system

Abstract

The system according to this embodiment aims to automatically collect and analyze traffic volume and other data and efficiently generate reports. [Solution] The system according to the embodiment comprises a projection unit, a counting unit, an upload unit, an analysis unit, and a report generation unit. The projection unit projects traffic conditions onto the user's field of view. The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. The upload unit uploads the data collected by the counting unit to the cloud in real time. The analysis unit analyzes the data uploaded by the upload unit. The report generation unit generates a report based on the data analyzed 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since data collection and analysis of traffic volume and traffic flow are performed manually, there is a problem that it takes time and effort.

[0005] The system according to the embodiment aims to automatically collect and analyze data on traffic volume and traffic flow and efficiently generate a report.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a projection unit, a counting unit, an upload unit, an analysis unit, and a report generation unit. The projection unit projects traffic conditions onto the user's field of view. The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. The upload unit uploads the data collected by the counting unit to the cloud in real time. The analysis unit analyzes the data uploaded by the upload unit. The report generation unit generates a report based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically collect and analyze traffic volume and other data, and efficiently generate reports. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated traffic volume and pedestrian flow measurement, analysis, and report generation system according to an embodiment of the present invention is a system that automatically measures and analyzes traffic volume and pedestrian flow using existing smart glasses and a generating AI, and generates reports. In this system, the user wears smart glasses, and the traffic situation is projected into their field of view. The smart glasses display the traffic situation in the user's field of view in real time, and the generating AI automatically counts the number of vehicles and pedestrians. It also has a customization function that extracts and analyzes specific information needed by the user, such as a specific gender or age group, a specific vehicle type, or a specific time of day. Next, the collected data is uploaded to the cloud in real time. The generating AI analyzes the data on the cloud and automatically generates a report. This eliminates the need for the user to manually organize and analyze data, and allows for efficient and highly accurate data acquisition. For example, traffic survey companies, public institutions, and urban planning departments can use this system to quickly and accurately collect and analyze traffic volume and pedestrian flow data, which can be used to improve urban planning and transportation planning. It also leads to a reduction in the workload of field work, contributing to work style reform. Thus, the present invention, by using smart glasses and generating AI, realizes automatic measurement, analysis, and report generation of pedestrian and traffic volume, and provides a function to customize, extract, and analyze specific information. This eliminates the inefficiencies of conventional manual counting work and enables efficient and highly accurate data collection and analysis. As a result, the automatic measurement, analysis, and report generation system for pedestrian and traffic volume eliminates the need for users to manually organize and analyze data, allowing for efficient and highly accurate data acquisition.

[0029] The automatic traffic volume and pedestrian flow measurement, analysis, and report generation system according to this embodiment comprises a projection unit, a counting unit, an upload unit, an analysis unit, and a report generation unit. The projection unit projects traffic conditions onto the user's field of view. The projection unit displays traffic conditions in real time on the user's field of view, for example, using smart glasses. The projection unit can display, for example, the status of traffic signals, the flow of vehicles, and the movement of pedestrians. The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. The counting unit automatically counts the number of vehicles and pedestrians, for example, using image recognition technology. The counting unit can also count the number of vehicles and pedestrians using sensors. Furthermore, the counting unit can also count the number of vehicles and pedestrians using generating AI. The upload unit uploads the data collected by the counting unit to the cloud in real time. The uploading unit uploads data to the cloud, for example, using wireless communication technology. Furthermore, the uploading unit can also upload data to the cloud using wired communication technology. Furthermore, the uploading unit can also upload data to the cloud using generating AI. The analysis unit analyzes the data uploaded by the upload unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit can also analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using generative AI. The report generation unit generates a report based on the data analyzed by the analysis unit. The report generation unit generates the report in, for example, text format. Furthermore, the report generation unit can also generate the report in graph format. Furthermore, the report generation unit can generate the report using generative AI. As a result, the automatic measurement, analysis, and report generation system for pedestrian and traffic volume according to this embodiment can project traffic conditions onto the user's field of view, automatically count the number of vehicles and pedestrians, upload the data to the cloud in real time, analyze it, and generate a report.

[0030] The projection unit projects traffic conditions onto the user's field of view. For example, using smart glasses, the projection unit displays real-time traffic conditions within the user's field of view. The smart glasses incorporate a transparent display, allowing the user to view additional information while maintaining normal vision. Specifically, the smart glasses' display shows traffic signal status, vehicle flow, pedestrian movement, and more. This allows the user to understand the surrounding traffic conditions in real time. Furthermore, the projection unit can use eye-tracking technology to display detailed information about specific areas the user is focusing on. For example, if the user looks at a particular intersection, information regarding the traffic signal status and vehicle flow at that intersection will be highlighted. This allows the user to quickly obtain necessary information and make appropriate decisions. The projection unit also features a voice assistant function, allowing the user to request specific information using voice commands. For example, if the user issues the voice command "Tell me the status of the next traffic light," the smart glasses' display will show the status of the next traffic light. Thus, the projection unit projects real-time traffic conditions onto the user's field of view and utilizes eye-tracking technology and voice assistant functions to enable the user to quickly and accurately obtain the necessary information.

[0031] The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. For example, the counting unit automatically counts vehicles and pedestrians using image recognition technology. Specifically, a camera built into the smart glasses captures images of the surroundings, and this image is analyzed in real time. The image recognition algorithm identifies vehicles and pedestrians and counts their numbers. The counting unit can also count vehicles and pedestrians using sensors. For example, infrared sensors or ultrasonic sensors built into the smart glasses are used to detect the movement of vehicles and pedestrians and count their numbers. Furthermore, the counting unit can also count vehicles and pedestrians using generative AI. Generative AI learns from past data and patterns to improve real-time counting accuracy. For example, generative AI learns patterns of vehicle and pedestrian movement at specific times and locations, and uses this information to improve counting accuracy. This allows the counting unit to accurately count vehicles and pedestrians by combining image recognition technology, sensor technology, and generative AI. Additionally, the counting unit records the counting results in real time for later analysis and report generation. This allows the counting unit to efficiently collect the data necessary for understanding and analyzing traffic conditions.

