system

Abstract

The system according to this embodiment aims to provide user-specific guidance using GPS information and to integrate with map applications. [Solution] The system according to the embodiment comprises a detection unit, a guide unit, a setting unit, and a linkage unit. The detection unit detects GPS information. The guide unit provides guidance based on the GPS information detected by the detection unit. The setting unit sets the guide content provided by the guide unit according to the user's preferences. The linkage unit links with a map application based on the guide content set by the setting unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, although a guide utilizing GPS information is provided, customization according to the user's preferences and cooperation with a map application are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a guide according to the user's preferences by utilizing GPS information and cooperate with a map application.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, a guide unit, a setting unit, and a linking unit. The detection unit detects GPS information. The guide unit provides guidance based on the GPS information detected by the detection unit. The setting unit sets the guide content provided by the guide unit according to the user's preferences. The linking unit links with a map application based on the guide content set by the setting unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize GPS information to provide guides tailored to the user's preferences and can be linked with a map application. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 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 guide system according to an embodiment of the present invention is a system that uses GPS location information to introduce the history and points of interest of a place. This guide system detects the user's current location using GPS and guides them with the history and points of interest related to that place. The guide level can be set according to the user's preferences, allowing them to acquire detailed information they want to know. Furthermore, it can be linked with a map application to display guide spots around the route. This system can be used in various situations such as travel, sightseeing, walking, and while on the go. For example, if the user is in a specific place, the system will display the history and points of interest of that place in audio guide or text. For example, it may provide information such as, "70 million years ago, this area was uplifted by crustal movement, 40 million years ago, river erosion began, and 2 million years ago..." This guide is provided not only at the current location but also while on the move, and is also multilingual. Users can set the guide level according to their preferences. For example, they can select categories and adjust the density of information. It is also possible to use the information sorting function to acquire information in order of rating, newness, or trend. This system can be linked with a map application to display guide spots around the route. For example, if a user sets a specific route, the system can display guide spots around that route and adjust the displayed content according to the user's guide level. It can also suggest the most suitable guide spots to the user using recommendation and "like" functions. This system allows users to easily learn about the history and points of interest around their current location or route in various situations such as travel, sightseeing, walks, and while commuting. For instance, when visiting a specific tourist spot during a trip, users can learn about the history and points of interest of that place through audio guides or text. They can also find nearby guide spots while walking and learn about guide spots around their route even while commuting. This allows users to enjoy more fulfilling trips, sightseeing, walks, and commutes. In this way, the guide system can provide appropriate guide information based on the user's current location and offer customized guidance tailored to the user's preferences.

[0029] The guide system according to this embodiment comprises a detection unit, a guide unit, a setting unit, and a linking unit. The detection unit detects GPS information. For example, the detection unit can detect the user's current location using GPS and acquire latitude, longitude, and altitude information. The guide unit provides guidance based on the GPS information detected by the detection unit. For example, if the user is in a specific location, the guide unit can display the history and points of interest of that location in audio or text. For example, the guide unit can play an audio file to provide an audio guide. The guide unit can also display text information to provide a text guide. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages. The setting unit configures the guide content provided by the guide unit according to the user's preferences. For example, the setting unit can allow the user to select categories of interest. The setting unit can also adjust the density of information. For example, the setting unit can choose whether to provide detailed or concise information. Furthermore, the setting unit can perform theme-based information filtering. For example, the settings unit can prioritize displaying information related to a specific theme. The integration unit interacts with the map application based on the guide content set by the settings unit. For example, the integration unit can display guide spots around the route. The integration unit can also save a history of visited spots. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments on spots visited by the user. This allows the guide system to provide appropriate guide information based on the user's current location and to provide customized guides tailored to the user's preferences.

[0030] The detection unit detects GPS information. Specifically, the detection unit can detect the user's current location using GPS and acquire latitude, longitude, and altitude information. This allows for accurate determination of the user's location. The detection unit receives signals from GPS satellites and calculates location information using the GPS module built into the user's device. Furthermore, the detection unit can utilize auxiliary data to improve the accuracy of location information. For example, by combining information from Wi-Fi access points and cell phone base stations, it can provide highly accurate location information even indoors or between high-rise buildings. The detection unit can also adjust the frequency of location information updates. For example, it can frequently update location information when the user is moving and reduce the update frequency when the user is stationary, thereby conserving battery power. This allows the detection unit to accurately and efficiently detect the user's current location and improve the overall performance of the guide system.

[0031] The guide unit provides guidance based on GPS information detected by the detection unit. Specifically, when the user is in a particular location, the guide unit can display the history and points of interest of that location in audio or text. To provide audio guidance, the guide unit plays pre-recorded audio files. The audio files are recorded by professional narrators, providing clear and easy-to-understand audio. In addition, to provide text guidance, the guide unit displays text information on the device screen. The text information is displayed in a user-friendly font and size, and includes visual elements such as images and maps as needed. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages ​​such as English, Japanese, Chinese, and French. Users can select their preferred language through the settings unit, and the guide information will be provided in the selected language. This allows the guide unit to provide appropriate guide information based on the user's current location and to provide customized guidance tailored to the user's language and preferences.

[0032] The settings section allows users to customize the guide content provided by the guide section to their preferences. Specifically, the settings section allows users to select categories of interest. For example, there are categories such as history, culture, nature, and gourmet food, and users can select categories according to their interests. The settings section also allows users to adjust the density of information. For example, they can choose to provide detailed information or concise information. If detailed information is selected, the guide section will provide more background information and anecdotes; if concise information is selected, it will provide only the essential points in a short summary. Furthermore, the settings section can filter information based on themes. For example, it can prioritize the display of information related to a specific theme. Users can select a theme through the settings section, and guide information related to the selected theme will be prioritized. This allows the settings section to provide a customized guide tailored to the user's preferences, thereby improving user satisfaction.

[0033] The integration unit interacts with the map application based on the guide content set by the settings unit. Specifically, the integration unit can display guide spots around a route. For example, when a user searches for a route in the map application, the integration unit displays guide spots around that route on the map. Guide spots include tourist attractions, historical buildings, restaurants, etc., and users can view these spots on the map. The integration unit can also save a history of visited spots. Information on spots visited by the user is automatically recorded by the integration unit and can be reviewed later. Furthermore, the integration unit can collect user feedback. For example, it can collect ratings and comments on spots visited by users and use this to improve the guide content. This allows the integration unit to provide appropriate guide information based on the user's current location and to provide customized guides tailored to the user's preferences. The integration unit can also interact with other applications and services. For example, it can interact with social media to share information about spots visited by users. This allows the integration unit to enrich the user experience and improve the overall usability of the guide system.

[0034] The guide unit may include audio guides, text displays, and multilingual support. For example, the guide unit may play an audio file to provide an audio guide. For example, if the user is in a specific location, the guide unit may provide an audio explanation of the history and points of interest of that location. The guide unit may also display text information to provide a text guide. For example, if the user is in a specific location, the guide unit may display the history and points of interest of that location in text. Furthermore, the guide unit may provide multilingual support. For example, the guide unit may provide guide information in multiple languages. This allows users to obtain guide information in a wider variety of ways by utilizing audio guides, text displays, and multilingual support. For audio guides, for example, the type of voice, language, and sound quality can be set. For text displays, for example, the font size, color, and display position can be set. For multilingual support, for example, the types of languages ​​supported and the accuracy of translation can be set. Some or all of the above processing in the guide unit may be performed using, for example, generative AI, or without using generative AI. For example, the guide unit can input the content of the audio guide into a generating AI, which can then generate the audio guide.

[0035] The settings section may include category selection, adjustment of information density, and theme-based information filtering. For example, the settings section may allow the user to select categories of interest. For example, the settings section may allow selection from categories such as history, nature, and culture. The settings section may also adjust the information density. For example, the settings section may allow selection whether to provide detailed or concise information. Furthermore, the settings section may perform theme-based information filtering. For example, the settings section may prioritize displaying information related to a specific theme. This allows the user to customize the guide information to their preferences. Category selection can be configured, for example, by setting the type of category and the selection method. Information density adjustment can be configured, for example, by setting the level of detail of the information and the amount of information to display. Theme-based information filtering can be configured, for example, by setting the type of theme and the filtering criteria. Some or all of the above processing in the settings section may be performed, for example, using a generative AI, or without using a generative AI. For example, the settings section may prompt a generative AI based on the user's preferences, and the generative AI may generate customized guide information.

