system

Abstract

The system according to this embodiment aims to provide information and support to enable Muslim tourists to enjoy traveling in Japan with peace of mind. [Solution] The system according to the embodiment comprises a reception unit, a tracking unit, a provision unit, a tourist information unit, and an emergency response unit. The reception unit inputs the user's basic information. The tracking unit tracks the user's current location based on the information entered by the reception unit. The provision unit provides information on halal restaurants and mosques based on the current location tracked by the tracking unit. The tourist information unit provides information on tourist spots based on the information provided by the provision unit. The emergency response unit provides emergency support based on the information provided by the tourist information 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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for Muslim tourists to find halal restaurants and prayer places in Japan, and there is a risk of feeling cultural and language gaps.

[0005] The system according to the embodiment aims to provide information and support for Muslim tourists to travel in Japan with confidence and enjoy their trips.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a tracking unit, a provision unit, a tourist information unit, and an emergency response unit. The reception unit inputs the user's basic information. The tracking unit tracks the user's current location based on the information entered by the reception unit. The provision unit provides information on halal restaurants and mosques based on the current location tracked by the tracking unit. The tourist information unit provides information on tourist spots based on the information provided by the provision unit. The emergency response unit provides emergency support based on the information provided by the tourist information unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide information and support to enable Muslim tourists to enjoy traveling in Japan with peace of mind. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 3...

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel support system according to an embodiment of the present invention is a personalized travel support system for Muslim foreign tourists visiting Japan. When a user first uses the service, they input basic information such as their religious preferences, language settings, and alert preferences. Next, the travel support system tracks the user's current location using GPS and notifies them in real time of nearby halal-certified restaurants and mosques. The travel support system also provides information on tourist attractions tailored to the user's schedule and interests. Furthermore, the travel support system provides information on Japanese manners and customs, offering cultural support. If a user encounters a problem, the travel support system provides directions to the nearest support facility. Finally, the travel support system continuously optimizes its services based on user feedback and history. This travel support system uses generative AI to provide real-time tourist information based on the traveler's location and interests, and also sends information to help understand Japanese culture. It also provides services that consider religious needs, such as guidance to halal-friendly restaurants and places of prayer. It also provides support for travel troubles and emergencies. As a result, the travel support system enables Muslim foreign tourists visiting Japan to stay with peace of mind and have an enjoyable travel experience.

[0029] The travel support system according to this embodiment comprises a reception unit, a tracking unit, a service provision unit, a tourist information unit, and an emergency response unit. The reception unit inputs the user's basic information. This basic information includes, but is not limited to, the user's name, address, contact information, and religious preferences. For example, when a user uses the service for the first time, the reception unit inputs basic information such as religious preferences, language settings, and alert preferences. The tracking unit tracks the user's current location based on the information entered by the reception unit. The tracking unit tracks the user's current location using, for example, GPS. GPS is used, for example, to clarify how and how accurate location information is obtained. The service provision unit provides information on halal-certified restaurants and mosques based on the current location tracked by the tracking unit. The service provision unit notifies the user of nearby halal-certified restaurants and mosques in real time. Real-time notifications are made, for example, to clarify the frequency of information updates and the notification method. The tourist information unit provides information on tourist spots based on the information provided by the service provision unit. The tourist information unit provides information on tourist spots tailored to the user's schedule and interests. Information on tourist spots includes, for example, the type of tourist spot and the level of detail provided. The emergency response unit provides emergency support based on the information provided by the tourist information unit. For example, if a user encounters a difficult situation, the emergency response unit will provide directions to the nearest support facility. Difficult situations include, for example, emergencies and types of troubles. As a result, the travel support system according to this embodiment can provide real-time tourist information and emergency support based on the user's basic information, location, and interests.

[0030] The reception desk inputs the user's basic information. This basic information includes, but is not limited to, name, address, contact information, and religious preferences. For example, when a user uses the service for the first time, the reception desk inputs basic information such as religious preferences, language settings, and alert preferences. Specifically, when a user first accesses the system, a dedicated input form is displayed, and the user enters their name, address, contact information (phone number and email address), religious preferences (e.g., halal dietary requirements or frequency of prayer), language settings (native language and language used when traveling), and alert preferences (notification frequency and method). This information is securely stored in the system's database and encrypted to protect user privacy. Furthermore, the reception desk also handles cases where users wish to update or add new information, allowing users to edit their basic information at any time. For example, if a travel destination changes or a new contact is added, the user can easily update their information. This allows the reception desk to always maintain the user's up-to-date basic information, improving the accuracy and reliability of the entire system.

[0031] The tracking unit tracks the user's current location based on information entered by the reception unit. The tracking unit uses, for example, GPS to track the user's current location. GPS is used, for example, to clarify the method and accuracy of location information acquisition. Specifically, it utilizes the GPS function built into the user's smartphone or mobile device to acquire the user's location information in real time. This allows the system to accurately determine the user's current location and provide necessary information. Furthermore, the tracking unit can also use Wi-Fi and Bluetooth® signals to improve the accuracy of location information. For example, in urban areas, GPS signals may be blocked by buildings, so Wi-Fi access points or Bluetooth beacons are used to supplement location information. This improves the accuracy of the user's location information, enabling the provision of more precise information. The tracking unit also records the user's movement history and analyzes past movement patterns to predict user behavior and identify places of interest. This allows the system to provide personalized information tailored to the user's needs.

[0032] The service provider provides information on halal-certified restaurants and mosques based on the user's current location, which is tracked by the tracking unit. For example, the service provider notifies users in real time of nearby halal-certified restaurants and mosques. Real-time notifications are provided, for example, to clarify the frequency of information updates and the notification method. Specifically, based on the user's current location, the service provider searches for information on the nearest halal-certified restaurants and mosques and notifies the user's smartphone. Notification methods include push notifications, email, and SMS, which users can choose according to their preference. Furthermore, the service provider also provides detailed information on restaurants and mosques. For example, this includes restaurant menus and opening hours, and mosque prayer times and facility information. This allows users to quickly obtain the necessary information and take appropriate action. The service provider also has a function to recommend the most suitable restaurants and mosques to the user based on their past usage history and ratings. This makes it easy for users to find places that suit their preferences.

[0033] The tourist information department provides information on tourist spots based on information provided by the service provider. For example, the tourist information department provides information on tourist spots tailored to the user's schedule and interests. This information on tourist spots includes, for example, the type of tourist spot and the level of detail provided. Specifically, it recommends the most suitable tourist spots based on the user's current location, schedule, and categories of interest (historical buildings, natural landscapes, shopping areas, etc.). This information on tourist spots includes an overview of the spot, how to get there, opening hours, admission fees, and information on special events. The tourist information department can also suggest efficient sightseeing routes considering the user's length of stay and means of transportation. For example, it may suggest spots that can be visited on foot or provide routes using public transportation. Furthermore, the tourist information department can collect user feedback and continuously improve the accuracy and quality of the information it provides. This allows users to have a fulfilling sightseeing experience.

