system

Abstract

The system according to this embodiment aims to help pedestrians and drivers reach their destinations smoothly. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a projection unit, and a guide unit. The reception unit receives input of the destination. The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The guide unit provides voice guidance based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, the assistance for pedestrians and drivers to smoothly reach their destinations is not sufficient, and there is room for improvement.

[0005] The system according to the embodiment aims to assist pedestrians and drivers to smoothly reach their destinations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a projection unit, and a guide unit. The reception unit receives input of the destination. The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The guide unit provides voice guidance based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can assist pedestrians and drivers in reaching their destinations smoothly. [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 manages communication between a plurality of computers. Examples of communication standards applicable 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 pedestrian navigation agent system according to an embodiment of the present invention is a glasses-type device that assists pedestrians and drivers in smoothly reaching their destinations. This pedestrian navigation agent system analyzes map information in real time and projects directions and corners onto the glasses. It also provides voice guidance through a bone conduction speaker, allowing users to receive directions without moving their eyes. This enables efficient and comfortable travel without getting lost. For example, when a user inputs a destination, the generating AI analyzes the latest map data and traffic information and provides the optimal route in real time. The generating AI projects directions and corners onto the glasses and provides voice guidance through a bone conduction speaker. This allows users to receive directions without moving their eyes. For example, when a user alights at a station platform for the first time, the generating AI proposes the optimal route and projects directions and corners onto the glasses. The user can receive directions without moving their eyes and reach their destination without getting lost. The generating AI also provides advice according to traffic conditions and weather. For example, if traffic congestion occurs, the generating AI suggests an alternative route and guides the user via voice. This supports a comfortable and efficient journey for the user. This device is extremely useful for travel enthusiasts, outdoor activity lovers, and people who travel on foot or by car on a daily basis. Especially in unfamiliar places, it is easy to get lost and the stress of reaching one's destination can be great, but using a pedestrian navigation agent can reduce this stress and improve the efficiency and safety of travel. In this way, the pedestrian navigation agent system can help users reach their destination smoothly.

[0029] The pedestrian navigation agent system according to this embodiment comprises a reception unit, an analysis unit, a projection unit, and a guide unit. The reception unit receives information from the user to input their destination. The reception unit can accept destination input by methods such as voice input, touch input, or text input. The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The analysis unit analyzes the latest map data and real-time traffic information using, for example, a generation AI to provide the optimal route. When the generation AI analyzes map data and traffic information, the analysis unit considers, for example, the type of algorithm and the accuracy of the analysis. The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions and corner indications by methods such as arrow displays or voice guidance. The guide unit provides voice guidance based on the analysis results obtained by the analysis unit. The guide unit provides voice guidance by means of, for example, a bone conduction speaker, allowing the user to receive directions without moving their eyes. As a result, the pedestrian navigation agent system can help the user reach their destination smoothly.

[0030] The reception unit receives information from the user to enter their destination. The reception unit can accept destination input via methods such as voice input, touch input, and text input. For voice input, the user simply speaks their destination into the microphone, and the system uses speech recognition technology to convert the input into text. For touch input, the user can select the destination on a map using the device's touchscreen or enter the destination's address or name using the keyboard. For text input, the user enters the destination using a keyboard or software keyboard. This allows the reception unit to offer diverse input methods, improving user convenience. Furthermore, the reception unit can quickly transmit the entered information to the analysis unit to proceed to the next processing step. To accurately recognize the user's input, the reception unit also has the ability to analyze the meaning of the input using natural language processing technology and correct erroneous or ambiguous inputs. For example, if a user enters "nearby cafe," the reception unit identifies the nearest cafe based on the user's current location and sends it to the analysis unit. This allows the reception unit to accurately understand the user's intent and provide appropriate information.

[0031] The analysis unit analyzes map data and traffic information based on the information received by the reception unit. For example, the analysis unit uses generative AI to analyze the latest map data and real-time traffic information to provide the optimal route. The generative AI rapidly processes a vast map database and traffic information to calculate the optimal route from the user's current location to their destination. Specifically, the generative AI obtains information such as road structure, pedestrian-only paths, and traffic light locations from the map database, and optimizes the route by considering congestion and construction information from real-time traffic information. For example, the generative AI uses deep learning algorithms to learn from past data and predict the optimal route. The analysis unit considers factors such as the type of algorithm and the accuracy of the analysis when the generative AI analyzes map data and traffic information. This allows the analysis unit to provide the user with the most efficient and safe route. Furthermore, the analysis unit can also provide customized routes by considering the user's walking speed and preferred routes (e.g., scenic routes or the shortest routes). Based on real-time updated information, the analysis unit immediately calculates a new route if a change is necessary and notifies the user. This allows the analysis unit to always provide the optimal route based on the latest information, helping users reach their destination smoothly.

[0032] The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions and corner indications using methods such as arrow displays or voice guidance. Specifically, the projection unit uses a display built into the smart glasses worn by the user to project arrows indicating the direction of travel and information about the next corner directly into the user's field of vision. This allows the user to confirm the direction of travel without moving their gaze. The projection unit can also provide a combination of visual and auditory information by using voice guidance in conjunction with the projection unit. For example, when approaching the next corner, it can provide voice guidance such as "Turn right at the next corner." This allows the user to obtain information from both sight and sound, enabling more intuitive navigation. Furthermore, the projection unit has a function to detect ambient brightness and the direction of the user's gaze and automatically adjust the displayed content. For example, it adjusts the brightness of the display at night or in dark places to make the information easier for the user to see. Also, if the user is looking in a particular direction, it prioritizes displaying information related to that direction. This allows the projection unit to provide flexible information tailored to the user's situation, offering a comfortable navigation experience.

[0033] The guide unit provides voice guidance based on the analysis results obtained by the analysis unit. For example, the guide unit provides voice guidance through a bone conduction speaker, allowing the user to receive directions without moving their eyes. Because the bone conduction speaker transmits sound directly to the inner ear through the bone without blocking the ears, the user can receive voice guidance while still hearing ambient sounds. This allows the user to safely navigate while checking their surroundings. The guide unit updates the content of the voice guidance in real time according to the user's current location and progress. For example, when the user approaches the next corner, it provides specific instructions such as "Turn right at the next corner," and when approaching the destination, it provides guidance such as "Your destination is on the left." Furthermore, the guide unit can adjust the timing and content of the voice guidance according to the user's walking speed and surrounding conditions. For example, if the user is walking quickly, it provides the next instruction earlier, and if the user is standing still, it provides information appropriate to the situation. This allows the guide unit to provide flexible voice guidance tailored to the user's situation, supporting smooth navigation. Additionally, the guide unit supports multiple languages ​​and can provide voice guidance in the appropriate language according to the user's language settings. This allows the guide system to provide appropriate navigation to a diverse range of users, including foreign tourists.

