system

Abstract

The system according to this embodiment aims to generate and provide an optimal wellness travel plan based on the user's health data and preferences. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data and preferences. The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The provision unit provides the plan generated by the generation unit.

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 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an optimal wellness travel plan based on a user's health data and preferences has not been sufficiently generated, and there is room for improvement.

[0005] The system according to an embodiment aims to generate and provide an optimal wellness travel plan based on a user's health data and preferences.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects a user's health data and preferences. The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The provision unit provides the plan generated by the generation unit. [Effects of the Invention]

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

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An AI agent system according to an embodiment of the present invention is a system that analyzes a user's health data and preferences and proposes an optimal wellness travel plan. This AI agent system collects the user's health data and preferences, and the AI ​​analyzes them to generate an optimal wellness travel plan. The generated plan is provided to the user via high-speed communication technology, enabling real-time health management. For example, the AI ​​agent system collects the user's health data and preferences. Health data includes the user's weight, blood pressure, heart rate, exercise history, etc., while preferences include travel destination preferences, activity preferences, and food preferences. This data is either entered by the user or automatically collected from a wearable device. Next, the AI ​​agent system analyzes the collected data. Based on the user's health data and preferences, the AI ​​generates an optimal wellness travel plan. For example, if the user is seeking relaxation, the AI ​​suggests a hot spring resort or spa resort. For users who prefer active travel, the AI ​​suggests a plan that includes activities such as hiking and cycling. The generated wellness travel plan is provided to the user via high-speed communication technology. This allows the user to receive real-time health management wherever they are. For example, if a user's health changes during a trip, the AI ​​adjusts the plan accordingly and notifies the user. This system allows users to enjoy their trip while maintaining their health. Improved health rates, increased travel satisfaction, and enhanced health literacy are expected. Furthermore, it can provide data-driven, personalized services that meet the needs of health-conscious travelers. In addition, the combination of AI and healthcare technology, along with high-speed communication technology, enables technological innovation and creativity through new service models. The target audience is health-conscious individuals aged 30 to 60, addressing the challenge of a lack of ways to enjoy travel while maintaining health. This AI agent generates optimal wellness travel plans based on health data and personal preferences, enabling users to enjoy both health and travel. The wellness travel market is estimated to be worth approximately 7 trillion yen globally, and market growth is expected due to the combination of increasing health awareness and travel needs.The increased awareness of health and wellness in the post-pandemic era, coupled with the recovery of the travel industry, provides a favorable opportunity for market entry at this time. By combining health and travel, the aim is to propose a richer, more active lifestyle and improve people's quality of life. This allows the AI ​​agent system to provide optimal wellness travel plans based on the user's health data and preferences.

[0029] The AI ​​agent system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data and preferences. The user's health data includes, but is not limited to, heart rate, blood pressure, body temperature, and exercise history. The collection unit can collect health data using, for example, a wearable device. The collection unit can also collect data entered by the user. For example, the collection unit collects health data entered by the user through a smartphone application. Furthermore, the collection unit collects the user's preferences. The user's preferences include, for example, preferences for travel destinations, activities, and food. The collection unit can collect user preferences using, for example, a questionnaire. The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit analyzes the data using, for example, a machine learning algorithm. For example, the generation unit analyzes the user's health data and preferences using a neural network and generates an optimal plan. The generation unit can also analyze the data using a support vector machine. Furthermore, the generation unit can also analyze the data using a clustering algorithm. For example, the generation unit clusters the user's health data and preferences and generates an optimal plan for each cluster. The delivery unit provides the plans generated by the generation unit. The delivery unit provides the plans to the user using, for example, high-speed communication technology. For example, the delivery unit provides the plans in real time using 5G communication. The delivery unit can also provide plans using fiber optic communication. Furthermore, the delivery unit can provide plans using satellite communication. For example, the delivery unit provides plans to users in remote locations using satellite communication. This allows the AI ​​agent system according to the embodiment to provide an optimal wellness travel plan based on the user's health data and preferences. Some or all of the processing described above in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the plans generated by the generation unit into an AI model and have the AI ​​perform the plan delivery.

[0030] The data collection unit collects user health data and preferences. User health data includes, but is not limited to, heart rate, blood pressure, body temperature, and exercise history. The data collection unit can collect health data using, for example, wearable devices. Specifically, wearable devices such as smartwatches and fitness trackers monitor the user's heart rate, blood pressure, and body temperature in real time and transmit this data to the data collection unit. This allows for continuous monitoring of the user's health status. The data collection unit can also collect data entered by the user. For example, the data collection unit collects health data entered by the user through a smartphone app. The user uses the app to enter their daily weight, diet, exercise level, etc., and this data is transmitted to the data collection unit. Furthermore, the data collection unit collects user preferences. User preferences include, but are not limited to, travel destination preferences, activity preferences, and food preferences. The data collection unit can collect user preferences using, for example, questionnaires. Questionnaires are provided through smartphone apps or websites, and users provide information to the data collection unit by selecting their preferences and interests from a list of options. This allows the data collection unit to comprehensively collect user health data and preferences, securing the foundational data needed to provide personalized wellness travel plans. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the generation and delivery units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit analyzes the data using, for example, machine learning algorithms. Specifically, it uses a neural network to analyze the user's health data and preferences and generate an optimal plan. The neural network learns complex patterns using multi-layered artificial neurons and proposes an optimal travel plan based on the user's health status and preferences. The generation unit can also analyze data using a support vector machine. A support vector machine is an algorithm excellent for data classification and regression analysis, extracting key features for generating an optimal travel plan based on the user's health data and preferences. Furthermore, the generation unit can analyze data using clustering algorithms. For example, it clusters the user's health data and preferences and generates an optimal plan for each cluster. Clustering algorithms group data with similar characteristics and propose an optimal travel plan for each group. This allows the generation unit to provide customized travel plans tailored to the user's individual needs. By combining these algorithms, the generation unit can achieve more accurate analysis and plan generation. For example, by combining neural networks and clustering algorithms, the system can analyze users' health data and preferences in more detail to generate optimal travel plans. Furthermore, the generation unit can consider past data and trends to propose plans that address future needs. This allows the generation unit to consistently provide users with the most up-to-date and optimal wellness travel plans.