[0032] The upload unit uploads data collected by the counting unit to the cloud in real time. The upload unit uploads data to the cloud using, for example, wireless communication technology. Specifically, it uses Wi-Fi or Bluetooth® modules built into the smart glasses to send the collected data to the cloud server. The upload unit can also upload data to the cloud using wired communication technology. For example, it connects the smart glasses to a computer with a USB cable and uploads the data to the cloud. Furthermore, the upload unit can also upload data to the cloud using generative AI. Generative AI compresses and optimizes the data to upload it to the cloud efficiently. For example, generative AI eliminates data duplication and extracts and uploads only the necessary information, thereby saving communication bandwidth and improving upload speed. As a result, the upload unit can combine wireless communication technology, wired communication technology, and generative AI to upload collected data to the cloud quickly and efficiently. Furthermore, the upload unit protects the data using encryption technology to ensure data security. For example, before uploading the data, it encrypts the data using encryption algorithms such as AES or RSA to prevent unauthorized access by third parties. This allows the upload unit to securely upload the collected data to the cloud, making it available for analysis and report generation.

[0033] The analysis unit analyzes the data uploaded by the upload unit. For example, the analysis unit uses data mining techniques to analyze the data. Specifically, it analyzes data stored on a cloud server using data mining algorithms to extract traffic volume and flow patterns. The analysis unit can also analyze data using statistical analysis techniques. For example, it analyzes collected data using statistical methods to evaluate fluctuations in traffic volume at specific times and locations. Furthermore, the analysis unit can analyze data using generative AI. Generative AI learns from past data and patterns to improve real-time analysis accuracy. For example, generative AI can predict traffic volume at specific times and locations, and forecast future traffic conditions. This allows the analysis unit to combine data mining techniques, statistical analysis techniques, and generative AI to analyze collected data from multiple perspectives and understand traffic volume and flow patterns and trends. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. For example, if an abnormally high traffic volume is detected at a specific intersection, the analysis unit can immediately issue a warning and take appropriate measures. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0034] The report generation unit generates reports based on data analyzed by the analysis unit. For example, the report generation unit generates reports in text format. Specifically, it creates text reports that detail traffic volume and flow patterns and trends based on the analysis results. The report generation unit can also generate reports in graph format. For example, it can create graphs that visually show fluctuations in traffic volume, or graphs that compare traffic volume at specific times or locations. Furthermore, the report generation unit can generate reports using a generation AI. The generation AI automatically creates reports based on the analysis results and provides them to the user. For example, the generation AI generates reports that summarize the analysis results and highlight important points. The generation AI can also create customized reports according to the user's needs. This allows the report generation unit to combine text format, graph format, and generation AI to produce reports that clearly and effectively communicate analysis results. Additionally, the report generation unit has a function to automatically distribute the generated reports. For example, it can automatically send reports via email or cloud storage, allowing users to access them at any time. This enables the report generation unit to provide analysis results to users quickly and efficiently, helping them understand traffic conditions and develop countermeasures.

[0035] The customization unit extracts and analyzes specific information in a customized manner. For example, the customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day. The customization unit can extract and analyze specific information using generative AI. For example, to extract information on a specific gender, the customization unit can determine gender using image recognition technology. The customization unit can also estimate age using facial recognition technology to extract information on a specific age group. Furthermore, the customization unit can analyze the shape and characteristics of a vehicle to extract information on a specific vehicle type. In this way, the customization unit can extract and analyze specific information in a customized manner.

[0036] The customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day. For example, to extract information on a specific gender, the customization unit can use image recognition technology to determine gender. For example, the customization unit can use facial recognition technology to determine gender. The customization unit can also use facial recognition technology to estimate age in order to extract information on a specific age group. For example, the customization unit can analyze facial features to estimate age. Furthermore, the customization unit can analyze the shape and characteristics of a vehicle in order to extract information on a specific vehicle type. For example, the customization unit can analyze the shape and characteristics of a vehicle to determine the vehicle type. As a result, the customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day.

[0037] The projection unit can display real-time traffic conditions within the user's field of view. For example, the projection unit can display real-time traffic conditions within the user's field of view using smart glasses. For instance, the projection unit can display traffic signal status, vehicle flow, pedestrian movement, and more. Furthermore, the projection unit can also display real-time traffic conditions using generative AI. For example, the projection unit can display real-time traffic conditions analyzed by generative AI. This allows the projection unit to display real-time traffic conditions within the user's field of view.

[0038] The counting unit can automatically count the number of vehicles and pedestrians. For example, the counting unit can automatically count the number of vehicles and pedestrians using image recognition technology. For example, the counting unit can analyze images captured by a camera to count the number of vehicles and pedestrians. The counting unit can also count the number of vehicles and pedestrians using sensors. For example, the counting unit can use infrared sensors to count the number of vehicles and pedestrians. Furthermore, the counting unit can also count the number of vehicles and pedestrians using generative AI. For example, the counting unit can count the number of vehicles and pedestrians based on data analyzed by generative AI. As a result, the counting unit can automatically count the number of vehicles and pedestrians.

[0039] The analysis unit can analyze data on the cloud. For example, the analysis unit can analyze data using data mining techniques. For instance, it can analyze data uploaded to the cloud to analyze trends in traffic volume and flow. Furthermore, the analysis unit can analyze data using statistical analysis techniques. For example, it can statistically analyze data on the cloud to understand fluctuations in traffic volume and flow. Additionally, the analysis unit can analyze data using generative AI. For example, it can perform detailed analyses of traffic volume and flow based on data analyzed by generative AI. This allows the analysis unit to analyze data on the cloud.

[0040] The projection unit can track the user's eye movements during projection and prioritize projecting important traffic information in the user's line of sight. For example, if the user is looking in a specific direction, the projection unit will prioritize projecting traffic information in that direction. For instance, the projection unit can track the user's eye movements using an eye-tracking device and display traffic information in that direction. The projection unit can also instantly project new traffic information in the user's line of sight when the user moves their gaze. For example, the projection unit can detect the user's eye movements using a camera and display new traffic information. Furthermore, if the user's gaze is fixed, the projection unit can project detailed traffic information in that direction. For example, the projection unit can detect the user's fixed gaze using an eye-tracking device and display detailed traffic information. This allows the projection unit to track the user's eye movements and prioritize projecting important traffic information in their line of sight.

