system

Abstract

The system according to this embodiment aims to enable the provision of navigation and gourmet information even in areas without network coverage. [Solution] The system according to the embodiment comprises a location identification unit, a navigation unit, a suggestion unit, and a download unit. The location identification unit identifies the current location using GPS information. The navigation unit performs navigation based on the current location identified by the location identification unit and downloaded map data. The suggestion unit collects and suggests gourmet information via the internet. The download unit downloads data before entering an area without coverage.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it becomes difficult to provide navigation and gourmet information when entering an out-of-service area during travel.

[0005] The system according to the embodiment aims to enable the provision of navigation and gourmet information even in an out-of-service area.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a location identification unit, a navigation unit, a suggestion unit, and a download unit. The location identification unit uses GPS information to determine the current location. The navigation unit performs navigation based on the current location determined by the location identification unit and downloaded map data. The suggestion unit collects and suggests gourmet information via the internet. The download unit downloads data before entering an area without coverage. [Effects of the Invention]

[0007] The system according to this embodiment can provide navigation and gourmet information even in areas without network coverage. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between 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 travel support system according to an embodiment of the present invention is a system that autonomously assists travelers with directions, recommendations for restaurants and tourist attractions, etc. Based on GPS information built into the smartphone and the latest maps downloaded to the terminal, the travel support system can respond to changes in destination even when out of range. The travel support system also suggests recommended restaurants in the vicinity according to the user's preferences via the internet and investigates information such as temporary closures through real-time searches. Furthermore, the travel support system utilizes the mobile network, and when the user enters an area where there is likely to be no signal, the AI ​​agent autonomously downloads data so that it can operate offline. This solves the problem of the system being useless when there is no internet connection. For example, if a user needs directions during a trip, the travel support system uses the smartphone's GPS information to determine the current location and guides the user to their destination based on downloaded map data. For example, if a user wants to go to a tourist attraction, the travel support system calculates the optimal route and provides guidance via voice and visuals. Next, if a user requests information on restaurants, the travel support system collects information on nearby restaurants and cafes via the internet and suggests recommended restaurants according to the user's preferences. For example, if a user prefers Japanese food, it suggests nearby Japanese restaurants and provides information on temporary closures and congestion in real time. Furthermore, if the user enters an area where cell service is likely to be lost, the travel support system can download necessary data in advance, enabling offline navigation and information provision. For example, before entering areas without cell service, such as mountainous regions or underground areas, the travel support system automatically downloads map data and tourist information, allowing users to enjoy their trip with peace of mind even without cell service. In this way, the travel support system makes maximum use of the various functions of smartphones to provide real-time support to make the user's trip the best possible experience. It functions without problems even in areas without cell service and provides information tailored to the user's preferences, reducing travel troubles and ensuring a comfortable trip. As a result, the travel support system can autonomously assist with directions, recommending restaurants and tourist attractions during travel.

[0029] The travel support system according to this embodiment comprises a location identification unit, a navigation unit, a suggestion unit, and a download unit. The location identification unit identifies the current location using GPS information. The location identification unit acquires the current location using, for example, the GPS sensor of a smartphone. The location identification unit can also correct the current location using Wi-Fi or Bluetooth® signals. For example, the location identification unit corrects the current location using the location information of a Wi-Fi access point. The location identification unit can also correct the current location using Bluetooth beacon signals. The navigation unit performs navigation based on downloaded map data according to the current location identified by the location identification unit. The navigation unit calculates the optimal route using, for example, the map data. The navigation unit can also provide guidance using voice or visuals. For example, the navigation unit guides the user on a route using voice guidance. The navigation unit can also guide the user on a route using visual guidance. The suggestion unit collects and suggests gourmet information via the internet. The suggestion unit suggests gourmet information tailored to the user's preferences, for example. The suggestion unit can also investigate temporary closures and congestion status in real time. For example, the suggestion unit collects information from restaurant official websites and review sites via the internet. The suggestion unit can also analyze the user's past behavior history to make more appropriate suggestions. The download unit downloads data before entering an area without coverage. For example, the download unit downloads necessary map data and tourist information before entering an area without coverage. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit downloads information about tourist attractions the user plans to visit in advance. The download unit can also download information tailored to the user's preferences. As a result, the travel support system according to this embodiment can autonomously assist with directions, recommended restaurants, tourist attractions, and more during travel.

[0030] The location tracking unit uses GPS information to determine the current location. For example, the location tracking unit obtains the current location using the GPS sensor of a smartphone. Specifically, the smartphone's GPS sensor receives signals from multiple satellites and calculates the current location based on the difference in the arrival times of those signals. This allows the location tracking unit to obtain highly accurate location information. The location tracking unit can also correct the current location using Wi-Fi and Bluetooth signals. For example, the location tracking unit corrects the current location using the location information of a Wi-Fi access point. The location information of Wi-Fi access points is registered in a database in advance, and the current location can be corrected based on the information of the Wi-Fi access point to which the smartphone is connected. The location tracking unit can also correct the current location using signals from Bluetooth beacons. Bluetooth beacons emit signals within a specific range, and smartphones that receive these signals correct their current location based on the beacon's location information. This allows the location tracking unit to obtain highly accurate location information even indoors or in urban areas where GPS signals are difficult to receive. Furthermore, the location tracking unit updates this location information in real time, allowing it to constantly and accurately determine the current location in accordance with the user's movement. This allows the specific unit to provide accurate location information wherever the user is, and to support other functions of the travel assistance system.

[0031] The navigation unit performs navigation based on downloaded map data, using the current location identified by the identification unit. For example, the navigation unit calculates the optimal route using the map data. Specifically, the navigation unit receives the user's current location and destination as input and calculates the optimal route using an algorithm. This algorithm considers road congestion, traffic regulations, and the user's mode of transportation (walking, cycling, driving, etc.) to provide the most efficient route. The navigation unit can also provide voice and visual guidance. For example, the navigation unit guides the user along a route using voice guidance. Voice guidance is particularly useful while driving or walking, as it allows the user to navigate without relying on visual information. The navigation unit can also guide the user along a route using visual guidance. Visual guidance displays the route on a map, allowing the user to visually confirm their route as they move. Furthermore, the navigation unit can recalculate the route based on real-time updated traffic information, providing the user with the latest information. This allows the navigation unit to support the user in reaching their destination efficiently and safely.

[0032] The suggestion department collects and proposes gourmet information via the internet. For example, the suggestion department proposes gourmet information tailored to the user's preferences. Specifically, the suggestion department analyzes the user's past search history and rating history to understand the user's preferences for dishes and restaurants. This allows them to provide the user with the most suitable gourmet information. The suggestion department can also investigate temporary closures and congestion levels in real time. For example, the suggestion department collects information from restaurant official websites and review sites via the internet. This allows them to determine whether a restaurant the user is planning to visit is temporarily closed or how crowded it is. Furthermore, the suggestion department can analyze the user's past behavior history to make more appropriate suggestions. For example, based on data from restaurants the user has visited and dishes they have rated in the past, they can suggest restaurants the user hasn't visited yet but might like. In this way, the suggestion department can provide valuable information to users and expand their dining options during their travels.