[0032] The service provider provides the plan generated by the generation unit. The service provider provides the plan to the user using, for example, high-speed communication technology. Specifically, the service provider provides the plan in real time using 5G communication. 5G communication enables high-speed and low-latency communication, allowing users to receive the plan quickly wherever they are. The service provider can also provide the plan using fiber optic communication. Fiber optic communication provides high-speed and stable communication and can transmit large amounts of data quickly. Furthermore, the service provider can also provide the plan using satellite communication. For example, the service provider provides the plan to a user in a remote location using satellite communication. Satellite communication enables communication even in areas where ground communication infrastructure is not well-developed, allowing users to receive the plan wherever they are. As a result, the AI ​​agent system according to the embodiment can provide an optimal wellness travel plan based on the user's health data and preferences. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the plan generated by the generation unit into an AI model and have the AI ​​perform the plan provision. The AI ​​model optimizes the plan according to the user's current situation and environment and provides it in real time. This allows the service provider to consistently offer users the most optimal plan and improve user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and content of the plans. For example, by providing evaluations and comments on the plans provided by users, the service provider can revise the plans based on that information and provide better service. This allows the service provider to provide users with quick and reliable instructions and minimize the risk of disaster.

[0033] The data collection unit can collect health data from wearable devices. For example, the data collection unit can collect heart rate and blood pressure using a smartwatch. For instance, the smartwatch measures heart rate in real time and the data is collected. The data collection unit can also collect exercise history using a fitness tracker. For example, the fitness tracker records the user's steps and exercise time and the data is collected. The data collection unit can also collect body temperature using a smart ring. For example, the smart ring measures the user's body temperature and the data is collected. This allows for an accurate understanding of the user's health status by collecting health data from wearable devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into an AI model and have the AI ​​perform the data collection.

[0034] The generation unit can analyze data using machine learning algorithms and generate an optimal wellness travel plan. For example, the generation unit can analyze data using a neural network. For instance, the neural network takes user health data and preferences as input and outputs the optimal plan. The generation unit can also analyze data using a support vector machine. For example, the support vector machine classifies user health data and preferences and generates the optimal plan. Furthermore, the generation unit can analyze data using a clustering algorithm. For example, the clustering algorithm clusters user health data and preferences and generates the optimal plan for each cluster. This allows the generation of an optimal wellness travel plan for the user by using machine learning algorithms. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can implement machine learning algorithms in an AI model and have the AI ​​perform data analysis and plan generation.

[0035] The service provider can provide users with plans generated using high-speed communication technology. For example, the service provider can provide plans in real time using 5G communication. For example, the service provider can transmit plans to users at high speed and with low latency using 5G communication. The service provider can also provide plans using fiber optic communication. For example, the service provider can transmit large amounts of data at high speed using fiber optic communication. The service provider can also provide plans using satellite communication. For example, the service provider can provide plans to users in remote locations using satellite communication. This allows for the rapid provision of plans to users by using high-speed communication technology. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated plans into an AI model and have the AI ​​perform the plan provision.

[0036] The service provider can perform health management in real time. For example, the service provider can monitor the user's health data in real time and adjust the plan according to the user's health status. For example, the service provider can monitor the user's heart rate and blood pressure in real time and change the plan if an abnormality is detected. The service provider can also monitor the user's exercise history in real time and adjust the plan according to the amount of exercise. For example, when the service provider starts exercising, it provides a plan that includes activities suitable for the exercise. The service provider can also monitor the user's dietary history in real time and adjust the plan according to the content of the meals. For example, when the service provider consumes a particular meal, it provides a plan that includes activities suitable for that meal. This enables appropriate responses according to the user's health status by performing health management in real time. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into an AI model and have the AI ​​perform real-time health management.

[0037] The data collection unit can analyze the user's past health data history and select the optimal collection method. For example, the data collection unit can collect data from the user's past data during the most stable time periods. For example, the data collection unit can analyze the user's past data and collect heart rate and blood pressure during stable time periods. The data collection unit can also focus on collecting data from the user's past data during time periods when specific health indicators fluctuate. For example, the data collection unit can analyze the user's past data and collect exercise history during specific time periods. The data collection unit can also collect data from the user's past data before and after specific events (such as meals or exercise). For example, the data collection unit can analyze the user's past data and collect blood glucose levels before and after meals. This allows the optimal collection method to be selected by analyzing the user's past health data history. 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 health data into an AI model and have the AI ​​select the optimal collection method.

[0038] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is exercising, the data collection unit will prioritize collecting exercise-related data. For instance, it might collect heart rate and exercise history during the time the user is exercising. Similarly, if the user is resting, the data collection unit can collect data related to relaxation. For example, it might collect blood pressure and body temperature during the time the user is resting. Furthermore, if the user is eating, the data collection unit can collect meal-related data. For example, it might collect blood glucose levels and meal details during the time the user is eating. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and activity level into an AI model and have the AI ​​perform the data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen concentration and heart rate data. For example, the data collection unit will collect oxygen concentration and heart rate data during the time the user is at high altitude. The data collection unit can also collect data related to air quality and noise levels if the user is in an urban area. For example, the data collection unit will collect air quality and noise levels during the time the user is in an urban area. The data collection unit can also collect data related to humidity and temperature if the user is at the beach. For example, the data collection unit will collect humidity and temperature during the time the user is at the beach. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 have the AI ​​perform the collection of highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to their stress level. For example, the data collection unit can collect heart rate and blood pressure during the times when the user is experiencing stress on social media. The data collection unit can also collect data such as heart rate and blood pressure when a user is relaxing on social media. For example, the data collection unit can collect exercise history during the times when the user is relaxing on social media. The data collection unit can also collect exercise-related data when a user is actively engaged on social media. For example, the data collection unit can collect exercise history during the times when the user is actively engaged on social media. This allows for the collection of relevant health 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 data into an AI model and have the AI ​​collect relevant health data.