[0036] The integration unit may include displaying guide spots around a route, saving a history of visited spots, and collecting user feedback. For example, the integration unit can display guide spots around a route. For example, the integration unit can display guide spots around a route set by the user. The integration unit can also save a history of visited spots. For example, the integration unit can save information about spots visited by the user so that it can be referenced later. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments on spots visited by the user. This allows the user to check guide spots around their route, save a history of visited spots, and provide feedback. For example, the display of guide spots around a route can be configured, such as the type of spots to display and the display method. For example, the saving of a history of visited spots can be configured, such as the type of information to save and the saving method. For example, the collection of user feedback can be configured, such as the type of feedback and the collection method. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit can input user feedback into a generative AI, which can then analyze the feedback.

[0037] The detection unit can analyze the user's past movement history and select the optimal method for acquiring GPS information. For example, the detection unit can analyze patterns of places the user has visited in the past and acquire detailed GPS information for frequently visited locations. For example, the detection unit can analyze the user's movement history data and calculate the frequency of visits to specific locations. The detection unit can also predict places the user will visit at specific times based on their movement history and focus on acquiring GPS information during those times. For example, the detection unit can analyze the user's movement history data in chronological order and identify visit patterns at specific times. The detection unit can also select the optimal method for acquiring GPS information to avoid congestion based on the user's movement history. For example, the detection unit can analyze the user's movement history data to identify congested times and locations and propose a GPS information acquisition method to avoid them. This allows for the provision of efficient guidance information by selecting the optimal GPS information acquisition method based on the user's past movement history. Past movement history can be configured, for example, by setting the type of history and analysis method. GPS information acquisition methods can be configured, for example, by setting the type of acquisition method and selection criteria. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user movement history data into a generating AI, which can then select the optimal method for acquiring GPS information.

[0038] The detection unit can filter GPS information based on the user's current activity status and areas of interest when acquiring it. For example, if the user is sightseeing, the detection unit will prioritize acquiring GPS information related to tourist spots. For example, the detection unit will analyze the user's current activity status and detect that they are sightseeing. The detection unit can also prioritize acquiring GPS information related to nature and parks if the user is taking a walk. For example, the detection unit will analyze the user's current activity status and detect that they are taking a walk. The detection unit can also prioritize acquiring GPS information related to traffic information and routes if the user is on the move. For example, the detection unit will analyze the user's current activity status and detect that they are on the move. This allows the system to provide highly relevant guide information by filtering based on the user's current activity status and areas of interest. Current activity status can be defined, for example, by setting the type of activity and filtering criteria. Areas of interest can be defined, for example, by setting the type of field and filtering criteria. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input user activity data into a generating AI, which can then perform filtering.

[0039] The detection unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring GPS information. For example, if the user is in a tourist area, the detection unit can prioritize acquiring information about the history and attractions related to that tourist area. For example, the detection unit analyzes the user's geographical location and detects that the user is in a tourist area. The detection unit can also prioritize acquiring information about nearby stores and sales if the user is in a shopping area. For example, the detection unit analyzes the user's geographical location and detects that the user is in a shopping area. The detection unit can also prioritize acquiring traffic information and route guidance if the user is near a transportation facility. For example, the detection unit analyzes the user's geographical location and detects that the user is near a transportation facility. By prioritizing the acquisition of highly relevant information by considering the user's geographical location, more appropriate guide information can be provided. Geographical location information can be defined, for example, by setting the type of location information and consideration criteria. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit inputs the user's geographical location information into the generating AI, which can then prioritize acquiring highly relevant information.

[0040] The detection unit can analyze the user's social media activity when acquiring GPS information and obtain relevant information. For example, if the user posts about a specific tourist destination on social media, the detection unit will prioritize obtaining information related to that tourist destination. For example, the detection unit will analyze the user's social media activity and detect posts about a specific tourist destination. The detection unit can also prioritize obtaining information related to an event if the user shows interest in a specific event on social media. For example, the detection unit will analyze the user's social media activity and detect interest in a specific event. The detection unit can also prioritize obtaining information related to a restaurant if the user mentions a specific restaurant on social media. For example, the detection unit will analyze the user's social media activity and detect mentions of a specific restaurant. By analyzing the user's social media activity and obtaining relevant information, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the analysis method. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI, which can then obtain relevant information.

[0041] The guide function can adjust the level of detail in a guide based on the importance of the location when providing a guide. For example, for important tourist spots, the guide function can provide detailed historical and noteworthy information. For instance, it can explain the historical background and important events of a tourist spot in detail. The guide function can also provide concise overview information for general locations. For example, it can briefly explain basic information and major points of interest of a location. The guide function can also provide additional detailed information for locations that the user is particularly interested in. For example, it can provide detailed information about a location based on the user's interests. This allows for the provision of more appropriate guide information by adjusting the level of detail based on the importance of the location. The importance of a location can be defined by setting criteria for importance, evaluation methods, etc. Some or all of the above processing in the guide function may be performed using AI, for example, or not. For example, the guide function can input location importance data into a generating AI, which can then adjust the level of detail in the guide.

[0042] The guide unit can apply different guide algorithms depending on the location category when providing guides. For example, in historical locations, the guide unit can provide guides that focus on historical background and important events. For example, the guide unit can provide detailed information about historical events and people. In natural locations, the guide unit can also provide information about the natural environment and flora and fauna. For example, the guide unit can describe the characteristics of the natural environment and information about specific flora and fauna. In shopping areas, the guide unit can also provide store information and sale information. For example, the guide unit can provide detailed information about stores and sales within the shopping area. By applying different guide algorithms depending on the location category, more appropriate guide information can be provided. Location categories can be defined, for example, by specifying the type of category and the details of the algorithm. Some or all of the above processing in the guide unit may be performed using AI, or not using AI. For example, the guide unit can input location category data into a generating AI, and the generating AI can apply a guide algorithm.

[0043] The guide unit can prioritize guides based on the time of year a location is visited. For example, the guide unit may prioritize seasonal events and attractions. For instance, it may provide detailed explanations of events and attractions held during specific seasons. The guide unit can also prioritize places that are best visited at specific times of day. For example, it may explain attractions and events that are best visited at specific times of day. Furthermore, the guide unit can provide optimal guide information tailored to the time of the user's visit. For example, it may provide relevant information based on the user's visit timing. This allows for the provision of more appropriate guide information by prioritizing guides based on the time of year a location is visited. The visit timing can be defined by setting criteria such as timing and evaluation methods. Some or all of the above processing in the guide unit may be performed using AI, or not. For example, the guide unit can input visit timing data into a generating AI, which can then determine the guide priorities.

[0044] The guide unit can adjust the order of guides based on the relevance of locations when providing guides. For example, the guide unit can first provide information most relevant to the user's current location. For example, the guide unit can analyze the user's current location and prioritize displaying highly relevant information. The guide unit can also prioritize providing information related to the next place the user plans to visit. For example, the guide unit can analyze the user's travel plans and display information related to the next place to visit. The guide unit can also sequentially provide highly relevant information based on the user's interests. For example, the guide unit can analyze the user's interest data and sequentially display highly relevant information. By adjusting the order of guides based on the relevance of locations, more appropriate guide information can be provided. The relevance of locations can be defined, for example, by setting criteria for relevance and evaluation methods. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input location relevance data into a generating AI, which can then adjust the order of guides.

[0045] The settings unit can analyze the user's past settings history to select the optimal settings method when setting up a guide. For example, the settings unit can suggest the optimal settings method based on the categories and information density previously selected by the user. For example, the settings unit can analyze the user's past settings history data to identify the optimal settings method. The settings unit can also suggest guides based on specific themes from the user's past settings history. For example, the settings unit can analyze the user's past settings history data to suggest guides related to specific themes. The settings unit can also analyze the user's past settings history to suggest the most efficient settings method. For example, the settings unit can analyze the user's past settings history data to identify the most efficient settings method. This allows for the provision of efficient guide information by selecting the optimal settings method based on the user's past settings history. The past settings history can be configured in terms of, for example, the type of history and the analysis method. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's past settings history data into a generating AI, which can then select the optimal settings method.