[0033] The download function downloads data before entering an area without coverage. For example, it downloads necessary map data and tourist information before entering an area without coverage. Specifically, the download function selects the necessary data based on the user's current location and travel route. For example, it downloads information about tourist attractions the user plans to visit in advance. The download function can also download information tailored to the user's preferences. For example, if the user is interested in historical buildings, the download function downloads information about historical buildings in the area they plan to visit. This allows the user to access necessary information even in areas without coverage, reducing inconvenience during travel. Furthermore, the download function manages the frequency and size of data updates, efficiently utilizing the user's device storage. For example, it can automatically delete old data and prioritize downloading new data, ensuring that the latest information is always provided. In this way, the download function can support users in enjoying a comfortable trip even in areas without coverage.

[0034] The suggestion unit can suggest information tailored to the user's preferences. For example, the suggestion unit can identify the user's preferences by analyzing the user's past behavior history. For example, the suggestion unit can understand the user's preferences based on information about restaurants and cafes the user has visited in the past. The suggestion unit can also identify the user's preferences based on keywords the user has searched for and websites the user has visited in the past. For example, if the user likes Japanese food, the suggestion unit can suggest a nearby Japanese restaurant. Similarly, if the user likes Italian food, the suggestion unit can suggest a nearby Italian restaurant. By providing information tailored to the user's preferences, it becomes possible to make suggestions that are more satisfying. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past behavior history into AI and have the AI ​​perform the identification of the user's preferences.

[0035] The suggestion department can investigate temporary closures and congestion levels in real time. The suggestion department collects information from, for example, the official websites and review sites of restaurants via the internet. For example, the suggestion department obtains information on temporary closures from the official websites of restaurants. The suggestion department can also obtain information on congestion levels from review sites. For example, the suggestion department analyzes user posts and reviews to understand congestion levels. The suggestion department can also update information in real time. For example, the suggestion department periodically collects information via the internet and provides the latest information. This allows the department to provide the latest information by investigating information in real time. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input information collected from the internet into AI and have the AI ​​perform investigations into temporary closures and congestion levels.

[0036] The navigation unit can provide guidance using voice and visuals. For example, the navigation unit can guide the user along a route using voice guidance. For example, the navigation unit can provide route guidance to the user using speech synthesis technology. The navigation unit can also guide the user along a route using visual guidance. For example, the navigation unit can display a map on the smartphone screen and show the route. The navigation unit can also overlay the route onto the real-world scenery using augmented reality (AR) technology. For example, the navigation unit can overlay the route onto the scenery visible through the smartphone's camera. This enables user-friendly navigation by providing guidance using both voice and visuals. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the content of the voice guidance into a generating AI and have the generating AI perform speech synthesis.

[0037] The download unit can download necessary data before entering an area without coverage. For example, the download unit can download necessary map data and tourist information before entering an area without coverage. For example, the download unit can pre-download information on tourist attractions that the user plans to visit. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit can pre-download information on tourist attractions that the user plans to visit. The download unit can also download information tailored to the user's preferences. For example, if the user likes Japanese food, the download unit will download information on Japanese restaurants. This allows users to stay active offline even in areas without coverage by pre-downloading necessary data. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input the user's current location and travel route into the AI ​​and have the AI ​​select the necessary data.

[0038] The location identification unit can improve its accuracy by referring to the user's past travel history when determining the current location. For example, the location identification unit can prioritize identifying places the user has frequently visited in the past. For example, the location identification unit can analyze the user's past travel history and identify frequently visited places. The location identification unit can also analyze the user's past travel patterns to improve accuracy. For example, the location identification unit can determine the current location based on the user's past travel patterns. The location identification unit can also predict places the user will visit at specific times based on the user's past travel history to improve accuracy. For example, the location identification unit predicts places the user will visit at specific times based on the user's past travel history. This allows the location identification accuracy to be improved by referring to the user's past travel history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the user's past travel history into AI and have AI perform the improvement of location accuracy.

[0039] The location identification unit can improve its accuracy by considering the user's movement speed and direction when determining the current location. For example, if the user is moving at high speed, the location identification unit can increase its accuracy to quickly determine the current location. For example, the location identification unit can adjust its accuracy based on the movement speed. Also, if the user is moving slowly, the location identification unit can maintain normal accuracy and reduce battery consumption. For example, the location identification unit can adjust its accuracy based on the movement speed. Furthermore, the location identification unit can also improve its accuracy by considering the user's movement direction. For example, the location identification unit can adjust its accuracy based on the movement direction. In this way, the accuracy of determining the current location can be improved by considering the user's movement speed and direction. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input data on the user's movement speed and direction into the AI ​​and have the AI ​​perform the improvement of the location identification accuracy.

[0040] The location identification unit can select a location identification method while considering the battery level of the user's device when determining the current location. For example, if the battery level is low, the location identification unit can reduce battery consumption by lowering the identification accuracy. For example, the location identification unit can reduce battery consumption by lowering the GPS accuracy. Also, if the battery level is sufficient, the location identification unit can increase the identification accuracy to quickly determine the current location. For example, the location identification unit can quickly determine the current location by increasing the GPS accuracy. Furthermore, the location identification unit can automatically adjust the identification method according to the battery level. For example, the location identification unit adjusts the identification method based on the battery level. In this way, by considering the battery level of the user's device, the current location can be determined while reducing battery consumption. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input battery level data into AI and have the AI ​​select the location identification method.

[0041] The location identification unit can improve its accuracy by referring to environmental information around the user when determining the current location. For example, the location identification unit can improve its accuracy by referring to surrounding buildings and landmarks. For example, the location identification unit can determine the current location based on the location information of surrounding buildings. The location identification unit can also improve its accuracy by considering surrounding traffic conditions. For example, the location identification unit can determine the current location based on traffic condition data. The location identification unit can also improve its accuracy by referring to surrounding weather information. For example, the location identification unit can determine the current location based on weather information. In this way, the accuracy of determining the current location can be improved by referring to environmental information around the user. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input surrounding environmental information data into AI and have AI perform the improvement of location accuracy.

[0042] The navigation unit can suggest the optimal route by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. For example, the navigation unit can analyze the user's past travel history and suggest the optimal route. The navigation unit can also suggest routes that avoid congestion based on the user's past travel history. For example, the navigation unit can suggest routes that avoid congestion based on the user's past travel history. The navigation unit can also analyze the user's past travel history and suggest the most efficient route. For example, the navigation unit can suggest the most efficient route based on the user's past travel history. In this way, the optimal route can be suggested by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into AI and have AI perform the task of suggesting the optimal route.

[0043] The navigation unit can adjust its guidance method during navigation, taking into account the user's movement speed and direction. For example, if the user is moving at high speed, the navigation unit can provide rapid guidance. For example, the navigation unit can adjust the guidance method based on the movement speed. The navigation unit can also provide detailed guidance if the user is moving slowly. For example, the navigation unit can adjust the guidance method based on the movement speed. Furthermore, the navigation unit can provide the optimal guidance method by considering the user's direction of movement. For example, the navigation unit can adjust the guidance method based on the direction of movement. In this way, by considering the user's movement speed and direction, a more appropriate guidance method can be provided. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on the user's movement speed and direction into the AI ​​and have the AI ​​perform the adjustment of the guidance method.