[0034] The advice unit can provide advice tailored to traffic conditions and weather. For example, the advice unit can acquire weather data and traffic sensor data and, based on this, suggest alternative routes and issue weather-related warnings to the user. For example, if there is traffic congestion, the advice unit can suggest alternative routes and provide voice guidance to the user. The advice unit can also provide voice guidance to warn the user if the weather deteriorates. In this way, the advice unit can support the user's comfortable and efficient travel by providing advice tailored to traffic conditions and weather. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input weather data and traffic sensor data into a generating AI, which can then analyze this data to generate advice.

[0035] The adjustment unit can adjust the navigation according to the user's walking speed. The adjustment unit measures the user's walking speed using, for example, an acceleration sensor or GPS data, and adjusts the timing of the navigation guidance based on this. For example, if the user is walking fast, the adjustment unit can advance the guidance timing. Conversely, if the user is walking slowly, the adjustment unit can delay the guidance timing. In this way, the adjustment unit can provide more appropriate guidance by adjusting the navigation according to the user's walking speed. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's walking speed data into a generating AI, and the generating AI can analyze this data to adjust the navigation.

[0036] The reception desk can analyze the user's past destination input history and automatically suggest frequently entered destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggested destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as suggested destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This allows the reception desk to reduce the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past destination input history data into a generating AI, which can analyze this data and automatically suggest frequently entered destinations.

[0037] The reception desk can suggest the optimal destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's schedule registered in their calendar and automatically set the destination. The reception desk can also suggest the optimal destination based on the user's current activity status (e.g., at work, on vacation, etc.). Furthermore, the reception desk can suggest the optimal destination by combining the user's current location information with their schedule. This allows the reception desk to improve convenience by suggesting the optimal destination based on the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information and location data into a generating AI, which can then analyze this data and suggest the optimal destination.

[0038] The reception unit can prioritize displaying highly relevant destinations when a destination is entered, taking into account the user's geographical location information. For example, the reception unit can prioritize displaying locations close to the user's current location as candidate destinations. The reception unit can also suggest highly relevant destinations by combining the user's current location with their past travel history. Furthermore, the reception unit can suggest the optimal destination by considering the user's current location and time of day. In this way, the reception unit can suggest highly relevant destinations by taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then analyze this data and prioritize displaying highly relevant destinations.

[0039] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest places the user has checked into on social media as potential destinations. It can also analyze the content of the user's social media posts and suggest relevant destinations. Furthermore, it can suggest places visited by the user's social media friends as potential destinations. In this way, the reception desk can suggest destinations that match the user's interests based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze this data and suggest relevant destinations.

[0040] The analysis unit can predict current traffic conditions by referring to past traffic data during analysis. For example, the analysis unit predicts current traffic conditions based on past traffic congestion data. The analysis unit can also predict current operating conditions by referring to past public transport operating conditions. Furthermore, the analysis unit can predict current traffic conditions based on past road construction information. As a result, the analysis unit can provide more accurate route guidance by predicting current traffic conditions based on past traffic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past traffic data into a generating AI, and the generating AI can analyze this data to predict current traffic conditions.

[0041] The analysis unit can provide the optimal route by considering the user's past travel history during analysis. For example, the analysis unit can propose the optimal route based on routes the user has used in the past. Furthermore, the analysis unit can propose routes that avoid congestion based on the user's past travel history. In addition, the analysis unit can analyze the user's past travel history and propose the most efficient route. Thus, by considering past travel history, the analysis unit can provide the user with the optimal route. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI, which can then analyze this data to provide the optimal route.

[0042] The analysis unit can provide the optimal route by considering the user's geographical location information during analysis. For example, the analysis unit can propose the optimal route from the user's current location. It can also propose the optimal route by considering the distance between the user's current location and the destination. Furthermore, it can propose the optimal route by combining the user's current location with traffic conditions. In this way, the analysis unit can provide the optimal route by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI, and the generating AI can analyze this data to provide the optimal route.

[0043] The analysis unit can analyze a user's social media activity during analysis and suggest relevant routes. For example, the analysis unit can suggest the optimal route based on the locations where the user has checked in on social media. It can also analyze the content of a user's social media posts and suggest relevant routes. Furthermore, the analysis unit can suggest the optimal route based on the locations visited by the user's social media friends. In this way, the analysis unit can provide route guidance tailored to the user's interests by suggesting routes based on social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, which can then analyze this data and suggest relevant routes.

[0044] The projection unit can detect the user's gaze movements during projection and select the optimal display position. For example, if the user's gaze is directed in a specific direction, the projection unit will project information in that direction. The projection unit can also track the user's gaze movements in real time and select the optimal display position. Furthermore, the projection unit can select the optimal display position considering the user's gaze movements and the environment. In this way, the projection unit can provide the optimal display position by detecting the user's gaze movements. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's gaze data into a generating AI, which can analyze this data to select the optimal display position.

[0045] The projection unit can adjust the brightness of the display based on the user's current ambient light during projection. For example, in a bright environment, the projection unit can automatically adjust the brightness to ensure visibility. In a dark environment, the projection unit can also automatically adjust the brightness to reduce eye strain. Furthermore, the projection unit can adjust the brightness in real time in response to changes in ambient light. This allows the projection unit to improve visibility by adjusting the brightness according to the ambient light. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input ambient light data into a generating AI, which can then analyze this data to adjust the brightness of the display.

[0046] The projection unit can prioritize displaying highly relevant information by considering the user's geographical location during projection. For example, the projection unit can prioritize displaying information about locations close to the user's current location. It can also combine the user's current location with their past travel history to display highly relevant information. Furthermore, the projection unit can display optimal information by considering the user's current location and time of day. In this way, the projection unit can provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's geographical location data into a generating AI, which can then analyze this data and prioritize displaying highly relevant information.