[0046] The settings unit can customize the settings based on the user's current interests when setting up the guide. For example, the settings unit can customize the guide settings based on the categories the user is currently interested in. For example, the settings unit can analyze the user's current interest data and identify relevant categories. The settings unit can also customize the guide settings based on information the user has recently searched for. For example, the settings unit can analyze the user's search history data and identify relevant information. The settings unit can also customize the guide settings based on events and activities the user is currently participating in. For example, the settings unit can analyze the user's event participation data and identify relevant information. By customizing the settings based on the user's current interests, more appropriate guide information can be provided. Current interests can be defined, for example, by setting the type of interest, customization criteria, etc. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input the user's interest data into a generating AI, which can then customize the settings.

[0047] The settings unit can select the optimal settings method when setting up a guide, taking into account the user's geographical location information. For example, if the user is in a tourist destination, the settings unit can suggest categories and information density related to that tourist destination. For example, the settings unit can analyze the user's geographical location information and detect that the user is in a tourist destination. The settings unit can also prioritize suggesting store information and sale information if the user is in a shopping area. For example, the settings unit can analyze the user's geographical location information and detect that the user is in a shopping area. The settings unit can also prioritize suggesting transportation information and route guidance if the user is near a transportation facility. For example, the settings unit can analyze the user's geographical location information and detect that the user is near a transportation facility. By selecting the optimal settings method considering the user's geographical location information, more appropriate guide information can be provided. Geographical location information can be configured, for example, by setting the type of location information and the criteria for consideration. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into a generating AI, which can then select the optimal settings method.

[0048] The settings unit can analyze the user's social media activity and suggest settings when setting up a guide. For example, if the user has posted about a specific tourist destination on social media, the settings unit can suggest categories and information density related to that destination. For example, the settings unit analyzes the user's social media activity and detects posts about a specific tourist destination. The settings unit can also suggest categories and information density related to an event if the user has shown interest in a specific event on social media. For example, the settings unit analyzes the user's social media activity and detects interest in a specific event. The settings unit can also suggest categories and information density related to a restaurant if the user has mentioned a specific restaurant on social media. For example, the settings unit analyzes the user's social media activity and detects mentions of a specific restaurant. By analyzing the user's social media activity and suggesting settings, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the method of analysis. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's social media data into a generating AI, which can then suggest settings.

[0049] The integration unit can analyze the user's past integration history to select the optimal integration method when integrating with a map application. For example, the integration unit can propose the optimal integration method based on the integration methods the user has used in the past. For example, the integration unit can analyze the user's past integration history data to identify the optimal integration method. The integration unit can also propose integration methods based on a specific theme from the user's past integration history. For example, the integration unit can analyze the user's past integration history data to propose integration methods related to a specific theme. The integration unit can also analyze the user's past integration history to propose the most efficient integration method. For example, the integration unit can analyze the user's past integration history data to identify the most efficient integration method. By selecting the optimal integration method based on the user's past integration history, efficient guide information can be provided. Past integration history can be configured, for example, by setting the type of history and the analysis method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past integration history data into a generating AI, which can then select the optimal integration method.

[0050] The integration unit can customize the integration content based on the user's current movement status when integrating with a map application. For example, if the user is traveling on foot, the integration unit can provide pedestrian-oriented integration content. For example, the integration unit can analyze the user's movement status and detect that they are traveling on foot. The integration unit can also provide driver-oriented integration content if the user is traveling by car. For example, the integration unit can analyze the user's movement status and detect that they are traveling by car. The integration unit can also provide public transport-oriented integration content if the user is using public transport. For example, the integration unit can analyze the user's movement status and detect that they are using public transport. By customizing the integration content based on the user's current movement status, more appropriate guidance information can be provided. The current movement status can be defined, for example, by setting the type of movement status and customization criteria. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the user's movement status data into a generating AI, which can then customize the integration content.

[0051] The integration unit can select the optimal integration method when integrating with a map application, taking into account the user's geographical location information. For example, if the user is in a tourist destination, the integration unit will prioritize integrating information related to that tourist destination. For example, the integration unit will analyze the user's geographical location information and detect that the user is in a tourist destination. The integration unit can also prioritize integrating information about nearby stores and sales if the user is in a shopping area. For example, the integration unit will analyze the user's geographical location information and detect that the user is in a shopping area. The integration unit can also prioritize integrating information about transportation and route guidance if the user is near a transportation facility. For example, the integration unit will analyze the user's geographical location information and detect that the user is near a transportation facility. By selecting the optimal integration method considering the user's geographical location information, more appropriate guide information can be provided. Geographical location information can be defined, for example, by setting the type of location information and the criteria for consideration. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI, which can then select the optimal integration method.

[0052] The integration unit can analyze the user's social media activity and suggest integration content when integrating with a map application. For example, if the user has posted about a specific tourist destination on social media, the integration unit will prioritize integrating information related to that destination. For example, the integration unit will analyze the user's social media activity and detect posts about a specific tourist destination. The integration unit can also prioritize integrating information related to an event if the user has shown interest in a specific event on social media. For example, the integration unit will analyze the user's social media activity and detect interest in a specific event. The integration unit can also prioritize integrating information related to a restaurant if the user has mentioned a specific restaurant on social media. For example, the integration unit will analyze the user's social media activity and detect mentions of a specific restaurant. By analyzing the user's social media activity and suggesting integration content, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the analysis method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media data into a generating AI, which can then suggest integration content.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The guide system can also include a health management unit that monitors the user's health status. This unit can acquire health data such as the user's heart rate, blood pressure, and steps taken, and adjust the guide information based on this data. For example, if the user's heart rate is high, the health management unit can prioritize guiding them to relaxing spots and rest areas. Similarly, if the user has taken many steps, the health management unit can suggest nearby tourist spots, taking into account the walking distance. Furthermore, the health management unit can analyze the user's health data and provide advice for maintaining good health. For example, it can suggest appropriate exercise and rest times. This allows users to enjoy sightseeing while maintaining their health.

[0055] The guide system may also include a hobby estimation unit that provides customized guides based on the user's hobbies and interests. For example, the hobby estimation unit can analyze the user's past search history and social media posts to estimate their hobbies and interests. If the user is interested in history, the hobby estimation unit can prioritize guiding them to historical sites and museums. Similarly, if the user is interested in nature, the hobby estimation unit can suggest nature parks and hiking trails. Furthermore, the hobby estimation unit can suggest event information and special activities based on the user's interests. For example, if the user is interested in music, it can provide information on nearby concerts and music festivals. This allows users to enjoy sightseeing that aligns with their hobbies and interests.

[0056] The guide system can also include a history analysis unit that analyzes the user's past guide usage history and proposes optimal guide content. For example, the history analysis unit can suggest places to visit next based on places the user has visited in the past and information they have been interested in. If the user has visited many historical sites in the past, the history analysis unit can suggest historical tourist spots to visit next. Similarly, if the user has visited many national parks in the past, the history analysis unit can suggest national parks to visit next. Furthermore, the history analysis unit can customize guide content based on the user's past feedback. For example, it can prioritize providing guide content that the user has previously given high ratings to. This allows users to efficiently obtain guide information that matches their interests and preferences.

[0057] The guide system may also include an activity estimation unit that customizes the guide content based on the user's current activity. For example, if the user is sightseeing, the activity estimation unit can prioritize providing information related to tourist attractions and historical sites. For example, the activity estimation unit analyzes the user's current activity and detects that they are sightseeing. Similarly, if the user is taking a walk, the activity estimation unit can prioritize providing information related to nature and parks. For example, the activity estimation unit analyzes the user's current activity and detects that they are taking a walk. Furthermore, if the user is on the move, the system can prioritize providing traffic information and route guidance. For example, the activity estimation unit analyzes the user's current activity and detects that they are on the move. This allows the system to provide more appropriate guide information by customizing the guide content based on the user's current activity.

[0058] The guide system can also include a travel history analysis unit that analyzes the user's past travel history and proposes optimal guide content. For example, the travel history analysis unit can suggest places the user should visit next based on places they have visited in the past and their travel patterns. For instance, it can suggest new tourist spots near places the user has frequently visited in the past. It can also suggest places the user should visit next based on their ratings of places they have visited in the past. For example, it can suggest tourist spots near places the user has given high ratings to. Furthermore, the travel history analysis unit can suggest efficient sightseeing routes based on the user's travel patterns. For example, it can suggest the optimal route connecting places the user has visited in the past. This allows the user to enjoy sightseeing more efficiently.

