system

Abstract

The system according to this embodiment aims to provide optimal tourist information by taking into account the user's interests, past travel history, and current situation. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, an analysis unit, a suggestion unit, and a response unit. The collection unit collects the user's interests, past travel history, and current situation. The analysis unit analyzes the information collected by the collection unit. The provision unit provides tourist information based on the analysis results obtained by the analysis unit. The analysis unit analyzes traffic conditions and facility congestion in real time. The suggestion unit suggests the optimal route and places to visit based on the analysis results obtained by the analysis unit. The response unit responds to the user's questions in a natural conversational format.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can provide optimal tourist information by taking into account the user's interests, past travel history, and current situation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 3 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The navigation system according to an embodiment of the present invention is a next-generation navigation system that revolutionizes the sightseeing experience while driving. This navigation system takes into account the user's interests, past travel history, and current circumstances (time, weather, companions, etc.) to provide sightseeing information optimized for each individual driver. This navigation system prioritizes safety while driving and provides information primarily through voice guidance. For example, when passing near a historical building, it will tell interesting anecdotes about the building, and when approaching a restaurant where local specialties can be enjoyed, it will introduce the restaurant's features and popular menu items. Furthermore, the AI ​​analyzes traffic conditions and facility congestion in real time and dynamically proposes the optimal route and points of interest. It also responds to user questions in a natural conversational format, providing an experience as if a knowledgeable local guide were accompanying the user. This navigation system goes beyond simply guiding users to their destination; it transforms the journey itself into an enjoyable adventure, supporting unexpected discoveries and the creation of memories. At the same time, by introducing the hidden charms of the region, it contributes to the decentralization of tourist destinations and the revitalization of local economies. It fuses technology and human curiosity to create a new way to enjoy travel. For example, the system collects information such as the user's interests, past travel history, and current circumstances (time, weather, companions, etc.). This information is then input into the AI. The AI ​​then analyzes the collected information and provides the user with the most relevant travel information. For instance, if the user is interested in historical buildings, the AI ​​will provide anecdotes about those buildings via voice guidance. As the user approaches a restaurant serving local specialties, the AI ​​will introduce the restaurant's features and popular dishes. Furthermore, the AI ​​analyzes traffic conditions and facility congestion in real time, dynamically suggesting optimal routes and points of interest. For example, if there is traffic congestion, the AI ​​will suggest an alternative route to avoid it. Similarly, if a facility is crowded, the AI ​​will suggest another spot to avoid the crowds. The AI ​​responds to user questions in a natural conversational format. For example, if the user asks, "What is the history of this building?", the AI ​​will provide a detailed explanation of its history. This gives the user an experience as if they had a knowledgeable local guide accompanying them.This navigation system goes beyond simply guiding you to your destination; it transforms the journey itself into an enjoyable adventure. Users can enjoy unexpected discoveries and create lasting memories. Furthermore, by showcasing the hidden charms of a region, it contributes to the diversification of tourist destinations and the revitalization of local economies. By fusing technology with human curiosity, it creates a new way to enjoy travel. As a result, the navigation system can provide optimal tourist information based on the user's interests, past travel history, and current circumstances, enhancing the sightseeing experience while driving.

[0029] The navigation system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, an analysis unit, a suggestion unit, and a response unit. The collection unit collects the user's interests, past travel history, and current situation. For example, the collection unit can collect information on the user's hobbies, interests, and specific themes. It can also collect information on places the user has visited in the past, the frequency of travel, and the purpose of travel. Furthermore, the collection unit can collect information such as the user's current location, weather, and traffic conditions. For example, the collection unit can obtain location information from the user's smartphone or in-vehicle system and obtain current weather and traffic conditions from the internet. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can use data analysis methods and algorithms to perform analysis to provide tourist information based on the user's interests. For example, the analysis unit can identify tourist spots that the user is likely to be interested in based on the user's past travel history. The analysis unit can also perform analysis to provide optimal tourist information based on the user's current situation. The provision unit provides tourist information based on the analysis results obtained by the analysis unit. The information provider unit can provide users with information such as tourist attractions, event information, and facility information. The information provider unit can provide tourist information to users using voice guidance. For example, the information provider unit can provide anecdotes about historical buildings via voice guidance. It can also introduce the features and popular menu items of restaurants where local specialties can be enjoyed. The analysis unit analyzes traffic conditions and facility congestion in real time. For example, the analysis unit can analyze traffic congestion information, traffic accident information, and the operating status of public transportation. It can also analyze the number of users at facilities, waiting times, and peak congestion times. The suggestion unit proposes optimal routes and points of interest based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose alternative routes to avoid traffic congestion. It can also propose alternative spots to avoid facility congestion. The response unit responds to user questions in a natural conversational format.The response unit can, for example, provide detailed information about the history of a building if the user asks, "What is the history of this building?". This allows the navigation system according to the embodiment to provide optimal tourist information based on the user's interests, past travel history, and current situation, thereby improving the sightseeing experience while driving.

[0030] The data collection unit collects information about users' interests, past travel history, and current situation. Specifically, it can collect information about users' hobbies, interests, and specific themes. For example, if a user is interested in historical buildings, the unit can collect that information and suggest tourist spots that match the user's interests. The data collection unit can also collect information about places users have visited in the past, the frequency of their travels, and the purpose of their travels. This allows the unit to understand the user's travel patterns and preferences, enabling more personalized suggestions. Furthermore, the data collection unit can collect information such as the user's current location, weather, and traffic conditions. For example, it can obtain location information from the user's smartphone or in-vehicle system and retrieve current weather and traffic conditions from the internet. This enables real-time situational awareness and allows the unit to provide users with the most relevant information. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provision departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can use data analysis methods and algorithms to perform analyses that provide tourism information based on the user's interests and preferences. For example, it can identify tourist spots that a user might be interested in based on their past travel history. This involves using machine learning algorithms to learn the user's behavior patterns and preferences and recommend the most suitable tourist spots. It can also perform analyses to provide optimal tourism information based on the user's current situation. For example, it can suggest the best tourist route and destinations for the user, taking into account the current weather and traffic conditions. Based on these analysis results, the analysis unit can provide personalized tourism information to the user. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict the popularity and congestion of tourist spots during specific seasons or event periods and suggest the best time to visit the user. In this way, the analysis unit can provide optimal tourism information based on the user's interests and current situation, improving the user's tourism experience.

[0032] The information provider unit provides tourist information based on the analysis results obtained by the analysis unit. Specifically, it can provide users with information on tourist spots, events, facilities, etc. The information provider unit can also provide tourist information to users using voice guidance. For example, it can provide anecdotes related to historical buildings via voice guidance. The information provider unit can also introduce the characteristics and popular menu items of restaurants where local specialties can be enjoyed. This allows users to understand and enjoy the charm of tourist destinations more deeply. The information provider unit can provide information at the optimal time based on the user's current location and interests. For example, when a user approaches a specific tourist spot, it can automatically provide information about that spot. The information provider unit can also collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the information provider unit to always provide users with the latest and most appropriate tourist information, improving their tourist experience. Furthermore, the information provider unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the information provider unit to provide users with tourist information quickly and reliably, improving their tourist experience.

[0033] The analytics department analyzes traffic conditions and facility congestion in real time. Specifically, it can analyze traffic congestion information, traffic accident information, and the operating status of public transportation. For example, based on traffic information obtained from the internet, it can grasp the current traffic situation in real time and suggest the optimal route to the user. The analytics department can also analyze the number of users, waiting times, and peak congestion times at facilities. For example, it can grasp the congestion status of tourist spots and restaurants in real time and suggest the optimal time to visit to the user. Based on this information, the analytics department can suggest the optimal sightseeing route and destination to the user. Furthermore, the analytics department can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict traffic conditions and facility congestion during specific seasons or event periods and suggest the optimal time to visit to the user. In this way, the analytics department can provide information to improve the user's sightseeing experience and suggest the optimal sightseeing route and destination to the user.