[0041] The generation unit can adjust the level of detail of the plan based on the user's health condition during generation. For example, if the user's health condition is good, the generation unit can generate a plan that includes detailed activities. The generation unit can also generate a plan that emphasizes rest if the user's health condition is unstable. The generation unit can also generate a plan that emphasizes rest if the user's health condition is unstable. The generation unit can also generate a plan that includes moderate exercise if the user's health condition is improving. By adjusting the level of detail of the plan based on the user's health condition, the generation unit can provide the user with the most suitable 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 health data into an AI model and have the AI ​​adjust the level of detail of the plan.

[0042] The generation unit can apply different generation algorithms to the user's preferences during generation. For example, if the user prefers nature, the generation unit can apply an algorithm that generates plans that emphasize natural scenery. The generation unit can also apply an algorithm that generates plans that emphasize urban tourist spots if the user prefers urban sightseeing. The generation unit can also apply an algorithm that generates plans that emphasize urban tourist spots if the user prefers urban sightseeing. The generation unit can also apply an algorithm that generates plans that allow users to enjoy local cuisine if the user prioritizes food. In this way, by applying different generation algorithms according to the user's preferences, the generation unit can provide the user with the most suitable 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 user preference data into an AI model and have the AI ​​execute the application of different generation algorithms.

[0043] The generation unit can determine the priority of plans based on when the user's health data was submitted during generation. For example, if the user has recently submitted health data, the generation unit can generate the latest plan based on that data. The generation unit can also generate plans that refer to past data based on health data previously submitted by the user. The generation unit can also generate plans based on trends in data if the user regularly submits health data. For example, if the user regularly submits health data, the generation unit can generate plans based on trends in that data. This allows the generation unit to provide plans based on the latest data by determining the priority of plans based on when the user's health data was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the user's health data submission into an AI model and have the AI ​​determine the priority of the plans.

[0044] The generation unit can adjust the order of plans based on user relevance during generation. For example, the generation unit can prioritize including highly relevant locations in the plan based on places the user has visited in the past. The generation unit can also prioritize including highly relevant activities in the plan based on user preferences. The generation unit can also prioritize including highly relevant health management activities in the plan based on the user's health status. By adjusting the order of plans based on user relevance, the generation unit can provide the user with the most suitable 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 user relevance data into an AI model and have the AI ​​perform the adjustment of the plan order.

[0045] The service provider can select the most suitable service delivery method by referring to the user's past travel history at the time of delivery. For example, the service provider can provide a highly relevant plan based on places the user has visited in the past. The service provider can also provide a plan that suits the user's preferences based on their past travel history. The service provider can also analyze the user's past travel history and provide the plan that will provide the highest level of satisfaction. This allows the service provider to select the most suitable service delivery method for the user by referring to their past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's past travel history data into an AI model and have the AI ​​select the most suitable service delivery method.

[0046] The service provider can customize the service offered based on the user's current health status at the time of delivery. For example, if the user's health status is good, the service provider can offer a plan that includes active activities. For example, if the user's health status is good, the service provider can offer a plan that includes active activities. The service provider can also offer a plan that emphasizes rest if the user's health status is unstable. For example, if the user's health status is improving, the service provider can offer a plan that includes moderate exercise. For example, if the user's health status is improving, the service provider can offer a plan that includes moderate exercise. By customizing the service offered based on the user's current health status, the service provider can provide the user with the most suitable plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health status data into an AI model and have the AI ​​perform the customization of the service offered.

[0047] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at high altitude, the service provider can provide activities suitable for high altitude. For example, if the user is at high altitude, the service provider can provide activities suitable for high altitude. The service provider can also provide activities suitable for urban sightseeing if the user is in an urban area. For example, if the user is at the beach, the service provider can provide activities suitable for the beach. For example, if the user is at the beach, the service provider can provide activities suitable for the beach. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​select the optimal service delivery method.

[0048] The service provider can analyze the user's social media activity and suggest content to offer at the time of delivery. For example, if the user is seeking relaxation on social media, the service provider can suggest relaxing activities. The service provider can also suggest active activities if the user is actively engaged on social media. The service provider can also suggest cultural activities if the user is seeking cultural experiences on social media. In this way, by analyzing the user's social media activity, relevant content can be suggested. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into an AI model and have the AI ​​suggest content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect not only user health data and preferences, but also user sleep patterns. For example, the unit can collect data such as the user's sleep duration, sleep quality, and heart rate during sleep. This allows the system to understand the user's sleep state and reflect it in wellness travel plans. For instance, if a user is sleep-deprived, the system can suggest a plan that includes relaxing activities. Conversely, if a user is getting enough sleep, the system can suggest a plan that includes active activities. Furthermore, based on the user's sleep patterns, the unit can suggest the optimal sleep environment during the trip. For example, it can suggest a plan that provides quiet accommodations and comfortable beds. This allows the system to provide an optimal wellness travel plan tailored to the user's sleep state.

[0051] The service provider can not only provide users with generated plans, but also collect user feedback to improve those plans. For example, the service provider can collect feedback on how users felt about the plans provided during their trip. This allows the service provider to adjust the plans based on user feedback and reflect it in future trips. For instance, if a user enjoyed a particular activity, that activity can be included in the next plan. Conversely, if a user did not like a particular activity, that activity can be excluded from the next plan. Furthermore, the service provider can suggest new activities or services based on user feedback. This allows them to provide optimal wellness travel plans that reflect user feedback.

[0052] The data collection unit can collect not only the user's health data and preferences, but also their dietary history. For example, the unit can collect data such as the content, calories, and nutrients of the meals the user has consumed. This allows the system to understand the user's eating habits and reflect them in wellness travel plans. For instance, if a user has specific dietary restrictions, the system can suggest a plan that provides meals tailored to those restrictions. Similarly, if a user needs to consume specific nutrients, the system can suggest a plan that includes those nutrients. Furthermore, based on the user's dietary history, the unit can suggest meal plans for the trip. For example, it can suggest a plan that offers healthy meals using local ingredients. This allows the system to provide an optimal wellness travel plan tailored to the user's eating habits.