[0059] The guide system may also include a location information customization unit that customizes the guide content by considering the user's current geographical location. For example, if the user is in a tourist destination, the location information customization unit can prioritize providing information related to the history and attractions of that destination. For example, the location information customization unit analyzes the user's geographical location and detects that the user is in a tourist destination. Also, if the user is in a shopping area, the location information customization unit can prioritize providing information on nearby stores and sales. For example, the location information customization unit analyzes the user's geographical location and detects that the user is in a shopping area. Furthermore, if the user is near a transportation facility, the system can prioritize providing transportation information and route guidance. For example, the location information customization unit analyzes the user's geographical location and detects that the user is near a transportation facility. In this way, by customizing the guide content by considering the user's current geographical location, more appropriate guide information can be provided.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The detection unit detects GPS information. For example, the detection unit can detect the user's current location using GPS and obtain latitude, longitude, and altitude information. Step 2: The guide unit provides guidance based on GPS information detected by the detection unit. For example, if the user is in a specific location, the guide unit can display the history and points of interest of that location in audio or text. For example, the guide unit can play an audio file to provide an audio guide. The guide unit can also display text information to provide a text guide. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages. Step 3: The settings section configures the guide content provided by the guide section to suit the user's preferences. For example, the settings section allows the user to select categories of interest. The settings section can also adjust the density of information. For example, the settings section can choose whether to provide detailed or concise information. Furthermore, the settings section can perform theme-based information filtering. For example, the settings section can prioritize displaying information related to a specific theme. Step 4: The integration unit interacts with the map application based on the guide content set by the settings unit. For example, the integration unit can display guide spots around the route. The integration unit can also save a history of visited spots. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments from users about the spots they have visited.

[0062] (Example of form 2) The guide system according to an embodiment of the present invention is a system that uses GPS location information to introduce the history and points of interest of a place. This guide system detects the user's current location using GPS and guides them with the history and points of interest related to that place. The guide level can be set according to the user's preferences, allowing them to acquire detailed information they want to know. Furthermore, it can be linked with a map application to display guide spots around the route. This system can be used in various situations such as travel, sightseeing, walking, and while on the go. For example, if the user is in a specific place, the system will display the history and points of interest of that place in audio guide or text. For example, it may provide information such as, "70 million years ago, this area was uplifted by crustal movement, 40 million years ago, river erosion began, and 2 million years ago..." This guide is provided not only at the current location but also while on the move, and is also multilingual. Users can set the guide level according to their preferences. For example, they can select categories and adjust the density of information. It is also possible to use the information sorting function to acquire information in order of rating, newness, or trend. This system can be linked with a map application to display guide spots around the route. For example, if a user sets a specific route, the system can display guide spots around that route and adjust the displayed content according to the user's guide level. It can also suggest the most suitable guide spots to the user using recommendation and "like" functions. This system allows users to easily learn about the history and points of interest around their current location or route in various situations such as travel, sightseeing, walks, and while commuting. For instance, when visiting a specific tourist spot during a trip, users can learn about the history and points of interest of that place through audio guides or text. They can also find nearby guide spots while walking and learn about guide spots around their route even while commuting. This allows users to enjoy more fulfilling trips, sightseeing, walks, and commutes. In this way, the guide system can provide appropriate guide information based on the user's current location and offer customized guidance tailored to the user's preferences.

[0063] The guide system according to this embodiment comprises a detection unit, a guide unit, a setting unit, and a linking unit. The detection unit detects GPS information. For example, the detection unit can detect the user's current location using GPS and acquire latitude, longitude, and altitude information. The guide unit provides guidance based on the GPS information detected by the detection unit. For example, if the user is in a specific location, the guide unit can display the history and points of interest of that location in audio or text. For example, the guide unit can play an audio file to provide an audio guide. The guide unit can also display text information to provide a text guide. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages. The setting unit configures the guide content provided by the guide unit according to the user's preferences. For example, the setting unit can allow the user to select categories of interest. The setting unit can also adjust the density of information. For example, the setting unit can choose whether to provide detailed or concise information. Furthermore, the setting unit can perform theme-based information filtering. For example, the settings unit can prioritize displaying information related to a specific theme. The integration unit interacts with the map application based on the guide content set by the settings unit. For example, the integration unit can display guide spots around the route. The integration unit can also save a history of visited spots. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments on spots visited by the user. This allows the guide system to provide appropriate guide information based on the user's current location and to provide customized guides tailored to the user's preferences.

[0064] The detection unit detects GPS information. Specifically, the detection unit can detect the user's current location using GPS and acquire latitude, longitude, and altitude information. This allows for accurate determination of the user's location. The detection unit receives signals from GPS satellites and calculates location information using the GPS module built into the user's device. Furthermore, the detection unit can utilize auxiliary data to improve the accuracy of location information. For example, by combining information from Wi-Fi access points and cell phone base stations, it can provide highly accurate location information even indoors or between high-rise buildings. The detection unit can also adjust the frequency of location information updates. For example, it can frequently update location information when the user is moving and reduce the update frequency when the user is stationary, thereby conserving battery power. This allows the detection unit to accurately and efficiently detect the user's current location and improve the overall performance of the guide system.

[0065] The guide unit provides guidance based on GPS information detected by the detection unit. Specifically, when the user is in a particular location, the guide unit can display the history and points of interest of that location in audio or text. To provide audio guidance, the guide unit plays pre-recorded audio files. The audio files are recorded by professional narrators, providing clear and easy-to-understand audio. In addition, to provide text guidance, the guide unit displays text information on the device screen. The text information is displayed in a user-friendly font and size, and includes visual elements such as images and maps as needed. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages ​​such as English, Japanese, Chinese, and French. Users can select their preferred language through the settings unit, and the guide information will be provided in the selected language. This allows the guide unit to provide appropriate guide information based on the user's current location and to provide customized guidance tailored to the user's language and preferences.

[0066] The settings section allows users to customize the guide content provided by the guide section to their preferences. Specifically, the settings section allows users to select categories of interest. For example, there are categories such as history, culture, nature, and gourmet food, and users can select categories according to their interests. The settings section also allows users to adjust the density of information. For example, they can choose to provide detailed information or concise information. If detailed information is selected, the guide section will provide more background information and anecdotes; if concise information is selected, it will provide only the essential points in a short summary. Furthermore, the settings section can filter information based on themes. For example, it can prioritize the display of information related to a specific theme. Users can select a theme through the settings section, and guide information related to the selected theme will be prioritized. This allows the settings section to provide a customized guide tailored to the user's preferences, thereby improving user satisfaction.

[0067] The integration unit interacts with the map application based on the guide content set by the settings unit. Specifically, the integration unit can display guide spots around a route. For example, when a user searches for a route in the map application, the integration unit displays guide spots around that route on the map. Guide spots include tourist attractions, historical buildings, restaurants, etc., and users can view these spots on the map. The integration unit can also save a history of visited spots. Information on spots visited by the user is automatically recorded by the integration unit and can be reviewed later. Furthermore, the integration unit can collect user feedback. For example, it can collect ratings and comments on spots visited by users and use this to improve the guide content. This allows the integration unit to provide appropriate guide information based on the user's current location and to provide customized guides tailored to the user's preferences. The integration unit can also interact with other applications and services. For example, it can interact with social media to share information about spots visited by users. This allows the integration unit to enrich the user experience and improve the overall usability of the guide system.

[0068] The guide unit may include audio guides, text displays, and multilingual support. For example, the guide unit may play an audio file to provide an audio guide. For example, if the user is in a specific location, the guide unit may provide an audio explanation of the history and points of interest of that location. The guide unit may also display text information to provide a text guide. For example, if the user is in a specific location, the guide unit may display the history and points of interest of that location in text. Furthermore, the guide unit may provide multilingual support. For example, the guide unit may provide guide information in multiple languages. This allows users to obtain guide information in a wider variety of ways by utilizing audio guides, text displays, and multilingual support. For audio guides, for example, the type of voice, language, and sound quality can be set. For text displays, for example, the font size, color, and display position can be set. For multilingual support, for example, the types of languages ​​supported and the accuracy of translation can be set. Some or all of the above processing in the guide unit may be performed using, for example, generative AI, or without using generative AI. For example, the guide unit can input the content of the audio guide into a generating AI, which can then generate the audio guide.