[0034] The suggestion department proposes optimal routes and points of interest based on the analysis results obtained by the analysis department. Specifically, it can suggest alternative routes to avoid traffic congestion. For example, it can calculate the optimal route for the user based on current traffic conditions and propose an alternative route to avoid congestion. The suggestion department can also suggest alternative spots to avoid crowded facilities. For example, it can suggest the best destinations for the user based on the congestion status of tourist spots and restaurants. The suggestion department can provide these suggestions to the user in real time. For example, when a user selects a particular route, the suggestion department monitors the traffic conditions on that route in real time and proposes an alternative route as needed. The suggestion department can also suggest optimal tourist spots and event information based on the user's interests. In this way, the suggestion department can propose optimal sightseeing routes and destinations to the user, improving the sightseeing experience. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the suggestion department can always provide the user with the best suggestions and improve the sightseeing experience.

[0035] The response unit responds to user questions in a natural conversational format. Specifically, if a user asks, "What is the history of this building?", it can provide a detailed explanation of the building's history. The response unit uses natural language processing technology to understand user questions and generate appropriate answers. For example, if a user asks about a specific tourist spot, the response unit searches for information about that spot and provides the user with a detailed explanation. The response unit can also provide relevant information based on the user's interests. For example, if a user is interested in historical buildings, the response unit provides anecdotes and background information related to those buildings. This allows the user to understand and enjoy the charm of tourist destinations more deeply. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. This allows the response unit to always provide users with the best possible information and enhance their travel experience. The response unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the response unit to provide users with information quickly and reliably, enhancing their travel experience.

[0036] The audio guide can provide anecdotes about historical buildings. For example, when passing near a historical building, the guide can narrate interesting stories about that building. The guide can also provide detailed information about the building's historical background and important events. For instance, it can explain when the building was constructed, who built it, and the historical events it was involved in. Furthermore, the guide can introduce legends and anecdotes related to the building. For example, it can recount anecdotes about famous people or events associated with the building. This enhances the user's sightseeing experience by providing interesting information about historical buildings through audio guidance.

[0037] The service provider can introduce the features and popular menu items of restaurants that offer local specialty dishes. For example, when users pass by a restaurant that serves local specialty dishes, the service provider can introduce the restaurant's features and popular menu items. The service provider can also describe the restaurant's atmosphere and the quality of service. For example, the service provider can describe the restaurant's interior and exterior, as well as the staff's service. Furthermore, the service provider can provide detailed information about the restaurant's popular and recommended dishes. For example, the service provider can introduce the chef's recommended dishes or seasonal menus. In this way, providing information about local specialty dishes can enhance the user's dining experience.

[0038] The suggestion function can propose alternative routes to avoid traffic congestion. For example, if traffic congestion is occurring, it can suggest an alternative route to avoid it. Furthermore, the suggestion function can also propose alternative routes to avoid routes affected by traffic accidents or construction. For example, it can suggest a detour route to avoid a route where a traffic accident has occurred. In addition, the suggestion function can propose the optimal route considering the operating status of public transportation. For example, it can propose the optimal route considering the operating status of trains and buses. By proposing alternative routes to avoid traffic congestion, it is possible to shorten the user's travel time and provide a more comfortable driving experience.

[0039] The suggestion function can propose alternative locations to avoid crowds at a facility. For example, if a facility is crowded, the suggestion function can suggest alternative locations to avoid the crowds. The suggestion function can also suggest alternative times to avoid peak hours. For example, the suggestion function can suggest visiting at a different time to avoid peak hours. Furthermore, the suggestion function can suggest methods of making reservations or purchasing tickets in advance to avoid crowds. For example, the suggestion function can suggest purchasing tickets online in advance to avoid crowds. In this way, by suggesting alternative locations to avoid crowds at a facility, the user's travel experience can be improved.

[0040] The response unit can respond to user questions in a natural conversational format. For example, if a user asks, "What is the history of this building?", the response unit can provide a detailed explanation of the building's history. Similarly, if a user asks, "What are the recommended dishes at this restaurant?", the response unit can provide a detailed explanation of the restaurant's recommended dishes. Furthermore, if a user asks, "What are the tourist attractions in this area?", the response unit can provide a detailed explanation of the area's tourist attractions. For example, the response unit can explain the characteristics, highlights, and access methods of the attractions. This allows the system to respond to user questions in a natural conversational format, providing an experience as if a knowledgeable local guide were accompanying the user.

[0041] The data collection unit can analyze a user's past travel history and select the most appropriate information collection method. For example, it can collect information on similar tourist destinations based on data of tourist destinations the user has visited in the past. It can also collect information on related activities based on data of activities the user has enjoyed in the past. Furthermore, it can collect information on similar accommodations based on data of accommodations the user has used in the past. For example, the data collection unit can use generative AI to perform data analysis in order to analyze a user's past travel history. This allows for the selection of a more appropriate information collection method by analyzing the user's past travel history.

[0042] The data collection unit can filter data based on the user's current interests and preferences during the collection process. For example, it can prioritize collecting information on theme parks that the user is currently interested in. It can also prioritize collecting information on historical buildings that the user is currently interested in. Furthermore, it can prioritize collecting information on outdoor activities that the user is currently interested in. For example, the data collection unit can use generative AI to analyze data in order to filter it based on the user's current interests and preferences. This allows for the provision of more relevant information by filtering information based on the user's current interests and preferences.

[0043] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, it can prioritize collecting information on tourist attractions close to the user's current location. It can also prioritize collecting information on restaurants easily accessible from the user's current location. Furthermore, it can prioritize collecting information on events that the user can attend in a short time from their current location. For example, the data collection unit can use generative AI to perform data analysis in order to filter information while considering the user's geographical location. This allows for the provision of more relevant information by collecting information while considering the user's geographical location.

[0044] The data collection unit can analyze users' social media activity and collect relevant information during the collection process. For example, it can collect information on relevant tourist destinations based on information about tourist destinations shared by users on social media. It can also collect information on relevant restaurants based on information about restaurants that users "liked" on social media. Furthermore, it can collect information on relevant activities based on information about activities that users follow on social media. For example, the data collection unit can use generative AI to perform data analysis in order to analyze users' social media activity. This allows for the provision of more relevant information by analyzing users' social media activity.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can analyze information about important tourist spots in detail. It can also analyze less important information concisely. Furthermore, the analysis unit can prioritize the analysis of highly important information according to the user's interests. For example, the analysis unit can use generative AI to perform data analysis to evaluate the importance of the collected information. This allows for more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information.

[0046] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply an analysis algorithm that emphasizes historical background to information about historical buildings. It can also apply an analysis algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply an analysis algorithm that emphasizes participant reviews and popularity to information about activities. For example, the analysis unit can perform data analysis using generative AI to apply different analysis algorithms depending on the category of information. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information.

[0047] The analysis unit can adjust the order of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can prioritize the analysis of information relevant to the user's current situation. For example, the analysis unit can perform data analysis using generative AI to evaluate when the information was collected. By adjusting the order of analysis based on when the information was collected, it can provide more appropriate analysis results.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's interests. It can also prioritize the analysis of information related to the user's current situation. Furthermore, it can prioritize the analysis of information related to the user's past travel history. For example, the analysis unit can perform data analysis using generative AI to evaluate the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it can provide more appropriate analysis results.

[0049] The information provider can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the provider can provide detailed information about important tourist spots. Conversely, it can provide less important information concisely. Furthermore, the provider can prioritize providing highly important information according to the user's interests. For example, the provider can use generative AI to perform data analysis to evaluate the importance of information. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the information.