[0044] The navigation unit can select a guidance method during navigation, taking into account the battery level of the user's device. For example, if the battery level is low, the navigation unit can reduce the frequency of guidance to conserve battery power. The navigation unit can also increase the frequency of guidance to provide more detailed guidance when the battery level is sufficient. The navigation unit can also automatically adjust the guidance method according to the battery level. For example, the navigation unit adjusts the guidance method based on the battery level. This allows navigation to be performed while conserving battery power by taking into account the battery level of the user's device. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit can input battery level data into the AI ​​and have the AI ​​select the guidance method.

[0045] The navigation unit can improve the accuracy of navigation by referring to environmental information around the user during navigation. For example, the navigation unit can improve the accuracy of navigation by referring to surrounding buildings and landmarks. For example, the navigation unit can improve the accuracy of navigation based on the location information of surrounding buildings. The navigation unit can also improve the accuracy of navigation by considering the surrounding traffic conditions. For example, the navigation unit can improve the accuracy of navigation based on traffic condition data. The navigation unit can also improve the accuracy of navigation by referring to surrounding weather information. For example, the navigation unit can improve the accuracy of navigation based on weather information. In this way, the accuracy of navigation can be improved by referring to environmental information around the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input data on the surrounding environment into the AI ​​and have the AI ​​perform the improvement of navigation accuracy.

[0046] The suggestion unit can make optimal suggestions by referring to the user's past consumption history. For example, the suggestion unit can make optimal suggestions based on products the user has purchased in the past. For example, the suggestion unit can analyze the user's past consumption history and make optimal suggestions. The suggestion unit can also analyze the user's past consumption patterns and make the most relevant suggestions. For example, the suggestion unit can make the most relevant suggestions based on the user's past consumption patterns. The suggestion unit can also predict and suggest products that the user will purchase at a specific time based on their past consumption history. For example, the suggestion unit predicts and suggests products that the user will purchase at a specific time based on their past consumption history. In this way, the suggestion unit can make optimal suggestions by referring to the user's past consumption history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past consumption history into AI and have the AI ​​execute the optimal suggestions.

[0047] The suggestion function can customize its suggestions based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the suggestion function can suggest health-related products, such as health foods or fitness equipment. It can also suggest travel-related products if the user is traveling, such as travel supplies or information on tourist destinations. Furthermore, the suggestion function can provide optimal suggestions based on the user's areas of interest, such as suggesting relevant products or services. This allows for more relevant suggestions by customizing the suggestions based on the user's current lifestyle and areas of interest. Some or all of the above-described processes in the suggestion function may be performed using AI, or not. For example, the suggestion function can input data on the user's current lifestyle and areas of interest into the AI, allowing the AI ​​to customize the suggestions.

[0048] The suggestion unit can prioritize suggesting highly relevant information by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting information about stores near the user's current location. For example, the suggestion unit can suggest information about nearby stores based on the user's current location. The suggestion unit can also suggest information related to a specific region if the user is in that region. For example, the suggestion unit can suggest tourist information or gourmet information for the region the user is in. The suggestion unit can also analyze the user's movement patterns and make optimal suggestions. For example, the suggestion unit can suggest highly relevant information based on the user's movement patterns. This allows the suggestion unit to prioritize suggesting highly relevant information by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​suggest highly relevant information.

[0049] The suggestion unit can analyze the user's social media activity and suggest relevant information when making suggestions. For example, the suggestion unit can suggest products that the user has shown interest in on social media. The suggestion unit can also analyze the user's social media activity to identify areas of interest and make suggestions. For example, the suggestion unit analyzes the user's social media activity to identify areas of interest and makes suggestions. The suggestion unit can also suggest products that the user's social media friends have shown interest in. For example, the suggestion unit can suggest products that the user's friends have shown interest in. In this way, by analyzing the user's social media activity, relevant information can be suggested. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's social media activity into AI and have AI make suggestions for relevant information.

[0050] The download unit can select necessary data by referring to the user's past travel history during the download process. For example, the download unit can prioritize downloading data of places the user has visited in the past. For example, the download unit can download data of places visited based on the user's past travel history. The download unit can also analyze the user's past travel patterns and select necessary data. For example, the download unit can select necessary data based on the user's past travel patterns. The download unit can also predict and download data needed for a specific time period based on the user's past travel history. For example, the download unit can predict and download data needed for a specific time period based on the user's past travel history. This allows the system to select necessary data by referring to the user's past travel history. Some or all of the above processing in the download unit may be performed using AI, for example, or without AI. For example, the download unit can input data from the user's past travel history into AI and have AI select the necessary data.

[0051] The download unit can customize data based on the user's current lifestyle and areas of interest during the download process. For example, if the user is health-conscious, the download unit can download health-related data. For example, the download unit can download health-related information. Similarly, if the user is traveling, the download unit can download travel-related data. For example, the download unit can download travel-related information. Furthermore, the download unit can download the most relevant data based on the user's areas of interest. For example, the download unit can download relevant information based on the user's areas of interest. This allows for the provision of more relevant data by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input data on the user's current lifestyle and areas of interest into an AI and have the AI ​​perform the data customization.

[0052] The download unit can prioritize data during download, taking into account the user's device's battery level. For example, if the battery level is low, the download unit will prioritize downloading important data. The download unit can also download detailed data if the battery level is sufficient. The download unit can also automatically adjust data priority according to the battery level. For example, the download unit adjusts data priority based on the battery level. This allows the download unit to download necessary data while minimizing battery consumption by considering the user's device's battery level. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input battery level data into AI and have AI determine data priority.

[0053] The download unit can select necessary data by referring to the user's surrounding environment information during the download process. For example, the download unit may prioritize downloading data related to surrounding buildings and landmarks. For example, the download unit may select data based on information about surrounding buildings. The download unit can also select necessary data by considering the surrounding traffic conditions. For example, the download unit may select necessary data based on traffic conditions data. The download unit can also download necessary data by referring to surrounding weather information. For example, the download unit may select necessary data based on weather information. In this way, the necessary data can be selected by referring to the user's surrounding environment information. Some or all of the above processing in the download unit may be performed using AI, for example, or without AI. For example, the download unit can input surrounding environment information data into AI and have the AI ​​perform the selection of necessary data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A travel support system can suggest future travel plans by referencing a user's past travel history. For example, it can suggest a next travel destination based on information about cities and tourist spots the user has visited in the past. It can also customize the next travel plan based on information about activities and restaurants the user has enjoyed in the past. Furthermore, it can suggest travel plans tailored to specific seasons or events based on the user's past travel history. In this way, it can provide more satisfying travel plans by utilizing the user's past travel history.

[0056] A travel support system can monitor a user's current health status and suggest health-conscious travel plans. For example, if a user is tired, the system can suggest relaxing tourist spots or spas. If the user is active, the system can suggest activities such as hiking or cycling. Furthermore, if a user has a specific health problem, the system can suggest a travel plan that takes that problem into consideration. This allows for the provision of an optimal travel plan tailored to the user's health condition.