[0053] The service provider not only provides users with generated plans, but can also adjust them based on the user's real-time health status. For example, the service provider can monitor the user's heart rate and blood pressure in real time and change the plan if an abnormality is detected. This enables appropriate responses tailored to the user's health condition. For instance, if a user's heart rate is high, the plan can be changed to include relaxing activities. Similarly, if a user's blood pressure is low, the plan can be changed to include light exercise. Furthermore, the service provider can suggest health management methods during the trip based on the user's health condition. For example, if a user is tired, a plan emphasizing rest can be suggested. This allows for the provision of an optimal wellness travel plan tailored to the user's real-time health condition.

[0054] The data collection unit can collect not only the user's health data and preferences, but also their exercise history. For example, the unit can collect data such as the type, duration, and intensity of exercise the user performed. This allows the system to understand the user's exercise habits and reflect them in wellness travel plans. For instance, if a user prefers a particular exercise, the system can suggest a plan that includes that exercise. Conversely, if a user needs to avoid a particular exercise, the system can suggest a plan that excludes it. Furthermore, based on the user's exercise history, the unit can suggest an exercise plan for the trip. For example, if a user prefers running, the system can suggest a plan that includes a running course. This allows the system to provide an optimal wellness travel plan tailored to the user's exercise habits.

[0055] The service provider can not only provide users with generated plans, but also adjust them based on the user's geographical location. For example, if a user is at high altitude, the service provider can provide a plan that includes activities suitable for high altitudes. This enables appropriate responses based on the user's geographical location. For instance, if a user is at high altitude, the service provider can provide a plan that includes activities considering oxygen concentration and heart rate. If a user is in an urban area, the service provider can provide a plan that includes activities suitable for urban sightseeing. Furthermore, if a user is by the sea, the service provider can provide a plan that includes activities suitable for the seaside. This allows for the provision of optimal wellness travel plans tailored to the user's geographical location.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's health data and preferences. This includes heart rate, blood pressure, body temperature, and exercise history. The data collection unit collects this data through wearable devices and smartphone apps. The data collection unit also collects data on the user's travel destination preferences, activity preferences, and food preferences through questionnaires. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit uses machine learning algorithms (e.g., neural networks, support vector machines, clustering algorithms) to analyze the data and generate an optimal plan based on the user's health data and preferences. Step 3: The provider unit provides the plan generated by the generator unit. The provider unit delivers the plan to the user using high-speed communication technology (e.g., 5G communication, fiber optic communication, satellite communication). This allows the user to receive the optimal wellness travel plan in real time.

[0058] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes a user's health data and preferences and proposes an optimal wellness travel plan. This AI agent system collects the user's health data and preferences, and the AI ​​analyzes them to generate an optimal wellness travel plan. The generated plan is provided to the user via high-speed communication technology, enabling real-time health management. For example, the AI ​​agent system collects the user's health data and preferences. Health data includes the user's weight, blood pressure, heart rate, exercise history, etc., while preferences include travel destination preferences, activity preferences, and food preferences. This data is either entered by the user or automatically collected from a wearable device. Next, the AI ​​agent system analyzes the collected data. Based on the user's health data and preferences, the AI ​​generates an optimal wellness travel plan. For example, if the user is seeking relaxation, the AI ​​suggests a hot spring resort or spa resort. For users who prefer active travel, the AI ​​suggests a plan that includes activities such as hiking and cycling. The generated wellness travel plan is provided to the user via high-speed communication technology. This allows the user to receive real-time health management wherever they are. For example, if a user's health changes during a trip, the AI ​​adjusts the plan accordingly and notifies the user. This system allows users to enjoy their trip while maintaining their health. Improved health rates, increased travel satisfaction, and enhanced health literacy are expected. Furthermore, it can provide data-driven, personalized services that meet the needs of health-conscious travelers. In addition, the combination of AI and healthcare technology, along with high-speed communication technology, enables technological innovation and creativity through new service models. The target audience is health-conscious individuals aged 30 to 60, addressing the challenge of a lack of ways to enjoy travel while maintaining health. This AI agent generates optimal wellness travel plans based on health data and personal preferences, enabling users to enjoy both health and travel. The wellness travel market is estimated to be worth approximately 7 trillion yen globally, and market growth is expected due to the combination of increasing health awareness and travel needs.The increased awareness of health and wellness in the post-pandemic era, coupled with the recovery of the travel industry, provides a favorable opportunity for market entry at this time. By combining health and travel, the aim is to propose a richer, more active lifestyle and improve people's quality of life. This allows the AI ​​agent system to provide optimal wellness travel plans based on the user's health data and preferences.

[0059] The AI ​​agent system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's health data and preferences. The user's health data includes, but is not limited to, heart rate, blood pressure, body temperature, and exercise history. The collection unit can collect health data using, for example, a wearable device. The collection unit can also collect data entered by the user. For example, the collection unit collects health data entered by the user through a smartphone application. Furthermore, the collection unit collects the user's preferences. The user's preferences include, for example, preferences for travel destinations, activities, and food. The collection unit can collect user preferences using, for example, a questionnaire. The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit analyzes the data using, for example, a machine learning algorithm. For example, the generation unit analyzes the user's health data and preferences using a neural network and generates an optimal plan. The generation unit can also analyze the data using a support vector machine. Furthermore, the generation unit can also analyze the data using a clustering algorithm. For example, the generation unit clusters the user's health data and preferences and generates an optimal plan for each cluster. The delivery unit provides the plans generated by the generation unit. The delivery unit provides the plans to the user using, for example, high-speed communication technology. For example, the delivery unit provides the plans in real time using 5G communication. The delivery unit can also provide plans using fiber optic communication. Furthermore, the delivery unit can provide plans using satellite communication. For example, the delivery unit provides plans to users in remote locations using satellite communication. This allows the AI ​​agent system according to the embodiment to provide an optimal wellness travel plan based on the user's health data and preferences. Some or all of the processing described above in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the plans generated by the generation unit into an AI model and have the AI ​​perform the plan delivery.