[0050] The information provider can apply different information provision algorithms depending on the information category. For example, it can apply an algorithm that emphasizes historical background to information about historical buildings. It can also apply an algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply an algorithm that emphasizes participant reviews and popularity to information about activities. For example, the information provider can use generative AI to perform data analysis in order to apply different information provision algorithms depending on the information category. This allows for the provision of more appropriate information by applying different information provision algorithms depending on the information category.

[0051] The information provider can determine the priority of information delivery based on when the information was collected. For example, the provider can prioritize the delivery of the latest information. Alternatively, it can postpone the delivery of older information. Furthermore, the provider can prioritize the delivery of information relevant to the user's current situation. For instance, the provider can use generative AI to perform data analysis to evaluate when the information was collected. This allows for the provision of more appropriate information by determining the priority of delivery based on when the information was collected.

[0052] The information provider can adjust the order of information delivery based on its relevance. For example, it can prioritize information related to the user's interests. It can also prioritize information related to the user's current situation. Furthermore, it can prioritize information related to the user's past travel history. For example, the information provider can use generative AI to perform data analysis to evaluate the relevance of the information. This allows for the provision of more relevant information by adjusting the order of delivery based on the relevance of the information.

[0053] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between traffic conditions and facility congestion. For example, if traffic congestion occurs, the analysis unit can suggest alternative routes by considering facility congestion. Furthermore, if a facility is crowded, the analysis unit can suggest alternative locations by considering traffic conditions. In addition, the analysis unit can comprehensively analyze traffic conditions and facility congestion to suggest the optimal route. For instance, the analysis unit can use generative AI to perform data analysis to evaluate the interrelationship between traffic conditions and facility congestion. This improves the accuracy of the analysis by considering the interrelationship between traffic conditions and facility congestion, thereby providing more appropriate analysis results.

[0054] The analysis unit can perform analyses while considering user attribute information. For example, the analysis unit can perform analyses to suggest appropriate tourist spots based on the user's age. It can also prioritize analyzing information likely to be of interest to users based on their gender. Furthermore, the analysis unit can analyze family-friendly spots and activities based on the user's family structure. For instance, the analysis unit can use generative AI to perform data analysis to evaluate user attribute information. This allows for more appropriate analysis results by considering user attribute information during the analysis process.

[0055] The analysis department can perform analyses while considering traffic conditions and the geographical distribution of facilities. For example, the analysis department can perform analyses while considering geographical distribution in order to avoid areas where traffic congestion occurs. Furthermore, the analysis department can perform analyses to propose efficient routes by considering the geographical distribution of facilities. In addition, the analysis department can perform analyses to propose the optimal route by comprehensively considering traffic conditions and the geographical distribution of facilities. For example, the analysis department can use generative AI to perform data analysis to evaluate traffic conditions and the geographical distribution of facilities. This allows for the provision of more appropriate analysis results by considering traffic conditions and the geographical distribution of facilities during the analysis.

[0056] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, it can improve the accuracy of its analysis by referring to the latest research on traffic conditions. It can also perform analysis to provide detailed information by referring to literature on tourist attractions. Furthermore, it can perform analysis to suggest optimal routes by referring to data on facility congestion levels. For example, the analysis department can use generative AI to perform data analysis in order to refer to relevant literature. This allows for the provision of more appropriate analysis results by improving the accuracy of the analysis through the referencing of relevant literature.

[0057] The suggestion function can adjust the level of detail in suggestions based on the importance of the information. For example, it can suggest detailed information about important tourist spots. It can also suggest less important information concisely. Furthermore, it can prioritize suggesting highly important information according to the user's interests. For instance, the suggestion function can use generative AI to perform data analysis to evaluate the importance of information. This allows it to provide more appropriate suggestions by adjusting the level of detail based on the importance of the information.

[0058] The suggestion function can apply different suggestion algorithms depending on the category of information when making suggestions. For example, it can apply a suggestion algorithm that emphasizes historical background to information about historical buildings. It can also apply a suggestion algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply a suggestion algorithm that emphasizes participant reviews and popularity to information about activities. For example, the suggestion function can use generative AI to perform data analysis in order to apply different suggestion algorithms depending on the category of information. This allows for the provision of more appropriate suggestions by applying different suggestion algorithms depending on the category of information.

[0059] The proposal function can prioritize proposals based on when the information was collected. For example, it can prioritize the most recent information. It can also postpone proposing older information. Furthermore, it can prioritize information relevant to the user's current situation. For instance, the proposal function can use generative AI to perform data analysis to evaluate when the information was collected. This allows it to provide more appropriate proposals by prioritizing them based on when the information was collected.

[0060] The suggestion function can adjust the order of suggestions based on the relevance of the information. For example, it can prioritize suggesting information related to the user's interests. It can also prioritize suggesting information related to the user's current situation. Furthermore, it can prioritize suggesting information related to the user's past travel history. For example, the suggestion function can perform data analysis using generative AI to evaluate the relevance of the information. This allows it to provide more appropriate suggestions by adjusting the order of suggestions based on the relevance of the information.

[0061] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide detailed responses to important questions, while providing concise responses to less important questions. Furthermore, it can prioritize responses to high-importance questions based on the user's interests. For instance, the response unit can use generative AI to analyze data to assess the importance of a question. This allows it to provide more appropriate responses by adjusting the level of detail based on the importance of the question.

[0062] The response unit can apply different response algorithms depending on the category of the question. For example, for questions about historical buildings, it can apply a response algorithm that emphasizes historical background. For questions about restaurants, it can apply a response algorithm that emphasizes the characteristics and ratings of the cuisine. Furthermore, for questions about activities, it can apply a response algorithm that emphasizes participant reviews and popularity. For example, the response unit can use generative AI to perform data analysis in order to apply different response algorithms depending on the category of the question. This allows for the provision of more appropriate responses by applying different response algorithms depending on the category of the question.

[0063] The response unit can prioritize responses based on when the question was submitted. For example, it can prioritize responding to the most recent question. It can also postpone responding to older questions. Furthermore, it can prioritize responding to questions relevant to the user's current situation. For instance, the response unit can use generative AI to perform data analysis to evaluate when a question was submitted. This allows it to provide more appropriate responses by prioritizing responses based on when the question was submitted.

[0064] The response unit can adjust the order of responses based on the relevance of the questions. For example, it can prioritize responding to questions related to the user's interests. It can also prioritize responding to questions related to the user's current situation. Furthermore, it can prioritize responding to questions related to the user's past travel history. For example, the response unit can perform data analysis using generative AI to evaluate the relevance of questions. This allows it to provide more appropriate responses by adjusting the order of responses based on the relevance of the questions.

[0065] The response unit can apply different response algorithms depending on the category of the question. For example, for questions about historical buildings, it can apply a response algorithm that emphasizes historical background. For questions about restaurants, it can apply a response algorithm that emphasizes the characteristics and ratings of the cuisine. Furthermore, for questions about activities, it can apply a response algorithm that emphasizes participant reviews and popularity. For example, the response unit can use generative AI to perform data analysis in order to apply different response algorithms depending on the category of the question. This allows for the provision of more appropriate responses by applying different response algorithms depending on the category of the question.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The navigation system can further monitor the user's current health status and adjust the information it provides based on that status. For example, if the user is feeling fatigued, it can prioritize providing information on rest stops and places to refresh. If the user is feeling stressed, it can provide information on relaxing natural landscapes and quiet places. Furthermore, if the user is in good health, it can provide information on activities and events. This allows for the provision of more relevant information by tailoring it to the user's health status.