[0034] The Emergency Response Department provides emergency support based on information provided by the Tourist Information Department. For example, if a user finds themselves in a difficult situation, the Emergency Response Department will provide directions to the nearest support facility. Difficult situations include various types of emergencies and troubles. Specifically, if a user gets lost, becomes ill, or becomes involved in a crime, the Emergency Response Department will respond quickly. Based on the user's current location, they will provide information and directions to the nearest support facility, such as a police station, hospital, or embassy. The Emergency Response Department can also send emergency notifications to the user's contacts to inform family and friends of the situation. Furthermore, the Emergency Response Department can provide translation services if the user faces a language barrier. For example, it can display necessary phrases in multiple languages ​​during emergencies and connect users to interpretation services. This allows users to enjoy their trip with peace of mind.

[0035] The optimization unit optimizes the service based on user feedback and history. For example, the optimization unit analyzes user feedback and history to improve the accuracy of the service. Optimization includes, for example, methods for analyzing feedback and methods for using history. As a result, the accuracy of the service is improved by optimizing the service based on user feedback and history. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input user feedback and history into a generative AI and have the generative AI perform the optimization of the service.

[0036] The reception desk inputs basic information such as the user's religious preferences, language settings, and alert preferences. For example, when a user uses the service for the first time, the reception desk inputs basic information such as religious preferences, language settings, and alert preferences. Religious preferences include, for example, a preference for halal food and the frequency of prayer. Language settings include, for example, the language to be used and the selection of display language. Alert preferences include, for example, the selection of notification frequency and notification method. This allows the system to provide appropriate information based on the user's religious preferences and language settings. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's religious preferences and language settings into a generative AI and have the generative AI provide appropriate information.

[0037] The tracking unit tracks the user's current location using GPS. The tracking unit, for example, tracks the user's current location using GPS. GPS is used, for example, to clarify the method and accuracy of acquiring location information. This allows for accurate tracking of the user's current location using GPS. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current location into a generative AI and have the generative AI perform location tracking.

[0038] The service provider notifies users of nearby halal-certified restaurants and mosques in real time. For example, the service provider notifies users of nearby halal-certified restaurants and mosques in real time. Real-time notifications are made, for example, to clarify the frequency of information updates and the notification method. This allows the service provider to meet the religious needs of users by providing information on halal-certified restaurants and mosques in real time. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information on nearby halal-certified restaurants and mosques into a generative AI and have the generative AI perform real-time notifications.

[0039] The tourist information department provides information on tourist spots tailored to the user's schedule and interests. For example, the tourist information department provides information on tourist spots tailored to the user's schedule and interests. The schedule includes, for example, how to input and manage appointments. Interests include, for example, categories of interests and how to input interests. This improves the user's travel experience by providing information on tourist spots based on the user's schedule and interests. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or not using a generative AI. For example, the tourist information department can input the user's schedule and interests into a generative AI and have the generative AI provide information on tourist spots.

[0040] The emergency response unit provides directions to the nearest support facility if the user finds themselves in a difficult situation. For example, if the user finds themselves in a difficult situation, the emergency response unit provides directions to the nearest support facility. Difficult situations include, for example, emergency situations or types of trouble. Support facilities include, for example, information on the type and location of the support facility. This ensures the user's safety by quickly providing directions to a support facility when the user finds themselves in a difficult situation. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emergency response unit can input the user's difficult situation into a generative AI and have the generative AI provide directions to a support facility.

[0041] The reception desk analyzes the user's past usage history and proposes the optimal input method. For example, the reception desk automatically displays basic information that the user has frequently entered in the past as a suggestion. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests basic information to be used during a specific time period based on the user's past usage history. This improves input efficiency by suggesting the optimal input method based on the user's past usage history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past usage history into a generative AI and have the generative AI suggest the optimal input method.

[0042] The reception desk customizes input fields based on the user's current situation and areas of interest when basic information is entered. For example, if the user is traveling, the reception desk prioritizes inputting basic information related to travel. For example, if the user is participating in a specific event, the reception desk inputs basic information related to that event. For example, if the user has a particular interest, the reception desk inputs basic information related to that interest. This improves the accuracy of input by customizing input fields based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's current situation and areas of interest into a generative AI and have the generative AI perform the customization of input fields.

[0043] The reception unit prioritizes inputting highly relevant information when basic information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes inputting basic information related to that region. For example, if the user is in a specific city, the reception unit prioritizes inputting basic information related to that city. For example, if the user is in a specific country, the reception unit prioritizes inputting basic information related to that country. This improves the accuracy of input by prioritizing the input of highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI and have the generative AI perform the input of highly relevant information.

[0044] The reception unit analyzes the user's social media activity and inputs relevant information when basic information is entered. For example, the reception unit inputs basic information based on information the user has shared on social media. For example, the reception unit inputs basic information based on information about accounts the user follows on social media. For example, the reception unit inputs basic information based on information about groups the user participates in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI and have the generative AI input the relevant information.

[0045] The tracking unit selects the optimal tracking method by referring to the user's past travel history during tracking. For example, the tracking unit selects the optimal tracking method based on routes previously used by the user. For example, the tracking unit selects a tracking method that avoids congestion from the user's past travel history. For example, the tracking unit analyzes the user's past travel history and selects the most efficient tracking method. In this way, the optimal tracking method can be selected by referring to the user's past travel history. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's past travel history into a generative AI and have the generative AI select the optimal tracking method.

[0046] The tracking unit adjusts the tracking frequency based on the user's current activity status during tracking. For example, if the user is on the move, the tracking unit increases the tracking frequency to provide real-time location information. For example, if the user is resting, the tracking unit decreases the tracking frequency to provide approximate location information. For example, if the user is staying in a specific location, the tracking unit adjusts the tracking frequency to provide optimal location information. This allows for the provision of appropriate location information by adjusting the tracking frequency based on the user's current activity status. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current activity status into a generative AI and have the generative AI adjust the tracking frequency.

[0047] The tracking unit improves tracking accuracy by considering the user's geographical distribution during tracking. For example, if the user is in a specific region, the tracking unit improves tracking accuracy related to that region. For example, if the user is in a specific city, the tracking unit improves tracking accuracy related to that city. For example, if the user is in a specific country, the tracking unit improves tracking accuracy related to that country. In this way, tracking accuracy is improved by considering the user's geographical distribution. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's geographical distribution into a generative AI and have the generative AI perform the improvement of tracking accuracy.

[0048] The tracking unit improves tracking accuracy by referring to the user's relevant literature during tracking. The tracking unit improves tracking accuracy based on, for example, literature the user has previously referenced. The tracking unit improves tracking accuracy based on, for example, literature the user is currently referencing. The tracking unit improves tracking accuracy related to, for example, a specific document the user is referencing. In this way, tracking accuracy is improved by referring to the user's relevant literature. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's relevant literature into a generative AI and have the generative AI perform the tracking accuracy improvement.

[0049] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, when providing important information, the provider provides a method of expression that includes detailed explanations. For example, when providing general information, the provider provides a concise method of expression. For example, when providing urgent information, the provider provides a quick and concise method of expression. In this way, appropriate information can be provided by adjusting the level of detail provided based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the importance of the information into the generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0050] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, when providing tourist information, the provider applies an algorithm that includes detailed descriptions of tourist spots. For example, when providing restaurant information, the provider applies an algorithm that includes menus and reviews. For example, when providing emergency information, the provider applies a fast and concise algorithm. This improves the accuracy of information provision by applying the appropriate information provision algorithm according to the information category. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the information category into a generative AI and have the generative AI perform the application of the information provision algorithm.