[0060] The data collection unit collects user health data and preferences. User health data includes, but is not limited to, heart rate, blood pressure, body temperature, and exercise history. The data collection unit can collect health data using, for example, wearable devices. Specifically, wearable devices such as smartwatches and fitness trackers monitor the user's heart rate, blood pressure, and body temperature in real time and transmit this data to the data collection unit. This allows for continuous monitoring of the user's health status. The data collection unit can also collect data entered by the user. For example, the data collection unit collects health data entered by the user through a smartphone app. The user uses the app to enter their daily weight, diet, exercise level, etc., and this data is transmitted to the data collection unit. Furthermore, the data collection unit collects user preferences. User preferences include, but are not limited to, travel destination preferences, activity preferences, and food preferences. The data collection unit can collect user preferences using, for example, questionnaires. Questionnaires are provided through smartphone apps or websites, and users provide information to the data collection unit by selecting their preferences and interests from a list of options. This allows the data collection unit to comprehensively collect user health data and preferences, securing the foundational data needed to provide personalized wellness travel plans. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the generation and delivery units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit analyzes the data using, for example, machine learning algorithms. Specifically, it uses a neural network to analyze the user's health data and preferences and generate an optimal plan. The neural network learns complex patterns using multi-layered artificial neurons and proposes an optimal travel plan based on the user's health status and preferences. The generation unit can also analyze data using a support vector machine. A support vector machine is an algorithm excellent for data classification and regression analysis, extracting key features for generating an optimal travel plan based on the user's health data and preferences. Furthermore, the generation unit can analyze data using clustering algorithms. For example, it clusters the user's health data and preferences and generates an optimal plan for each cluster. Clustering algorithms group data with similar characteristics and propose an optimal travel plan for each group. This allows the generation unit to provide customized travel plans tailored to the user's individual needs. By combining these algorithms, the generation unit can achieve more accurate analysis and plan generation. For example, by combining neural networks and clustering algorithms, the system can analyze users' health data and preferences in more detail to generate optimal travel plans. Furthermore, the generation unit can consider past data and trends to propose plans that address future needs. This allows the generation unit to consistently provide users with the most up-to-date and optimal wellness travel plans.

[0062] The service provider provides the plan generated by the generation unit. The service provider provides the plan to the user using, for example, high-speed communication technology. Specifically, the service provider provides the plan in real time using 5G communication. 5G communication enables high-speed and low-latency communication, allowing users to receive the plan quickly wherever they are. The service provider can also provide the plan using fiber optic communication. Fiber optic communication provides high-speed and stable communication and can transmit large amounts of data quickly. Furthermore, the service provider can also provide the plan using satellite communication. For example, the service provider provides the plan to a user in a remote location using satellite communication. Satellite communication enables communication even in areas where ground communication infrastructure is not well-developed, allowing users to receive the plan wherever they are. As a result, the AI ​​agent system according to the embodiment can provide an optimal wellness travel plan based on the user's health data and preferences. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the plan generated by the generation unit into an AI model and have the AI ​​perform the plan provision. The AI ​​model optimizes the plan according to the user's current situation and environment and provides it in real time. This allows the service provider to consistently offer users the most optimal plan and improve user satisfaction. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and content of the plans. For example, by providing evaluations and comments on the plans provided by users, the service provider can revise the plans based on that information and provide better service. This allows the service provider to provide users with quick and reliable instructions and minimize the risk of disaster.

[0063] The data collection unit can collect health data from wearable devices. For example, the data collection unit can collect heart rate and blood pressure using a smartwatch. For instance, the smartwatch measures heart rate in real time and the data is collected. The data collection unit can also collect exercise history using a fitness tracker. For example, the fitness tracker records the user's steps and exercise time and the data is collected. The data collection unit can also collect body temperature using a smart ring. For example, the smart ring measures the user's body temperature and the data is collected. This allows for an accurate understanding of the user's health status by collecting health data from wearable devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into an AI model and have the AI ​​perform the data collection.

[0064] The generation unit can analyze data using machine learning algorithms and generate an optimal wellness travel plan. For example, the generation unit can analyze data using a neural network. For instance, the neural network takes user health data and preferences as input and outputs the optimal plan. The generation unit can also analyze data using a support vector machine. For example, the support vector machine classifies user health data and preferences and generates the optimal plan. Furthermore, the generation unit can analyze data using a clustering algorithm. For example, the clustering algorithm clusters user health data and preferences and generates the optimal plan for each cluster. This allows the generation of an optimal wellness travel plan for the user by using machine learning algorithms. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can implement machine learning algorithms in an AI model and have the AI ​​perform data analysis and plan generation.

[0065] The service provider can provide users with plans generated using high-speed communication technology. For example, the service provider can provide plans in real time using 5G communication. For example, the service provider can transmit plans to users at high speed and with low latency using 5G communication. The service provider can also provide plans using fiber optic communication. For example, the service provider can transmit large amounts of data at high speed using fiber optic communication. The service provider can also provide plans using satellite communication. For example, the service provider can provide plans to users in remote locations using satellite communication. This allows for the rapid provision of plans to users by using high-speed communication technology. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated plans into an AI model and have the AI ​​perform the plan provision.

[0066] The service provider can perform health management in real time. For example, the service provider can monitor the user's health data in real time and adjust the plan according to the user's health status. For example, the service provider can monitor the user's heart rate and blood pressure in real time and change the plan if an abnormality is detected. The service provider can also monitor the user's exercise history in real time and adjust the plan according to the amount of exercise. For example, when the service provider starts exercising, it provides a plan that includes activities suitable for the exercise. The service provider can also monitor the user's dietary history in real time and adjust the plan according to the content of the meals. For example, when the service provider consumes a particular meal, it provides a plan that includes activities suitable for that meal. This enables appropriate responses according to the user's health status by performing health management in real time. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into an AI model and have the AI ​​perform real-time health management.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health data during times when the user is relaxed. For example, the data collection unit will collect heart rate and blood pressure during times when the user is relaxed. The data collection unit can also collect data during activities when the user is relaxed. For example, the data collection unit will collect exercise history during times when the user is exercising. The data collection unit can also prioritize data that can be collected in a short time if the user is in a hurry. For example, the data collection unit will collect easily measurable data when the user is in a hurry. By adjusting the timing of health data collection according to the user's emotions, more accurate 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 have the AI ​​adjust the timing of health data collection.