[0057] A travel support system can customize travel plans based on the user's current lifestyle and interests. For example, if a user is health-conscious, the system can suggest health-related tourist attractions and activities. If a user is interested in culture, the system can suggest museums and historical sites. Furthermore, if a user is interested in food, the system can suggest local restaurants and cafes. This allows the system to provide the optimal travel plan based on the user's current lifestyle and interests.

[0058] The travel support system can suggest shopping plans during a trip by referencing the user's past spending history. For example, it can suggest shopping spots at the travel destination based on information about products and brands the user has purchased in the past. It can also customize the next travel plan based on information about shopping areas the user has previously enjoyed. Furthermore, it can suggest shopping plans tailored to specific seasons or events based on the user's past spending history. In this way, it can provide more satisfying shopping plans by utilizing the user's past spending history.

[0059] A travel support system can suggest transportation options during a trip, taking into account the user's current geographical location. For example, if the user is in an urban area, the system can provide information on public transportation. If the user is in a suburban area, it can also provide information on rental cars and taxis. Furthermore, if the user is in a specific tourist destination, it can suggest transportation options within that destination. This allows the system to provide the most suitable transportation options, taking into account the user's current geographical location.

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

[0061] Step 1: The location unit determines the current location using GPS information. The location unit can obtain the current location using, for example, the GPS sensor of a smartphone. The location unit can also correct the current location using Wi-Fi or Bluetooth signals. For example, the location unit can correct the current location using the location information of a Wi-Fi access point. The location unit can also correct the current location using Bluetooth beacon signals. Step 2: The navigation unit performs navigation based on the downloaded map data, using the current location identified by the identification unit. The navigation unit calculates the optimal route using the map data, for example. The navigation unit can also provide guidance via voice or visual means. For example, the navigation unit guides the user along the route using voice guidance. The navigation unit can also guide the user along the route using visual guidance. Step 3: The suggestion team collects and proposes gourmet information via the internet. For example, the suggestion team proposes gourmet information tailored to the user's preferences. The suggestion team can also investigate temporary closures and congestion levels in real time. For example, the suggestion team collects information from restaurant official websites and review sites via the internet. The suggestion team can also analyze the user's past behavior history to make more appropriate suggestions. Step 4: The download unit downloads data before entering an area without coverage. For example, the download unit downloads necessary map data and tourist information before entering an area without coverage. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit downloads information about tourist attractions the user plans to visit in advance. The download unit can also download information tailored to the user's preferences.

[0062] (Example of form 2) The travel support system according to an embodiment of the present invention is a system that autonomously assists travelers with directions, recommendations for restaurants and tourist attractions, etc. Based on GPS information built into the smartphone and the latest maps downloaded to the terminal, the travel support system can respond to changes in destination even when out of range. The travel support system also suggests recommended restaurants in the vicinity according to the user's preferences via the internet and investigates information such as temporary closures through real-time searches. Furthermore, the travel support system utilizes the mobile network, and when the user enters an area where there is likely to be no signal, the AI ​​agent autonomously downloads data so that it can operate offline. This solves the problem of the system being useless when there is no internet connection. For example, if a user needs directions during a trip, the travel support system uses the smartphone's GPS information to determine the current location and guides the user to their destination based on downloaded map data. For example, if a user wants to go to a tourist attraction, the travel support system calculates the optimal route and provides guidance via voice and visuals. Next, if a user requests information on restaurants, the travel support system collects information on nearby restaurants and cafes via the internet and suggests recommended restaurants according to the user's preferences. For example, if a user prefers Japanese food, it suggests nearby Japanese restaurants and provides information on temporary closures and congestion in real time. Furthermore, if the user enters an area where cell service is likely to be lost, the travel support system can download necessary data in advance, enabling offline navigation and information provision. For example, before entering areas without cell service, such as mountainous regions or underground areas, the travel support system automatically downloads map data and tourist information, allowing users to enjoy their trip with peace of mind even without cell service. In this way, the travel support system makes maximum use of the various functions of smartphones to provide real-time support to make the user's trip the best possible experience. It functions without problems even in areas without cell service and provides information tailored to the user's preferences, reducing travel troubles and ensuring a comfortable trip. As a result, the travel support system can autonomously assist with directions, recommending restaurants and tourist attractions during travel.

[0063] The travel support system according to this embodiment comprises a location identification unit, a navigation unit, a suggestion unit, and a download unit. The location identification unit identifies the current location using GPS information. The location identification unit acquires the current location using, for example, the GPS sensor of a smartphone. The location identification unit can also correct the current location using Wi-Fi or Bluetooth signals. For example, the location identification unit corrects the current location using the location information of a Wi-Fi access point. The location identification unit can also correct the current location using Bluetooth beacon signals. The navigation unit performs navigation based on downloaded map data according to the current location identified by the location identification unit. The navigation unit calculates the optimal route using, for example, the map data. The navigation unit can also provide guidance using voice or visuals. For example, the navigation unit guides the user on a route using voice guidance. The navigation unit can also guide the user on a route using visual guidance. The suggestion unit collects and suggests gourmet information via the internet. The suggestion unit suggests gourmet information tailored to the user's preferences, for example. The suggestion unit can also investigate temporary closures and congestion status in real time. For example, the suggestion unit collects information from restaurant official websites and review sites via the internet. The suggestion unit can also analyze the user's past behavior history to make more appropriate suggestions. The download unit downloads data before entering an area without coverage. For example, the download unit downloads necessary map data and tourist information before entering an area without coverage. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit downloads information about tourist attractions the user plans to visit in advance. The download unit can also download information tailored to the user's preferences. As a result, the travel support system according to this embodiment can autonomously assist with directions, recommended restaurants, tourist attractions, and more during travel.

[0064] The location tracking unit uses GPS information to determine the current location. For example, the location tracking unit obtains the current location using the GPS sensor of a smartphone. Specifically, the smartphone's GPS sensor receives signals from multiple satellites and calculates the current location based on the difference in the arrival times of those signals. This allows the location tracking unit to obtain highly accurate location information. The location tracking unit can also correct the current location using Wi-Fi and Bluetooth signals. For example, the location tracking unit corrects the current location using the location information of a Wi-Fi access point. The location information of Wi-Fi access points is registered in a database in advance, and the current location can be corrected based on the information of the Wi-Fi access point to which the smartphone is connected. The location tracking unit can also correct the current location using signals from Bluetooth beacons. Bluetooth beacons emit signals within a specific range, and smartphones that receive these signals correct their current location based on the beacon's location information. This allows the location tracking unit to obtain highly accurate location information even indoors or in urban areas where GPS signals are difficult to receive. Furthermore, the location tracking unit updates this location information in real time, allowing it to constantly and accurately determine the current location in accordance with the user's movement. This allows the specific unit to provide accurate location information wherever the user is, and to support other functions of the travel assistance system.