[0068] The navigation system can further analyze the user's social media activity and provide relevant information. For example, it can provide information on relevant tourist destinations based on information about tourist destinations the user has shared on social media. It can also provide information on relevant restaurants based on information about restaurants the user has "liked" on social media. Furthermore, it can provide information on relevant activities based on information about activities the user follows on social media. In this way, by analyzing the user's social media activity, it can provide more relevant information.

[0069] The navigation system can further analyze the user's past travel history and select the most appropriate information gathering method. For example, it can collect information on similar tourist destinations based on data of tourist destinations the user has visited in the past. It can also collect information on related activities based on data of activities the user has enjoyed in the past. Furthermore, it can collect information on similar accommodations based on data of accommodations the user has used in the past. In this way, by analyzing the user's past travel history, a more appropriate information gathering method can be selected.

[0070] Navigation systems can further prioritize the collection of highly relevant information by considering the user's geographical location. For example, they can prioritize information on tourist attractions close to the user's current location. They can also prioritize information on restaurants easily accessible from the user's current location. Furthermore, they can prioritize information on events that the user can attend in a short time from their current location. By considering the user's geographical location when collecting information, the system can provide more relevant information.

[0071] Navigation systems can further analyze user attributes. For example, they can analyze to suggest appropriate tourist spots based on the user's age. They can also prioritize information likely to interest the user based on their gender. Furthermore, they can analyze family-friendly spots and activities based on the user's family structure. By considering user attributes, they can provide more appropriate analysis results.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects information about the user's interests, past travel history, and current situation. For example, it collects information such as the user's hobbies and interests, information on specific themes, places visited in the past, frequency of travel, purpose of travel, current location information, weather, and traffic conditions. The data collection unit can obtain location information from the user's smartphone or in-vehicle system, and obtain current weather and traffic conditions from the internet. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses data analysis methods and algorithms to perform analysis in order to provide tourist information based on the user's interests and preferences. Based on the user's past travel history, it identifies tourist spots that might be of interest and performs analysis to provide optimal tourist information based on the current situation. Step 3: The service provider provides tourist information based on the analysis results obtained by the analysis unit. For example, it provides users with information on tourist spots, events, and facilities, and provides tourist information using voice guidance. It introduces anecdotes related to historical buildings and the characteristics and popular menu items of restaurants where you can enjoy local specialties. Step 4: The analysis department analyzes traffic conditions and facility congestion in real time. For example, it analyzes traffic congestion information, traffic accident information, public transportation operating status, the number of facility users and waiting times, and peak congestion times. Step 5: The proposal team proposes the optimal route and points of interest based on the analysis results obtained by the analysis team. For example, they might suggest alternative routes to avoid traffic congestion or alternative spots to avoid crowded facilities. Step 6: The response unit responds to the user's question in a natural conversational format. For example, if the user asks, "What is the history of this building?", it will provide a detailed explanation of the building's history.

[0074] (Example of form 2) The navigation system according to an embodiment of the present invention is a next-generation navigation system that revolutionizes the sightseeing experience while driving. This navigation system takes into account the user's interests, past travel history, and current circumstances (time, weather, companions, etc.) to provide sightseeing information optimized for each individual driver. This navigation system prioritizes safety while driving and provides information primarily through voice guidance. For example, when passing near a historical building, it will tell interesting anecdotes about the building, and when approaching a restaurant where local specialties can be enjoyed, it will introduce the restaurant's features and popular menu items. Furthermore, the AI ​​analyzes traffic conditions and facility congestion in real time and dynamically proposes the optimal route and points of interest. It also responds to user questions in a natural conversational format, providing an experience as if a knowledgeable local guide were accompanying the user. This navigation system goes beyond simply guiding users to their destination; it transforms the journey itself into an enjoyable adventure, supporting unexpected discoveries and the creation of memories. At the same time, by introducing the hidden charms of the region, it contributes to the decentralization of tourist destinations and the revitalization of local economies. It fuses technology and human curiosity to create a new way to enjoy travel. For example, the system collects information such as the user's interests, past travel history, and current circumstances (time, weather, companions, etc.). This information is then input into the AI. The AI ​​then analyzes the collected information and provides the user with the most relevant travel information. For instance, if the user is interested in historical buildings, the AI ​​will provide anecdotes about those buildings via voice guidance. As the user approaches a restaurant serving local specialties, the AI ​​will introduce the restaurant's features and popular dishes. Furthermore, the AI ​​analyzes traffic conditions and facility congestion in real time, dynamically suggesting optimal routes and points of interest. For example, if there is traffic congestion, the AI ​​will suggest an alternative route to avoid it. Similarly, if a facility is crowded, the AI ​​will suggest another spot to avoid the crowds. The AI ​​responds to user questions in a natural conversational format. For example, if the user asks, "What is the history of this building?", the AI ​​will provide a detailed explanation of its history. This gives the user an experience as if they had a knowledgeable local guide accompanying them.This navigation system goes beyond simply guiding you to your destination; it transforms the journey itself into an enjoyable adventure. Users can enjoy unexpected discoveries and create lasting memories. Furthermore, by showcasing the hidden charms of a region, it contributes to the diversification of tourist destinations and the revitalization of local economies. By fusing technology with human curiosity, it creates a new way to enjoy travel. As a result, the navigation system can provide optimal tourist information based on the user's interests, past travel history, and current circumstances, enhancing the sightseeing experience while driving.

[0075] The navigation system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, an analysis unit, a suggestion unit, and a response unit. The collection unit collects the user's interests, past travel history, and current situation. For example, the collection unit can collect information on the user's hobbies, interests, and specific themes. It can also collect information on places the user has visited in the past, the frequency of travel, and the purpose of travel. Furthermore, the collection unit can collect information such as the user's current location, weather, and traffic conditions. For example, the collection unit can obtain location information from the user's smartphone or in-vehicle system and obtain current weather and traffic conditions from the internet. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can use data analysis methods and algorithms to perform analysis to provide tourist information based on the user's interests. For example, the analysis unit can identify tourist spots that the user is likely to be interested in based on the user's past travel history. The analysis unit can also perform analysis to provide optimal tourist information based on the user's current situation. The provision unit provides tourist information based on the analysis results obtained by the analysis unit. The information provider unit can provide users with information such as tourist attractions, event information, and facility information. The information provider unit can provide tourist information to users using voice guidance. For example, the information provider unit can provide anecdotes about historical buildings via voice guidance. It can also introduce the features and popular menu items of restaurants where local specialties can be enjoyed. The analysis unit analyzes traffic conditions and facility congestion in real time. For example, the analysis unit can analyze traffic congestion information, traffic accident information, and the operating status of public transportation. It can also analyze the number of users at facilities, waiting times, and peak congestion times. The suggestion unit proposes optimal routes and points of interest based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose alternative routes to avoid traffic congestion. It can also propose alternative spots to avoid facility congestion. The response unit responds to user questions in a natural conversational format.The response unit can, for example, provide detailed information about the history of a building if the user asks, "What is the history of this building?". This allows the navigation system according to the embodiment to provide optimal tourist information based on the user's interests, past travel history, and current situation, thereby improving the sightseeing experience while driving.