[0051] The information provision unit determines the priority of information provision based on the timing of information submission. For example, when providing urgent information, the information provision unit provides it with the highest priority. For example, when providing important information, the information provision unit provides it with priority. For example, when providing general information, the information provision unit provides it with normal priority. This ensures that information is provided at the appropriate time by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the timing of information submission into a generative AI and have the generative AI determine the priority of information provision.

[0052] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider prioritizes providing information related to the user's current situation. For example, the provider prioritizes providing information related to the user's interests. For example, the provider prioritizes providing information related to the user's past usage history. By adjusting the order of information delivery based on the relevance of the information, appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the order of delivery.

[0053] The tourist information department selects the most suitable guidance method by referring to the user's past travel history during the guidance process. For example, the tourist information department selects the most suitable guidance method based on the tourist spots the user has visited in the past. For example, the tourist information department selects a guidance method that avoids crowds based on the user's past travel history. For example, the tourist information department analyzes the user's past travel history and selects the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past travel history. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's past travel history into a generative AI and have the generative AI select the optimal guidance method.

[0054] The tourist information department customizes the content of the tour based on the user's current interests. For example, if the user has a specific interest, the tourist information department will guide them to tourist spots related to that interest. For example, if the user is participating in a specific event, the tourist information department will guide them to tourist spots related to that event. For example, if the user is interested in a specific region, the tourist information department will guide them to tourist spots related to that region. In this way, appropriate tourist information can be provided by customizing the content of the tour based on the user's current interests. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's current interests into a generative AI and have the generative AI perform the customization of the tour content.

[0055] The tourist information department selects the most appropriate guidance method when providing tourist information, taking into account the user's geographical location. For example, if the user is in a specific region, the tourist information department will guide them to tourist spots related to that region. For example, if the user is in a specific city, the tourist information department will guide them to tourist spots related to that city. For example, if the user is in a specific country, the tourist information department will guide them to tourist spots related to that country. In this way, the tourist information department can provide the most appropriate tourist information by taking into account the user's geographical location. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's geographical location information into a generative AI and have the generative AI select the most appropriate guidance method.

[0056] The tourist information department analyzes the user's social media activity and provides relevant tourist information during the tourist information session. For example, the tourist information department guides users to relevant tourist spots based on information shared by the user on social media. For example, the tourist information department guides users to relevant tourist spots based on information from accounts the user follows on social media. For example, the tourist information department guides users to relevant tourist spots based on information from groups the user participates in on social media. In this way, relevant tourist information can be provided by analyzing the user's social media activity. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's social media activity into a generative AI and have the generative AI provide relevant tourist information.

[0057] The emergency response unit selects the optimal response method by referring to the user's past trouble history during an emergency. For example, the emergency response unit selects the optimal response method based on troubles the user has experienced in the past. For example, the emergency response unit selects a rapid response method from the user's past trouble history. For example, the emergency response unit analyzes the user's past trouble history and selects the most effective response method. In this way, the optimal response method can be selected by referring to the user's past trouble history. Some or all of the above processing in the emergency response unit may be performed using, for example, a generation AI, or without a generation AI. For example, the emergency response unit can input the user's past trouble history into a generation AI and have the generation AI select the optimal response method.

[0058] The emergency response unit customizes the response measures based on the user's current situation during an emergency. For example, if the user is on the move, the emergency response unit provides a rapid response measure. For example, if the user is on a break, the emergency response unit provides a response measure that includes a detailed explanation. For example, if the user is staying in a specific location, the emergency response unit provides a response measure relevant to that location. This allows for an appropriate response by customizing the response measures based on the user's current situation. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's current situation into a generative AI and have the generative AI perform the customization of the response measures.

[0059] The emergency response unit selects the optimal response method during an emergency, taking into account the user's geographical location. For example, if the user is in a specific region, the emergency response unit selects an emergency response method relevant to that region. For example, if the user is in a specific city, the emergency response unit selects an emergency response method relevant to that city. For example, if the user is in a specific country, the emergency response unit selects an emergency response method relevant to that country. This enables optimal emergency response by considering the user's geographical location. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal response method.

[0060] The emergency response unit analyzes the user's social media activity during an emergency and proposes appropriate response measures. For example, the emergency response unit proposes the optimal response measures based on information shared by the user on social media. For example, the emergency response unit proposes the optimal response measures based on information about accounts the user follows on social media. For example, the emergency response unit proposes the optimal response measures based on information about groups the user participates in on social media. In this way, the optimal response measures can be proposed by analyzing the user's social media activity. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of response measures.

[0061] The optimization unit selects the optimal optimization method by referring to the user's past feedback during optimization. For example, the optimization unit selects the optimal optimization method based on feedback previously provided by the user. For example, the optimization unit selects an effective optimization method from the user's past feedback. For example, the optimization unit analyzes the user's past feedback and selects the most efficient optimization method. In this way, the optimal optimization method can be selected by referring to the user's past feedback. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the user's past feedback into a generative AI and have the generative AI perform the selection of the optimal optimization method.

[0062] The optimization unit customizes the optimization methods based on the user's current usage during optimization. For example, the optimization unit provides the optimal optimization method based on the functions the user is currently using. For example, the optimization unit provides the optimal optimization method according to the user's current usage. For example, if the user is using a specific function, the optimization unit provides the optimization method related to that function. This makes it possible to perform appropriate optimization by customizing the optimization method based on the user's current usage. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the user's current usage into a generative AI and have the generative AI perform the customization of the optimization method.

[0063] The optimization unit selects the optimal optimization method during optimization, taking into account the user's geographical location information. For example, if the user is in a specific region, the optimization unit selects an optimization method related to that region. For example, if the user is in a specific city, the optimization unit selects an optimization method related to that city. For example, if the user is in a specific country, the optimization unit selects an optimization method related to that country. This makes it possible to optimize by taking into account the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal optimization method.

[0064] The optimization unit analyzes the user's social media activity during optimization and proposes optimization methods. For example, the optimization unit proposes the optimal optimization method based on information shared by the user on social media. For example, the optimization unit proposes the optimal optimization method based on information of accounts followed by the user on social media. For example, the optimization unit proposes the optimal optimization method based on information of groups the user participates in on social media. In this way, the optimal optimization method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of optimization methods.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can be enhanced with a function to input the user's health information. For example, if a user has allergies, they can input this information, allowing the service desk to prioritize notifying them of allergy-friendly restaurants. Furthermore, if a user has a specific health condition (e.g., diabetes or high blood pressure), the service desk can use this information to recommend restaurants that offer appropriate meals. Additionally, the tourist information desk can suggest suitable sightseeing spots based on the user's health condition. This enables travel support that takes the user's health into consideration.

[0067] The optimization unit can analyze a user's travel activity history and suggest a plan for their next trip. For example, based on the user's past visits to tourist spots and restaurants, it can suggest new places to visit on their next trip. It can also predict the user's preferred activities and events from their past travel history and customize the travel plan accordingly. Furthermore, it can analyze user feedback and suggest areas for improvement in the next trip. This allows for the provision of more personalized travel plans by leveraging the user's past travel history.

[0068] The tracking unit can adjust the tracking accuracy based on the user's movement speed. For example, if the user is moving at high speed, the tracking accuracy is increased to provide detailed location information. If the user is moving slowly, the tracking accuracy is reduced to provide rough location information. If the user is staying in a specific location, the tracking frequency is adjusted to provide optimal location information. In this way, by adjusting the tracking accuracy according to the user's movement speed, appropriate location information can be provided.