[0047] The projection unit can analyze the user's social media activity during projection and display relevant information. For example, the projection unit can display information about places the user has checked into on social media. It can also analyze the content of the user's social media posts and display relevant information. Furthermore, the projection unit can display information about places visited by the user's social media friends. In this way, the projection unit can display information tailored to the user's interests by providing information based on social media activity. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's social media activity data into a generating AI, which can then analyze this data and display relevant information.

[0048] The guide unit can provide optimal guidance content by referring to the user's past travel history during guidance. For example, the guide unit can provide optimal guidance content based on routes the user has used in the past. The guide unit can also provide guidance content that avoids congestion based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guidance content. In this way, the guide unit can provide more appropriate guidance by providing guidance content based on past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI, and the generating AI can analyze this data to provide optimal guidance content.

[0049] The guide unit can adjust the volume of the voice guide based on the user's current ambient noise during guidance. For example, the guide unit can automatically increase the volume of the voice guide in a noisy environment. Conversely, the guide unit can automatically decrease the volume of the voice guide in a quiet environment. Furthermore, the guide unit can adjust the volume of the voice guide in real time in response to changes in ambient noise. This allows the guide unit to improve the audibility of the voice guide by adjusting the volume according to the ambient noise. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input ambient noise data into a generating AI, which can then analyze this data to adjust the volume of the voice guide.

[0050] The guiding unit can prioritize guiding users to highly relevant information by considering the user's geographical location. For example, the guiding unit can prioritize guiding users to information about locations close to the user's current location. The guiding unit can also combine the user's current location with their past travel history to guide users to highly relevant information. Furthermore, the guiding unit can consider the user's current location and time of day to guide users to the most relevant information. In this way, the guiding unit can provide highly relevant information by considering geographical location. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's geographical location data into a generating AI, which can analyze this data to prioritize guiding users to highly relevant information.

[0051] The guiding unit can analyze the user's social media activity and guide them with relevant information during the guiding process. For example, the guiding unit can guide users with information about places they have checked into on social media. It can also analyze the content of the user's social media posts and guide them with relevant information. Furthermore, the guiding unit can guide users with information about places visited by the user's social media friends. In this way, the guiding unit can provide information tailored to the user's interests by providing information based on their social media activity. Some or all of the above processing in the guiding unit may be performed using AI, for example, or not using AI. For example, the guiding unit can input the user's social media activity data into a generating AI, which can then analyze this data and guide the user with relevant information.

[0052] The advisory unit can predict the current situation by referring to past traffic data when providing advice. For example, the advisory unit can predict the current traffic situation based on past traffic congestion data. It can also predict the current operating situation by referring to past public transport operating conditions. Furthermore, it can predict the current traffic situation based on past road construction information. As a result, the advisory unit can provide more accurate advice by predicting the current situation based on past traffic data. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input past traffic data into a generating AI, and the generating AI can analyze this data to predict the current situation.

[0053] The advice unit can provide optimal advice by considering the user's past travel history. For example, the advice unit can provide optimal advice based on routes the user has used in the past. The advice unit can also provide advice to avoid congestion based on the user's past travel history. Furthermore, the advice unit can analyze the user's past travel history and provide the most efficient advice. As a result, the advice unit can provide more appropriate guidance by providing advice based on past travel history. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past travel history data into a generating AI, which can then analyze this data to provide optimal advice.

[0054] The advice unit can provide highly relevant advice by considering the user's geographical location information when providing advice. For example, the advice unit can prioritize providing advice about locations close to the user's current location. It can also provide highly relevant advice by combining the user's current location with their past travel history. Furthermore, the advice unit can provide optimal advice by considering the user's current location and time of day. In this way, the advice unit can provide highly relevant advice by considering geographical location information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location data into a generating AI, which can then analyze this data to provide highly relevant advice.

[0055] The adjustment unit can perform optimal adjustments by referring to the user's past walking speed data during the adjustment process. For example, the adjustment unit can adjust the optimal navigation speed based on the speed at which the user has walked in the past. The adjustment unit can also suggest the optimal navigation method based on the user's past walking speed data. Furthermore, the adjustment unit can analyze the user's past walking speed and provide the most efficient navigation. In this way, the adjustment unit can provide more appropriate navigation by performing adjustments based on past walking speed data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past walking speed data into a generating AI, which can then analyze this data and perform optimal adjustments.

[0056] The adjustment unit can perform optimal adjustments while considering the user's geographical location information. For example, the adjustment unit can perform optimal navigation adjustments based on the user's current location. It can also perform optimal navigation adjustments by combining the user's current location with their past travel history. Furthermore, the adjustment unit can perform optimal navigation adjustments by considering the user's current location and time of day. In this way, the adjustment unit can provide optimal navigation by considering geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information data into a generating AI, which can then analyze this data to perform optimal adjustments.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can monitor the user's health status and provide navigation tailored to that status. For example, if the user is tired, the reception desk can suggest a resting place. If the user is in good health, it can also suggest a longer route. Furthermore, the reception desk can adjust the frequency and timing of navigation based on the user's health status. This allows the reception desk to support a more comfortable journey by providing navigation that is appropriate to the user's health condition.

[0059] The analytics unit can provide information on tourist attractions and events along a route based on the user's hobbies and interests. For example, if a user is interested in history, the analytics unit can suggest a route that includes historical landmarks. If a user is interested in food, the analytics unit can suggest a route that includes popular restaurants and cafes. Furthermore, if a user wants to attend a specific event, the analytics unit can suggest the most suitable route for that event. In this way, the analytics unit can make travel more enjoyable by providing routes tailored to the user's hobbies and interests.

[0060] The guide unit can provide multilingual audio guides according to the user's language settings. For example, if the user selects English, the guide unit can provide an audio guide in English. If the user selects Japanese, it can provide an audio guide in Japanese. Furthermore, if the user uses multiple languages, the guide unit can switch the audio guide according to those languages. This allows the guide unit to provide more easily understandable guidance by offering audio guides tailored to the user's language settings.

[0061] The projection unit can adjust the displayed content according to the user's degree of visual impairment. For example, if the user has color blindness, the projection unit can adjust the color contrast. Also, if the user has reduced vision, the projection unit can enlarge the font size. Furthermore, if the user is completely visually impaired, the projection unit can prioritize providing audio guidance. In this way, the projection unit can provide more appropriate guidance by offering displayed content tailored to the user's degree of visual impairment.