[0068] The data collection unit can analyze the user's past health data history and select the optimal collection method. For example, the data collection unit can collect data from the user's past data during the most stable time periods. For example, the data collection unit can analyze the user's past data and collect heart rate and blood pressure during stable time periods. The data collection unit can also focus on collecting data from the user's past data during time periods when specific health indicators fluctuate. For example, the data collection unit can analyze the user's past data and collect exercise history during specific time periods. The data collection unit can also collect data from the user's past data before and after specific events (such as meals or exercise). For example, the data collection unit can analyze the user's past data and collect blood glucose levels before and after meals. This allows the optimal collection method to be selected by analyzing the user's past health data history. 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 health data into an AI model and have the AI ​​select the optimal collection method.

[0069] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is exercising, the data collection unit will prioritize collecting exercise-related data. For instance, it might collect heart rate and exercise history during the time the user is exercising. Similarly, if the user is resting, the data collection unit can collect data related to relaxation. For example, it might collect blood pressure and body temperature during the time the user is resting. Furthermore, if the user is eating, the data collection unit can collect meal-related data. For example, it might collect blood glucose levels and meal details during the time the user is eating. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and activity level into an AI model and have the AI ​​perform the data filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress levels. For example, the data collection unit will collect heart rate and blood pressure during times when the user is stressed. The data collection unit can also prioritize collecting data such as heart rate and blood pressure when the user is relaxed. For example, the data collection unit will collect exercise history during times when the user is relaxed. The data collection unit can also prioritize data that can be collected in a short time when the user is in a hurry. For example, the data collection unit will collect easily measurable data when the user is in a hurry. This allows for the priority collection of important data by determining the priority of health data 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 without AI. For example, the data collection unit can input user emotional data into an AI model and have the AI ​​determine the prioritization of health data.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of oxygen concentration and heart rate data. For example, the data collection unit will collect oxygen concentration and heart rate data during the time the user is at high altitude. The data collection unit can also collect data related to air quality and noise levels if the user is in an urban area. For example, the data collection unit will collect air quality and noise levels during the time the user is in an urban area. The data collection unit can also collect data related to humidity and temperature if the user is at the beach. For example, the data collection unit will collect humidity and temperature during the time the user is at the beach. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 have the AI ​​perform the collection of highly relevant data.

[0072] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit can collect data related to their stress level. For example, the data collection unit can collect heart rate and blood pressure during the times when the user is experiencing stress on social media. The data collection unit can also collect data such as heart rate and blood pressure when a user is relaxing on social media. For example, the data collection unit can collect exercise history during the times when the user is relaxing on social media. The data collection unit can also collect exercise-related data when a user is actively engaged on social media. For example, the data collection unit can collect exercise history during the times when the user is actively engaged on social media. This allows for the collection of relevant health 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 data into an AI model and have the AI ​​collect relevant health data.

[0073] The generation unit can estimate the user's emotions and adjust the content of the wellness travel plan it generates based on those emotions. For example, if the user is seeking relaxation, the generation unit can generate a plan that includes hot springs or spa resorts. For example, if the user is seeking relaxation, the generation unit can generate a plan that includes hot springs or spa resorts. The generation unit can also generate a plan that includes activities such as hiking or cycling if the user prefers active travel. For example, if the generation unit is seeking active travel, the generation unit can generate a plan that includes activities such as hiking or cycling. The generation unit can also generate a plan that includes visits to museums or historical sites if the user is seeking cultural experiences. For example, if the generation unit is seeking cultural experiences, the generation unit can generate a plan that includes visits to museums or historical sites. In this way, by adjusting the content of the wellness travel plan according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI model and have the AI ​​adjust the content of the wellness travel plan.

[0074] The generation unit can adjust the level of detail of the plan based on the user's health condition during generation. For example, if the user's health condition is good, the generation unit can generate a plan that includes detailed activities. The generation unit can also generate a plan that emphasizes rest if the user's health condition is unstable. The generation unit can also generate a plan that emphasizes rest if the user's health condition is unstable. The generation unit can also generate a plan that includes moderate exercise if the user's health condition is improving. By adjusting the level of detail of the plan based on the user's health condition, the generation unit can provide the user with the most suitable 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 health data into an AI model and have the AI ​​adjust the level of detail of the plan.

[0075] The generation unit can apply different generation algorithms to the user's preferences during generation. For example, if the user prefers nature, the generation unit can apply an algorithm that generates plans that emphasize natural scenery. The generation unit can also apply an algorithm that generates plans that emphasize urban tourist spots if the user prefers urban sightseeing. The generation unit can also apply an algorithm that generates plans that emphasize urban tourist spots if the user prefers urban sightseeing. The generation unit can also apply an algorithm that generates plans that allow users to enjoy local cuisine if the user prioritizes food. In this way, by applying different generation algorithms according to the user's preferences, the generation unit can provide the user with the most suitable 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 user preference data into an AI model and have the AI ​​execute the application of different generation algorithms.

[0076] The generation unit can estimate the user's emotions and adjust the length of the plan it generates based on the estimated emotions. For example, if the user is seeking relaxation, the generation unit can generate a plan that includes a longer stay. For example, if the user is seeking relaxation, the generation unit can generate a plan that includes a longer stay. The generation unit can also generate a plan that includes a shorter stay if the user is looking for a short trip. For example, if the user is looking for a short trip, the generation unit can generate a plan that includes a shorter stay. For example, if the user is looking for a medium-length stay, the generation unit can generate a plan of a moderate length. For example, if the user is looking for a medium-length stay, the generation unit can generate a plan of a moderate length. In this way, by adjusting the length of the plan according to the user's emotions, the system can provide the user with the most suitable travel plan. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI model and have the AI ​​adjust the length of the plan.

[0077] The generation unit can determine the priority of plans based on when the user's health data was submitted during generation. For example, if the user has recently submitted health data, the generation unit can generate the latest plan based on that data. The generation unit can also generate plans that refer to past data based on health data previously submitted by the user. The generation unit can also generate plans based on trends in data if the user regularly submits health data. For example, if the user regularly submits health data, the generation unit can generate plans based on trends in that data. This allows the generation unit to provide plans based on the latest data by determining the priority of plans based on when the user's health data was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the user's health data submission into an AI model and have the AI ​​determine the priority of the plans.