[0069] The tourist information department can select the most suitable guidance method by referring to the user's past travel history. For example, it can select the most suitable guidance method based on the tourist spots the user has visited in the past. It can select a guidance method that avoids crowds based on the user's past travel history. It can analyze the user's past travel history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past travel history.

[0070] The reception desk can analyze users' social media activity and input relevant information. For example, it can input basic information based on information users have shared on social media, information on accounts users follow on social media, and information on groups users participate in on social media. This allows for the efficient input of relevant information by analyzing users' social media activity.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk enters the user's basic information. This information includes name, address, contact information, religious preferences, language settings, and alert preferences. Step 2: The tracking unit tracks the user's current location based on the information entered by the reception unit. The tracking unit tracks the user's current location, for example, using GPS. Step 3: The service provider provides information on halal-certified restaurants and mosques based on the user's current location, which has been tracked by the tracking unit. For example, the service provider will notify the user in real time of nearby halal-certified restaurants and mosques. Step 4: The tourist information department provides information about tourist spots based on the information provided by the service provider. For example, the tourist information department provides information about tourist spots tailored to the user's schedule and interests. Step 5: The Emergency Response Department provides emergency support based on the information provided by the Tourist Information Department. For example, if a user is in a difficult situation, the Emergency Response Department will provide directions to the nearest support facility.

[0073] (Example of form 2) The travel support system according to an embodiment of the present invention is a personalized travel support system for Muslim foreign tourists visiting Japan. When a user first uses the service, they input basic information such as their religious preferences, language settings, and alert preferences. Next, the travel support system tracks the user's current location using GPS and notifies them in real time of nearby halal-certified restaurants and mosques. The travel support system also provides information on tourist attractions tailored to the user's schedule and interests. Furthermore, the travel support system provides information on Japanese manners and customs, offering cultural support. If a user encounters a problem, the travel support system provides directions to the nearest support facility. Finally, the travel support system continuously optimizes its services based on user feedback and history. This travel support system uses generative AI to provide real-time tourist information based on the traveler's location and interests, and also sends information to help understand Japanese culture. It also provides services that consider religious needs, such as guidance to halal-friendly restaurants and places of prayer. It also provides support for travel troubles and emergencies. As a result, the travel support system enables Muslim foreign tourists visiting Japan to stay with peace of mind and have an enjoyable travel experience.

[0074] The travel support system according to this embodiment comprises a reception unit, a tracking unit, a service provision unit, a tourist information unit, and an emergency response unit. The reception unit inputs the user's basic information. This basic information includes, but is not limited to, the user's name, address, contact information, and religious preferences. For example, when a user uses the service for the first time, the reception unit inputs basic information such as religious preferences, language settings, and alert preferences. The tracking unit tracks the user's current location based on the information entered by the reception unit. The tracking unit tracks the user's current location using, for example, GPS. GPS is used, for example, to clarify how and how accurate location information is obtained. The service provision unit provides information on halal-certified restaurants and mosques based on the current location tracked by the tracking unit. The service provision unit notifies the user of nearby halal-certified restaurants and mosques in real time. Real-time notifications are made, for example, to clarify the frequency of information updates and the notification method. The tourist information unit provides information on tourist spots based on the information provided by the service provision unit. The tourist information unit provides information on tourist spots tailored to the user's schedule and interests. Information on tourist spots includes, for example, the type of tourist spot and the level of detail provided. The emergency response unit provides emergency support based on the information provided by the tourist information unit. For example, if a user encounters a difficult situation, the emergency response unit will provide directions to the nearest support facility. Difficult situations include, for example, emergencies and types of troubles. As a result, the travel support system according to this embodiment can provide real-time tourist information and emergency support based on the user's basic information, location, and interests.

[0075] The reception desk inputs the user's basic information. This basic information includes, but is not limited to, name, address, contact information, and religious preferences. For example, when a user uses the service for the first time, the reception desk inputs basic information such as religious preferences, language settings, and alert preferences. Specifically, when a user first accesses the system, a dedicated input form is displayed, and the user enters their name, address, contact information (phone number and email address), religious preferences (e.g., halal dietary requirements or frequency of prayer), language settings (native language and language used when traveling), and alert preferences (notification frequency and method). This information is securely stored in the system's database and encrypted to protect user privacy. Furthermore, the reception desk also handles cases where users wish to update or add new information, allowing users to edit their basic information at any time. For example, if a travel destination changes or a new contact is added, the user can easily update their information. This allows the reception desk to always maintain the user's up-to-date basic information, improving the accuracy and reliability of the entire system.

[0076] The tracking unit tracks the user's current location based on information entered by the reception unit. The tracking unit uses, for example, GPS to track the user's location. GPS is used, for example, to clarify the method and accuracy of location information acquisition. Specifically, it utilizes the GPS function built into the user's smartphone or mobile device to acquire the user's location information in real time. This allows the system to accurately determine the user's current location and provide necessary information. Furthermore, the tracking unit can also use Wi-Fi and Bluetooth signals to improve the accuracy of location information. For example, in urban areas, GPS signals may be blocked by buildings, so Wi-Fi access points or Bluetooth beacons are used to supplement location information. This improves the accuracy of the user's location information, enabling the provision of more precise information. The tracking unit also records the user's movement history and analyzes past movement patterns to predict user behavior and identify places of interest. This allows the system to provide personalized information tailored to the user's needs.

[0077] The service provider provides information on halal-certified restaurants and mosques based on the user's current location, which is tracked by the tracking unit. For example, the service provider notifies users in real time of nearby halal-certified restaurants and mosques. Real-time notifications are provided, for example, to clarify the frequency of information updates and the notification method. Specifically, based on the user's current location, the service provider searches for information on the nearest halal-certified restaurants and mosques and notifies the user's smartphone. Notification methods include push notifications, email, and SMS, which users can choose according to their preference. Furthermore, the service provider also provides detailed information on restaurants and mosques. For example, this includes restaurant menus and opening hours, and mosque prayer times and facility information. This allows users to quickly obtain the necessary information and take appropriate action. The service provider also has a function to recommend the most suitable restaurants and mosques to the user based on their past usage history and ratings. This makes it easy for users to find places that suit their preferences.

[0078] The tourist information department provides information on tourist spots based on information provided by the service provider. For example, the tourist information department provides information on tourist spots tailored to the user's schedule and interests. This information on tourist spots includes, for example, the type of tourist spot and the level of detail provided. Specifically, it recommends the most suitable tourist spots based on the user's current location, schedule, and categories of interest (historical buildings, natural landscapes, shopping areas, etc.). This information on tourist spots includes an overview of the spot, how to get there, opening hours, admission fees, and information on special events. The tourist information department can also suggest efficient sightseeing routes considering the user's length of stay and means of transportation. For example, it may suggest spots that can be visited on foot or provide routes using public transportation. Furthermore, the tourist information department can collect user feedback and continuously improve the accuracy and quality of the information it provides. This allows users to have a fulfilling sightseeing experience.