[0062] The adjustment unit can adjust the navigation according to the user's mode of transportation. For example, if the user is traveling on foot, the adjustment unit can provide a pedestrian-friendly route. If the user is traveling by bicycle, it can provide a dedicated bicycle route. Furthermore, if the user is using public transport, the adjustment unit can provide transfer information and the optimal boarding location. In this way, the adjustment unit can support smoother travel by providing navigation tailored to the user's mode of transportation.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives information from the user regarding their destination. The reception desk can accept destination input using methods such as voice input, touch input, or text input. Step 2: The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The analysis unit uses, for example, a generating AI to analyze the latest map data and real-time traffic information and provide the optimal route. When the generating AI analyzes the map data and traffic information, the analysis unit considers, for example, the type of algorithm and the accuracy of the analysis. Step 3: The projection unit projects directions for travel and corners onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions for travel and corners using methods such as arrow displays or voice guidance. Step 4: The guide unit provides audio guidance based on the analysis results obtained by the analysis unit. The guide unit provides audio guidance, for example, through a bone conduction speaker, allowing the user to receive directions without moving their eyes.

[0065] (Example of form 2) The pedestrian navigation agent system according to an embodiment of the present invention is a glasses-type device that assists pedestrians and drivers in smoothly reaching their destinations. This pedestrian navigation agent system analyzes map information in real time and projects directions and corners onto the glasses. It also provides voice guidance through a bone conduction speaker, allowing users to receive directions without moving their eyes. This enables efficient and comfortable travel without getting lost. For example, when a user inputs a destination, the generating AI analyzes the latest map data and traffic information and provides the optimal route in real time. The generating AI projects directions and corners onto the glasses and provides voice guidance through a bone conduction speaker. This allows users to receive directions without moving their eyes. For example, when a user alights at a station platform for the first time, the generating AI proposes the optimal route and projects directions and corners onto the glasses. The user can receive directions without moving their eyes and reach their destination without getting lost. The generating AI also provides advice according to traffic conditions and weather. For example, if traffic congestion occurs, the generating AI suggests an alternative route and guides the user via voice. This supports a comfortable and efficient journey for the user. This device is extremely useful for travel enthusiasts, outdoor activity lovers, and people who travel on foot or by car on a daily basis. Especially in unfamiliar places, it is easy to get lost and the stress of reaching one's destination can be great, but using a pedestrian navigation agent can reduce this stress and improve the efficiency and safety of travel. In this way, the pedestrian navigation agent system can help users reach their destination smoothly.

[0066] The pedestrian navigation agent system according to this embodiment comprises a reception unit, an analysis unit, a projection unit, and a guide unit. The reception unit receives information from the user to input their destination. The reception unit can accept destination input by methods such as voice input, touch input, or text input. The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The analysis unit analyzes the latest map data and real-time traffic information using, for example, a generation AI to provide the optimal route. When the generation AI analyzes map data and traffic information, the analysis unit considers, for example, the type of algorithm and the accuracy of the analysis. The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions and corner indications by methods such as arrow displays or voice guidance. The guide unit provides voice guidance based on the analysis results obtained by the analysis unit. The guide unit provides voice guidance by means of, for example, a bone conduction speaker, allowing the user to receive directions without moving their eyes. As a result, the pedestrian navigation agent system can help the user reach their destination smoothly.

[0067] The reception unit receives information from the user to enter their destination. The reception unit can accept destination input via methods such as voice input, touch input, and text input. For voice input, the user simply speaks their destination into the microphone, and the system uses speech recognition technology to convert the input into text. For touch input, the user can select the destination on a map using the device's touchscreen or enter the destination's address or name using the keyboard. For text input, the user enters the destination using a keyboard or software keyboard. This allows the reception unit to offer diverse input methods, improving user convenience. Furthermore, the reception unit can quickly transmit the entered information to the analysis unit to proceed to the next processing step. To accurately recognize the user's input, the reception unit also has the ability to analyze the meaning of the input using natural language processing technology and correct erroneous or ambiguous inputs. For example, if a user enters "nearby cafe," the reception unit identifies the nearest cafe based on the user's current location and sends it to the analysis unit. This allows the reception unit to accurately understand the user's intent and provide appropriate information.

[0068] The analysis unit analyzes map data and traffic information based on the information received by the reception unit. For example, the analysis unit uses generative AI to analyze the latest map data and real-time traffic information to provide the optimal route. The generative AI rapidly processes a vast map database and traffic information to calculate the optimal route from the user's current location to their destination. Specifically, the generative AI obtains information such as road structure, pedestrian-only paths, and traffic light locations from the map database, and optimizes the route by considering congestion and construction information from real-time traffic information. For example, the generative AI uses deep learning algorithms to learn from past data and predict the optimal route. The analysis unit considers factors such as the type of algorithm and the accuracy of the analysis when the generative AI analyzes map data and traffic information. This allows the analysis unit to provide the user with the most efficient and safe route. Furthermore, the analysis unit can also provide customized routes by considering the user's walking speed and preferred routes (e.g., scenic routes or the shortest routes). Based on real-time updated information, the analysis unit immediately calculates a new route if a change is necessary and notifies the user. This allows the analysis unit to always provide the optimal route based on the latest information, helping users reach their destination smoothly.

[0069] The projection unit projects directions and corner indications onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions and corner indications using methods such as arrow displays or voice guidance. Specifically, the projection unit uses a display built into the smart glasses worn by the user to project arrows indicating the direction of travel and information about the next corner directly into the user's field of vision. This allows the user to confirm the direction of travel without moving their gaze. The projection unit can also provide a combination of visual and auditory information by using voice guidance in conjunction with the projection unit. For example, when approaching the next corner, it can provide voice guidance such as "Turn right at the next corner." This allows the user to obtain information from both sight and sound, enabling more intuitive navigation. Furthermore, the projection unit has a function to detect ambient brightness and the direction of the user's gaze and automatically adjust the displayed content. For example, it adjusts the brightness of the display at night or in dark places to make the information easier for the user to see. Also, if the user is looking in a particular direction, it prioritizes displaying information related to that direction. This allows the projection unit to provide flexible information tailored to the user's situation, offering a comfortable navigation experience.