[0076] The data collection unit collects information about users' interests, past travel history, and current situation. Specifically, it can collect information about users' hobbies, interests, and specific themes. For example, if a user is interested in historical buildings, the unit can collect that information and suggest tourist spots that match the user's interests. The data collection unit can also collect information about places users have visited in the past, the frequency of their travels, and the purpose of their travels. This allows the unit to understand the user's travel patterns and preferences, enabling more personalized suggestions. Furthermore, the data collection unit can collect information such as the user's current location, weather, and traffic conditions. For example, it can obtain location information from the user's smartphone or in-vehicle system and retrieve current weather and traffic conditions from the internet. This enables real-time situational awareness and allows the unit to provide users with the most relevant information. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provision departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can use data analysis methods and algorithms to perform analyses that provide tourism information based on the user's interests and preferences. For example, it can identify tourist spots that a user might be interested in based on their past travel history. This involves using machine learning algorithms to learn the user's behavior patterns and preferences and recommend the most suitable tourist spots. It can also perform analyses to provide optimal tourism information based on the user's current situation. For example, it can suggest the best tourist route and destinations for the user, taking into account the current weather and traffic conditions. Based on these analysis results, the analysis unit can provide personalized tourism information to the user. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict the popularity and congestion of tourist spots during specific seasons or event periods and suggest the best time to visit the user. In this way, the analysis unit can provide optimal tourism information based on the user's interests and current situation, improving the user's tourism experience.

[0078] The information provider unit provides tourist information based on the analysis results obtained by the analysis unit. Specifically, it can provide users with information on tourist spots, events, facilities, etc. The information provider unit can also provide tourist information to users using voice guidance. For example, it can provide anecdotes related to historical buildings via voice guidance. The information provider unit can also introduce the characteristics and popular menu items of restaurants where local specialties can be enjoyed. This allows users to understand and enjoy the charm of tourist destinations more deeply. The information provider unit can provide information at the optimal time based on the user's current location and interests. For example, when a user approaches a specific tourist spot, it can automatically provide information about that spot. The information provider unit can also collect user feedback and continuously improve the accuracy and content of the information it provides. This allows the information provider unit to always provide users with the latest and most appropriate tourist information, improving their tourist experience. Furthermore, the information provider unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the information provider unit to provide users with tourist information quickly and reliably, improving their tourist experience.

[0079] The analytics department analyzes traffic conditions and facility congestion in real time. Specifically, it can analyze traffic congestion information, traffic accident information, and the operating status of public transportation. For example, based on traffic information obtained from the internet, it can grasp the current traffic situation in real time and suggest the optimal route to the user. The analytics department can also analyze the number of users, waiting times, and peak congestion times at facilities. For example, it can grasp the congestion status of tourist spots and restaurants in real time and suggest the optimal time to visit to the user. Based on this information, the analytics department can suggest the optimal sightseeing route and destination to the user. Furthermore, the analytics department can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, it can predict traffic conditions and facility congestion during specific seasons or event periods and suggest the optimal time to visit to the user. In this way, the analytics department can provide information to improve the user's sightseeing experience and suggest the optimal sightseeing route and destination to the user.

[0080] The suggestion department proposes optimal routes and points of interest based on the analysis results obtained by the analysis department. Specifically, it can suggest alternative routes to avoid traffic congestion. For example, it can calculate the optimal route for the user based on current traffic conditions and propose an alternative route to avoid congestion. The suggestion department can also suggest alternative spots to avoid crowded facilities. For example, it can suggest the best destinations for the user based on the congestion status of tourist spots and restaurants. The suggestion department can provide these suggestions to the user in real time. For example, when a user selects a particular route, the suggestion department monitors the traffic conditions on that route in real time and proposes an alternative route as needed. The suggestion department can also suggest optimal tourist spots and event information based on the user's interests. In this way, the suggestion department can propose optimal sightseeing routes and destinations to the user, improving the sightseeing experience. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the suggestion department can always provide the user with the best suggestions and improve the sightseeing experience.

[0081] The response unit responds to user questions in a natural conversational format. Specifically, if a user asks, "What is the history of this building?", it can provide a detailed explanation of the building's history. The response unit uses natural language processing technology to understand user questions and generate appropriate answers. For example, if a user asks about a specific tourist spot, the response unit searches for information about that spot and provides the user with a detailed explanation. The response unit can also provide relevant information based on the user's interests. For example, if a user is interested in historical buildings, the response unit provides anecdotes and background information related to those buildings. This allows the user to understand and enjoy the charm of tourist destinations more deeply. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. This allows the response unit to always provide users with the best possible information and enhance their travel experience. The response unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the response unit to provide users with information quickly and reliably, enhancing their travel experience.

[0082] The audio guide can provide anecdotes about historical buildings. For example, when passing near a historical building, the guide can narrate interesting stories about that building. The guide can also provide detailed information about the building's historical background and important events. For instance, it can explain when the building was constructed, who built it, and the historical events it was involved in. Furthermore, the guide can introduce legends and anecdotes related to the building. For example, it can recount anecdotes about famous people or events associated with the building. This enhances the user's sightseeing experience by providing interesting information about historical buildings through audio guidance.

[0083] The service provider can introduce the features and popular menu items of restaurants that offer local specialty dishes. For example, when users pass by a restaurant that serves local specialty dishes, the service provider can introduce the restaurant's features and popular menu items. The service provider can also describe the restaurant's atmosphere and the quality of service. For example, the service provider can describe the restaurant's interior and exterior, as well as the staff's service. Furthermore, the service provider can provide detailed information about the restaurant's popular and recommended dishes. For example, the service provider can introduce the chef's recommended dishes or seasonal menus. In this way, providing information about local specialty dishes can enhance the user's dining experience.

[0084] The suggestion function can propose alternative routes to avoid traffic congestion. For example, if traffic congestion is occurring, it can suggest an alternative route to avoid it. Furthermore, the suggestion function can also propose alternative routes to avoid routes affected by traffic accidents or construction. For example, it can suggest a detour route to avoid a route where a traffic accident has occurred. In addition, the suggestion function can propose the optimal route considering the operating status of public transportation. For example, it can propose the optimal route considering the operating status of trains and buses. By proposing alternative routes to avoid traffic congestion, it is possible to shorten the user's travel time and provide a more comfortable driving experience.

[0085] The suggestion function can propose alternative locations to avoid crowds at a facility. For example, if a facility is crowded, the suggestion function can suggest alternative locations to avoid the crowds. The suggestion function can also suggest alternative times to avoid peak hours. For example, the suggestion function can suggest visiting at a different time to avoid peak hours. Furthermore, the suggestion function can suggest methods of making reservations or purchasing tickets in advance to avoid crowds. For example, the suggestion function can suggest purchasing tickets online in advance to avoid crowds. In this way, by suggesting alternative locations to avoid crowds at a facility, the user's travel experience can be improved.

[0086] The response unit can respond to user questions in a natural conversational format. For example, if a user asks, "What is the history of this building?", the response unit can provide a detailed explanation of the building's history. Similarly, if a user asks, "What are the recommended dishes at this restaurant?", the response unit can provide a detailed explanation of the restaurant's recommended dishes. Furthermore, if a user asks, "What are the tourist attractions in this area?", the response unit can provide a detailed explanation of the area's tourist attractions. For example, the response unit can explain the characteristics, highlights, and access methods of the attractions. This allows the system to respond to user questions in a natural conversational format, providing an experience as if a knowledgeable local guide were accompanying the user.

[0087] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is excited, the unit can prioritize collecting information about activities and events. If the user is relaxed, the unit can prioritize collecting information about natural scenery and relaxing spots. Furthermore, if the user is tired, the unit can prioritize collecting information about rest spots and places to refresh. For example, the data collection unit can use an emotion engine or generative AI to estimate the user's emotions. This allows the unit to provide more relevant information by prioritizing information based on the user's emotions.

[0088] The data collection unit can analyze a user's past travel history and select the most appropriate information collection method. For example, it can collect information on similar tourist destinations based on data of tourist destinations the user has visited in the past. It can also collect information on related activities based on data of activities the user has enjoyed in the past. Furthermore, it can collect information on similar accommodations based on data of accommodations the user has used in the past. For example, the data collection unit can use generative AI to perform data analysis in order to analyze a user's past travel history. This allows for the selection of a more appropriate information collection method by analyzing the user's past travel history.