[0069] The settings section may include category selection, adjustment of information density, and theme-based information filtering. For example, the settings section may allow the user to select categories of interest. For example, the settings section may allow selection from categories such as history, nature, and culture. The settings section may also adjust the information density. For example, the settings section may allow selection whether to provide detailed or concise information. Furthermore, the settings section may perform theme-based information filtering. For example, the settings section may prioritize displaying information related to a specific theme. This allows the user to customize the guide information to their preferences. Category selection can be configured, for example, by setting the type of category and the selection method. Information density adjustment can be configured, for example, by setting the level of detail of the information and the amount of information to display. Theme-based information filtering can be configured, for example, by setting the type of theme and the filtering criteria. Some or all of the above processing in the settings section may be performed, for example, using a generative AI, or without using a generative AI. For example, the settings section may prompt a generative AI based on the user's preferences, and the generative AI may generate customized guide information.

[0070] The integration unit may include displaying guide spots around a route, saving a history of visited spots, and collecting user feedback. For example, the integration unit can display guide spots around a route. For example, the integration unit can display guide spots around a route set by the user. The integration unit can also save a history of visited spots. For example, the integration unit can save information about spots visited by the user so that it can be referenced later. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments on spots visited by the user. This allows the user to check guide spots around their route, save a history of visited spots, and provide feedback. For example, the display of guide spots around a route can be configured, such as the type of spots to display and the display method. For example, the saving of a history of visited spots can be configured, such as the type of information to save and the saving method. For example, the collection of user feedback can be configured, such as the type of feedback and the collection method. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the integration unit can input user feedback into a generative AI, which can then analyze the feedback.

[0071] The detection unit can estimate the user's emotions and adjust the timing of GPS information acquisition based on the estimated emotions. For example, if the user is excited, the detection unit can acquire GPS information frequently and provide guidance information in real time. For example, the detection unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. Also, if the user is relaxed, the detection unit can acquire GPS information at regular intervals and provide guidance information at a relaxed pace. For example, the detection unit can analyze the user's voice tone and posture to detect the state of relaxation. Also, if the user is tired, the detection unit can acquire GPS information at the minimum necessary timing and provide concise guidance information. For example, the detection unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. This allows for the provision of more appropriate guidance information by adjusting the timing of GPS information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion.

[0072] The detection unit can analyze the user's past movement history and select the optimal method for acquiring GPS information. For example, the detection unit can analyze patterns of places the user has visited in the past and acquire detailed GPS information for frequently visited locations. For example, the detection unit can analyze the user's movement history data and calculate the frequency of visits to specific locations. The detection unit can also predict places the user will visit at specific times based on their movement history and focus on acquiring GPS information during those times. For example, the detection unit can analyze the user's movement history data in chronological order and identify visit patterns at specific times. The detection unit can also select the optimal method for acquiring GPS information to avoid congestion based on the user's movement history. For example, the detection unit can analyze the user's movement history data to identify congested times and locations and propose a GPS information acquisition method to avoid them. This allows for the provision of efficient guidance information by selecting the optimal GPS information acquisition method based on the user's past movement history. Past movement history can be configured, for example, by setting the type of history and analysis method. GPS information acquisition methods can be configured, for example, by setting the type of acquisition method and selection criteria. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user movement history data into a generating AI, which can then select the optimal method for acquiring GPS information.

[0073] The detection unit can filter GPS information based on the user's current activity status and areas of interest when acquiring it. For example, if the user is sightseeing, the detection unit will prioritize acquiring GPS information related to tourist spots. For example, the detection unit will analyze the user's current activity status and detect that they are sightseeing. The detection unit can also prioritize acquiring GPS information related to nature and parks if the user is taking a walk. For example, the detection unit will analyze the user's current activity status and detect that they are taking a walk. The detection unit can also prioritize acquiring GPS information related to traffic information and routes if the user is on the move. For example, the detection unit will analyze the user's current activity status and detect that they are on the move. This allows the system to provide highly relevant guide information by filtering based on the user's current activity status and areas of interest. Current activity status can be defined, for example, by setting the type of activity and filtering criteria. Areas of interest can be defined, for example, by setting the type of field and filtering criteria. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input user activity data into a generating AI, which can then perform filtering.

[0074] The detection unit can estimate the user's emotions and determine the priority of GPS information to acquire based on the estimated emotions. For example, if the user is excited, the detection unit will prioritize acquiring information on tourist spots and events. For example, the detection unit will analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. The detection unit can also prioritize acquiring information related to nature and parks if the user is relaxed. For example, the detection unit will analyze the user's voice tone and posture to detect the state of relaxation. The detection unit can also prioritize acquiring information related to rest spots and cafes if the user is tired. For example, the detection unit will analyze the user's skin electrical activity and movement speed to detect the state of fatigue. By prioritizing GPS information according to the user's emotions, more appropriate guidance information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion.

[0075] The detection unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring GPS information. For example, if the user is in a tourist area, the detection unit can prioritize acquiring information about the history and attractions related to that tourist area. For example, the detection unit analyzes the user's geographical location and detects that the user is in a tourist area. The detection unit can also prioritize acquiring information about nearby stores and sales if the user is in a shopping area. For example, the detection unit analyzes the user's geographical location and detects that the user is in a shopping area. The detection unit can also prioritize acquiring traffic information and route guidance if the user is near a transportation facility. For example, the detection unit analyzes the user's geographical location and detects that the user is near a transportation facility. By prioritizing the acquisition of highly relevant information by considering the user's geographical location, more appropriate guide information can be provided. Geographical location information can be defined, for example, by setting the type of location information and consideration criteria. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit inputs the user's geographical location information into the generating AI, which can then prioritize acquiring highly relevant information.

[0076] The detection unit can analyze the user's social media activity when acquiring GPS information and obtain relevant information. For example, if the user posts about a specific tourist destination on social media, the detection unit will prioritize obtaining information related to that tourist destination. For example, the detection unit will analyze the user's social media activity and detect posts about a specific tourist destination. The detection unit can also prioritize obtaining information related to an event if the user shows interest in a specific event on social media. For example, the detection unit will analyze the user's social media activity and detect interest in a specific event. The detection unit can also prioritize obtaining information related to a restaurant if the user mentions a specific restaurant on social media. For example, the detection unit will analyze the user's social media activity and detect mentions of a specific restaurant. By analyzing the user's social media activity and obtaining relevant information, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the analysis method. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI, which can then obtain relevant information.

[0077] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated emotions. For example, if the user is excited, the guide unit can provide a guide with visually stimulating effects. For example, the guide unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. The guide unit can also provide a guide in a calm tone if the user is relaxed. For example, the guide unit can analyze the user's voice tone and posture to detect the state of relaxation. The guide unit can also provide a concise and to-the-point guide if the user is tired. For example, the guide unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. By adjusting the way the guide is presented according to the user's emotions, more appropriate guide information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user emotion data into a generating AI, which can then adjust how the guide is presented.

[0078] The guide function can adjust the level of detail in a guide based on the importance of the location when providing a guide. For example, for important tourist spots, the guide function can provide detailed historical and noteworthy information. For instance, it can explain the historical background and important events of a tourist spot in detail. The guide function can also provide concise overview information for general locations. For example, it can briefly explain basic information and major points of interest of a location. The guide function can also provide additional detailed information for locations that the user is particularly interested in. For example, it can provide detailed information about a location based on the user's interests. This allows for the provision of more appropriate guide information by adjusting the level of detail based on the importance of the location. The importance of a location can be defined by setting criteria for importance, evaluation methods, etc. Some or all of the above processing in the guide function may be performed using AI, for example, or not. For example, the guide function can input location importance data into a generating AI, which can then adjust the level of detail in the guide.

[0079] The guide unit can apply different guide algorithms depending on the location category when providing guides. For example, in historical locations, the guide unit can provide guides that focus on historical background and important events. For example, the guide unit can provide detailed information about historical events and people. In natural locations, the guide unit can also provide information about the natural environment and flora and fauna. For example, the guide unit can describe the characteristics of the natural environment and information about specific flora and fauna. In shopping areas, the guide unit can also provide store information and sale information. For example, the guide unit can provide detailed information about stores and sales within the shopping area. By applying different guide algorithms depending on the location category, more appropriate guide information can be provided. Location categories can be defined, for example, by specifying the type of category and the details of the algorithm. Some or all of the above processing in the guide unit may be performed using AI, or not using AI. For example, the guide unit can input location category data into a generating AI, and the generating AI can apply a guide algorithm.