[0041] The projection unit can automatically adjust the brightness and contrast of the projection in response to ambient light during projection. For example, in bright daytime environments, the projection unit can increase the brightness of the projection to improve visibility. For example, the projection unit can measure the ambient light using a light sensor and adjust the brightness of the projection. The projection unit can also reduce eye strain by decreasing the brightness of the projection at night or in dark environments. For example, the projection unit can measure the ambient light using a light sensor and adjust the brightness of the projection. Furthermore, the projection unit can adjust the brightness and contrast of the projection in real time when the ambient light changes. For example, the projection unit can detect changes in ambient light using a light sensor and adjust the brightness and contrast of the projection in real time. As a result, the projection unit can automatically adjust the brightness and contrast of the projection in response to ambient light.

[0042] The projection unit can project region-specific traffic information based on the user's location information during projection. For example, if the user is in a specific region, the projection unit will prioritize projecting traffic information for that region. For instance, the projection unit can obtain the user's location information using GPS and display traffic information for that region. Furthermore, if the user is on the move, the projection unit can update and project traffic information based on the user's current location in real time. For example, the projection unit can track the user's current location using GPS and display the latest traffic information. In addition, the projection unit can project traffic information for a specific region in advance when the user is approaching that region. For example, the projection unit can predict the user's direction of movement using GPS and display traffic information for the destination. In this way, the projection unit can project region-specific traffic information based on the user's location information.

[0043] The projection unit can project highly relevant information by referring to the user's past behavioral history during projection. For example, the projection unit can prioritize projecting traffic information for places the user has frequently visited in the past. For example, the projection unit can analyze the user's location history and display traffic information for places visited in the past. The projection unit can also project traffic information related to specific time periods based on the user's past behavioral history. For example, the projection unit can analyze the user's behavioral patterns and display traffic information related to specific time periods. Furthermore, the projection unit can analyze the user's past behavioral patterns and project the most relevant traffic information. For example, the projection unit can prioritize displaying highly relevant information based on the user's behavioral history. In this way, the projection unit can project highly relevant information by referring to the user's past behavioral history.

[0044] The counting unit can improve its counting accuracy by considering the speed and direction of vehicles and pedestrians during counting. For example, the counting unit can perform accurate counting by considering the speed of vehicles. For example, the counting unit can improve the accuracy of counting by measuring the speed of vehicles using radar. The counting unit can also perform accurate counting by considering the direction of pedestrians. For example, the counting unit can improve the accuracy of counting by detecting the direction of pedestrians using a camera. Furthermore, the counting unit can improve the accuracy of counting by considering the speed and direction of vehicles and pedestrians simultaneously. For example, the counting unit can improve the accuracy of counting by combining radar and a camera to measure the speed and direction of vehicles and pedestrians. In this way, the counting unit can improve the accuracy of counting by considering the speed and direction of vehicles and pedestrians.

[0045] The counting unit can adjust the timing of counting based on specific events. For example, the counting unit can temporarily pause counting vehicles when the traffic light turns red. For example, the counting unit can detect the change in traffic light and stop counting vehicles while the light is red. The counting unit can also resume counting vehicles when the traffic light turns green. For example, the counting unit can detect the change in traffic light and resume counting vehicles while the light is green. Furthermore, the counting unit can also adjust the timing of counting pedestrians based on the change in traffic light. For example, the counting unit can detect the change in traffic light and adjust the timing of counting pedestrians. In this way, the counting unit can adjust the timing of counting based on specific events.

[0046] The counting unit can perform counting while considering the attribute information of vehicles and pedestrians. For example, the counting unit can prioritize counting specific vehicle types. For example, the counting unit can use image recognition technology to determine specific vehicle types and then perform the count. The counting unit can also prioritize counting pedestrians wearing specific clothing. For example, the counting unit can use image recognition technology to determine specific clothing and then perform the count. Furthermore, the counting unit can perform counting while simultaneously considering the attribute information of vehicles and pedestrians. For example, the counting unit can use image recognition technology to analyze the attribute information of vehicles and pedestrians and then perform the count. As a result, the counting unit can perform counting while considering the attribute information of vehicles and pedestrians.

[0047] The counting unit can analyze ambient sound information during counting and correct the count based on specific sounds. For example, the counting unit can correct the vehicle count when it detects the sound of a car horn. For example, the counting unit can use speech recognition technology to detect the sound of a car horn and correct the count. The counting unit can also correct the pedestrian count when it detects the voice of a pedestrian. For example, the counting unit can use speech recognition technology to detect the voice of a pedestrian and correct the count. Furthermore, the counting unit can analyze ambient sound information to improve the accuracy of the count. For example, the counting unit can use speech recognition technology to analyze ambient sound information and improve the accuracy of the count. As a result, the counting unit can analyze ambient sound information and correct the count based on specific sounds.

[0048] The upload unit can determine upload priorities based on data importance during the upload process. For example, the upload unit can prioritize uploading important data. For instance, it can select important data based on data type and urgency and prioritize its upload. Furthermore, the upload unit can adjust the upload order based on data importance. For example, it can evaluate data importance and upload important data first. Additionally, the upload unit can upload high-priority data in real time. For example, it can upload high-priority data immediately for rapid processing. This allows the upload unit to determine upload priorities based on data importance.

[0049] The upload unit can adjust the data compression ratio during upload according to the network conditions. For example, if the network is congested, the upload unit will increase the data compression ratio before uploading. For example, the upload unit can monitor network bandwidth and compress the data before uploading when it is congested. Conversely, if the network is not congested, the upload unit can also upload high-quality data with a lower compression ratio. For example, the upload unit can monitor the network conditions and upload data without compression when it is not congested. Furthermore, the upload unit can adjust the data compression ratio in real time according to the network conditions. For example, the upload unit can monitor the network conditions in real time, select an appropriate compression ratio, and upload the data. In this way, the upload unit can adjust the data compression ratio according to the network conditions.

[0050] The upload unit can enhance security by adjusting the data encryption level during upload. For example, the upload unit can increase the encryption level when uploading important data. For instance, the upload unit can encrypt data using advanced encryption technologies such as AES-256 or RSA to enhance security. The upload unit can also adjust the encryption level when uploading general data. For example, the upload unit can select an encryption level according to the importance of the data and perform appropriate encryption. Furthermore, the upload unit can adjust the encryption level in real time according to the importance of the data. For example, the upload unit can evaluate the importance of the data and adjust the encryption level in real time before uploading. This allows the upload unit to enhance security by adjusting the data encryption level.