[0079] The Emergency Response Department provides emergency support based on information provided by the Tourist Information Department. For example, if a user finds themselves in a difficult situation, the Emergency Response Department will provide directions to the nearest support facility. Difficult situations include various types of emergencies and troubles. Specifically, if a user gets lost, becomes ill, or becomes involved in a crime, the Emergency Response Department will respond quickly. Based on the user's current location, they will provide information and directions to the nearest support facility, such as a police station, hospital, or embassy. The Emergency Response Department can also send emergency notifications to the user's contacts to inform family and friends of the situation. Furthermore, the Emergency Response Department can provide translation services if the user faces a language barrier. For example, it can display necessary phrases in multiple languages ​​during emergencies and connect users to interpretation services. This allows users to enjoy their trip with peace of mind.

[0080] The optimization unit optimizes the service based on user feedback and history. For example, the optimization unit analyzes user feedback and history to improve the accuracy of the service. Optimization includes, for example, methods for analyzing feedback and methods for using history. As a result, the accuracy of the service is improved by optimizing the service based on user feedback and history. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input user feedback and history into a generative AI and have the generative AI perform the optimization of the service.

[0081] The reception desk inputs basic information such as the user's religious preferences, language settings, and alert preferences. For example, when a user uses the service for the first time, the reception desk inputs basic information such as religious preferences, language settings, and alert preferences. Religious preferences include, for example, a preference for halal food and the frequency of prayer. Language settings include, for example, the language to be used and the selection of display language. Alert preferences include, for example, the selection of notification frequency and notification method. This allows the system to provide appropriate information based on the user's religious preferences and language settings. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's religious preferences and language settings into a generative AI and have the generative AI provide appropriate information.

[0082] The tracking unit tracks the user's current location using GPS. The tracking unit, for example, tracks the user's current location using GPS. GPS is used, for example, to clarify the method and accuracy of acquiring location information. This allows for accurate tracking of the user's current location using GPS. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current location into a generative AI and have the generative AI perform location tracking.

[0083] The service provider notifies users of nearby halal-certified restaurants and mosques in real time. For example, the service provider notifies users of nearby halal-certified restaurants and mosques in real time. Real-time notifications are made, for example, to clarify the frequency of information updates and the notification method. This allows the service provider to meet the religious needs of users by providing information on halal-certified restaurants and mosques in real time. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information on nearby halal-certified restaurants and mosques into a generative AI and have the generative AI perform real-time notifications.

[0084] The tourist information department provides information on tourist spots tailored to the user's schedule and interests. For example, the tourist information department provides information on tourist spots tailored to the user's schedule and interests. The schedule includes, for example, how to input and manage appointments. Interests include, for example, categories of interests and how to input interests. This improves the user's travel experience by providing information on tourist spots based on the user's schedule and interests. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or not using a generative AI. For example, the tourist information department can input the user's schedule and interests into a generative AI and have the generative AI provide information on tourist spots.

[0085] The emergency response unit provides directions to the nearest support facility if the user finds themselves in a difficult situation. For example, if the user finds themselves in a difficult situation, the emergency response unit provides directions to the nearest support facility. Difficult situations include, for example, emergency situations or types of trouble. Support facilities include, for example, information on the type and location of the support facility. This ensures the user's safety by quickly providing directions to a support facility when the user finds themselves in a difficult situation. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emergency response unit can input the user's difficult situation into a generative AI and have the generative AI provide directions to a support facility.

[0086] The reception desk estimates the user's emotions and adjusts the method of inputting basic information based on the estimated emotions. For example, if the user is nervous, the reception desk provides a simple and intuitive interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of basic information. This reduces user stress by adjusting the method of inputting basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using a generative AI or not. For example, the reception desk can input the user's emotions into a generative AI and have the generative AI adjust the method of inputting basic information.

[0087] The reception desk analyzes the user's past usage history and proposes the optimal input method. For example, the reception desk automatically displays basic information that the user has frequently entered in the past as a suggestion. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests basic information to be used during a specific time period based on the user's past usage history. This improves input efficiency by suggesting the optimal input method based on the user's past usage history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past usage history into a generative AI and have the generative AI suggest the optimal input method.

[0088] The reception desk customizes input fields based on the user's current situation and areas of interest when basic information is entered. For example, if the user is traveling, the reception desk prioritizes inputting basic information related to travel. For example, if the user is participating in a specific event, the reception desk inputs basic information related to that event. For example, if the user has a particular interest, the reception desk inputs basic information related to that interest. This improves the accuracy of input by customizing input fields based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's current situation and areas of interest into a generative AI and have the generative AI perform the customization of input fields.

[0089] The reception unit estimates the user's emotions and determines the priority of the information to be entered based on the estimated emotions. For example, if the user is nervous, the reception unit prioritizes important information. For example, if the user is relaxed, the reception unit prioritizes detailed information. For example, if the user is in a hurry, the reception unit prioritizes minimal information. This ensures that important information is prioritized by determining the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input the user's emotions into a generative AI and have the generative AI determine the priority of the information.

[0090] The reception unit prioritizes inputting highly relevant information when basic information is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes inputting basic information related to that region. For example, if the user is in a specific city, the reception unit prioritizes inputting basic information related to that city. For example, if the user is in a specific country, the reception unit prioritizes inputting basic information related to that country. This improves the accuracy of input by prioritizing the input of highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI and have the generative AI perform the input of highly relevant information.

[0091] The reception unit analyzes the user's social media activity and inputs relevant information when basic information is entered. For example, the reception unit inputs basic information based on information the user has shared on social media. For example, the reception unit inputs basic information based on information about accounts the user follows on social media. For example, the reception unit inputs basic information based on information about groups the user participates in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI and have the generative AI input the relevant information.

[0092] The tracking unit estimates the user's emotions and adjusts the tracking accuracy based on the estimated emotions. For example, if the user is stressed, the tracking unit increases the tracking accuracy to provide detailed location information. For example, if the user is relaxed, the tracking unit reduces the tracking accuracy to provide rough location information. For example, if the user is in a hurry, the tracking unit increases the tracking accuracy to provide rapid location information. In this way, by adjusting the tracking accuracy according to the user's emotions, more appropriate location information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the tracking unit may be performed using a generative AI, or not using a generative AI. For example, the tracking unit can input the user's emotions into a generative AI and have the generative AI adjust the tracking accuracy.

[0093] The tracking unit selects the optimal tracking method by referring to the user's past travel history during tracking. For example, the tracking unit selects the optimal tracking method based on routes previously used by the user. For example, the tracking unit selects a tracking method that avoids congestion from the user's past travel history. For example, the tracking unit analyzes the user's past travel history and selects the most efficient tracking method. In this way, the optimal tracking method can be selected by referring to the user's past travel history. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's past travel history into a generative AI and have the generative AI select the optimal tracking method.

[0094] The tracking unit adjusts the tracking frequency based on the user's current activity status during tracking. For example, if the user is on the move, the tracking unit increases the tracking frequency to provide real-time location information. For example, if the user is resting, the tracking unit decreases the tracking frequency to provide approximate location information. For example, if the user is staying in a specific location, the tracking unit adjusts the tracking frequency to provide optimal location information. This allows for the provision of appropriate location information by adjusting the tracking frequency based on the user's current activity status. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current activity status into a generative AI and have the generative AI adjust the tracking frequency.