[0080] The guide unit can estimate the user's emotions and adjust the length of the guide based on the estimated emotions. For example, if the user is in a hurry, the guide unit can provide a short, concise guide. For example, the guide unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the hurried state. The guide unit can also provide a longer guide with detailed explanations if the user is relaxed. For example, the guide unit can analyze the user's voice tone and posture to detect the relaxed state. The guide unit can also provide a guide with visually stimulating effects if the user is excited. For example, the guide unit can analyze the user's skin electrical activity and movement speed to detect the excited state. By adjusting the length of the guide according to the user's emotions, more appropriate guide information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user emotion data into a generating AI, which can then adjust the length of the guide.

[0081] The guide unit can prioritize guides based on the time of year a location is visited. For example, the guide unit may prioritize seasonal events and attractions. For instance, it may provide detailed explanations of events and attractions held during specific seasons. The guide unit can also prioritize places that are best visited at specific times of day. For example, it may explain attractions and events that are best visited at specific times of day. Furthermore, the guide unit can provide optimal guide information tailored to the time of the user's visit. For example, it may provide relevant information based on the user's visit timing. This allows for the provision of more appropriate guide information by prioritizing guides based on the time of year a location is visited. The visit timing can be defined by setting criteria such as timing and evaluation methods. Some or all of the above processing in the guide unit may be performed using AI, or not. For example, the guide unit can input visit timing data into a generating AI, which can then determine the guide priorities.

[0082] The guide unit can adjust the order of guides based on the relevance of locations when providing guides. For example, the guide unit can first provide information most relevant to the user's current location. For example, the guide unit can analyze the user's current location and prioritize displaying highly relevant information. The guide unit can also prioritize providing information related to the next place the user plans to visit. For example, the guide unit can analyze the user's travel plans and display information related to the next place to visit. The guide unit can also sequentially provide highly relevant information based on the user's interests. For example, the guide unit can analyze the user's interest data and sequentially display highly relevant information. By adjusting the order of guides based on the relevance of locations, more appropriate guide information can be provided. The relevance of locations can be defined, for example, by setting criteria for relevance and evaluation methods. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input location relevance data into a generating AI, which can then adjust the order of guides.

[0083] The settings unit can estimate the user's emotions and adjust the guide settings based on the estimated emotions. For example, if the user is excited, the settings unit can provide detailed setting options and suggest a customizable guide. For example, the settings unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. Alternatively, if the user is relaxed, the settings unit can provide simple setting options and suggest an intuitively operable guide. For example, the settings unit can analyze the user's voice tone and posture to detect the state of relaxation. Furthermore, if the user is tired, the settings unit can provide minimal setting options and suggest an easy-to-configure guide. For example, the settings unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. This allows for the provision of more appropriate guide information by adjusting the guide settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input user emotion data into a generating AI, which can then adjust the method of setting the guide.

[0084] The settings unit can analyze the user's past settings history to select the optimal settings method when setting up a guide. For example, the settings unit can suggest the optimal settings method based on the categories and information density previously selected by the user. For example, the settings unit can analyze the user's past settings history data to identify the optimal settings method. The settings unit can also suggest guides based on specific themes from the user's past settings history. For example, the settings unit can analyze the user's past settings history data to suggest guides related to specific themes. The settings unit can also analyze the user's past settings history to suggest the most efficient settings method. For example, the settings unit can analyze the user's past settings history data to identify the most efficient settings method. This allows for the provision of efficient guide information by selecting the optimal settings method based on the user's past settings history. The past settings history can be configured in terms of, for example, the type of history and the analysis method. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's past settings history data into a generating AI, which can then select the optimal settings method.

[0085] The settings unit can customize the settings based on the user's current interests when setting up the guide. For example, the settings unit can customize the guide settings based on the categories the user is currently interested in. For example, the settings unit can analyze the user's current interest data and identify relevant categories. The settings unit can also customize the guide settings based on information the user has recently searched for. For example, the settings unit can analyze the user's search history data and identify relevant information. The settings unit can also customize the guide settings based on events and activities the user is currently participating in. For example, the settings unit can analyze the user's event participation data and identify relevant information. By customizing the settings based on the user's current interests, more appropriate guide information can be provided. Current interests can be defined, for example, by setting the type of interest, customization criteria, etc. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input the user's interest data into a generating AI, which can then customize the settings.

[0086] The settings unit can estimate the user's emotions and determine the priority of guide settings based on the estimated emotions. For example, if the user is excited, the settings unit may prioritize providing detailed setting options. For example, the settings unit may analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. The settings unit may also prioritize providing simple setting options if the user is relaxed. For example, the settings unit may analyze the user's voice tone and posture to detect the state of relaxation. The settings unit may also prioritize providing minimal setting options if the user is tired. For example, the settings unit may analyze the user's skin electrical activity and movement speed to detect the state of fatigue. By prioritizing guide settings according to the user's emotions, more appropriate guide information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input user emotion data into a generating AI, which can then determine the priority of the guide settings.

[0087] The settings unit can select the optimal settings method when setting up a guide, taking into account the user's geographical location information. For example, if the user is in a tourist destination, the settings unit can suggest categories and information density related to that tourist destination. For example, the settings unit can analyze the user's geographical location information and detect that the user is in a tourist destination. The settings unit can also prioritize suggesting store information and sale information if the user is in a shopping area. For example, the settings unit can analyze the user's geographical location information and detect that the user is in a shopping area. The settings unit can also prioritize suggesting transportation information and route guidance if the user is near a transportation facility. For example, the settings unit can analyze the user's geographical location information and detect that the user is near a transportation facility. By selecting the optimal settings method considering the user's geographical location information, more appropriate guide information can be provided. Geographical location information can be configured, for example, by setting the type of location information and the criteria for consideration. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's geographical location information into a generating AI, which can then select the optimal settings method.

[0088] The settings unit can analyze the user's social media activity and suggest settings when setting up a guide. For example, if the user has posted about a specific tourist destination on social media, the settings unit can suggest categories and information density related to that destination. For example, the settings unit analyzes the user's social media activity and detects posts about a specific tourist destination. The settings unit can also suggest categories and information density related to an event if the user has shown interest in a specific event on social media. For example, the settings unit analyzes the user's social media activity and detects interest in a specific event. The settings unit can also suggest categories and information density related to a restaurant if the user has mentioned a specific restaurant on social media. For example, the settings unit analyzes the user's social media activity and detects mentions of a specific restaurant. By analyzing the user's social media activity and suggesting settings, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the method of analysis. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the user's social media data into a generating AI, which can then suggest settings.

[0089] The integration unit can estimate the user's emotions and adjust the integration method with the map application based on the estimated emotions. For example, if the user is excited, the integration unit can provide detailed integration options and suggest a customizable integration method. For example, the integration unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. If the user is relaxed, the integration unit can also provide simple integration options and suggest an intuitively operable integration method. For example, the integration unit can analyze the user's voice tone and posture to detect the state of relaxation. If the user is tired, the integration unit can provide minimal integration options and suggest an easy-to-use integration method. For example, the integration unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. This allows for the provision of more appropriate guidance information by adjusting the integration method with the map application according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input user emotion data into a generating AI, which can then adjust the collaboration method.

[0090] The integration unit can analyze the user's past integration history to select the optimal integration method when integrating with a map application. For example, the integration unit can propose the optimal integration method based on the integration methods the user has used in the past. For example, the integration unit can analyze the user's past integration history data to identify the optimal integration method. The integration unit can also propose integration methods based on a specific theme from the user's past integration history. For example, the integration unit can analyze the user's past integration history data to propose integration methods related to a specific theme. The integration unit can also analyze the user's past integration history to propose the most efficient integration method. For example, the integration unit can analyze the user's past integration history data to identify the most efficient integration method. By selecting the optimal integration method based on the user's past integration history, efficient guide information can be provided. Past integration history can be configured, for example, by setting the type of history and the analysis method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past integration history data into a generating AI, which can then select the optimal integration method.