[0051] The upload unit can automatically back up data during upload, preventing data loss. For example, the upload unit can automatically create a backup when uploading data. For instance, it can use cloud storage to create a backup, preventing data loss. Furthermore, the upload unit can create a backup before uploading data to prevent data loss. For example, it can create a backup in local storage before uploading data, ensuring data security. Additionally, the upload unit can create a backup after uploading data to ensure data security. For example, it can create a backup in cloud storage after uploading data, preventing data loss. In this way, the upload unit can automatically back up data and prevent data loss.

[0052] The analysis unit can detect outliers by comparing them with past data during analysis and correct the analysis results. For example, the analysis unit can detect outliers by comparing them with past data and correct the analysis results. For example, the analysis unit can identify outliers by referring to past data and correct the analysis results. The analysis unit can also perform corrections by referring to past data when it detects outliers. For example, when the analysis unit detects outliers, it can perform corrections based on past data. Furthermore, the analysis unit can perform outlier detection and correction in real time to improve the accuracy of the analysis results. For example, the analysis unit can detect outliers in real time and correct them immediately. This allows the analysis unit to detect outliers by comparing them with past data and correct the analysis results.

[0053] The analysis unit can perform multidimensional analysis while considering the correlation between data. For example, the analysis unit can analyze the correlation between vehicle speed and traffic volume. For example, the analysis unit can analyze vehicle speed data and traffic volume data and identify the correlation. The analysis unit can also analyze the correlation between the number of pedestrians and weather. For example, the analysis unit can analyze the number of pedestrians and weather data and identify the correlation. Furthermore, the analysis unit can analyze the correlation between vehicle type and traffic accident rate. For example, the analysis unit can analyze vehicle type data and traffic accident data and identify the correlation. As a result, the analysis unit can perform multidimensional analysis while considering the correlation between data.

[0054] The analysis unit can improve the accuracy of its analysis by referring to external data sources during the analysis process. For example, the analysis unit can improve the accuracy of traffic volume analysis by referring to weather information. For example, the analysis unit can acquire weather information and perform analysis in combination with traffic volume data. The analysis unit can also correct the analysis results by referring to the operating status of public transportation. For example, the analysis unit can acquire operating data of public transportation and perform analysis in combination with traffic volume data. Furthermore, the analysis unit can improve the accuracy of its analysis results by referring to road construction information. For example, the analysis unit can acquire road construction information and perform analysis in combination with traffic volume data. In this way, the analysis unit can improve the accuracy of its analysis by referring to external data sources.

[0055] The analysis unit can automatically optimize the analysis algorithm by feeding back data in real time during analysis. For example, the analysis unit can adjust the analysis algorithm based on data acquired in real time to improve accuracy. Furthermore, the analysis unit can also adjust the analysis algorithm based on the feedback data. For example, the analysis unit can analyze the feedback data and adjust the algorithm parameters. In addition, the analysis unit can improve the accuracy of the analysis algorithm through real-time data feedback. For example, the analysis unit can improve the accuracy of the algorithm by feeding back data in real time. This allows the analysis unit to automatically optimize the analysis algorithm by feeding back data in real time.

[0056] The report generation unit can adjust the level of detail in a report based on the importance of the data during report generation. For example, the report generation unit can prioritize and describe important data in detail. For example, the report generation unit can evaluate the importance of the data and describe important data in detail. The report generation unit can also adjust the level of detail in a report based on the importance of the data. For example, the report generation unit can generate a report with adjusted detail based on the importance of the data. Furthermore, the report generation unit can describe highly important data in detail and less important data concisely. For example, the report generation unit can evaluate the importance of the data and describe highly important data in detail and less important data concisely. In this way, the report generation unit can adjust the level of detail in a report based on the importance of the data.

[0057] The report generation unit can generate reports in different formats. For example, it can generate reports in PDF format. For example, it can convert data to PDF format and generate a report. It can also generate reports in Excel format. For example, it can convert data to Excel format and generate a report. Furthermore, the report generation unit can generate reports in different formats according to user specifications. For example, it can generate reports in PDF, Excel, CSV, etc., according to user requests. This allows the report generation unit to generate reports in different formats.

[0058] The report generation unit can prioritize displaying highly relevant information by referring to the user's past report viewing history when generating reports. For example, the report generation unit can prioritize displaying information from reports the user has previously viewed. For example, the report generation unit can analyze the user's past report viewing history and display highly relevant information. The report generation unit can also display highly relevant information by referring to the user's past report viewing history. For example, the report generation unit can select and display highly relevant information based on the user's past viewing history. Furthermore, the report generation unit can analyze the user's past report viewing history and display the most relevant information. For example, the report generation unit can analyze the user's past viewing history and select and display the most relevant information. As a result, the report generation unit can prioritize displaying highly relevant information by referring to the user's past report viewing history.

[0059] The report generation unit can add an automatic report distribution function when generating reports, allowing reports to be sent at a specified time. For example, the report generation unit can automatically distribute reports at a specified time. For instance, the report generation unit can generate and automatically distribute reports at a time specified by the user. Furthermore, the report generation unit can automatically distribute reports according to user specifications. For example, the report generation unit can generate and distribute reports at a specified time in response to user requests. Additionally, the report generation unit can add an automatic report distribution function, allowing reports to be sent at a specified time. For example, the report generation unit can generate and automatically distribute reports at a time specified by the user. This allows the report generation unit to add an automatic report distribution function and send reports at a specified time.

[0060] The customization unit can propose the optimal customization by referring to the user's past customization history during the customization process. For example, the customization unit can propose the optimal customization by referring to the user's past customization history. For example, the customization unit can analyze the user's past customization history and propose the optimal customization. Furthermore, the customization unit can propose the optimal customization based on the customization options the user has previously selected. For example, the customization unit can propose the optimal customization options based on the user's past customization history. In addition, the customization unit can analyze the user's past customization history and propose the most suitable customization. For example, the customization unit can analyze the user's past customization history and propose the most suitable customization options. This allows the customization unit to propose the optimal customization by referring to the user's past customization history.