[0070] The guide unit provides voice guidance based on the analysis results obtained by the analysis unit. For example, the guide unit provides voice guidance through a bone conduction speaker, allowing the user to receive directions without moving their eyes. Because the bone conduction speaker transmits sound directly to the inner ear through the bone without blocking the ears, the user can receive voice guidance while still hearing ambient sounds. This allows the user to safely navigate while checking their surroundings. The guide unit updates the content of the voice guidance in real time according to the user's current location and progress. For example, when the user approaches the next corner, it provides specific instructions such as "Turn right at the next corner," and when approaching the destination, it provides guidance such as "Your destination is on the left." Furthermore, the guide unit can adjust the timing and content of the voice guidance according to the user's walking speed and surrounding conditions. For example, if the user is walking quickly, it provides the next instruction earlier, and if the user is standing still, it provides information appropriate to the situation. This allows the guide unit to provide flexible voice guidance tailored to the user's situation, supporting smooth navigation. Additionally, the guide unit supports multiple languages ​​and can provide voice guidance in the appropriate language according to the user's language settings. This allows the guide system to provide appropriate navigation to a diverse range of users, including foreign tourists.

[0071] The advice unit can provide advice tailored to traffic conditions and weather. For example, the advice unit can acquire weather data and traffic sensor data and, based on this, suggest alternative routes and issue weather-related warnings to the user. For example, if there is traffic congestion, the advice unit can suggest alternative routes and provide voice guidance to the user. The advice unit can also provide voice guidance to warn the user if the weather deteriorates. In this way, the advice unit can support the user's comfortable and efficient travel by providing advice tailored to traffic conditions and weather. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input weather data and traffic sensor data into a generating AI, which can then analyze this data to generate advice.

[0072] The adjustment unit can adjust the navigation according to the user's walking speed. The adjustment unit measures the user's walking speed using, for example, an acceleration sensor or GPS data, and adjusts the timing of the navigation guidance based on this. For example, if the user is walking fast, the adjustment unit can advance the guidance timing. Conversely, if the user is walking slowly, the adjustment unit can delay the guidance timing. In this way, the adjustment unit can provide more appropriate guidance by adjusting the navigation according to the user's walking speed. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's walking speed data into a generating AI, and the generating AI can analyze this data to adjust the navigation.

[0073] The reception desk can estimate the user's emotions and customize the destination input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick destination input. In this way, the reception desk makes destination input easier by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can analyze this data to estimate the user's emotions.

[0074] The reception desk can analyze the user's past destination input history and automatically suggest frequently entered destinations. For example, the reception desk can automatically display places the user has frequently visited in the past as suggested destinations. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as suggested destinations. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This allows the reception desk to reduce the effort required for input by suggesting destinations based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past destination input history data into a generating AI, which can analyze this data and automatically suggest frequently entered destinations.

[0075] The reception desk can suggest the optimal destination based on the user's current activity status and schedule when the user enters a destination. For example, the reception desk can refer to the user's schedule registered in their calendar and automatically set the destination. The reception desk can also suggest the optimal destination based on the user's current activity status (e.g., at work, on vacation, etc.). Furthermore, the reception desk can suggest the optimal destination by combining the user's current location information with their schedule. This allows the reception desk to improve convenience by suggesting the optimal destination based on the user's activity status and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information and location data into a generating AI, which can then analyze this data and suggest the optimal destination.

[0076] The reception unit can estimate the user's emotions and select an input method based on the estimated emotions. For example, if the user is stressed, the reception unit may prioritize voice input to allow for quick entry of the destination. If the user is relaxed, the reception unit may offer handwriting input or more detailed input options. Furthermore, if the user is in a hurry, the reception unit may provide a simple voice input interface. This makes input easier by providing input methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI, which can analyze this data to estimate the user's emotions.

[0077] The reception unit can prioritize displaying highly relevant destinations when a destination is entered, taking into account the user's geographical location information. For example, the reception unit can prioritize displaying locations close to the user's current location as candidate destinations. The reception unit can also suggest highly relevant destinations by combining the user's current location with their past travel history. Furthermore, the reception unit can suggest the optimal destination by considering the user's current location and time of day. In this way, the reception unit can suggest highly relevant destinations by taking into account the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then analyze this data and prioritize displaying highly relevant destinations.

[0078] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest places the user has checked into on social media as potential destinations. It can also analyze the content of the user's social media posts and suggest relevant destinations. Furthermore, it can suggest places visited by the user's social media friends as potential destinations. In this way, the reception desk can suggest destinations that match the user's interests based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze this data and suggest relevant destinations.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can improve visibility by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI, and the generative AI can analyze this data to estimate the user's emotions.

[0080] The analysis unit can predict current traffic conditions by referring to past traffic data during analysis. For example, the analysis unit predicts current traffic conditions based on past traffic congestion data. The analysis unit can also predict current operating conditions by referring to past public transport operating conditions. Furthermore, the analysis unit can predict current traffic conditions based on past road construction information. As a result, the analysis unit can provide more accurate route guidance by predicting current traffic conditions based on past traffic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past traffic data into a generating AI, and the generating AI can analyze this data to predict current traffic conditions.

[0081] The analysis unit can provide the optimal route by considering the user's past travel history during analysis. For example, the analysis unit can propose the optimal route based on routes the user has used in the past. Furthermore, the analysis unit can propose routes that avoid congestion based on the user's past travel history. In addition, the analysis unit can analyze the user's past travel history and propose the most efficient route. Thus, by considering past travel history, the analysis unit can provide the user with the optimal route. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into a generating AI, which can then analyze this data to provide the optimal route.

[0082] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can prioritize displaying important information. If the user is relaxed, the analysis unit can display analysis results that include detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that summarize the key points. In this way, the analysis unit can prioritize providing important information by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI, and the generative AI can analyze this data to estimate the user's emotions.

[0083] The analysis unit can provide the optimal route by considering the user's geographical location information during analysis. For example, the analysis unit can propose the optimal route from the user's current location. It can also propose the optimal route by considering the distance between the user's current location and the destination. Furthermore, it can propose the optimal route by combining the user's current location with traffic conditions. In this way, the analysis unit can provide the optimal route by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI, and the generating AI can analyze this data to provide the optimal route.