[0089] The data collection unit can filter data based on the user's current interests and preferences during the collection process. For example, it can prioritize collecting information on theme parks that the user is currently interested in. It can also prioritize collecting information on historical buildings that the user is currently interested in. Furthermore, it can prioritize collecting information on outdoor activities that the user is currently interested in. For example, the data collection unit can use generative AI to analyze data in order to filter it based on the user's current interests and preferences. This allows for the provision of more relevant information by filtering information based on the user's current interests and preferences.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is excited, the unit can collect and provide event information in real time. If the user is relaxed, the unit can collect and provide tourist information at a slower pace. Furthermore, if the user is tired, the unit can collect and provide information about rest stops earlier. For example, the data collection unit can use an emotion engine or generative AI to estimate the user's emotions. This allows the system to adjust the timing of information collection based on the user's emotions, providing information at a more appropriate time.

[0091] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, it can prioritize collecting information on tourist attractions close to the user's current location. It can also prioritize collecting information on restaurants easily accessible from the user's current location. Furthermore, it can prioritize collecting information on events that the user can attend in a short time from their current location. For example, the data collection unit can use generative AI to perform data analysis in order to filter information while considering the user's geographical location. This allows for the provision of more relevant information by collecting information while considering the user's geographical location.

[0092] The data collection unit can analyze users' social media activity and collect relevant information during the collection process. For example, it can collect information on relevant tourist destinations based on information about tourist destinations shared by users on social media. It can also collect information on relevant restaurants based on information about restaurants that users "liked" on social media. Furthermore, it can collect information on relevant activities based on information about activities that users follow on social media. For example, the data collection unit can use generative AI to perform data analysis in order to analyze users' social media activity. This allows for the provision of more relevant information by analyzing users' social media activity.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is excited, the analysis unit can increase the accuracy of the analysis to provide more detailed information. Conversely, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide concise information. Furthermore, if the user is tired, the analysis unit can prioritize analyzing important information. For instance, the analysis unit can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows for more appropriate analysis results by adjusting the analysis method based on the user's emotions.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit can analyze information about important tourist spots in detail. It can also analyze less important information concisely. Furthermore, the analysis unit can prioritize the analysis of highly important information according to the user's interests. For example, the analysis unit can use generative AI to perform data analysis to evaluate the importance of the collected information. This allows for more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information.

[0095] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply an analysis algorithm that emphasizes historical background to information about historical buildings. It can also apply an analysis algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply an analysis algorithm that emphasizes participant reviews and popularity to information about activities. For example, the analysis unit can perform data analysis using generative AI to apply different analysis algorithms depending on the category of information. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information.

[0096] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize the analysis of activity information. Similarly, if the user is relaxed, the analysis unit can prioritize the analysis of natural scenery information. Furthermore, if the user is tired, the analysis unit can prioritize the analysis of rest spot information. For instance, the analysis unit can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows for more appropriate analysis results by prioritizing analysis based on the user's emotions.

[0097] The analysis unit can adjust the order of analysis based on when the information was collected. For example, the analysis unit can prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can prioritize the analysis of information relevant to the user's current situation. For example, the analysis unit can perform data analysis using generative AI to evaluate when the information was collected. By adjusting the order of analysis based on when the information was collected, it can provide more appropriate analysis results.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's interests. It can also prioritize the analysis of information related to the user's current situation. Furthermore, it can prioritize the analysis of information related to the user's past travel history. For example, the analysis unit can perform data analysis using generative AI to evaluate the relevance of the information. By adjusting the order of analysis based on the relevance of the information, it can provide more appropriate analysis results.

[0099] The service provider can estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is excited, the service provider can provide visually stimulating presentations. If the user is relaxed, the service provider can provide calm presentations. Furthermore, if the user is tired, the service provider can provide concise and easy-to-understand presentations. For instance, the service provider can use an emotion engine or generative AI to estimate the user's emotions. This allows the service provider to provide more appropriate information by adjusting the way information is presented based on the user's emotions.

[0100] The information provider can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the provider can provide detailed information about important tourist spots. Conversely, it can provide less important information concisely. Furthermore, the provider can prioritize providing highly important information according to the user's interests. For example, the provider can use generative AI to perform data analysis to evaluate the importance of information. This allows for the provision of more appropriate information by adjusting the level of detail based on the importance of the information.

[0101] The information provider can apply different information provision algorithms depending on the information category. For example, it can apply an algorithm that emphasizes historical background to information about historical buildings. It can also apply an algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply an algorithm that emphasizes participant reviews and popularity to information about activities. For example, the information provider can use generative AI to perform data analysis in order to apply different information provision algorithms depending on the information category. This allows for the provision of more appropriate information by applying different information provision algorithms depending on the information category.

[0102] The service provider can estimate the user's emotions and adjust the length of the information provided based on those emotions. For example, if the user is excited, the service provider can provide detailed information. If the user is relaxed, the service provider can provide concise information. Furthermore, if the user is tired, the service provider can provide short, to-the-point information. For example, the service provider can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows the service provider to provide more appropriate information by adjusting the length of the information based on the user's emotions.

[0103] The information provider can determine the priority of information delivery based on when the information was collected. For example, the provider can prioritize the delivery of the latest information. Alternatively, it can postpone the delivery of older information. Furthermore, the provider can prioritize the delivery of information relevant to the user's current situation. For instance, the provider can use generative AI to perform data analysis to evaluate when the information was collected. This allows for the provision of more appropriate information by determining the priority of delivery based on when the information was collected.

[0104] The information provider can adjust the order of information delivery based on its relevance. For example, it can prioritize information related to the user's interests. It can also prioritize information related to the user's current situation. Furthermore, it can prioritize information related to the user's past travel history. For example, the information provider can use generative AI to perform data analysis to evaluate the relevance of the information. This allows for the provision of more relevant information by adjusting the order of delivery based on the relevance of the information.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is excited, the analysis unit can perform a detailed analysis. If the user is relaxed, the analysis unit can perform a concise analysis. Furthermore, if the user is tired, the analysis unit can prioritize the analysis of important information. For example, the analysis unit can use an emotion engine or generative AI to estimate the user's emotions. This allows for more appropriate analysis results by adjusting the analysis criteria based on the user's emotions.

[0106] The analysis unit can improve the accuracy of its analysis by considering the interrelationship between traffic conditions and facility congestion. For example, if traffic congestion occurs, the analysis unit can suggest alternative routes by considering facility congestion. Furthermore, if a facility is crowded, the analysis unit can suggest alternative locations by considering traffic conditions. In addition, the analysis unit can comprehensively analyze traffic conditions and facility congestion to suggest the optimal route. For instance, the analysis unit can use generative AI to perform data analysis to evaluate the interrelationship between traffic conditions and facility congestion. This improves the accuracy of the analysis by considering the interrelationship between traffic conditions and facility congestion, thereby providing more appropriate analysis results.

[0107] The analysis unit can perform analyses while considering user attribute information. For example, the analysis unit can perform analyses to suggest appropriate tourist spots based on the user's age. It can also prioritize analyzing information likely to be of interest to users based on their gender. Furthermore, the analysis unit can analyze family-friendly spots and activities based on the user's family structure. For instance, the analysis unit can use generative AI to perform data analysis to evaluate user attribute information. This allows for more appropriate analysis results by considering user attribute information during the analysis process.

[0108] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can display activity information first. Similarly, if the user is relaxed, it can display natural scenery information first. Furthermore, if the user is tired, it can display rest spot information first. For instance, the analysis unit can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows for the provision of more relevant information by adjusting the display order of the analysis results based on the user's emotions.