[0061] The customization unit can provide optimal customization by considering the user's device information during the customization process. For example, if the user is using a smartphone, the customization unit can provide the optimal customization for a smartphone. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a smartphone. Furthermore, if the user is using a tablet, the customization unit can provide the optimal customization for a tablet. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a tablet. In addition, if the user is using a smartwatch, the customization unit can provide the optimal customization for a smartwatch. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a smartwatch. This allows the customization unit to provide optimal customization by considering the user's device information.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The projection unit can track the user's eye movements and prioritize projecting important traffic information in their line of sight. For example, if the user is looking in a specific direction, it will prioritize projecting traffic information in that direction. Using an eye-tracking device, it can track the user's eye movements and display traffic information in their line of sight. Furthermore, when the user moves their gaze, it can instantly project new traffic information in their line of sight. Using a camera, it can detect the user's eye movements and display new traffic information. Additionally, if the user fixates their gaze, it can project detailed traffic information in their line of sight. Using an eye-tracking device, it can detect when the user's gaze is fixed and display detailed traffic information. This allows the projection unit to track the user's eye movements and prioritize projecting important traffic information in their line of sight.

[0064] The counting unit can improve its accuracy by considering the speed and direction of vehicles and pedestrians. For example, it can perform accurate counting by considering the speed of vehicles. By using radar to measure the speed of vehicles, the accuracy of the counting can be improved. It can also perform accurate counting by considering the direction of pedestrians. By using a camera to detect the direction of pedestrians, the accuracy of the counting can be improved. Furthermore, it can improve the accuracy of the counting by considering the speed and direction of vehicles and pedestrians simultaneously. By combining radar and a camera to measure the speed and direction of vehicles and pedestrians, the accuracy of the counting can be improved. As a result, the counting unit can improve its accuracy by considering the speed and direction of vehicles and pedestrians.

[0065] The analysis unit can improve the accuracy of its analysis by referring to external data sources. For example, it can improve the accuracy of traffic volume analysis by referring to weather information. Weather information can be acquired and combined with traffic volume data for analysis. It can also correct the analysis results by referring to the operating status of public transportation. Public transportation operation data can be acquired and combined with traffic volume data for analysis. Furthermore, it can improve the accuracy of the analysis results by referring to road construction information. Road construction information can be acquired and combined with traffic volume data for analysis. In this way, the analysis unit can improve the accuracy of its analysis by referring to external data sources.

[0066] The upload unit can adjust the data compression ratio according to network conditions. For example, if the network is congested, it can increase the data compression ratio before uploading. It monitors network bandwidth and compresses data before uploading when congested. Conversely, if the network is not congested, it can upload high-quality data with a lower compression ratio. It monitors network conditions and uploads data without compression when the network is not congested. Furthermore, it can adjust the data compression ratio in real time according to network conditions. It monitors network conditions in real time and selects an appropriate compression ratio before uploading data. In this way, the upload unit can adjust the data compression ratio according to network conditions.

[0067] The report generation unit can generate reports in different formats. For example, it can generate reports in PDF format. It can convert data to PDF format and generate a report. It can also generate reports in Excel format. It can convert data to Excel format and generate a report. Furthermore, it can generate reports in different formats according to user specifications. It can generate reports in PDF, Excel, CSV, etc., according to user requests. In this way, the report generation unit can generate reports in different formats.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The projection unit projects traffic conditions into the user's field of view. For example, smart glasses can be used to display real-time traffic conditions in the user's field of view. The projection unit can display traffic signal status, vehicle flow, pedestrian movement, and more. Step 2: The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. For example, it automatically counts the number of vehicles and pedestrians using image recognition technology, sensors, and generative AI. Step 3: The upload unit uploads the data collected by the counting unit to the cloud in real time. For example, it uploads the data to the cloud using wireless communication technology, wired communication technology, or generative AI. Step 4: The analysis unit analyzes the data uploaded by the upload unit. For example, it analyzes the data using data mining techniques, statistical analysis techniques, and generative AI. Step 5: The report generation unit generates a report based on the data analyzed by the analysis unit. For example, it can generate reports in text format, graph format, or using AI generation.

[0070] (Example of form 2) The automated traffic volume and pedestrian flow measurement, analysis, and report generation system according to an embodiment of the present invention is a system that automatically measures and analyzes traffic volume and pedestrian flow using existing smart glasses and a generating AI, and generates reports. In this system, the user wears smart glasses, and the traffic situation is projected into their field of view. The smart glasses display the traffic situation in the user's field of view in real time, and the generating AI automatically counts the number of vehicles and pedestrians. It also has a customization function that extracts and analyzes specific information needed by the user, such as a specific gender or age group, a specific vehicle type, or a specific time of day. Next, the collected data is uploaded to the cloud in real time. The generating AI analyzes the data on the cloud and automatically generates a report. This eliminates the need for the user to manually organize and analyze data, and allows for efficient and highly accurate data acquisition. For example, traffic survey companies, public institutions, and urban planning departments can use this system to quickly and accurately collect and analyze traffic volume and pedestrian flow data, which can be used to improve urban planning and transportation planning. It also leads to a reduction in the workload of field work, contributing to work style reform. Thus, the present invention, by using smart glasses and generating AI, realizes automatic measurement, analysis, and report generation of pedestrian and traffic volume, and provides a function to customize, extract, and analyze specific information. This eliminates the inefficiencies of conventional manual counting work and enables efficient and highly accurate data collection and analysis. As a result, the automatic measurement, analysis, and report generation system for pedestrian and traffic volume eliminates the need for users to manually organize and analyze data, allowing for efficient and highly accurate data acquisition.