[0078] The generation unit can adjust the order of plans based on user relevance during generation. For example, the generation unit can prioritize including highly relevant locations in the plan based on places the user has visited in the past. The generation unit can also prioritize including highly relevant activities in the plan based on user preferences. The generation unit can also prioritize including highly relevant health management activities in the plan based on the user's health status. By adjusting the order of plans based on user relevance, the generation unit can provide the user with the most suitable 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 user relevance data into an AI model and have the AI ​​perform the adjustment of the plan order.

[0079] The service provider can estimate the user's emotions and adjust how the plan is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display the plan with a calming color scheme interface. For example, if the user is relaxed, the service provider can display the plan with a calming color scheme interface. The service provider can also display the plan with a bright color scheme interface if the user is excited. For example, if the user is excited, the service provider can display the plan with a bright color scheme interface. The service provider can also display the plan with a simple and highly visible interface if the user is in a hurry. For example, if the user is in a hurry, the service provider can display the plan with a simple and highly visible interface. By adjusting how the plan is displayed according to the user's emotions, it becomes possible to provide a user-friendly display. Emotion estimation is achieved using an emotion estimation function, for example, 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​adjust how the plan is displayed.

[0080] The service provider can select the most suitable service delivery method by referring to the user's past travel history at the time of delivery. For example, the service provider can provide a highly relevant plan based on places the user has visited in the past. The service provider can also provide a plan that suits the user's preferences based on their past travel history. The service provider can also analyze the user's past travel history and provide the plan that will provide the highest level of satisfaction. This allows the service provider to select the most suitable service delivery method for the user by referring to their past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's past travel history data into an AI model and have the AI ​​select the most suitable service delivery method.

[0081] The service provider can customize the service offered based on the user's current health status at the time of delivery. For example, if the user's health status is good, the service provider can offer a plan that includes active activities. For example, if the user's health status is good, the service provider can offer a plan that includes active activities. The service provider can also offer a plan that emphasizes rest if the user's health status is unstable. For example, if the user's health status is improving, the service provider can offer a plan that includes moderate exercise. For example, if the user's health status is improving, the service provider can offer a plan that includes moderate exercise. By customizing the service offered based on the user's current health status, the service provider can provide the user with the most suitable plan. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health status data into an AI model and have the AI ​​perform the customization of the service offered.

[0082] The service provider can estimate the user's emotions and determine the priority of the plans offered based on the estimated emotions. For example, if the user is seeking relaxation, the service provider will prioritize offering relaxing activities. For example, if the user is seeking relaxation, the service provider will prioritize offering relaxing activities. For example, if the user prefers active travel, the service provider will prioritize offering active activities. For example, if the user is seeking cultural experiences, the service provider will prioritize offering cultural activities. For example, if the user is seeking cultural experiences, the service provider will prioritize offering cultural activities. In this way, by determining the priority of plans according to the user's emotions, the service provider can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into an AI model and have the AI ​​determine the priority of plans.

[0083] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at high altitude, the service provider can provide activities suitable for high altitude. For example, if the user is at high altitude, the service provider can provide activities suitable for high altitude. The service provider can also provide activities suitable for urban sightseeing if the user is in an urban area. For example, if the user is at the beach, the service provider can provide activities suitable for the beach. For example, if the user is at the beach, the service provider can provide activities suitable for the beach. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into an AI model and have the AI ​​select the optimal service delivery method.

[0084] The service provider can analyze the user's social media activity and suggest content to offer at the time of delivery. For example, if the user is seeking relaxation on social media, the service provider can suggest relaxing activities. The service provider can also suggest active activities if the user is actively engaged on social media. The service provider can also suggest cultural activities if the user is seeking cultural experiences on social media. In this way, by analyzing the user's social media activity, relevant content can be suggested. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into an AI model and have the AI ​​suggest content.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can collect not only user health data and preferences, but also user sleep patterns. For example, the unit can collect data such as the user's sleep duration, sleep quality, and heart rate during sleep. This allows the system to understand the user's sleep state and reflect it in wellness travel plans. For instance, if a user is sleep-deprived, the system can suggest a plan that includes relaxing activities. Conversely, if a user is getting enough sleep, the system can suggest a plan that includes active activities. Furthermore, based on the user's sleep patterns, the unit can suggest the optimal sleep environment during the trip. For example, it can suggest a plan that provides quiet accommodations and comfortable beds. This allows the system to provide an optimal wellness travel plan tailored to the user's sleep state.

[0087] The generation unit not only generates wellness travel plans based on the user's health data and preferences, but can also adjust the plan to take into account the user's stress level. For example, if the user's stress level is high, the generation unit can generate a plan that includes many relaxing activities. Conversely, if the user's stress level is low, it can generate a plan that includes many active activities. Furthermore, the generation unit can also suggest stress management methods during the trip, depending on the user's stress level. For example, it can suggest a plan that includes stress-reducing activities such as meditation or yoga. This allows the system to provide an optimal wellness travel plan tailored to the user's stress level.

[0088] The service provider can not only provide users with generated plans, but also collect user feedback to improve those plans. For example, the service provider can collect feedback on how users felt about the plans provided during their trip. This allows the service provider to adjust the plans based on user feedback and reflect it in future trips. For instance, if a user enjoyed a particular activity, that activity can be included in the next plan. Conversely, if a user did not like a particular activity, that activity can be excluded from the next plan. Furthermore, the service provider can suggest new activities or services based on user feedback. This allows them to provide optimal wellness travel plans that reflect user feedback.

[0089] The data collection unit can collect not only the user's health data and preferences, but also their dietary history. For example, the unit can collect data such as the content, calories, and nutrients of the meals the user has consumed. This allows the system to understand the user's eating habits and reflect them in wellness travel plans. For instance, if a user has specific dietary restrictions, the system can suggest a plan that provides meals tailored to those restrictions. Similarly, if a user needs to consume specific nutrients, the system can suggest a plan that includes those nutrients. Furthermore, based on the user's dietary history, the unit can suggest meal plans for the trip. For example, it can suggest a plan that offers healthy meals using local ingredients. This allows the system to provide an optimal wellness travel plan tailored to the user's eating habits.