[0095] The tracking unit estimates the user's emotions and adjusts the display method of the tracking results based on the estimated user emotions. For example, if the user is nervous, the tracking unit provides a simple and highly visible display method. For example, if the user is relaxed, the tracking unit provides a display method that includes detailed information. For example, if the user is in a hurry, the tracking unit provides a display method that gets straight to the point. In this way, highly visible information can be provided by adjusting the display method of the tracking results 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the tracking unit may be performed using a generative AI, for example, or without a generative AI. For example, the tracking unit can input the user's emotions into a generative AI and have the generative AI adjust the display method of the tracking results.

[0096] The tracking unit improves tracking accuracy by considering the user's geographical distribution during tracking. For example, if the user is in a specific region, the tracking unit improves tracking accuracy related to that region. For example, if the user is in a specific city, the tracking unit improves tracking accuracy related to that city. For example, if the user is in a specific country, the tracking unit improves tracking accuracy related to that country. In this way, tracking accuracy is improved by considering the user's geographical distribution. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's geographical distribution into a generative AI and have the generative AI perform the improvement of tracking accuracy.

[0097] The tracking unit improves tracking accuracy by referring to the user's relevant literature during tracking. The tracking unit improves tracking accuracy based on, for example, literature the user has previously referenced. The tracking unit improves tracking accuracy based on, for example, literature the user is currently referencing. The tracking unit improves tracking accuracy related to, for example, a specific document the user is referencing. In this way, tracking accuracy is improved by referring to the user's relevant literature. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's relevant literature into a generative AI and have the generative AI perform the tracking accuracy improvement.

[0098] The service provider estimates the user's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and easily understandable presentation. If the user is relaxed, the service provider provides a presentation that includes detailed information. If the user is in a hurry, the service provider provides a presentation that gets straight to the point. By adjusting the presentation of information according to the user's emotions, the service provider can provide easily understandable information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's emotions into a generative AI and have the generative AI adjust the presentation of the information.

[0099] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, when providing important information, the provider provides a method of expression that includes detailed explanations. For example, when providing general information, the provider provides a concise method of expression. For example, when providing urgent information, the provider provides a quick and concise method of expression. In this way, appropriate information can be provided by adjusting the level of detail provided based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provider can input the importance of the information into the generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0100] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, when providing tourist information, the provider applies an algorithm that includes detailed descriptions of tourist spots. For example, when providing restaurant information, the provider applies an algorithm that includes menus and reviews. For example, when providing emergency information, the provider applies a fast and concise algorithm. This improves the accuracy of information provision by applying the appropriate information provision algorithm according to the information category. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the information category into a generative AI and have the generative AI perform the application of the information provision algorithm.

[0101] The information provider estimates the user's emotions and adjusts the length of the information provided based on the estimated emotions. For example, if the user is nervous, the provider provides short, concise information. If the user is relaxed, the provider provides longer information including detailed explanations. If the user is in a hurry, the provider provides quick and concise information. This allows the provider to provide appropriate information by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 information provider may be performed using a generative AI, or not. For example, the information provider can input the user's emotions into a generative AI and have the generative AI adjust the length of the information.

[0102] The information provision unit determines the priority of information provision based on the timing of information submission. For example, when providing urgent information, the information provision unit provides it with the highest priority. For example, when providing important information, the information provision unit provides it with priority. For example, when providing general information, the information provision unit provides it with normal priority. This ensures that information is provided at the appropriate time by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input the timing of information submission into a generative AI and have the generative AI determine the priority of information provision.

[0103] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider prioritizes providing information related to the user's current situation. For example, the provider prioritizes providing information related to the user's interests. For example, the provider prioritizes providing information related to the user's past usage history. By adjusting the order of information delivery based on the relevance of the information, appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the order of delivery.

[0104] The tourist information department estimates the user's emotions and adjusts the display method of the tourist information based on the estimated emotions. For example, if the user is nervous, the tourist information department provides a simple and highly visible display method. For example, if the user is relaxed, the tourist information department provides a display method that includes detailed information. For example, if the user is in a hurry, the tourist information department provides a display method that gets straight to the point. In this way, by adjusting the display method of the tourist information according to the user's emotions, highly visible information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the tourist information department may be performed using a generative AI, for example, or without a generative AI. For example, the tourist information department can input the user's emotions into a generative AI and have the generative AI adjust the display method of the tourist information.

[0105] The tourist information department selects the most suitable guidance method by referring to the user's past travel history during the guidance process. For example, the tourist information department selects the most suitable guidance method based on the tourist spots the user has visited in the past. For example, the tourist information department selects a guidance method that avoids crowds based on the user's past travel history. For example, the tourist information department analyzes the user's past travel history and selects the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past travel history. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's past travel history into a generative AI and have the generative AI select the optimal guidance method.

[0106] The tourist information department customizes the content of the tour based on the user's current interests. For example, if the user has a specific interest, the tourist information department will guide them to tourist spots related to that interest. For example, if the user is participating in a specific event, the tourist information department will guide them to tourist spots related to that event. For example, if the user is interested in a specific region, the tourist information department will guide them to tourist spots related to that region. In this way, appropriate tourist information can be provided by customizing the content of the tour based on the user's current interests. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's current interests into a generative AI and have the generative AI perform the customization of the tour content.

[0107] The tourist information department estimates the user's emotions and determines the priority of tourist information based on the estimated emotions. For example, if the user is nervous, the tourist information department will prioritize showing important tourist spots. For example, if the user is relaxed, the tourist information department will prioritize showing detailed tourist spots. For example, if the user is in a hurry, the tourist information department will prioritize showing the most efficient tourist spots. In this way, by determining the priority of tourist information according to the user's emotions, important tourist spots can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the tourist information department may be performed using a generative AI, or not using a generative AI. For example, the tourist information department can input the user's emotions into a generative AI and have the generative AI perform the determination of the priority of tourist information.

[0108] The tourist information department selects the most appropriate guidance method when providing tourist information, taking into account the user's geographical location. For example, if the user is in a specific region, the tourist information department will guide them to tourist spots related to that region. For example, if the user is in a specific city, the tourist information department will guide them to tourist spots related to that city. For example, if the user is in a specific country, the tourist information department will guide them to tourist spots related to that country. In this way, the tourist information department can provide the most appropriate tourist information by taking into account the user's geographical location. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's geographical location information into a generative AI and have the generative AI select the most appropriate guidance method.

[0109] The tourist information department analyzes the user's social media activity and provides relevant tourist information during the tourist information session. For example, the tourist information department guides users to relevant tourist spots based on information shared by the user on social media. For example, the tourist information department guides users to relevant tourist spots based on information from accounts the user follows on social media. For example, the tourist information department guides users to relevant tourist spots based on information from groups the user participates in on social media. In this way, relevant tourist information can be provided by analyzing the user's social media activity. Some or all of the above processing in the tourist information department may be performed using, for example, a generative AI, or without a generative AI. For example, the tourist information department can input the user's social media activity into a generative AI and have the generative AI provide relevant tourist information.

[0110] The emergency response unit estimates the user's emotions and adjusts its emergency response method based on the estimated emotions. For example, if the user is nervous, the emergency response unit will provide an emergency response in a calm voice. If the user is relaxed, the emergency response unit will provide an emergency response that includes detailed explanations. If the user is in a hurry, the emergency response unit will provide a quick and concise emergency response. This allows for an appropriate response by adjusting the emergency response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the emergency response unit may be performed using a generative AI, or not using a generative AI. For example, the emergency response unit can input the user's emotions into a generative AI and have the generative AI adjust the emergency response method.