[0071] The automatic traffic volume and pedestrian flow measurement, analysis, and report generation system according to this embodiment comprises a projection unit, a counting unit, an upload unit, an analysis unit, and a report generation unit. The projection unit projects traffic conditions onto the user's field of view. The projection unit displays traffic conditions in real time on the user's field of view, for example, using smart glasses. The projection unit can display, for example, the status of traffic signals, the flow of vehicles, and the movement of pedestrians. The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. The counting unit automatically counts the number of vehicles and pedestrians, for example, using image recognition technology. The counting unit can also count the number of vehicles and pedestrians using sensors. Furthermore, the counting unit can also count the number of vehicles and pedestrians using generating AI. The upload unit uploads the data collected by the counting unit to the cloud in real time. The uploading unit uploads data to the cloud, for example, using wireless communication technology. Furthermore, the uploading unit can also upload data to the cloud using wired communication technology. Furthermore, the uploading unit can also upload data to the cloud using generating AI. The analysis unit analyzes the data uploaded by the upload unit. The analysis unit analyzes the data using, for example, data mining techniques. The analysis unit can also analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using generative AI. The report generation unit generates a report based on the data analyzed by the analysis unit. The report generation unit generates the report in, for example, text format. Furthermore, the report generation unit can also generate the report in graph format. Furthermore, the report generation unit can generate the report using generative AI. As a result, the automatic measurement, analysis, and report generation system for pedestrian and traffic volume according to this embodiment can project traffic conditions onto the user's field of view, automatically count the number of vehicles and pedestrians, upload the data to the cloud in real time, analyze it, and generate a report.

[0072] The projection unit projects traffic conditions onto the user's field of view. For example, using smart glasses, the projection unit displays real-time traffic conditions within the user's field of view. The smart glasses incorporate a transparent display, allowing the user to view additional information while maintaining normal vision. Specifically, the smart glasses' display shows traffic signal status, vehicle flow, pedestrian movement, and more. This allows the user to understand the surrounding traffic conditions in real time. Furthermore, the projection unit can use eye-tracking technology to display detailed information about specific areas the user is focusing on. For example, if the user looks at a particular intersection, information regarding the traffic signal status and vehicle flow at that intersection will be highlighted. This allows the user to quickly obtain necessary information and make appropriate decisions. The projection unit also features a voice assistant function, allowing the user to request specific information using voice commands. For example, if the user issues the voice command "Tell me the status of the next traffic light," the smart glasses' display will show the status of the next traffic light. Thus, the projection unit projects real-time traffic conditions onto the user's field of view and utilizes eye-tracking technology and voice assistant functions to enable the user to quickly and accurately obtain the necessary information.

[0073] The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. For example, the counting unit automatically counts vehicles and pedestrians using image recognition technology. Specifically, a camera built into the smart glasses captures images of the surroundings, and this image is analyzed in real time. The image recognition algorithm identifies vehicles and pedestrians and counts their numbers. The counting unit can also count vehicles and pedestrians using sensors. For example, infrared sensors or ultrasonic sensors built into the smart glasses are used to detect the movement of vehicles and pedestrians and count their numbers. Furthermore, the counting unit can also count vehicles and pedestrians using generative AI. Generative AI learns from past data and patterns to improve real-time counting accuracy. For example, generative AI learns patterns of vehicle and pedestrian movement at specific times and locations, and uses this information to improve counting accuracy. This allows the counting unit to accurately count vehicles and pedestrians by combining image recognition technology, sensor technology, and generative AI. Additionally, the counting unit records the counting results in real time for later analysis and report generation. This allows the counting unit to efficiently collect the data necessary for understanding and analyzing traffic conditions.

[0074] The upload unit uploads data collected by the counting unit to the cloud in real time. The upload unit uploads data to the cloud using, for example, wireless communication technology. Specifically, it uses Wi-Fi or Bluetooth modules built into the smart glasses to transmit collected data to a cloud server. The upload unit can also upload data to the cloud using wired communication technology. For example, it connects the smart glasses to a computer via a USB cable to upload data to the cloud. Furthermore, the upload unit can upload data to the cloud using generative AI. Generative AI compresses and optimizes data to efficiently upload it to the cloud. For example, it eliminates data duplication and extracts only the necessary information for upload, saving bandwidth and improving upload speed. This allows the upload unit to quickly and efficiently upload collected data to the cloud by combining wireless communication technology, wired communication technology, and generative AI. In addition, the upload unit protects data using encryption technology to ensure data security. For example, it encrypts data using encryption algorithms such as AES or RSA before uploading to prevent unauthorized access by third parties. This allows the upload unit to securely upload the collected data to the cloud, making it available for analysis and report generation.

[0075] The analysis unit analyzes the data uploaded by the upload unit. For example, the analysis unit uses data mining techniques to analyze the data. Specifically, it analyzes data stored on a cloud server using data mining algorithms to extract traffic volume and flow patterns. The analysis unit can also analyze data using statistical analysis techniques. For example, it analyzes collected data using statistical methods to evaluate fluctuations in traffic volume at specific times and locations. Furthermore, the analysis unit can analyze data using generative AI. Generative AI learns from past data and patterns to improve real-time analysis accuracy. For example, generative AI can predict traffic volume at specific times and locations, and forecast future traffic conditions. This allows the analysis unit to combine data mining techniques, statistical analysis techniques, and generative AI to analyze collected data from multiple perspectives and understand traffic volume and flow patterns and trends. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. For example, if an abnormally high traffic volume is detected at a specific intersection, the analysis unit can immediately issue a warning and take appropriate measures. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0076] The report generation unit generates reports based on data analyzed by the analysis unit. For example, the report generation unit generates reports in text format. Specifically, it creates text reports that detail traffic volume and flow patterns and trends based on the analysis results. The report generation unit can also generate reports in graph format. For example, it can create graphs that visually show fluctuations in traffic volume, or graphs that compare traffic volume at specific times or locations. Furthermore, the report generation unit can generate reports using a generation AI. The generation AI automatically creates reports based on the analysis results and provides them to the user. For example, the generation AI generates reports that summarize the analysis results and highlight important points. The generation AI can also create customized reports according to the user's needs. This allows the report generation unit to combine text format, graph format, and generation AI to produce reports that clearly and effectively communicate analysis results. Additionally, the report generation unit has a function to automatically distribute the generated reports. For example, it can automatically send reports via email or cloud storage, allowing users to access them at any time. This enables the report generation unit to provide analysis results to users quickly and efficiently, helping them understand traffic conditions and develop countermeasures.

[0077] The customization unit extracts and analyzes specific information in a customized manner. For example, the customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day. The customization unit can extract and analyze specific information using generative AI. For example, to extract information on a specific gender, the customization unit can determine gender using image recognition technology. The customization unit can also estimate age using facial recognition technology to extract information on a specific age group. Furthermore, the customization unit can analyze the shape and characteristics of a vehicle to extract information on a specific vehicle type. In this way, the customization unit can extract and analyze specific information in a customized manner.