[0065] The navigation unit performs navigation based on downloaded map data, using the current location identified by the identification unit. For example, the navigation unit calculates the optimal route using the map data. Specifically, the navigation unit receives the user's current location and destination as input and calculates the optimal route using an algorithm. This algorithm considers road congestion, traffic regulations, and the user's mode of transportation (walking, cycling, driving, etc.) to provide the most efficient route. The navigation unit can also provide voice and visual guidance. For example, the navigation unit guides the user along a route using voice guidance. Voice guidance is particularly useful while driving or walking, as it allows the user to navigate without relying on visual information. The navigation unit can also guide the user along a route using visual guidance. Visual guidance displays the route on a map, allowing the user to visually confirm their route as they move. Furthermore, the navigation unit can recalculate the route based on real-time updated traffic information, providing the user with the latest information. This allows the navigation unit to support the user in reaching their destination efficiently and safely.

[0066] The suggestion department collects and proposes gourmet information via the internet. For example, the suggestion department proposes gourmet information tailored to the user's preferences. Specifically, the suggestion department analyzes the user's past search history and rating history to understand the user's preferences for dishes and restaurants. This allows them to provide the user with the most suitable gourmet information. The suggestion department can also investigate temporary closures and congestion levels in real time. For example, the suggestion department collects information from restaurant official websites and review sites via the internet. This allows them to determine whether a restaurant the user is planning to visit is temporarily closed or how crowded it is. Furthermore, the suggestion department can analyze the user's past behavior history to make more appropriate suggestions. For example, based on data from restaurants the user has visited and dishes they have rated in the past, they can suggest restaurants the user hasn't visited yet but might like. In this way, the suggestion department can provide valuable information to users and expand their dining options during their travels.

[0067] The download function downloads data before entering an area without coverage. For example, it downloads necessary map data and tourist information before entering an area without coverage. Specifically, the download function selects the necessary data based on the user's current location and travel route. For example, it downloads information about tourist attractions the user plans to visit in advance. The download function can also download information tailored to the user's preferences. For example, if the user is interested in historical buildings, the download function downloads information about historical buildings in the area they plan to visit. This allows the user to access necessary information even in areas without coverage, reducing inconvenience during travel. Furthermore, the download function manages the frequency and size of data updates, efficiently utilizing the user's device storage. For example, it can automatically delete old data and prioritize downloading new data, ensuring that the latest information is always provided. In this way, the download function can support users in enjoying a comfortable trip even in areas without coverage.

[0068] The suggestion unit can suggest information tailored to the user's preferences. For example, the suggestion unit can identify the user's preferences by analyzing the user's past behavior history. For example, the suggestion unit can understand the user's preferences based on information about restaurants and cafes the user has visited in the past. The suggestion unit can also identify the user's preferences based on keywords the user has searched for and websites the user has visited in the past. For example, if the user likes Japanese food, the suggestion unit can suggest a nearby Japanese restaurant. Similarly, if the user likes Italian food, the suggestion unit can suggest a nearby Italian restaurant. By providing information tailored to the user's preferences, it becomes possible to make suggestions that are more satisfying. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's past behavior history into AI and have the AI ​​perform the identification of the user's preferences.

[0069] The suggestion department can investigate temporary closures and congestion levels in real time. The suggestion department collects information from, for example, the official websites and review sites of restaurants via the internet. For example, the suggestion department obtains information on temporary closures from the official websites of restaurants. The suggestion department can also obtain information on congestion levels from review sites. For example, the suggestion department analyzes user posts and reviews to understand congestion levels. The suggestion department can also update information in real time. For example, the suggestion department periodically collects information via the internet and provides the latest information. This allows the department to provide the latest information by investigating information in real time. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input information collected from the internet into AI and have the AI ​​perform investigations into temporary closures and congestion levels.

[0070] The navigation unit can provide guidance using voice and visuals. For example, the navigation unit can guide the user along a route using voice guidance. For example, the navigation unit can provide route guidance to the user using speech synthesis technology. The navigation unit can also guide the user along a route using visual guidance. For example, the navigation unit can display a map on the smartphone screen and show the route. The navigation unit can also overlay the route onto the real-world scenery using augmented reality (AR) technology. For example, the navigation unit can overlay the route onto the scenery visible through the smartphone's camera. This enables user-friendly navigation by providing guidance using both voice and visuals. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the content of the voice guidance into a generating AI and have the generating AI perform speech synthesis.

[0071] The download unit can download necessary data before entering an area without coverage. For example, the download unit can download necessary map data and tourist information before entering an area without coverage. For example, the download unit can pre-download information on tourist attractions that the user plans to visit. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit can pre-download information on tourist attractions that the user plans to visit. The download unit can also download information tailored to the user's preferences. For example, if the user likes Japanese food, the download unit will download information on Japanese restaurants. This allows users to stay active offline even in areas without coverage by pre-downloading necessary data. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input the user's current location and travel route into the AI ​​and have the AI ​​select the necessary data.

[0072] The location tracking unit can estimate the user's emotions and adjust the accuracy of location tracking based on the estimated emotions. For example, if the user is stressed, the location tracking unit can increase accuracy to quickly pinpoint the current location. For example, it can maximize GPS accuracy to pinpoint the current location. Also, if the user is relaxed, the location tracking unit can maintain normal accuracy to conserve battery power. For example, it can maintain normal GPS accuracy to conserve battery power. Also, if the user is in a hurry, the location tracking unit can maximize accuracy to pinpoint the current location in the shortest possible time. For example, it can maximize GPS accuracy to quickly pinpoint the current location. This allows for more appropriate navigation by adjusting the accuracy of location tracking according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI, for example, or without AI. For example, the specific unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0073] The location identification unit can improve its accuracy by referring to the user's past travel history when determining the current location. For example, the location identification unit can prioritize identifying places the user has frequently visited in the past. For example, the location identification unit can analyze the user's past travel history and identify frequently visited places. The location identification unit can also analyze the user's past travel patterns to improve accuracy. For example, the location identification unit can determine the current location based on the user's past travel patterns. The location identification unit can also predict places the user will visit at specific times based on the user's past travel history to improve accuracy. For example, the location identification unit predicts places the user will visit at specific times based on the user's past travel history. This allows the location identification accuracy to be improved by referring to the user's past travel history. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the user's past travel history into AI and have AI perform the improvement of location accuracy.

[0074] The location identification unit can improve its accuracy by considering the user's movement speed and direction when determining the current location. For example, if the user is moving at high speed, the location identification unit can increase its accuracy to quickly determine the current location. For example, the location identification unit can adjust its accuracy based on the movement speed. Also, if the user is moving slowly, the location identification unit can maintain normal accuracy and reduce battery consumption. For example, the location identification unit can adjust its accuracy based on the movement speed. Furthermore, the location identification unit can also improve its accuracy by considering the user's movement direction. For example, the location identification unit can adjust its accuracy based on the movement direction. In this way, the accuracy of determining the current location can be improved by considering the user's movement speed and direction. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input data on the user's movement speed and direction into the AI ​​and have the AI ​​perform the improvement of the location identification accuracy.