[0090] The generation unit not only generates wellness travel plans based on the user's health data and preferences, but can also estimate the user's emotions and adjust the plan accordingly. For example, if the user is seeking relaxation, the generation unit can generate a plan that includes many relaxing activities. Similarly, if the user prefers an active trip, it can generate a plan that includes many active activities. Furthermore, the generation unit can suggest ways to manage emotions during the trip based on the user's feelings. For example, if the user is feeling stressed, it can suggest a plan that includes stress-reducing activities. This allows the system to provide an optimal wellness travel plan tailored to the user's emotions.

[0091] The service provider not only provides users with generated plans, but can also adjust them based on the user's real-time health status. For example, the service provider can monitor the user's heart rate and blood pressure in real time and change the plan if an abnormality is detected. This enables appropriate responses tailored to the user's health condition. For instance, if a user's heart rate is high, the plan can be changed to include relaxing activities. Similarly, if a user's blood pressure is low, the plan can be changed to include light exercise. Furthermore, the service provider can suggest health management methods during the trip based on the user's health condition. For example, if a user is tired, a plan emphasizing rest can be suggested. This allows for the provision of an optimal wellness travel plan tailored to the user's real-time health condition.

[0092] The data collection unit can collect not only the user's health data and preferences, but also their exercise history. For example, the unit can collect data such as the type, duration, and intensity of exercise the user performed. This allows the system to understand the user's exercise habits and reflect them in wellness travel plans. For instance, if a user prefers a particular exercise, the system can suggest a plan that includes that exercise. Conversely, if a user needs to avoid a particular exercise, the system can suggest a plan that excludes it. Furthermore, based on the user's exercise history, the unit can suggest an exercise plan for the trip. For example, if a user prefers running, the system can suggest a plan that includes a running course. This allows the system to provide an optimal wellness travel plan tailored to the user's exercise habits.

[0093] The generation unit not only generates wellness travel plans based on the user's health data and preferences, but can also estimate the user's emotions to prioritize the plan. For example, if the user is seeking relaxation, the generation unit can prioritize including relaxing activities in the plan. Similarly, if the user prefers active travel, it can prioritize including active activities. Furthermore, the generation unit can suggest ways to manage emotions during the trip based on the user's emotions. For example, if the user is feeling stressed, it can prioritize including stress-reducing activities in the plan. This allows the system to provide an optimal wellness travel plan tailored to the user's emotions.

[0094] The service provider can not only provide users with generated plans, but also adjust them based on the user's geographical location. For example, if a user is at high altitude, the service provider can provide a plan that includes activities suitable for high altitudes. This enables appropriate responses based on the user's geographical location. For instance, if a user is at high altitude, the service provider can provide a plan that includes activities considering oxygen concentration and heart rate. If a user is in an urban area, the service provider can provide a plan that includes activities suitable for urban sightseeing. Furthermore, if a user is by the sea, the service provider can provide a plan that includes activities suitable for the seaside. This allows for the provision of optimal wellness travel plans tailored to the user's geographical location.

[0095] The generation unit not only generates wellness travel plans based on the user's health data and preferences, but can also estimate the user's emotions and adjust how the plan is displayed. For example, if the user is relaxed, the generation unit can display the plan with a calming color scheme interface. If the user is excited, it can display the plan with a bright color scheme interface. Furthermore, if the user is in a hurry, it can display the plan with a simple and highly visible interface. This allows the system to provide the optimal display method according to the user's emotions. For example, if the user is relaxed, displaying the plan with a calming color scheme interface makes it easier for the user to view. If the user is excited, displaying the plan with a bright color scheme interface can enhance the user's excitement. This allows the system to provide the optimal wellness travel plan according to the user's emotions.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects the user's health data and preferences. This includes heart rate, blood pressure, body temperature, and exercise history. The data collection unit collects this data through wearable devices and smartphone apps. The data collection unit also collects data on the user's travel destination preferences, activity preferences, and food preferences through questionnaires. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan. The generation unit uses machine learning algorithms (e.g., neural networks, support vector machines, clustering algorithms) to analyze the data and generate an optimal plan based on the user's health data and preferences. Step 3: The provider unit provides the plan generated by the generator unit. The provider unit delivers the plan to the user using high-speed communication technology (e.g., 5G communication, fiber optic communication, satellite communication). This allows the user to receive the optimal wellness travel plan in real time.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's health data and preferences using the smart device 14's wearable device or smartphone app. The generation unit is implemented in the data processing unit 12, for example, by the identification processing unit 290, which analyzes the data using a machine learning algorithm to generate an optimal wellness travel plan. The provision unit is implemented in the smart device 14, for example, by the control unit 46A, which provides the generated plan to the user using high-speed communication technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's health data and preferences using the wearable device of the smart glasses 214 or a smartphone app. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the data using a machine learning algorithm and generates an optimal wellness travel plan. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides the generated plan to the user using high-speed communication technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's health data and preferences using the wearable device of the headset terminal 314 or a smartphone application. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the data using a machine learning algorithm and generates an optimal wellness travel plan. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides the generated plan to the user using high-speed communication technology. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's health data and preferences using the robot 414's wearable device or a smartphone app. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the data using a machine learning algorithm to generate an optimal wellness travel plan. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated plan to the user using high-speed communication technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects user health data and preferences, A generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan, The system comprises a providing unit that provides the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect health data from wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is We use machine learning algorithms to analyze data and generate the optimal wellness travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide users with plans generated using high-speed communication technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Real-time health management The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past health data history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health 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 11) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the content of the wellness travel plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the level of detail in the plan is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied according to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the plan's priority is determined based on when the user's health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of plans is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust how the plans we offer are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the plans to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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 health data and preferences, A generation unit analyzes the data collected by the collection unit and generates an optimal wellness travel plan, The system comprises a providing unit that provides the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect health data from wearable devices. The system according to feature 1.

3. The generating unit is We use machine learning algorithms to analyze data and generate the optimal wellness travel plan. The system according to feature 1.

4. The aforementioned supply unit is, We provide users with plans generated using high-speed communication technology. The system according to feature 1.

5. The aforementioned supply unit is, Real-time health management The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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