[0109] The analysis department can perform analyses while considering traffic conditions and the geographical distribution of facilities. For example, the analysis department can perform analyses while considering geographical distribution in order to avoid areas where traffic congestion occurs. Furthermore, the analysis department can perform analyses to propose efficient routes by considering the geographical distribution of facilities. In addition, the analysis department can perform analyses to propose the optimal route by comprehensively considering traffic conditions and the geographical distribution of facilities. For example, the analysis department can use generative AI to perform data analysis to evaluate traffic conditions and the geographical distribution of facilities. This allows for the provision of more appropriate analysis results by considering traffic conditions and the geographical distribution of facilities during the analysis.

[0110] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, it can improve the accuracy of its analysis by referring to the latest research on traffic conditions. It can also perform analysis to provide detailed information by referring to literature on tourist attractions. Furthermore, it can perform analysis to suggest optimal routes by referring to data on facility congestion levels. For example, the analysis department can use generative AI to perform data analysis in order to refer to relevant literature. This allows for the provision of more appropriate analysis results by improving the accuracy of the analysis through the referencing of relevant literature.

[0111] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is excited, the suggestion function can present suggestions in a visually stimulating way. If the user is relaxed, the suggestion function can present suggestions in a calming way. Furthermore, if the user is tired, the suggestion function can present suggestions in a concise and easy-to-understand way. For instance, the suggestion function can use an emotion engine or generative AI to estimate the user's emotions. This allows it to provide more appropriate suggestions by adjusting the way it presents suggestions based on the user's emotions.

[0112] The suggestion function can adjust the level of detail in suggestions based on the importance of the information. For example, it can suggest detailed information about important tourist spots. It can also suggest less important information concisely. Furthermore, it can prioritize suggesting highly important information according to the user's interests. For instance, the suggestion function can use generative AI to perform data analysis to evaluate the importance of information. This allows it to provide more appropriate suggestions by adjusting the level of detail based on the importance of the information.

[0113] The suggestion function can apply different suggestion algorithms depending on the category of information when making suggestions. For example, it can apply a suggestion algorithm that emphasizes historical background to information about historical buildings. It can also apply a suggestion algorithm that emphasizes the characteristics and ratings of the cuisine to information about restaurants. Furthermore, it can apply a suggestion algorithm that emphasizes participant reviews and popularity to information about activities. For example, the suggestion function can use generative AI to perform data analysis in order to apply different suggestion algorithms depending on the category of information. This allows for the provision of more appropriate suggestions by applying different suggestion algorithms depending on the category of information.

[0114] The suggestion function can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is excited, the suggestion function can provide detailed suggestions. If the user is relaxed, it can provide concise suggestions. Furthermore, if the user is tired, it can provide short, to-the-point suggestions. For instance, the suggestion function can use an emotion engine or generative AI to estimate the user's emotions. This allows it to provide more appropriate suggestions by adjusting the length of the suggestions based on the user's emotions.

[0115] The proposal function can prioritize proposals based on when the information was collected. For example, it can prioritize the most recent information. It can also postpone proposing older information. Furthermore, it can prioritize information relevant to the user's current situation. For instance, the proposal function can use generative AI to perform data analysis to evaluate when the information was collected. This allows it to provide more appropriate proposals by prioritizing them based on when the information was collected.

[0116] The suggestion function can adjust the order of suggestions based on the relevance of the information. For example, it can prioritize suggesting information related to the user's interests. It can also prioritize suggesting information related to the user's current situation. Furthermore, it can prioritize suggesting information related to the user's past travel history. For example, the suggestion function can perform data analysis using generative AI to evaluate the relevance of the information. This allows it to provide more appropriate suggestions by adjusting the order of suggestions based on the relevance of the information.

[0117] The response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is excited, the response unit can respond in a bright and cheerful voice. If the user is relaxed, it can respond in a calm voice. Furthermore, if the user is tired, it can respond in a gentle voice. For example, the response unit can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows it to provide a more appropriate response by adjusting the way it expresses its response based on the user's emotions.

[0118] The response unit can adjust the level of detail in its response based on the importance of the question. For example, it can provide detailed responses to important questions, while providing concise responses to less important questions. Furthermore, it can prioritize responses to high-importance questions based on the user's interests. For instance, the response unit can use generative AI to analyze data to assess the importance of a question. This allows it to provide more appropriate responses by adjusting the level of detail based on the importance of the question.

[0119] The response unit can apply different response algorithms depending on the category of the question. For example, for questions about historical buildings, it can apply a response algorithm that emphasizes historical background. For questions about restaurants, it can apply a response algorithm that emphasizes the characteristics and ratings of the cuisine. Furthermore, for questions about activities, it can apply a response algorithm that emphasizes participant reviews and popularity. For example, the response unit can use generative AI to perform data analysis in order to apply different response algorithms depending on the category of the question. This allows for the provision of more appropriate responses by applying different response algorithms depending on the category of the question.

[0120] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is excited, the response unit can provide a detailed response. If the user is relaxed, the response unit can provide a concise response. Furthermore, if the user is tired, the response unit can provide a short, to-the-point response. For example, the response unit can use an emotion estimation function with an emotion engine or generative AI to estimate the user's emotions. This allows it to provide a more appropriate response by adjusting the length of the response based on the user's emotions.

[0121] The response unit can prioritize responses based on when the question was submitted. For example, it can prioritize responding to the most recent question. It can also postpone responding to older questions. Furthermore, it can prioritize responding to questions relevant to the user's current situation. For instance, the response unit can use generative AI to perform data analysis to evaluate when a question was submitted. This allows it to provide more appropriate responses by prioritizing responses based on when the question was submitted.

[0122] The response unit can adjust the order of responses based on the relevance of the questions. For example, it can prioritize responding to questions related to the user's interests. It can also prioritize responding to questions related to the user's current situation. Furthermore, it can prioritize responding to questions related to the user's past travel history. For example, the response unit can perform data analysis using generative AI to evaluate the relevance of questions. This allows it to provide more appropriate responses by adjusting the order of responses based on the relevance of the questions.

[0123] The response unit can apply different response algorithms depending on the category of the question. For example, for questions about historical buildings, it can apply a response algorithm that emphasizes historical background. For questions about restaurants, it can apply a response algorithm that emphasizes the characteristics and ratings of the cuisine. Furthermore, for questions about activities, it can apply a response algorithm that emphasizes participant reviews and popularity. For example, the response unit can use generative AI to perform data analysis in order to apply different response algorithms depending on the category of the question. This allows for the provision of more appropriate responses by applying different response algorithms depending on the category of the question.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The navigation system can further estimate the user's emotions and adjust the content of the information it provides based on those emotions. For example, if the user is excited, it can prioritize providing information about activities and events. If the user is relaxed, it can provide information about natural scenery and relaxing spots. Furthermore, if the user is tired, it can provide information about rest stops and places to refresh. In this way, by adjusting the content of information based on the user's emotions, it can provide more appropriate information.

[0126] The navigation system can further monitor the user's current health status and adjust the information it provides based on that status. For example, if the user is feeling fatigued, it can prioritize providing information on rest stops and places to refresh. If the user is feeling stressed, it can provide information on relaxing natural landscapes and quiet places. Furthermore, if the user is in good health, it can provide information on activities and events. This allows for the provision of more relevant information by tailoring it to the user's health status.

[0127] The navigation system can further analyze the user's social media activity and provide relevant information. For example, it can provide information on relevant tourist destinations based on information about tourist destinations the user has shared on social media. It can also provide information on relevant restaurants based on information about restaurants the user has "liked" on social media. Furthermore, it can provide information on relevant activities based on information about activities the user follows on social media. In this way, by analyzing the user's social media activity, it can provide more relevant information.