[0091] The integration unit can customize the integration content based on the user's current movement status when integrating with a map application. For example, if the user is traveling on foot, the integration unit can provide pedestrian-oriented integration content. For example, the integration unit can analyze the user's movement status and detect that they are traveling on foot. The integration unit can also provide driver-oriented integration content if the user is traveling by car. For example, the integration unit can analyze the user's movement status and detect that they are traveling by car. The integration unit can also provide public transport-oriented integration content if the user is using public transport. For example, the integration unit can analyze the user's movement status and detect that they are using public transport. By customizing the integration content based on the user's current movement status, more appropriate guidance information can be provided. The current movement status can be defined, for example, by setting the type of movement status and customization criteria. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input the user's movement status data into a generating AI, which can then customize the integration content.

[0092] The integration unit can estimate the user's emotions and determine the priority of the information to be integrated based on the estimated emotions. For example, if the user is excited, the integration unit will prioritize integrating information about tourist attractions and events. For example, the integration unit can analyze the user's heart rate and facial expressions using an emotion estimation algorithm to detect the state of excitement. The integration unit can also prioritize integrating information related to nature and parks if the user is relaxed. For example, the integration unit can analyze the user's voice tone and posture to detect the state of relaxation. The integration unit can also prioritize integrating information related to rest spots and cafes if the user is tired. For example, the integration unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. By prioritizing the information to be integrated according to the user's emotions, more appropriate guide information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input user emotion data into a generating AI and determine the priority of the information that the generating AI will collaborate with.

[0093] The integration unit can select the optimal integration method when integrating with a map application, taking into account the user's geographical location information. For example, if the user is in a tourist destination, the integration unit will prioritize integrating information related to that tourist destination. For example, the integration unit will analyze the user's geographical location information and detect that the user is in a tourist destination. The integration unit can also prioritize integrating information about nearby stores and sales if the user is in a shopping area. For example, the integration unit will analyze the user's geographical location information and detect that the user is in a shopping area. The integration unit can also prioritize integrating information about transportation and route guidance if the user is near a transportation facility. For example, the integration unit will analyze the user's geographical location information and detect that the user is near a transportation facility. By selecting the optimal integration method considering the user's geographical location information, more appropriate guide information can be provided. Geographical location information can be defined, for example, by setting the type of location information and the criteria for consideration. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI, which can then select the optimal integration method.

[0094] The integration unit can analyze the user's social media activity and suggest integration content when integrating with a map application. For example, if the user has posted about a specific tourist destination on social media, the integration unit will prioritize integrating information related to that destination. For example, the integration unit will analyze the user's social media activity and detect posts about a specific tourist destination. The integration unit can also prioritize integrating information related to an event if the user has shown interest in a specific event on social media. For example, the integration unit will analyze the user's social media activity and detect interest in a specific event. The integration unit can also prioritize integrating information related to a restaurant if the user has mentioned a specific restaurant on social media. For example, the integration unit will analyze the user's social media activity and detect mentions of a specific restaurant. By analyzing the user's social media activity and suggesting integration content, more appropriate guide information can be provided. Social media activity can be defined, for example, by specifying the type of activity and the analysis method. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media data into a generating AI, which can then suggest integration content.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The guide system can also include a health management unit that monitors the user's health status. This unit can acquire health data such as the user's heart rate, blood pressure, and steps taken, and adjust the guide information based on this data. For example, if the user's heart rate is high, the health management unit can prioritize guiding them to relaxing spots and rest areas. Similarly, if the user has taken many steps, the health management unit can suggest nearby tourist spots, taking into account the walking distance. Furthermore, the health management unit can analyze the user's health data and provide advice for maintaining good health. For example, it can suggest appropriate exercise and rest times. This allows users to enjoy sightseeing while maintaining their health.

[0097] The guide system may also include a hobby estimation unit that provides customized guides based on the user's hobbies and interests. For example, the hobby estimation unit can analyze the user's past search history and social media posts to estimate their hobbies and interests. If the user is interested in history, the hobby estimation unit can prioritize guiding them to historical sites and museums. Similarly, if the user is interested in nature, the hobby estimation unit can suggest nature parks and hiking trails. Furthermore, the hobby estimation unit can suggest event information and special activities based on the user's interests. For example, if the user is interested in music, it can provide information on nearby concerts and music festivals. This allows users to enjoy sightseeing that aligns with their hobbies and interests.

[0098] The guide system may also include an emotion adjustment unit that estimates the user's emotions and adjusts the tone and style of the guide based on those emotions. For example, if the user is excited, the emotion adjustment unit can provide the guide in an energetic and lively tone. For example, the emotion adjustment unit can analyze the user's heart rate and facial expressions to detect their state of excitement. If the user is relaxed, the guide can provide the guide in a calm and soothing tone. For example, the emotion adjustment unit can analyze the user's voice tone and posture to detect their state of relaxation. Furthermore, if the user is tired, the guide can provide a concise and to-the-point guide. For example, the emotion adjustment unit can analyze the user's skin electrical activity and movement speed to detect their state of fatigue. By adjusting the tone and style of the guide according to the user's emotions, more appropriate guide information can be provided.

[0099] The guide system can also include a history analysis unit that analyzes the user's past guide usage history and proposes optimal guide content. For example, the history analysis unit can suggest places to visit next based on places the user has visited in the past and information they have been interested in. If the user has visited many historical sites in the past, the history analysis unit can suggest historical tourist spots to visit next. Similarly, if the user has visited many national parks in the past, the history analysis unit can suggest national parks to visit next. Furthermore, the history analysis unit can customize guide content based on the user's past feedback. For example, it can prioritize providing guide content that the user has previously given high ratings to. This allows users to efficiently obtain guide information that matches their interests and preferences.

[0100] The guide system may also include an emotion adaptation unit that estimates the user's emotions and dynamically changes the guide content based on the estimated emotions. For example, if the user is excited, the emotion adaptation unit can prioritize providing information on active activities and events. For example, the emotion adaptation unit can analyze the user's heart rate and facial expressions to detect their state of excitement. If the user is relaxed, it can also prioritize providing information on quiet places and relaxing spots. For example, the emotion adaptation unit can analyze the user's voice tone and posture to detect their state of relaxation. Furthermore, if the user is tired, it can also prioritize providing information on rest spots and cafes. For example, the emotion adaptation unit can analyze the user's skin electrical activity and movement speed to detect their state of fatigue. This allows the system to dynamically change the guide content according to the user's emotions, thereby providing more appropriate guide information.

[0101] The guide system may also include an activity estimation unit that customizes the guide content based on the user's current activity. For example, if the user is sightseeing, the activity estimation unit can prioritize providing information related to tourist attractions and historical sites. For example, the activity estimation unit analyzes the user's current activity and detects that they are sightseeing. Similarly, if the user is taking a walk, the activity estimation unit can prioritize providing information related to nature and parks. For example, the activity estimation unit analyzes the user's current activity and detects that they are taking a walk. Furthermore, if the user is on the move, the system can prioritize providing traffic information and route guidance. For example, the activity estimation unit analyzes the user's current activity and detects that they are on the move. This allows the system to provide more appropriate guide information by customizing the guide content based on the user's current activity.

[0102] The guide system may further include an emotion-providing unit that estimates the user's emotions and adjusts the way the guide is delivered based on the estimated emotions. For example, if the user is excited, the emotion-providing unit can provide a guide with visually stimulating effects. For example, the emotion-providing unit can analyze the user's heart rate and facial expressions to detect the state of excitement. If the user is relaxed, the emotion-providing unit can also provide a guide in a calm and soothing tone. For example, the emotion-providing unit can analyze the user's voice tone and posture to detect the state of relaxation. Furthermore, if the user is tired, the emotion-providing unit can provide a concise and to-the-point guide. For example, the emotion-providing unit can analyze the user's skin electrical activity and movement speed to detect the state of fatigue. By adjusting the way the guide is delivered according to the user's emotions, more appropriate guide information can be provided.

[0103] The guide system can also include a travel history analysis unit that analyzes the user's past travel history and proposes optimal guide content. For example, the travel history analysis unit can suggest places the user should visit next based on places they have visited in the past and their travel patterns. For instance, it can suggest new tourist spots near places the user has frequently visited in the past. It can also suggest places the user should visit next based on their ratings of places they have visited in the past. For example, it can suggest tourist spots near places the user has given high ratings to. Furthermore, the travel history analysis unit can suggest efficient sightseeing routes based on the user's travel patterns. For example, it can suggest the optimal route connecting places the user has visited in the past. This allows the user to enjoy sightseeing more efficiently.