[0075] The identification unit can estimate the user's emotions and adjust the frequency of location identification based on the estimated emotions. For example, if the user is stressed, the identification unit increases the identification frequency to update the current location quickly. For example, the identification unit increases the GPS update frequency to update the current location. Also, if the user is relaxed, the identification unit can maintain a normal identification frequency to conserve battery power. For example, the identification unit maintains a normal GPS update frequency to conserve battery power. Also, if the user is in a hurry, the identification unit maximizes the identification frequency to update the current location in the shortest possible time. For example, the identification unit maximizes the GPS update frequency to update the current location quickly. By adjusting the frequency of location identification according to the user's emotions, more appropriate navigation becomes possible. 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 identification unit may be performed using AI, for example, or without AI. For example, the specific unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0076] The location identification unit can select a location identification method while considering the battery level of the user's device when determining the current location. For example, if the battery level is low, the location identification unit can reduce battery consumption by lowering the identification accuracy. For example, the location identification unit can reduce battery consumption by lowering the GPS accuracy. Also, if the battery level is sufficient, the location identification unit can increase the identification accuracy to quickly determine the current location. For example, the location identification unit can quickly determine the current location by increasing the GPS accuracy. Furthermore, the location identification unit can automatically adjust the identification method according to the battery level. For example, the location identification unit adjusts the identification method based on the battery level. In this way, by considering the battery level of the user's device, the current location can be determined while reducing battery consumption. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input battery level data into AI and have the AI ​​select the location identification method.

[0077] The location identification unit can improve its accuracy by referring to environmental information around the user when determining the current location. For example, the location identification unit can improve its accuracy by referring to surrounding buildings and landmarks. For example, the location identification unit can determine the current location based on the location information of surrounding buildings. The location identification unit can also improve its accuracy by considering surrounding traffic conditions. For example, the location identification unit can determine the current location based on traffic condition data. The location identification unit can also improve its accuracy by referring to surrounding weather information. For example, the location identification unit can determine the current location based on weather information. In this way, the accuracy of determining the current location can be improved by referring to environmental information around the user. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input surrounding environmental information data into AI and have AI perform the improvement of location accuracy.

[0078] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated user emotions. For example, if the user is nervous, the navigation unit can provide a simple and highly visible guidance method. For example, the navigation unit can provide concise voice guidance. Also, if the user is relaxed, the navigation unit can provide guidance that includes detailed information. For example, the navigation unit can provide detailed voice guidance. Also, if the user is in a hurry, the navigation unit can provide guidance that gets to the point. For example, the navigation unit can provide voice guidance that gets to the point. By adjusting the navigation guidance method according to the user's emotions, more appropriate guidance becomes possible. 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 navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The navigation unit can suggest the optimal route by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. For example, the navigation unit can analyze the user's past travel history and suggest the optimal route. The navigation unit can also suggest routes that avoid congestion based on the user's past travel history. For example, the navigation unit can suggest routes that avoid congestion based on the user's past travel history. The navigation unit can also analyze the user's past travel history and suggest the most efficient route. For example, the navigation unit can suggest the most efficient route based on the user's past travel history. In this way, the optimal route can be suggested by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into AI and have AI perform the task of suggesting the optimal route.

[0080] The navigation unit can adjust its guidance method during navigation, taking into account the user's movement speed and direction. For example, if the user is moving at high speed, the navigation unit can provide rapid guidance. For example, the navigation unit can adjust the guidance method based on the movement speed. The navigation unit can also provide detailed guidance if the user is moving slowly. For example, the navigation unit can adjust the guidance method based on the movement speed. Furthermore, the navigation unit can provide the optimal guidance method by considering the user's direction of movement. For example, the navigation unit can adjust the guidance method based on the direction of movement. In this way, by considering the user's movement speed and direction, a more appropriate guidance method can be provided. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on the user's movement speed and direction into the AI ​​and have the AI ​​perform the adjustment of the guidance method.

[0081] The navigation unit can estimate the user's emotions and adjust the frequency of navigation guidance based on the estimated emotions. For example, if the user is nervous, the navigation unit can increase the guidance frequency to provide a sense of security. The navigation unit can also maintain a normal guidance frequency to conserve battery power if the user is relaxed. The navigation unit can also maximize the guidance frequency to provide quick guidance if the user is in a hurry. By adjusting the frequency of navigation guidance according to the user's emotions, more appropriate guidance becomes possible. 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 navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0082] The navigation unit can select a guidance method during navigation, taking into account the battery level of the user's device. For example, if the battery level is low, the navigation unit can reduce the frequency of guidance to conserve battery power. The navigation unit can also increase the frequency of guidance to provide more detailed guidance when the battery level is sufficient. The navigation unit can also automatically adjust the guidance method according to the battery level. For example, the navigation unit adjusts the guidance method based on the battery level. This allows navigation to be performed while conserving battery power by taking into account the battery level of the user's device. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit can input battery level data into the AI ​​and have the AI ​​select the guidance method.

[0083] The navigation unit can improve the accuracy of navigation by referring to environmental information around the user during navigation. For example, the navigation unit can improve the accuracy of navigation by referring to surrounding buildings and landmarks. For example, the navigation unit can improve the accuracy of navigation based on the location information of surrounding buildings. The navigation unit can also improve the accuracy of navigation by considering the surrounding traffic conditions. For example, the navigation unit can improve the accuracy of navigation based on traffic condition data. The navigation unit can also improve the accuracy of navigation by referring to surrounding weather information. For example, the navigation unit can improve the accuracy of navigation based on weather information. In this way, the accuracy of navigation can be improved by referring to environmental information around the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input data on the surrounding environment into the AI ​​and have the AI ​​perform the improvement of navigation accuracy.

[0084] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is tense, the suggestion section can provide a simple and easily understandable presentation. For example, it can use concise text and images to make suggestions. If the user is relaxed, the suggestion section can also provide a presentation that includes detailed information. For example, it can use detailed text and images to make suggestions. If the user is in a hurry, the suggestion section can provide a concise presentation. For example, it can use concise text and images to make suggestions. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0085] The suggestion unit can make optimal suggestions by referring to the user's past consumption history. For example, the suggestion unit can make optimal suggestions based on products the user has purchased in the past. For example, the suggestion unit can analyze the user's past consumption history and make optimal suggestions. The suggestion unit can also analyze the user's past consumption patterns and make the most relevant suggestions. For example, the suggestion unit can make the most relevant suggestions based on the user's past consumption patterns. The suggestion unit can also predict and suggest products that the user will purchase at a specific time based on their past consumption history. For example, the suggestion unit predicts and suggests products that the user will purchase at a specific time based on their past consumption history. In this way, the suggestion unit can make optimal suggestions by referring to the user's past consumption history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past consumption history into AI and have the AI ​​execute the optimal suggestions.

[0086] The suggestion function can customize its suggestions based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the suggestion function can suggest health-related products, such as health foods or fitness equipment. It can also suggest travel-related products if the user is traveling, such as travel supplies or information on tourist destinations. Furthermore, the suggestion function can provide optimal suggestions based on the user's areas of interest, such as suggesting relevant products or services. This allows for more relevant suggestions by customizing the suggestions based on the user's current lifestyle and areas of interest. Some or all of the above-described processes in the suggestion function may be performed using AI, or not. For example, the suggestion function can input data on the user's current lifestyle and areas of interest into the AI, allowing the AI ​​to customize the suggestions.