[0078] The customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day. For example, to extract information on a specific gender, the customization unit can use image recognition technology to determine gender. For example, the customization unit can use facial recognition technology to determine gender. The customization unit can also use facial recognition technology to estimate age in order to extract information on a specific age group. For example, the customization unit can analyze facial features to estimate age. Furthermore, the customization unit can analyze the shape and characteristics of a vehicle in order to extract information on a specific vehicle type. For example, the customization unit can analyze the shape and characteristics of a vehicle to determine the vehicle type. As a result, the customization unit can extract and analyze information such as specific gender, age group, vehicle type, and time of day.

[0079] The projection unit can display real-time traffic conditions within the user's field of view. For example, the projection unit can display real-time traffic conditions within the user's field of view using smart glasses. For instance, the projection unit can display traffic signal status, vehicle flow, pedestrian movement, and more. Furthermore, the projection unit can also display real-time traffic conditions using generative AI. For example, the projection unit can display real-time traffic conditions analyzed by generative AI. This allows the projection unit to display real-time traffic conditions within the user's field of view.

[0080] The counting unit can automatically count the number of vehicles and pedestrians. For example, the counting unit can automatically count the number of vehicles and pedestrians using image recognition technology. For example, the counting unit can analyze images captured by a camera to count the number of vehicles and pedestrians. The counting unit can also count the number of vehicles and pedestrians using sensors. For example, the counting unit can use infrared sensors to count the number of vehicles and pedestrians. Furthermore, the counting unit can also count the number of vehicles and pedestrians using generative AI. For example, the counting unit can count the number of vehicles and pedestrians based on data analyzed by generative AI. As a result, the counting unit can automatically count the number of vehicles and pedestrians.

[0081] The analysis unit can analyze data on the cloud. For example, the analysis unit can analyze data using data mining techniques. For instance, it can analyze data uploaded to the cloud to analyze trends in traffic volume and flow. Furthermore, the analysis unit can analyze data using statistical analysis techniques. For example, it can statistically analyze data on the cloud to understand fluctuations in traffic volume and flow. Additionally, the analysis unit can analyze data using generative AI. For example, it can perform detailed analyses of traffic volume and flow based on data analyzed by generative AI. This allows the analysis unit to analyze data on the cloud.

[0082] The projection unit can estimate the user's emotions and adjust the type of information projected based on the estimated emotions. For example, if the user is stressed, the projection unit can reduce the amount of information by projecting only essential traffic information. For example, the projection unit can analyze the user's facial expressions to determine if they are stressed and display only essential information. Conversely, if the user is relaxed, the projection unit can increase the amount of information by projecting detailed traffic information. For example, the projection unit can analyze the user's voice to determine if they are relaxed and display detailed information. Furthermore, if the user is in a hurry, the projection unit can prioritize projecting the most important traffic information. For example, the projection unit can analyze the user's heart rate to determine if they are in a hurry and prioritize displaying essential information. In this way, the projection unit can adjust the type of information projected based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The projection unit can track the user's eye movements during projection and prioritize projecting important traffic information in the user's line of sight. For example, if the user is looking in a specific direction, the projection unit will prioritize projecting traffic information in that direction. For instance, the projection unit can track the user's eye movements using an eye-tracking device and display traffic information in that direction. The projection unit can also instantly project new traffic information in the user's line of sight when the user moves their gaze. For example, the projection unit can detect the user's eye movements using a camera and display new traffic information. Furthermore, if the user's gaze is fixed, the projection unit can project detailed traffic information in that direction. For example, the projection unit can detect the user's fixed gaze using an eye-tracking device and display detailed traffic information. This allows the projection unit to track the user's eye movements and prioritize projecting important traffic information in their line of sight.

[0084] The projection unit can automatically adjust the brightness and contrast of the projection in response to ambient light during projection. For example, in bright daytime environments, the projection unit can increase the brightness of the projection to improve visibility. For example, the projection unit can measure the ambient light using a light sensor and adjust the brightness of the projection. The projection unit can also reduce eye strain by decreasing the brightness of the projection at night or in dark environments. For example, the projection unit can measure the ambient light using a light sensor and adjust the brightness of the projection. Furthermore, the projection unit can adjust the brightness and contrast of the projection in real time when the ambient light changes. For example, the projection unit can detect changes in ambient light using a light sensor and adjust the brightness and contrast of the projection in real time. As a result, the projection unit can automatically adjust the brightness and contrast of the projection in response to ambient light.

[0085] The projection unit can estimate the user's emotions and determine the priority of the information to project based on the estimated emotions. For example, if the user is stressed, the projection unit will prioritize projecting the most important traffic information. For instance, it can analyze the user's facial expressions to determine if they are stressed and prioritize displaying important information. Similarly, if the user is relaxed, the projection unit can prioritize projecting detailed traffic information. For example, it can analyze the user's voice to determine if they are relaxed and display detailed information. Furthermore, if the user is in a hurry, the projection unit can prioritize projecting urgent traffic information. For example, it can analyze the user's heart rate to determine if they are in a hurry and prioritize displaying urgent information. Thus, the projection unit can determine the priority of the information to project based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The projection unit can project region-specific traffic information based on the user's location information during projection. For example, if the user is in a specific region, the projection unit will prioritize projecting traffic information for that region. For instance, the projection unit can obtain the user's location information using GPS and display traffic information for that region. Furthermore, if the user is on the move, the projection unit can update and project traffic information based on the user's current location in real time. For example, the projection unit can track the user's current location using GPS and display the latest traffic information. In addition, the projection unit can project traffic information for a specific region in advance when the user is approaching that region. For example, the projection unit can predict the user's direction of movement using GPS and display traffic information for the destination. In this way, the projection unit can project region-specific traffic information based on the user's location information.

[0087] The projection unit can project highly relevant information by referring to the user's past behavioral history during projection. For example, the projection unit can prioritize projecting traffic information for places the user has frequently visited in the past. For example, the projection unit can analyze the user's location history and display traffic information for places visited in the past. The projection unit can also project traffic information related to specific time periods based on the user's past behavioral history. For example, the projection unit can analyze the user's behavioral patterns and display traffic information related to specific time periods. Furthermore, the projection unit can analyze the user's past behavioral patterns and project the most relevant traffic information. For example, the projection unit can prioritize displaying highly relevant information based on the user's behavioral history. In this way, the projection unit can project highly relevant information by referring to the user's past behavioral history.

