into an AI model to generate an optimal plan.

[0105] A travel planning system can analyze a user's social media activity and propose travel plans. For example, it can suggest plans related to destinations the user has shared on social media. It can also suggest plans that the user's social media followers might be interested in. Furthermore, it can suggest plans related to posts the user has "liked" on social media. In this way, by analyzing the user's social media activity, it can provide more relevant travel plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into an AI model and propose plans.

[0106] The travel planning system can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is relaxed, it can perform detailed optimization. If the user is in a hurry, it can perform concise optimization that gets straight to the point. Furthermore, if the user is excited, it can perform visually appealing optimization. In this way, by adjusting the optimization method according to the user's emotions, a more appropriate travel plan can be provided. Emotion estimation is achieved using, for example, 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 optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into an AI model and adjust the optimization method.

[0107] The travel planning system can adjust its plans by referring to real-time weather information during optimization. For example, in rainy weather, it can suggest a plan that prioritizes indoor activities. In sunny weather, it can suggest a plan that prioritizes outdoor activities. Furthermore, on snowy days, it can suggest a plan that includes less slippery routes. By adjusting the plan by referring to real-time weather information, it can provide a more appropriate travel plan. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time weather information into an AI model and adjust the plan.

[0108] The travel planning system can adjust its plans by referencing real-time event information during optimization. For example, it can suggest plans that include events based on currently ongoing events. It can also suggest plans that include events based on nearby events. Furthermore, it can suggest plans that include events based on events related to the user's interests. This allows the system to provide more appropriate travel plans by adjusting them by referencing real-time event information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input real-time event information into an AI model and adjust the plan.

[0109] A travel planning system can estimate a user's emotions and determine optimization priorities based on those emotions. For example, if the user is relaxed, detailed optimizations may be prioritized. If the user is in a hurry, concise optimizations focusing on the essentials may be prioritized. Furthermore, if the user is excited, visually appealing optimizations may be prioritized. This allows the system to provide a more appropriate travel plan by prioritizing optimizations according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into an AI model to determine optimization priorities.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user behavior data. User behavior data includes, for example, search history, booking history, and reviews. The data collection unit collects website browsing history, booking history, and review data. For example, it records the URLs of web pages visited by the user and the time spent browsing them, and collects information on booked accommodations and transportation, as well as reviews and ratings posted. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the user's preferences and lifestyle. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the user's search history to identify their interests, analyzes their booking history to understand their travel patterns, and analyzes review data to understand their evaluation trends. Step 3: The generation unit generates a travel plan based on the analysis results obtained by the analysis unit. Generation is performed using generation AI. For example, text generation AI (e.g., LLM) or multimodal generation AI is used to generate a travel plan tailored to the user's preferences and lifestyle. This allows the system to suggest tourist destinations and activities based on the user's interests. Step 4: The optimization unit optimizes the travel plan generated by the generation unit in real time. Optimization is performed based on real-time weather and event information. For example, it suggests indoor activities if the weather deteriorates, adjusts the travel plan based on nearby events, and suggests alternative plans to deal with unforeseen circumstances.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and optimization 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 behavior data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel plan based on real-time weather information and event information. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using a generation AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel plan based on real-time weather information and event information. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and optimization 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 behavior data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using a generation AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel plan based on real-time weather information and event information. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In 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.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using a generation AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel plan based on real-time weather information and event information. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's preferences and lifestyle, A generation unit that generates a travel plan based on the analysis results obtained by the analysis unit, The system includes an optimization unit that optimizes the travel plan generated by the generation unit in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as users' search history, booking history, and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine the user's preferences and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a travel plan based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, Optimize your travel plans based on real-time weather and event information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We propose alternative plans to deal with unforeseen circumstances. 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 the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data 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 travel plans and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a travel plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a travel plan, customize the plan based on the user's current lifestyle. 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 prioritizes travel plans 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 suggest plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, the plan is adjusted by referring to real-time weather information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, During optimization, the plan is adjusted by referring to real-time event information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, the system proposes the optimal plan while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, During optimization, we analyze users' social media activity and adjust the plan accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's preferences and lifestyle, A generation unit that generates a travel plan based on the analysis results obtained by the analysis unit, The system includes an optimization unit that optimizes the travel plan generated by the generation unit in real time. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as users' search history, booking history, and reviews. The system according to feature 1.

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

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

5. The optimization unit, Optimize your travel plans based on real-time weather and event information. The system according to feature 1.

6. The optimization unit, We propose alternative plans to deal with unforeseen circumstances. 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 the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

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