[0087] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. For example, it will prioritize providing the user with important information. If the user is relaxed, the suggestion unit can also provide detailed suggestions. For example, it will provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can quickly provide important suggestions. For example, it will quickly provide the user with important information. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The suggestion unit can prioritize suggesting highly relevant information by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting information about stores near the user's current location. For example, the suggestion unit can suggest information about nearby stores based on the user's current location. The suggestion unit can also suggest information related to a specific region if the user is in that region. For example, the suggestion unit can suggest tourist information or gourmet information for the region the user is in. The suggestion unit can also analyze the user's movement patterns and make optimal suggestions. For example, the suggestion unit can suggest highly relevant information based on the user's movement patterns. This allows the suggestion unit to prioritize suggesting highly relevant information by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location data into AI and have the AI ​​suggest highly relevant information.

[0089] The suggestion unit can analyze the user's social media activity and suggest relevant information when making suggestions. For example, the suggestion unit can suggest products that the user has shown interest in on social media. The suggestion unit can also analyze the user's social media activity to identify areas of interest and make suggestions. For example, the suggestion unit analyzes the user's social media activity to identify areas of interest and makes suggestions. The suggestion unit can also suggest products that the user's social media friends have shown interest in. For example, the suggestion unit can suggest products that the user's friends have shown interest in. In this way, by analyzing the user's social media activity, relevant information can be suggested. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's social media activity into AI and have AI make suggestions for relevant information.

[0090] The download unit can select necessary data by referring to the user's past travel history during the download process. For example, the download unit can prioritize downloading data of places the user has visited in the past. For example, the download unit can download data of places visited based on the user's past travel history. The download unit can also analyze the user's past travel patterns and select necessary data. For example, the download unit can select necessary data based on the user's past travel patterns. The download unit can also predict and download data needed for a specific time period based on the user's past travel history. For example, the download unit can predict and download data needed for a specific time period based on the user's past travel history. This allows the system to select necessary data by referring to the user's past travel history. Some or all of the above processing in the download unit may be performed using AI, for example, or without AI. For example, the download unit can input data from the user's past travel history into AI and have AI select the necessary data.

[0091] The download unit can customize data based on the user's current lifestyle and areas of interest during the download process. For example, if the user is health-conscious, the download unit can download health-related data. For example, the download unit can download health-related information. Similarly, if the user is traveling, the download unit can download travel-related data. For example, the download unit can download travel-related information. Furthermore, the download unit can download the most relevant data based on the user's areas of interest. For example, the download unit can download relevant information based on the user's areas of interest. This allows for the provision of more relevant data by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input data on the user's current lifestyle and areas of interest into an AI and have the AI ​​perform the data customization.

[0092] The download unit can estimate the user's emotions and adjust the download timing based on the estimated emotions. For example, if the user is stressed, the download unit can quickly download the necessary data. If the user is relaxed, the download unit can download the data at a normal time. If the user is in a hurry, the download unit can download the necessary data in the shortest possible time. By adjusting the download timing according to the user's emotions, data can be downloaded at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, with 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 download unit may be performed using AI or not. For example, the download unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The download unit can prioritize data during download, taking into account the user's device's battery level. For example, if the battery level is low, the download unit will prioritize downloading important data. The download unit can also download detailed data if the battery level is sufficient. The download unit can also automatically adjust data priority according to the battery level. For example, the download unit adjusts data priority based on the battery level. This allows the download unit to download necessary data while minimizing battery consumption by considering the user's device's battery level. Some or all of the above processing in the download unit may be performed using AI, or not. For example, the download unit can input battery level data into AI and have AI determine data priority.

[0094] The download unit can select necessary data by referring to the user's surrounding environment information during the download process. For example, the download unit may prioritize downloading data related to surrounding buildings and landmarks. For example, the download unit may select data based on information about surrounding buildings. The download unit can also select necessary data by considering the surrounding traffic conditions. For example, the download unit may select necessary data based on traffic conditions data. The download unit can also download necessary data by referring to surrounding weather information. For example, the download unit may select necessary data based on weather information. In this way, the necessary data can be selected by referring to the user's surrounding environment information. Some or all of the above processing in the download unit may be performed using AI, for example, or without AI. For example, the download unit can input surrounding environment information data into AI and have the AI ​​perform the selection of necessary data.

[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] A travel support system can estimate a user's emotions and automatically adjust the travel plan based on those emotions. For example, if a user is feeling stressed, the system can suggest relaxing tourist spots or spas. If the user is excited, it can suggest active activities or events. Furthermore, if the user is tired, the system can suggest cafes or parks where they can rest. This allows the system to provide an optimal travel plan tailored to the user's emotions.

[0097] A travel support system can suggest future travel plans by referencing a user's past travel history. For example, it can suggest a next travel destination based on information about cities and tourist spots the user has visited in the past. It can also customize the next travel plan based on information about activities and restaurants the user has enjoyed in the past. Furthermore, it can suggest travel plans tailored to specific seasons or events based on the user's past travel history. In this way, it can provide more satisfying travel plans by utilizing the user's past travel history.

[0098] A travel support system can monitor a user's current health status and suggest health-conscious travel plans. For example, if a user is tired, the system can suggest relaxing tourist spots or spas. If the user is active, the system can suggest activities such as hiking or cycling. Furthermore, if a user has a specific health problem, the system can suggest a travel plan that takes that problem into consideration. This allows for the provision of an optimal travel plan tailored to the user's health condition.

[0099] A travel support system can estimate a user's emotions and adjust its communication methods during travel based on those estimates. For example, if a user is stressed, the system can provide simple, easy-to-read messages. If the user is relaxed, it can provide messages with more detailed information. Furthermore, if the user is in a hurry, it can provide concise, to-the-point messages. This allows the system to provide the most appropriate communication method according to the user's emotions.

[0100] A travel support system can customize travel plans based on the user's current lifestyle and interests. For example, if a user is health-conscious, the system can suggest health-related tourist attractions and activities. If a user is interested in culture, the system can suggest museums and historical sites. Furthermore, if a user is interested in food, the system can suggest local restaurants and cafes. This allows the system to provide the optimal travel plan based on the user's current lifestyle and interests.

[0101] The travel support system can estimate the user's emotions and provide emergency assistance during travel based on those estimates. For example, if the user is feeling stressed, the system will send a notification to their emergency contacts. If the user is panicking, the system can also provide information on the nearest medical facilities or police stations. Furthermore, if the user gets lost, the system can pinpoint their current location and guide them to the nearest safe place. This ensures safety during travel by providing emergency assistance tailored to the user's emotions.

[0102] The travel support system can suggest shopping plans during a trip by referencing the user's past spending history. For example, it can suggest shopping spots at the travel destination based on information about products and brands the user has purchased in the past. It can also customize the next travel plan based on information about shopping areas the user has previously enjoyed. Furthermore, it can suggest shopping plans tailored to specific seasons or events based on the user's past spending history. In this way, it can provide more satisfying shopping plans by utilizing the user's past spending history.

[0103] A travel support system can estimate a user's emotions and suggest entertainment plans during their trip based on those emotions. For example, if the user is relaxed, the system can suggest relaxing movies or music. If the user is excited, it can suggest active events or live performances. Furthermore, if the user is tired, the system can suggest relaxing activities such as reading or meditation. This allows the system to provide the optimal entertainment plan tailored to the user's emotions.