[0111] The emergency response unit selects the optimal response method by referring to the user's past trouble history during an emergency. For example, the emergency response unit selects the optimal response method based on troubles the user has experienced in the past. For example, the emergency response unit selects a rapid response method from the user's past trouble history. For example, the emergency response unit analyzes the user's past trouble history and selects the most effective response method. In this way, the optimal response method can be selected by referring to the user's past trouble history. Some or all of the above processing in the emergency response unit may be performed using, for example, a generation AI, or without a generation AI. For example, the emergency response unit can input the user's past trouble history into a generation AI and have the generation AI select the optimal response method.

[0112] The emergency response unit customizes the response measures based on the user's current situation during an emergency. For example, if the user is on the move, the emergency response unit provides a rapid response measure. For example, if the user is on a break, the emergency response unit provides a response measure that includes a detailed explanation. For example, if the user is staying in a specific location, the emergency response unit provides a response measure relevant to that location. This allows for an appropriate response by customizing the response measures based on the user's current situation. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's current situation into a generative AI and have the generative AI perform the customization of the response measures.

[0113] The emergency response unit estimates the user's emotions and determines the priority of emergency responses based on the estimated emotions. For example, if the user is anxious, the emergency response unit will prioritize emergency response. If the user is relaxed, the emergency response unit will prioritize emergency response with normal priority. If the user is in a hurry, the emergency response unit will prioritize a rapid response. This enables a rapid response by determining the priority of emergency responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using a generative AI, or not. For example, the emergency response unit can input the user's emotions into a generative AI and have the generative AI determine the priority of emergency responses.

[0114] The emergency response unit selects the optimal response method during an emergency, taking into account the user's geographical location. For example, if the user is in a specific region, the emergency response unit selects an emergency response method relevant to that region. For example, if the user is in a specific city, the emergency response unit selects an emergency response method relevant to that city. For example, if the user is in a specific country, the emergency response unit selects an emergency response method relevant to that country. This enables optimal emergency response by considering the user's geographical location. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal response method.

[0115] The emergency response unit analyzes the user's social media activity during an emergency and proposes appropriate response measures. For example, the emergency response unit proposes the optimal response measures based on information shared by the user on social media. For example, the emergency response unit proposes the optimal response measures based on information about accounts the user follows on social media. For example, the emergency response unit proposes the optimal response measures based on information about groups the user participates in on social media. In this way, the optimal response measures can be proposed by analyzing the user's social media activity. Some or all of the above processing in the emergency response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emergency response unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of response measures.

[0116] The optimization unit estimates the user's emotions and adjusts the optimization method based on the estimated emotions. For example, if the user is nervous, the optimization unit provides a simple and intuitive optimization method. For example, if the user is relaxed, the optimization unit provides a detailed optimization method. For example, if the user is in a hurry, the optimization unit provides a quick and concise optimization method. This allows for appropriate optimization by adjusting the optimization method 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input the user's emotions into a generative AI and have the generative AI perform the adjustment of the optimization method.

[0117] The optimization unit selects the optimal optimization method by referring to the user's past feedback during optimization. For example, the optimization unit selects the optimal optimization method based on feedback previously provided by the user. For example, the optimization unit selects an effective optimization method from the user's past feedback. For example, the optimization unit analyzes the user's past feedback and selects the most efficient optimization method. In this way, the optimal optimization method can be selected by referring to the user's past feedback. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the user's past feedback into a generative AI and have the generative AI perform the selection of the optimal optimization method.

[0118] The optimization unit customizes the optimization methods based on the user's current usage during optimization. For example, the optimization unit provides the optimal optimization method based on the functions the user is currently using. For example, the optimization unit provides the optimal optimization method according to the user's current usage. For example, if the user is using a specific function, the optimization unit provides the optimization method related to that function. This makes it possible to perform appropriate optimization by customizing the optimization method based on the user's current usage. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the user's current usage into a generative AI and have the generative AI perform the customization of the optimization method.

[0119] The optimization unit estimates the user's emotions and determines optimization priorities based on the estimated emotions. For example, if the user is tense, the optimization unit prioritizes optimization. If the user is relaxed, the optimization unit prioritizes optimization with normal priority. If the user is in a hurry, the optimization unit prioritizes rapid optimization. This enables rapid optimization by determining optimization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not. For example, the optimization unit can input the user's emotions into a generative AI and have the generative AI determine the optimization priorities.

[0120] The optimization unit selects the optimal optimization method during optimization, taking into account the user's geographical location information. For example, if the user is in a specific region, the optimization unit selects an optimization method related to that region. For example, if the user is in a specific city, the optimization unit selects an optimization method related to that city. For example, if the user is in a specific country, the optimization unit selects an optimization method related to that country. This makes it possible to optimize by taking into account the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal optimization method.

[0121] The optimization unit analyzes the user's social media activity during optimization and proposes optimization methods. For example, the optimization unit proposes the optimal optimization method based on information shared by the user on social media. For example, the optimization unit proposes the optimal optimization method based on information of accounts followed by the user on social media. For example, the optimization unit proposes the optimal optimization method based on information of groups the user participates in on social media. In this way, the optimal optimization method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input the user's social media activity into a generative AI and have the generative AI execute the proposal of optimization methods.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception desk can be enhanced with a function to input the user's health information. For example, if a user has allergies, they can input this information, allowing the service desk to prioritize notifying them of allergy-friendly restaurants. Furthermore, if a user has a specific health condition (e.g., diabetes or high blood pressure), the service desk can use this information to recommend restaurants that offer appropriate meals. Additionally, the tourist information desk can suggest suitable sightseeing spots based on the user's health condition. This enables travel support that takes the user's health into consideration.

[0124] The optimization unit can analyze a user's travel activity history and suggest a plan for their next trip. For example, based on the user's past visits to tourist spots and restaurants, it can suggest new places to visit on their next trip. It can also predict the user's preferred activities and events from their past travel history and customize the travel plan accordingly. Furthermore, it can analyze user feedback and suggest areas for improvement in the next trip. This allows for the provision of more personalized travel plans by leveraging the user's past travel history.

[0125] The reception desk can estimate the user's emotions and change the design of the input interface based on those emotions. For example, if the user is nervous, it can provide a simple and intuitive design and minimize the number of input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick entry of basic information. In this way, by adjusting the input interface according to the user's emotions, it is possible to reduce user stress and provide a comfortable input experience.

[0126] The tracking unit can adjust the tracking accuracy based on the user's movement speed. For example, if the user is moving at high speed, the tracking accuracy is increased to provide detailed location information. If the user is moving slowly, the tracking accuracy is reduced to provide rough location information. If the user is staying in a specific location, the tracking frequency is adjusted to provide optimal location information. In this way, by adjusting the tracking accuracy according to the user's movement speed, appropriate location information can be provided.

[0127] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is nervous, a simple and easily visible presentation is provided. If the user is relaxed, a presentation including detailed information is provided. If the user is in a hurry, a presentation that gets straight to the point is provided. By adjusting the way information is presented according to the user's emotions, it is possible to provide information that is easily visible.

[0128] The tourist information department can select the most suitable guidance method by referring to the user's past travel history. For example, it can select the most suitable guidance method based on the tourist spots the user has visited in the past. It can select a guidance method that avoids crowds based on the user's past travel history. It can analyze the user's past travel history and select the most efficient guidance method. In this way, the optimal guidance method can be selected by referring to the user's past travel history.