[0084] The analysis unit can analyze a user's social media activity during analysis and suggest relevant routes. For example, the analysis unit can suggest the optimal route based on the locations where the user has checked in on social media. It can also analyze the content of a user's social media posts and suggest relevant routes. Furthermore, the analysis unit can suggest the optimal route based on the locations visited by the user's social media friends. In this way, the analysis unit can provide route guidance tailored to the user's interests by suggesting routes based on social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI, which can then analyze this data and suggest relevant routes.

[0085] The projection unit can estimate the user's emotions and adjust the display method of the projected content based on the estimated user emotions. For example, if the user is tense, the projection unit can provide a simple and highly visible display method. If the user is relaxed, the projection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the projection unit can provide a concise display method. In this way, the projection unit can improve visibility by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the projection unit may be performed using AI, or not using AI. For example, the projection unit can input user facial expression data into the generative AI, and the generative AI can analyze this data to estimate the user's emotions.

[0086] The projection unit can detect the user's gaze movements during projection and select the optimal display position. For example, if the user's gaze is directed in a specific direction, the projection unit will project information in that direction. The projection unit can also track the user's gaze movements in real time and select the optimal display position. Furthermore, the projection unit can select the optimal display position considering the user's gaze movements and the environment. In this way, the projection unit can provide the optimal display position by detecting the user's gaze movements. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's gaze data into a generating AI, which can analyze this data to select the optimal display position.

[0087] The projection unit can adjust the brightness of the display based on the user's current ambient light during projection. For example, in a bright environment, the projection unit can automatically adjust the brightness to ensure visibility. In a dark environment, the projection unit can also automatically adjust the brightness to reduce eye strain. Furthermore, the projection unit can adjust the brightness in real time in response to changes in ambient light. This allows the projection unit to improve visibility by adjusting the brightness according to the ambient light. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input ambient light data into a generating AI, which can then analyze this data to adjust the brightness of the display.

[0088] The projection unit can estimate the user's emotions and determine the priority of projected content based on the estimated emotions. For example, if the user is tense, the projection unit will prioritize displaying important information. If the user is relaxed, the projection unit can display projected content that includes detailed information. Furthermore, if the user is in a hurry, the projection unit can prioritize displaying projected content that gets straight to the point. In this way, the projection unit can prioritize providing important information by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the projection unit may be performed using AI, or not using AI. For example, the projection unit can input user facial expression data into the generative AI, which can analyze this data to estimate the user's emotions.

[0089] The projection unit can prioritize displaying highly relevant information by considering the user's geographical location during projection. For example, the projection unit can prioritize displaying information about locations close to the user's current location. It can also combine the user's current location with their past travel history to display highly relevant information. Furthermore, the projection unit can display optimal information by considering the user's current location and time of day. In this way, the projection unit can provide highly relevant information by considering the user's geographical location. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's geographical location data into a generating AI, which can then analyze this data and prioritize displaying highly relevant information.

[0090] The projection unit can analyze the user's social media activity during projection and display relevant information. For example, the projection unit can display information about places the user has checked into on social media. It can also analyze the content of the user's social media posts and display relevant information. Furthermore, the projection unit can display information about places visited by the user's social media friends. In this way, the projection unit can display information tailored to the user's interests by providing information based on social media activity. Some or all of the above processing in the projection unit may be performed using AI, for example, or without AI. For example, the projection unit can input the user's social media activity data into a generating AI, which can then analyze this data and display relevant information.

[0091] The guide unit can estimate the user's emotions and adjust the tone and speed of the voice guide based on the estimated emotions. For example, if the user is nervous, the guide unit can provide guidance in a calm and slow voice. If the user is relaxed, the guide unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the guide unit can provide quick and concise voice guidance. In this way, the guide unit makes the guidance more effective by providing voice guidance that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not using AI. For example, the guide unit can input user facial expression data into the generative AI, which can analyze this data to estimate the user's emotions.

[0092] The guide unit can provide optimal guidance content by referring to the user's past travel history during guidance. For example, the guide unit can provide optimal guidance content based on routes the user has used in the past. The guide unit can also provide guidance content that avoids congestion based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guidance content. In this way, the guide unit can provide more appropriate guidance by providing guidance content based on past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI, and the generating AI can analyze this data to provide optimal guidance content.

[0093] The guide unit can adjust the volume of the voice guide based on the user's current ambient noise during guidance. For example, the guide unit can automatically increase the volume of the voice guide in a noisy environment. Conversely, the guide unit can automatically decrease the volume of the voice guide in a quiet environment. Furthermore, the guide unit can adjust the volume of the voice guide in real time in response to changes in ambient noise. This allows the guide unit to improve the audibility of the voice guide by adjusting the volume according to the ambient noise. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input ambient noise data into a generating AI, which can then analyze this data to adjust the volume of the voice guide.

[0094] The guide unit can estimate the user's emotions and customize the content of the audio guide based on the estimated emotions. For example, if the user is nervous, the guide unit can provide an audio guide with reassuring content. If the user is relaxed, the guide unit can provide an audio guide with detailed information. Furthermore, if the user is in a hurry, the guide unit can provide an audio guide that gets straight to the point. In this way, the guide unit makes the guidance more effective by providing an audio guide that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI, or not using AI. For example, the guide unit can input user facial expression data into the generative AI, and the generative AI can analyze this data to estimate the user's emotions.

[0095] The guiding unit can prioritize guiding users to highly relevant information by considering the user's geographical location. For example, the guiding unit can prioritize guiding users to information about locations close to the user's current location. The guiding unit can also combine the user's current location with their past travel history to guide users to highly relevant information. Furthermore, the guiding unit can consider the user's current location and time of day to guide users to the most relevant information. In this way, the guiding unit can provide highly relevant information by considering geographical location. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's geographical location data into a generating AI, which can analyze this data to prioritize guiding users to highly relevant information.

[0096] The guiding unit can analyze the user's social media activity and guide them with relevant information during the guiding process. For example, the guiding unit can guide users with information about places they have checked into on social media. It can also analyze the content of the user's social media posts and guide them with relevant information. Furthermore, the guiding unit can guide users with information about places visited by the user's social media friends. In this way, the guiding unit can provide information tailored to the user's interests by providing information based on their social media activity. Some or all of the above processing in the guiding unit may be performed using AI, for example, or not using AI. For example, the guiding unit can input the user's social media activity data into a generating AI, which can then analyze this data and guide the user with relevant information.