[0104] A travel support system can suggest transportation options during a trip, taking into account the user's current geographical location. For example, if the user is in an urban area, the system can provide information on public transportation. If the user is in a suburban area, it can also provide information on rental cars and taxis. Furthermore, if the user is in a specific tourist destination, it can suggest transportation options within that destination. This allows the system to provide the most suitable transportation options, taking into account the user's current geographical location.

[0105] A travel support system can estimate a user's emotions and suggest meal plans during their trip based on those emotions. For example, if a user is feeling stressed, the system can suggest relaxing cafes or restaurants. If the user is excited, it can suggest restaurants or bars with an active atmosphere. Furthermore, if the user is tired, the system can suggest restaurants that offer relaxing meals. This allows the system to provide the optimal meal plan tailored to the user's emotions.

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

[0107] Step 1: The location unit determines the current location using GPS information. The location unit can obtain the current location using, for example, the GPS sensor of a smartphone. The location unit can also correct the current location using Wi-Fi or Bluetooth signals. For example, the location unit can correct the current location using the location information of a Wi-Fi access point. The location unit can also correct the current location using Bluetooth beacon signals. Step 2: The navigation unit performs navigation based on the downloaded map data, using the current location identified by the identification unit. The navigation unit calculates the optimal route using the map data, for example. The navigation unit can also provide guidance via voice or visual means. For example, the navigation unit guides the user along the route using voice guidance. The navigation unit can also guide the user along the route using visual guidance. Step 3: The suggestion team collects and proposes gourmet information via the internet. For example, the suggestion team proposes gourmet information tailored to the user's preferences. The suggestion team can also investigate temporary closures and congestion levels in real time. For example, the suggestion team collects information from restaurant official websites and review sites via the internet. The suggestion team can also analyze the user's past behavior history to make more appropriate suggestions. Step 4: The download unit downloads data before entering an area without coverage. For example, the download unit downloads necessary map data and tourist information before entering an area without coverage. The download unit can also select necessary data based on the user's current location and travel route. For example, the download unit downloads information about tourist attractions the user plans to visit in advance. The download unit can also download information tailored to the user's preferences.

[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] Each of the multiple elements described above, including the identification unit, navigation unit, suggestion unit, and download unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the identification unit uses the GPS sensor, Wi-Fi, and Bluetooth signals of the smart device 14 to determine the current location. The navigation unit calculates the optimal route using map data downloaded by the control unit 46A of the smart device 14 and provides guidance in voice and visuals. The suggestion unit collects gourmet information via the internet using the identification processing unit 290 of the data processing unit 12 and makes suggestions tailored to the user's preferences. The download unit downloads necessary data before entering an area without coverage using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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] Each of the multiple elements described above, including the identification unit, navigation unit, suggestion unit, and download unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the identification unit uses the GPS sensor, Wi-Fi, and Bluetooth signals of the smart glasses 214 to determine the current location. The navigation unit calculates the optimal route using map data downloaded by the control unit 46A of the smart glasses 214 and provides guidance via voice and visuals. The suggestion unit collects gourmet information via the internet using the identification processing unit 290 of the data processing unit 12 and makes suggestions tailored to the user's preferences. The download unit downloads necessary data before entering an area without coverage using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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] Each of the multiple elements described above, including the identification unit, navigation unit, suggestion unit, and download unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the identification unit uses the GPS sensor, Wi-Fi, and Bluetooth signals of the headset terminal 314 to determine the current location. The navigation unit calculates the optimal route using downloaded map data by the control unit 46A of the headset terminal 314 and provides guidance via voice and visuals. The suggestion unit collects gourmet information via the internet by the identification processing unit 290 of the data processing unit 12 and provides suggestions tailored to the user's preferences. The download unit downloads necessary data before entering an area without coverage by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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] Each of the multiple elements described above, including the identification unit, navigation unit, suggestion unit, and download unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the identification unit uses the robot 414's GPS sensor, Wi-Fi, and Bluetooth signals to determine the current location. The navigation unit calculates the optimal route using map data downloaded by the robot 414's control unit 46A and provides guidance via voice and visuals. The suggestion unit collects gourmet information via the internet using the identification processing unit 290 of the data processing unit 12 and makes suggestions tailored to the user's preferences. The download unit downloads necessary data before entering an area without coverage using the robot 414's control unit 46A. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[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 unit that identifies the current location using GPS information, A navigation unit that performs navigation based on downloaded map data, using the current location identified by the aforementioned identification unit, The proposal department collects and proposes gourmet information via the internet, It includes a download unit that downloads data before entering an area with no signal. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest information tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Investigate temporary closures and congestion levels in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Provide guidance using audio and visuals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned download section is Download necessary data before entering an area with no signal. The system described in Appendix 1, characterized by the features described herein. (Note 6) The specified part is, It estimates the user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The specified part is, When determining the current location, the system improves accuracy by referencing the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, When determining the current location, the accuracy of the location is improved by taking into account the user's movement speed and direction. The system described in Appendix 1, characterized by the features described herein. (Note 9) The specified part is, It estimates the user's emotions and adjusts the frequency of location detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, When determining the current location, the method of location selection is chosen considering the battery level of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 11) The specified part is, When determining the current location, the system improves accuracy by referencing environmental information around the user. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned navigation unit is During navigation, the system suggests the optimal route by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned navigation unit is During navigation, the guidance method is adjusted considering the user's movement speed and direction. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned navigation unit is The system estimates the user's emotions and adjusts the frequency of navigation prompts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned navigation unit is During navigation, the navigation system selects a guidance method that takes into account the battery level of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned navigation unit is During navigation, the system improves guidance accuracy by referencing information about the user's surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, we refer to the user's past consumption history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, customize the content based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, the system prioritizes suggesting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned download section is During the download process, the system selects the necessary data by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned download section is During the download process, the data is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned download section is It estimates the user's emotions and adjusts the download timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned download section is During downloads, the system prioritizes data based on the user's device's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned download section is During download, the system selects the necessary data by referring to the user's surrounding environment information. 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 unit that identifies the current location using GPS information, A navigation unit that performs navigation based on downloaded map data, using the current location identified by the aforementioned identification unit, The proposal department collects and proposes gourmet information via the internet, It includes a download unit that downloads data before entering an area with no signal. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest information tailored to the user's preferences. The system according to feature 1.

3. The aforementioned proposal section is, Investigate temporary closures and congestion levels in real time. The system according to feature 1.

4. The aforementioned navigation unit is Provide guidance using audio and visuals. The system according to feature 1.

5. The aforementioned download section is Download necessary data before entering an area with no signal. The system according to feature 1.

6. The specified part is, It estimates the user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. The system according to feature 1.

7. The specified part is, When determining the current location, the system improves accuracy by referencing the user's past movement history. The system according to feature 1.

8. The specified part is, When determining the current location, the accuracy of the location is improved by taking into account the user's movement speed and direction. The system according to feature 1.

9. The specified part is, It estimates the user's emotions and adjusts the frequency of location detection based on the estimated user emotions. The system according to feature 1.

10. The specified part is, When determining the current location, the method of location selection is chosen considering the battery level of the user's device. The system according to feature 1.

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