[0129] The emergency response unit can estimate the user's emotions and adjust its emergency response method based on those emotions. For example, if the user is nervous, it will respond in a calm voice. If the user is relaxed, it will provide an emergency response that includes detailed explanations. If the user is in a hurry, it will provide a quick and concise emergency response. This allows for an appropriate response by adjusting the emergency response method according to the user's emotions.

[0130] The optimization unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is nervous, it provides a simple and intuitive optimization method. If the user is relaxed, it provides a detailed optimization method. If the user is in a hurry, it provides a quick and concise optimization method. This allows for appropriate optimization by adjusting the optimization method according to the user's emotions.

[0131] The reception desk can analyze users' social media activity and input relevant information. For example, it can input basic information based on information users have shared on social media, information on accounts users follow on social media, and information on groups users participate in on social media. This allows for the efficient input of relevant information by analyzing users' social media activity.

[0132] The tourist information department can estimate the user's emotions and prioritize tourist information based on those emotions. For example, if the user is nervous, it will prioritize important tourist spots. If the user is relaxed, it will prioritize detailed tourist spots. If the user is in a hurry, it will prioritize the most efficient tourist spots. In this way, by prioritizing tourist information according to the user's emotions, it can prioritize showing them important tourist spots.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk enters the user's basic information. This information includes name, address, contact information, religious preferences, language settings, and alert preferences. Step 2: The tracking unit tracks the user's current location based on the information entered by the reception unit. The tracking unit tracks the user's current location, for example, using GPS. Step 3: The service provider provides information on halal-certified restaurants and mosques based on the user's current location, which has been tracked by the tracking unit. For example, the service provider will notify the user in real time of nearby halal-certified restaurants and mosques. Step 4: The tourist information department provides information about tourist spots based on the information provided by the service provider. For example, the tourist information department provides information about tourist spots tailored to the user's schedule and interests. Step 5: The Emergency Response Department provides emergency support based on the information provided by the Tourist Information Department. For example, if a user is in a difficult situation, the Emergency Response Department will provide directions to the nearest support facility.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, tracking unit, provision unit, tourist information unit, emergency response unit, and optimization unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's basic information. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and tracks the user's current location using GPS. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and notifies the user of nearby halal-certified restaurants and mosques in real time. The tourist information unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides information on tourist spots according to the user's schedule and interests. The emergency response unit is implemented, for example, by the control unit 46A of the smart device 14 and provides directions to the nearest support facility. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the service based on user feedback and history. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, tracking unit, service provision unit, tourist information unit, emergency response unit, and optimization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes in the user's basic information. The tracking unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and tracks the user's current location using GPS. The service provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of nearby halal-certified restaurants and mosques in real time. The tourist information unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides information on tourist spots according to the user's schedule and interests. The emergency response unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides directions to the nearest support facility. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the service based on user feedback and history. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, tracking unit, service provision unit, tourist information unit, emergency response unit, and optimization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs the user's basic information. The tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and tracks the user's current location using GPS. The service provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and notifies the user of nearby halal-certified restaurants and mosques in real time. The tourist information unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information on tourist spots according to the user's schedule and interests. The emergency response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides directions to the nearest support facility. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the service based on user feedback and history. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, tracking unit, service provision unit, tourist information unit, emergency response unit, and optimization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the user's basic information. The tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and tracks the user's current location using GPS. The service provision unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user of nearby halal-certified restaurants and mosques in real time. The tourist information unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information on tourist spots according to the user's schedule and interests. The emergency response unit is implemented by, for example, the control unit 46A of the robot 414 and provides directions to the nearest support facility. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the service based on user feedback and history. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A reception area where users enter their basic information, A tracking unit tracks the user's current location based on the information entered by the reception unit, A provision unit that provides information on halal-friendly restaurants and mosques based on the current location tracked by the aforementioned tracking unit, A tourist information department provides information on tourist spots based on the information provided by the aforementioned provision department, The facility includes an emergency response unit that provides emergency support based on information provided by the aforementioned tourist information unit. A system characterized by the following features. (Note 2) It also includes an optimization unit that optimizes the service based on user feedback and history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Enter basic information such as the user's religious preferences, language settings, and alert preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Use GPS to track the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Real-time notifications of nearby halal-certified restaurants and mosques. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tourist information department, Provides information on tourist spots tailored to the user's schedule and interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned emergency response unit, If a user encounters a difficult situation, the system will provide directions to the nearest support facility. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is We analyze the user's past usage history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering basic information, the input fields are customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering basic information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering basic information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking accuracy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned tracking unit is During tracking, the system selects the optimal tracking method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned tracking unit is During tracking, the frequency of tracking is adjusted based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned tracking unit is It estimates the user's emotions and adjusts how tracking results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned tracking unit is During tracking, we improve tracking accuracy by considering the user's geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is During tracking, we improve tracking accuracy by referring to the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tourist information department, The system estimates the user's emotions and adjusts how tourist information is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned tourist information department, When providing tourist information, the system selects the most suitable guidance method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned tourist information department, When providing tourist information, customize the content of the information based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tourist information department, The system estimates the user's emotions and prioritizes tourist information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tourist information department, When providing tourist information, the system selects the most suitable guidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tourist information department, When providing tourist information, we analyze the user's social media activity to provide relevant tourist information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned emergency response unit, It estimates the user's emotions and adjusts emergency response methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emergency response unit, During emergency situations, the system selects the optimal response method by referring to the user's past trouble history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emergency response unit, During emergency situations, the response method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emergency response unit, It estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emergency response unit, During emergency response, the system selects the optimal response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned emergency response unit, During emergency situations, we analyze users' social media activity and propose appropriate response measures. The system described in Appendix 1, characterized by the features described herein. (Note 38) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The optimization unit, During optimization, the system selects the optimal optimization method by referring to past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 40) The optimization unit, During optimization, the optimization methods are customized based on the user's current usage. The system described in Appendix 1, characterized by the features described herein. (Note 41) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The optimization unit, During optimization, the optimal optimization method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The optimization unit, During optimization, we analyze users' social media activity and propose optimization methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their basic information, A tracking unit tracks the user's current location based on the information entered by the reception unit, A provision unit that provides information on halal-friendly restaurants and mosques based on the current location tracked by the aforementioned tracking unit, A tourist information department provides information on tourist spots based on the information provided by the aforementioned provision department, The facility includes an emergency response unit that provides emergency support based on information provided by the aforementioned tourist information unit. A system characterized by the following features.

2. It also includes an optimization unit that optimizes the service based on user feedback and history. The system according to feature 1.

3. The aforementioned reception unit is Enter basic information such as the user's religious preferences, language settings, and alert preferences. The system according to feature 1.

4. The aforementioned tracking unit is Use GPS to track the user's current location. The system according to feature 1.

5. The aforementioned supply unit is, Real-time notifications of nearby halal-certified restaurants and mosques. The system according to feature 1.

6. The aforementioned tourist information department, Provides information on tourist spots tailored to the user's schedule and interests. The system according to feature 1.

7. The aforementioned emergency response unit, If a user encounters a difficult situation, the system will provide directions to the nearest support facility. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic information based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is We analyze the user's past usage history and suggest the optimal input method. The system according to feature 1.

Images