[0097] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is nervous, the advice unit can provide reassuring advice. If the user is relaxed, the advice unit can provide advice that includes detailed information. Furthermore, if the user is in a hurry, the advice unit can provide concise advice. In this way, the guidance becomes more effective when the advice unit provides advice that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user facial expression data into the generative AI, which can analyze this data to estimate the user's emotions.

[0098] The advisory unit can predict the current situation by referring to past traffic data when providing advice. For example, the advisory unit can predict the current traffic situation based on past traffic congestion data. It can also predict the current operating situation by referring to past public transport operating conditions. Furthermore, it can predict the current traffic situation based on past road construction information. As a result, the advisory unit can provide more accurate advice by predicting the current situation based on past traffic data. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input past traffic data into a generating AI, and the generating AI can analyze this data to predict the current situation.

[0099] The advice unit can provide optimal advice by considering the user's past travel history. For example, the advice unit can provide optimal advice based on routes the user has used in the past. The advice unit can also provide advice to avoid congestion based on the user's past travel history. Furthermore, the advice unit can analyze the user's past travel history and provide the most efficient advice. As a result, the advice unit can provide more appropriate guidance by providing advice based on past travel history. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past travel history data into a generating AI, which can then analyze this data to provide optimal advice.

[0100] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is nervous, the advice unit will prioritize important information in its advice. If the user is relaxed, the advice unit can provide advice that includes detailed information. Furthermore, if the user is in a hurry, the advice unit can prioritize providing concise advice. In this way, the advice unit can prioritize important information by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user facial expression data into a generative AI, which can analyze this data to estimate the user's emotions.

[0101] The advice unit can provide highly relevant advice by considering the user's geographical location information when providing advice. For example, the advice unit can prioritize providing advice about locations close to the user's current location. It can also provide highly relevant advice by combining the user's current location with their past travel history. Furthermore, the advice unit can provide optimal advice by considering the user's current location and time of day. In this way, the advice unit can provide highly relevant advice by considering geographical location information. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location data into a generating AI, which can then analyze this data to provide highly relevant advice.

[0102] The adjustment unit can estimate the user's emotions and change the navigation adjustment method based on the estimated user emotions. For example, if the user is tense, the adjustment unit can provide simple and highly visible navigation. If the user is relaxed, the adjustment unit can provide navigation with detailed information. Furthermore, if the user is in a hurry, the adjustment unit can provide concise navigation. In this way, the adjustment unit makes guidance more effective by providing navigation that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user facial expression data into the generative AI, and the generative AI can analyze this data to estimate the user's emotions.

[0103] The adjustment unit can perform optimal adjustments by referring to the user's past walking speed data during the adjustment process. For example, the adjustment unit can adjust the optimal navigation speed based on the speed at which the user has walked in the past. The adjustment unit can also suggest the optimal navigation method based on the user's past walking speed data. Furthermore, the adjustment unit can analyze the user's past walking speed and provide the most efficient navigation. In this way, the adjustment unit can provide more appropriate navigation by performing adjustments based on past walking speed data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past walking speed data into a generating AI, which can then analyze this data and perform optimal adjustments.

[0104] The adjustment unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is tense, the adjustment unit can prioritize navigation to important information. If the user is relaxed, the adjustment unit can provide navigation that includes detailed information. Furthermore, if the user is in a hurry, the adjustment unit can prioritize providing concise navigation. In this way, the adjustment unit can prioritize providing important information by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user facial expression data into the generative AI, which can analyze this data to estimate the user's emotions.

[0105] The adjustment unit can perform optimal adjustments while considering the user's geographical location information. For example, the adjustment unit can perform optimal navigation adjustments based on the user's current location. It can also perform optimal navigation adjustments by combining the user's current location with their past travel history. Furthermore, the adjustment unit can perform optimal navigation adjustments by considering the user's current location and time of day. In this way, the adjustment unit can provide optimal navigation by considering geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information data into a generating AI, which can then analyze this data to perform optimal adjustments.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The reception desk can monitor the user's health status and provide navigation tailored to that status. For example, if the user is tired, the reception desk can suggest a resting place. If the user is in good health, it can also suggest a longer route. Furthermore, the reception desk can adjust the frequency and timing of navigation based on the user's health status. This allows the reception desk to support a more comfortable journey by providing navigation that is appropriate to the user's health condition.

[0108] The analytics unit can provide information on tourist attractions and events along a route based on the user's hobbies and interests. For example, if a user is interested in history, the analytics unit can suggest a route that includes historical landmarks. If a user is interested in food, the analytics unit can suggest a route that includes popular restaurants and cafes. Furthermore, if a user wants to attend a specific event, the analytics unit can suggest the most suitable route for that event. In this way, the analytics unit can make travel more enjoyable by providing routes tailored to the user's hobbies and interests.

[0109] The guide unit can provide multilingual audio guides according to the user's language settings. For example, if the user selects English, the guide unit can provide an audio guide in English. If the user selects Japanese, it can provide an audio guide in Japanese. Furthermore, if the user uses multiple languages, the guide unit can switch the audio guide according to those languages. This allows the guide unit to provide more easily understandable guidance by offering audio guides tailored to the user's language settings.

[0110] The projection unit can adjust the displayed content according to the user's degree of visual impairment. For example, if the user has color blindness, the projection unit can adjust the color contrast. Also, if the user has reduced vision, the projection unit can enlarge the font size. Furthermore, if the user is completely visually impaired, the projection unit can prioritize providing audio guidance. In this way, the projection unit can provide more appropriate guidance by offering displayed content tailored to the user's degree of visual impairment.

[0111] The adjustment unit can adjust the navigation according to the user's mode of transportation. For example, if the user is traveling on foot, the adjustment unit can provide a pedestrian-friendly route. If the user is traveling by bicycle, it can provide a dedicated bicycle route. Furthermore, if the user is using public transport, the adjustment unit can provide transfer information and the optimal boarding location. In this way, the adjustment unit can support smoother travel by providing navigation tailored to the user's mode of transportation.

[0112] The reception desk can estimate the user's emotions and customize the destination input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick destination entry. In this way, the reception desk makes destination entry easier by providing an interface that responds to the user's emotions.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. In this way, the analysis unit can improve visibility by providing a display method that matches the user's emotions.