[0128] The navigation system can further estimate the user's emotions and adjust the way information is presented based on those emotions. For example, if the user is excited, it can provide visually stimulating presentations. If the user is relaxed, it can provide calm presentations. Furthermore, if the user is tired, it can provide concise and easy-to-understand presentations. By adjusting the presentation of information based on the user's emotions, the system can provide more appropriate information.

[0129] The navigation system can further analyze the user's past travel history and select the most appropriate information gathering method. For example, it can collect information on similar tourist destinations based on data of tourist destinations the user has visited in the past. It can also collect information on related activities based on data of activities the user has enjoyed in the past. Furthermore, it can collect information on similar accommodations based on data of accommodations the user has used in the past. In this way, by analyzing the user's past travel history, a more appropriate information gathering method can be selected.

[0130] The navigation system can further estimate the user's emotions and adjust the length of the information provided based on those emotions. For example, if the user is excited, detailed information can be provided. If the user is relaxed, concise information can be provided. Furthermore, if the user is tired, short, to-the-point information can be provided. By adjusting the length of information based on the user's emotions, the system can provide more relevant information.

[0131] Navigation systems can further prioritize the collection of highly relevant information by considering the user's geographical location. For example, they can prioritize information on tourist attractions close to the user's current location. They can also prioritize information on restaurants easily accessible from the user's current location. Furthermore, they can prioritize information on events that the user can attend in a short time from their current location. By considering the user's geographical location when collecting information, the system can provide more relevant information.

[0132] The navigation system can further estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is excited, suggestions can be presented in a visually stimulating way. If the user is relaxed, suggestions can be presented in a calming way. Furthermore, if the user is tired, suggestions can be presented in a concise and easy-to-understand way. In this way, by adjusting the presentation of suggestions based on the user's emotions, more appropriate suggestions can be provided.

[0133] Navigation systems can further analyze user attributes. For example, they can analyze to suggest appropriate tourist spots based on the user's age. They can also prioritize information likely to interest the user based on their gender. Furthermore, they can analyze family-friendly spots and activities based on the user's family structure. By considering user attributes, they can provide more appropriate analysis results.

[0134] The navigation system can further estimate the user's emotions and adjust the way it expresses its responses based on those emotions. For example, if the user is excited, it can respond in a bright, cheerful voice. If the user is relaxed, it can respond in a calm voice. Furthermore, if the user is tired, it can respond in a gentle voice. By adjusting the way it expresses its responses based on the user's emotions, it can provide more appropriate responses.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit collects information about the user's interests, past travel history, and current situation. For example, it collects information such as the user's hobbies and interests, information on specific themes, places visited in the past, frequency of travel, purpose of travel, current location information, weather, and traffic conditions. The data collection unit can obtain location information from the user's smartphone or in-vehicle system, and obtain current weather and traffic conditions from the internet. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses data analysis methods and algorithms to perform analysis in order to provide tourist information based on the user's interests and preferences. Based on the user's past travel history, it identifies tourist spots that might be of interest and performs analysis to provide optimal tourist information based on the current situation. Step 3: The service provider provides tourist information based on the analysis results obtained by the analysis unit. For example, it provides users with information on tourist spots, events, and facilities, and provides tourist information using voice guidance. It introduces anecdotes related to historical buildings and the characteristics and popular menu items of restaurants where you can enjoy local specialties. Step 4: The analysis department analyzes traffic conditions and facility congestion in real time. For example, it analyzes traffic congestion information, traffic accident information, public transportation operating status, the number of facility users and waiting times, and peak congestion times. Step 5: The proposal team proposes the optimal route and points of interest based on the analysis results obtained by the analysis team. For example, they might suggest alternative routes to avoid traffic congestion or alternative spots to avoid crowded facilities. Step 6: The response unit responds to the user's question in a natural conversational format. For example, if the user asks, "What is the history of this building?", it will provide a detailed explanation of the building's history.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, analysis unit, suggestion unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's interests, past travel history, and current situation by the control unit 46A of the smart device 14. The analysis unit analyzes the collected information by the identification processing unit 290 of the data processing unit 12. The provision unit provides tourist information via voice guidance based on the analysis results. The analysis unit analyzes traffic conditions and facility congestion in real time. The suggestion unit suggests the optimal route and points of interest based on the analysis results. The response unit responds to the user's questions in a natural conversational format. The collection unit estimates the user's emotions and determines the priority of information based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, analysis unit, suggestion unit, and response unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's interests, past travel history, and current situation by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information by the identification processing unit 290 of the data processing unit 12. The provision unit provides tourist information via voice guidance based on the analysis results. The analysis unit analyzes traffic conditions and facility congestion in real time. The suggestion unit suggests the optimal route and points of interest based on the analysis results. The response unit responds to the user's questions in a natural conversational format. The collection unit estimates the user's emotions and determines the priority of information based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, analysis unit, suggestion unit, and response unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit collects the user's interests, past travel history, and current situation using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information using the identification processing unit 290 of the data processing unit 12. The provision unit provides tourist information via voice guidance based on the analysis results. The analysis unit analyzes traffic conditions and facility congestion in real time. The suggestion unit suggests the optimal route and points of interest based on the analysis results. The response unit responds to the user's questions in a natural conversational format. The collection unit estimates the user's emotions and determines the priority of information based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, analysis unit, suggestion unit, and response unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's interests, past travel history, and current situation by the control unit 46A of the robot 414. The analysis unit analyzes the collected information by the identification processing unit 290 of the data processing unit 12. The provision unit provides tourist information via voice guidance based on the analysis results. The analysis unit analyzes traffic conditions and facility congestion in real time. The suggestion unit suggests the optimal route and points of interest based on the analysis results. The response unit responds to the user's questions in a natural conversational format. The collection unit estimates the user's emotions and determines the priority of information based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A data collection unit that gathers user interests, past travel history, and current status, An analysis unit analyzes the information collected by the aforementioned collection unit, A provisioning unit that provides tourism information based on the analysis results obtained by the aforementioned analysis unit, The analysis department analyzes traffic conditions and facility congestion in real time, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal route and places to stop along the way. It includes a response unit that responds to user questions in a natural conversational format. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Audio guides provide anecdotes related to historical buildings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, This article introduces the features and popular menu items of restaurants where you can enjoy local specialty dishes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose alternative routes to avoid traffic congestion. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Suggesting alternative spots to avoid crowds at the facility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The response unit is Respond to user questions in a natural conversational format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) 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 21) 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 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, consider the interrelationship between traffic conditions and facility congestion to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is When performing analysis, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, traffic conditions and the geographical distribution of facilities should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The response unit is When responding, adjust the level of detail in your response based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 39) The response unit is When responding, apply a different response algorithm depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 40) The response unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The response unit is When responding, we will prioritize responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The response unit is When responding, adjust the order of responses based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The response unit is When responding, apply a different response algorithm depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that gathers user interests, past travel history, and current status, An analysis unit analyzes the information collected by the aforementioned collection unit, A provisioning unit that provides tourism information based on the analysis results obtained by the aforementioned analysis unit, The analysis department analyzes traffic conditions and facility congestion in real time, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal route and places to stop along the way. It includes a response unit that responds to user questions in a natural conversational format. A system characterized by the following features.

2. The aforementioned supply unit is, Audio guides provide anecdotes related to historical buildings. The system according to feature 1.

3. The aforementioned supply unit is, This article introduces the features and popular menu items of restaurants where you can enjoy local specialty dishes. The system according to feature 1.

4. The aforementioned proposal section is, We propose alternative routes to avoid traffic congestion. The system according to feature 1.

5. The aforementioned proposal section is, Suggesting alternative spots to avoid crowds at the facility. The system according to feature 1.

6. The response unit is Respond to user questions in a natural conversational format. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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