[0104] The guide system may also include an emotion personalization unit that estimates the user's emotions and personalizes the guide content based on those emotions. For example, if the user is excited, the emotion personalization unit can prioritize providing information on active activities and events. For example, it can analyze the user's heart rate and facial expressions to detect their state of excitement. If the user is relaxed, it can also prioritize providing information on quiet places and relaxing spots. For example, it can analyze the user's voice tone and posture to detect their state of relaxation. Furthermore, if the user is tired, it can prioritize providing information on rest spots and cafes. For example, it can analyze the user's skin electrical activity and movement speed to detect their state of fatigue. By personalizing the guide content according to the user's emotions, more appropriate guide information can be provided.

[0105] The guide system may also include a location information customization unit that customizes the guide content by considering the user's current geographical location. For example, if the user is in a tourist destination, the location information customization unit can prioritize providing information related to the history and attractions of that destination. For example, the location information customization unit analyzes the user's geographical location and detects that the user is in a tourist destination. Also, if the user is in a shopping area, the location information customization unit can prioritize providing information on nearby stores and sales. For example, the location information customization unit analyzes the user's geographical location and detects that the user is in a shopping area. Furthermore, if the user is near a transportation facility, the system can prioritize providing transportation information and route guidance. For example, the location information customization unit analyzes the user's geographical location and detects that the user is near a transportation facility. In this way, by customizing the guide content by considering the user's current geographical location, more appropriate guide information can be provided.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The detection unit detects GPS information. For example, the detection unit can detect the user's current location using GPS and obtain latitude, longitude, and altitude information. Step 2: The guide unit provides guidance based on GPS information detected by the detection unit. For example, if the user is in a specific location, the guide unit can display the history and points of interest of that location in audio or text. For example, the guide unit can play an audio file to provide an audio guide. The guide unit can also display text information to provide a text guide. Furthermore, the guide unit can support multiple languages. For example, the guide unit can provide guide information in multiple languages. Step 3: The settings section configures the guide content provided by the guide section to suit the user's preferences. For example, the settings section allows the user to select categories of interest. The settings section can also adjust the density of information. For example, the settings section can choose whether to provide detailed or concise information. Furthermore, the settings section can perform theme-based information filtering. For example, the settings section can prioritize displaying information related to a specific theme. Step 4: The integration unit interacts with the map application based on the guide content set by the settings unit. For example, the integration unit can display guide spots around the route. The integration unit can also save a history of visited spots. Furthermore, the integration unit can collect user feedback. For example, the integration unit can collect ratings and comments from users about the spots they have visited.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] For example, the detection unit can detect the user's current location using the GPS module of the smart device 14, and acquire latitude, longitude, and altitude information using the specific processing unit 290 of the data processing device 12. For example, the guide unit can provide voice and text guides using the control unit 46A of the smart device 14, and enable multilingual support using the specific processing unit 290 of the data processing device 12. For example, the setting unit can set guide content tailored to the user's preferences using the control unit 46A of the smart device 14, and adjust the density of information using the specific processing unit 290 of the data processing device 12. For example, the collaboration unit can collaborate with a map application using the communication I / F 44 of the smart device 14, and save a history of visited spots using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] For example, the detection unit can detect the user's current location using the GPS module of the smart glasses 214, and acquire latitude, longitude, and altitude information using the specific processing unit 290 of the data processing device 12. For example, the guide unit can provide voice and text guides using the control unit 46A of the smart glasses 214, and enable multilingual support using the specific processing unit 290 of the data processing device 12. For example, the setting unit can set guide content tailored to the user's preferences using the control unit 46A of the smart glasses 214, and adjust the density of information using the specific processing unit 290 of the data processing device 12. For example, the collaboration unit can collaborate with a map application using the communication I / F 44 of the smart glasses 214, and save a history of visited spots using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] For example, the detection unit can detect the user's current location using the GPS module of the headset terminal 314, and acquire latitude, longitude, and altitude information using the specific processing unit 290 of the data processing device 12. For example, the guide unit can provide voice and text guides using the control unit 46A of the headset terminal 314, and enable multilingual support using the specific processing unit 290 of the data processing device 12. For example, the setting unit can set guide content tailored to the user's preferences using the control unit 46A of the headset terminal 314, and adjust the density of information using the specific processing unit 290 of the data processing device 12. For example, the collaboration unit can collaborate with a map application using the communication I / F 44 of the headset terminal 314, and save a history of visited spots using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] For example, the detection unit can detect the user's current location using the GPS module of the robot 414, and acquire latitude, longitude, and altitude information using the specific processing unit 290 of the data processing device 12. For example, the guide unit can provide voice and text guides using the control unit 46A of the robot 414, and enable multilingual support using the specific processing unit 290 of the data processing device 12. For example, the setting unit can set guide content tailored to the user's preferences using the control unit 46A of the robot 414, and adjust the density of information using the specific processing unit 290 of the data processing device 12. For example, the collaboration unit can collaborate with a map application using the communication I / F 44 of the robot 414, and save a history of visited spots using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A detection unit that detects GPS information, A guide unit that provides guidance based on GPS information detected by the aforementioned detection unit, A setting unit that configures the guide content provided by the aforementioned guide unit according to the user's preferences, A linking unit that interacts with a map application based on the guide content set by the aforementioned setting unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned guide portion is Includes audio guides or text displays, and multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 3) The setting unit is, This includes category selection or adjusting the density of information, and theme-based information filtering. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, This includes displaying guide spots around the route, saving a history of visited spots, and collecting user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit, The system estimates the user's emotions and adjusts the timing of GPS information acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit, Analyze the user's past movement history and select the appropriate method for obtaining GPS information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit, When acquiring GPS information, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit, The system estimates the user's emotions and determines the priority of GPS information to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit, When acquiring GPS information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit, When acquiring GPS information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned guide portion is The system estimates the user's emotions and adjusts the way the guide is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned guide portion is When providing a guide, adjust the level of detail in the guide based on the importance of the location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned guide portion is When providing a guide, different guide algorithms are applied depending on the location category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned guide portion is It estimates the user's emotions and adjusts the length of the guide based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned guide portion is When providing a guide, we prioritize guides based on the timing of their visit to each location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide portion is When providing the guide, we adjust the order of the guides based on the relevance of the locations. The system described in Appendix 1, characterized by the features described herein. (Note 17) The setting unit is, It estimates the user's emotions and adjusts the guide settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The setting unit is, When setting up the guide, the system analyzes the user's past settings history to select the optimal setting method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The setting unit is, When setting up the guide, customize the settings based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 20) The setting unit is, It estimates the user's emotions and determines the priority of guide settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The setting unit is, When setting up the guide, the optimal setting method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The setting unit is, When setting up the guide, the system analyzes the user's social media activity and suggests settings accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts how it interacts with the map application based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When integrating with a map application, the system analyzes the user's past integration history to select the optimal integration method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, When integrating with a map application, the integration details are customized based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, It estimates the user's emotions and prioritizes the information to link based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, When integrating with map applications, the system selects the optimal integration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, When integrating with map applications, the system analyzes the user's social media activity and suggests integration options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 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 detection unit that detects GPS information, A guide unit that provides guidance based on GPS information detected by the aforementioned detection unit, A setting unit that configures the guide content provided by the aforementioned guide unit according to the user's preferences, A linking unit that interacts with a map application based on the guide content set by the aforementioned setting unit, Equipped with A system characterized by the following features.

2. The aforementioned guide section is Includes audio guides or text displays, and multilingual support. The system according to feature 1.

3. The setting unit is, This includes category selection or adjusting the density of information, and theme-based information filtering. The system according to feature 1.

4. The aforementioned linkage unit is, This includes displaying guide spots around the route, saving a history of visited spots, and collecting user feedback. The system according to feature 1.

5. The detection unit, The system estimates the user's emotions and adjusts the timing of GPS information acquisition based on the estimated emotions. The system according to feature 1.

6. The detection unit, Analyze the user's past movement history and select the appropriate method for obtaining GPS information. The system according to feature 1.

7. The detection unit, When acquiring GPS information, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

8. The detection unit, The system estimates the user's emotions and determines the priority of GPS information to acquire based on those estimated emotions. The system according to feature 1.

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