[0114] The guide unit can estimate the user's emotions and adjust the tone and speed of the voice guide based on those estimates. For example, if the user is nervous, it can provide guidance in a calm and slow voice. If the user is relaxed, it can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, it can provide quick and concise voice guidance. In this way, the guide unit makes the guidance more effective by providing voice guidance that is tailored to the user's emotions.

[0115] The projection unit can estimate the user's emotions and adjust the display method of the projected content based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the projection unit can improve visibility by providing a display method that responds to the user's emotions.

[0116] The adjustment unit can estimate the user's emotions and change the navigation adjustment method based on the estimated user emotions. For example, if the user is stressed, it can provide simple and highly visible navigation. If the user is relaxed, it can provide navigation that includes detailed information. Furthermore, if the user is in a hurry, it can provide concise navigation. In this way, the adjustment unit makes guidance more effective by providing navigation that is tailored to the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk receives information from the user regarding their destination. The reception desk can accept destination input using methods such as voice input, touch input, or text input. Step 2: The analysis unit analyzes map data and traffic information based on the information received by the reception unit. The analysis unit uses, for example, a generating AI to analyze the latest map data and real-time traffic information and provide the optimal route. When the generating AI analyzes the map data and traffic information, the analysis unit considers, for example, the type of algorithm and the accuracy of the analysis. Step 3: The projection unit projects directions for travel and corners onto the glasses based on the analysis results obtained by the analysis unit. The projection unit can display directions for travel and corners using methods such as arrow displays or voice guidance. Step 4: The guide unit provides audio guidance based on the analysis results obtained by the analysis unit. The guide unit provides audio guidance, for example, through a bone conduction speaker, allowing the user to receive directions without moving their eyes.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, projection unit, guide unit, advice unit, and adjustment unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's destination input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes map data and traffic information. The projection unit is implemented by the control unit 46A of the smart device 14 and projects directions and corner indications onto the glasses. The guide unit is implemented by the control unit 46A of the smart device 14 and provides voice guidance through a bone conduction speaker. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice according to traffic conditions and weather. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the navigation according to the user's walking speed. 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.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, projection unit, guide unit, advice unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's destination input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes map data and traffic information. The projection unit is implemented by the control unit 46A of the smart glasses 214 and projects directions and corner indications onto the glasses. The guide unit is implemented by the control unit 46A of the smart glasses 214 and provides voice guidance through a bone conduction speaker. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice according to traffic conditions and weather. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the navigation according to the user's walking speed. 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.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The 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.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the 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.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 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.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, projection unit, guide unit, advice unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's destination input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes map data and traffic information. The projection unit is implemented by the control unit 46A of the headset terminal 314 and projects directions and corner indications onto the glasses. The guide unit is implemented by the control unit 46A of the headset terminal 314 and provides voice guidance through a bone conduction speaker. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice according to traffic conditions and weather. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the navigation according to the user's walking speed. 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.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The 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.

[0159] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, projection unit, guide unit, advice unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's destination input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes map data and traffic information. The projection unit is implemented by the control unit 46A of the robot 414 and projects directions and corner indications onto the glasses. The guide unit is implemented by the control unit 46A of the robot 414 and provides voice guidance through a bone conduction speaker. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice according to traffic conditions and weather. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the navigation according to the user's walking speed. 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A reception desk that accepts destination inputs, An analysis unit analyzes map data and traffic information based on the information received by the reception unit, A projection unit projects indications of the direction of travel and corners onto the glasses based on the analysis results obtained by the aforementioned analysis unit, The system includes a guide unit that provides audio guidance based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) We have an advisory department that provides advice based on traffic conditions and weather. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an adjustment unit that adjusts the navigation according to the user's walking speed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and customizes the destination input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system analyzes the user's past destination input history and automatically suggests frequently entered destinations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When you enter a destination, the system will suggest the most suitable destination based on your current activities and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and selects an input method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering a destination, the system prioritizes displaying the most relevant destinations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a destination is entered, the system analyzes the user's social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, past traffic data is referenced to predict current traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the system provides the optimal route by considering the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the system provides the optimal route while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The projection unit is It estimates the user's emotions and adjusts how projected content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The projection unit is During projection, the system detects the user's eye movements to select the optimal display position. The system described in Appendix 1, characterized by the features described herein. (Note 18) The projection unit is During projection, the display brightness is adjusted based on the user's current ambient light. The system described in Appendix 1, characterized by the features described herein. (Note 19) The projection unit is It estimates the user's emotions and determines the priority of projection content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The projection unit is During projection, the system prioritizes displaying highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The projection unit is During projection, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is It estimates the user's emotions and adjusts the tone and speed of the voice guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is During guidance, the system provides optimal guidance content by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is During guidance, the volume of the audio guide is adjusted based on the user's current ambient noise. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is It estimates the user's emotions and customizes the content of the voice guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When providing guidance, the system prioritizes relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is During the guidance process, we analyze the user's social media activity and guide them with relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we refer to past traffic data to predict the current situation. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, we take into account the user's past travel history to offer the most appropriate advice. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide more relevant advice. The system described in Appendix 2, characterized by the features described herein. (Note 33) The adjustment unit is, It estimates the user's emotions and changes how navigation is adjusted based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The adjustment unit is, During adjustment, the system references the user's past walking speed data to perform optimal adjustments. The system described in Appendix 3, characterized by the features described herein. (Note 35) The adjustment unit is, It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The adjustment unit is, During the adjustment process, the optimal adjustments are made considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts destination inputs, An analysis unit analyzes map data and traffic information based on the information received by the reception unit, A projection unit projects indications of the direction of travel and corners onto the glasses based on the analysis results obtained by the aforementioned analysis unit, The system includes a guide unit that provides audio guidance based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. We have an advisory department that provides advice based on traffic conditions and weather. The system according to feature 1.

3. It features an adjustment unit that adjusts the navigation according to the user's walking speed. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and customizes the destination input interface based on those estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is The system analyzes the user's past destination input history and automatically suggests frequently entered destinations. The system according to feature 1.

6. The aforementioned reception unit is When you enter a destination, the system will suggest the most suitable destination based on your current activities and schedule. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and selects an input method based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering a destination, the system prioritizes displaying the most relevant destinations, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When a destination is entered, the system analyzes the user's social media activity and suggests relevant destinations. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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