[0088] The counting unit can estimate the user's emotions and adjust the accuracy of the count based on the estimated emotions. For example, if the user is stressed, the counting unit can increase the accuracy of the count to provide more precise data. For example, the counting unit can analyze the user's facial expressions to determine if they are stressed and adjust the accuracy of the count. Also, if the user is relaxed, the counting unit can increase the level of detail in the data by adjusting the accuracy of the count. For example, the counting unit can analyze the user's voice to determine if they are relaxed and adjust the accuracy of the count. Furthermore, if the user is in a hurry, the counting unit can optimize the accuracy of the count to provide data quickly. For example, the counting unit can analyze the user's heart rate to determine if they are in a hurry and adjust the accuracy of the count. In this way, the counting unit can adjust the accuracy of the count based on 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.

[0089] The counting unit can improve its counting accuracy by considering the speed and direction of vehicles and pedestrians during counting. For example, the counting unit can perform accurate counting by considering the speed of vehicles. For example, the counting unit can improve the accuracy of counting by measuring the speed of vehicles using radar. The counting unit can also perform accurate counting by considering the direction of pedestrians. For example, the counting unit can improve the accuracy of counting by detecting the direction of pedestrians using a camera. Furthermore, the counting unit can improve the accuracy of counting by considering the speed and direction of vehicles and pedestrians simultaneously. For example, the counting unit can improve the accuracy of counting by combining radar and a camera to measure the speed and direction of vehicles and pedestrians. In this way, the counting unit can improve the accuracy of counting by considering the speed and direction of vehicles and pedestrians.

[0090] The counting unit can adjust the timing of counting based on specific events. For example, the counting unit can temporarily pause counting vehicles when the traffic light turns red. For example, the counting unit can detect the change in traffic light and stop counting vehicles while the light is red. The counting unit can also resume counting vehicles when the traffic light turns green. For example, the counting unit can detect the change in traffic light and resume counting vehicles while the light is green. Furthermore, the counting unit can also adjust the timing of counting pedestrians based on the change in traffic light. For example, the counting unit can detect the change in traffic light and adjust the timing of counting pedestrians. In this way, the counting unit can adjust the timing of counting based on specific events.

[0091] The counting unit can estimate the user's emotions and determine the priority of items to count based on the estimated emotions. For example, if the user is stressed, the counting unit will prioritize counting important items. For example, it can analyze the user's facial expressions to determine if they are stressed and prioritize counting important items. Also, if the user is relaxed, the counting unit can prioritize counting detailed items. For example, it can analyze the user's voice to determine if they are relaxed and prioritize counting detailed items. Furthermore, if the user is in a hurry, the counting unit can prioritize counting items of high urgency. For example, it can analyze the user's heart rate to determine if they are in a hurry and prioritize counting items of high urgency. In this way, the counting unit can determine the priority of items to count based on 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.

[0092] The counting unit can perform counting while considering the attribute information of vehicles and pedestrians. For example, the counting unit can prioritize counting specific vehicle types. For example, the counting unit can use image recognition technology to determine specific vehicle types and then perform the count. The counting unit can also prioritize counting pedestrians wearing specific clothing. For example, the counting unit can use image recognition technology to determine specific clothing and then perform the count. Furthermore, the counting unit can perform counting while simultaneously considering the attribute information of vehicles and pedestrians. For example, the counting unit can use image recognition technology to analyze the attribute information of vehicles and pedestrians and then perform the count. As a result, the counting unit can perform counting while considering the attribute information of vehicles and pedestrians.

[0093] The counting unit can analyze ambient sound information during counting and correct the count based on specific sounds. For example, the counting unit can correct the vehicle count when it detects the sound of a car horn. For example, the counting unit can use speech recognition technology to detect the sound of a car horn and correct the count. The counting unit can also correct the pedestrian count when it detects the voice of a pedestrian. For example, the counting unit can use speech recognition technology to detect the voice of a pedestrian and correct the count. Furthermore, the counting unit can analyze ambient sound information to improve the accuracy of the count. For example, the counting unit can use speech recognition technology to analyze ambient sound information and improve the accuracy of the count. As a result, the counting unit can analyze ambient sound information and correct the count based on specific sounds.

[0094] The upload unit can estimate the user's emotions and adjust the upload frequency based on the estimated emotions. For example, if the user is stressed, the upload unit can reduce the upload frequency to alleviate the burden. For example, the upload unit can analyze the user's facial expressions to determine if they are stressed and adjust the upload frequency accordingly. The upload unit can also increase the upload frequency to provide more detailed data if the user is relaxed. For example, the upload unit can analyze the user's voice to determine if they are relaxed and adjust the upload frequency accordingly. Furthermore, if the user is in a hurry, the upload unit can optimize the upload frequency to provide data quickly. For example, the upload unit can analyze the user's heart rate to determine if they are in a hurry and adjust the upload frequency accordingly. In this way, the upload unit can adjust the upload frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The upload unit can determine upload priorities based on data importance during the upload process. For example, the upload unit can prioritize uploading important data. For instance, it can select important data based on data type and urgency and prioritize its upload. Furthermore, the upload unit can adjust the upload order based on data importance. For example, it can evaluate data importance and upload important data first. Additionally, the upload unit can upload high-priority data in real time. For example, it can upload high-priority data immediately for rapid processing. This allows the upload unit to determine upload priorities based on data importance.

[0096] The upload unit can adjust the data compression ratio during upload according to the network conditions. For example, if the network is congested, the upload unit will increase the data compression ratio before uploading. For example, the upload unit can monitor network bandwidth and compress the data before uploading when it is congested. Conversely, if the network is not congested, the upload unit can also upload high-quality data with a lower compression ratio. For example, the upload unit can monitor the network conditions and upload data without compression when it is not congested. Furthermore, the upload unit can adjust the data compression ratio in real time according to the network conditions. For example, the upload unit can monitor the network conditions in real time, select an appropriate compression ratio, and upload the data. In this way, the upload unit can adjust the data compression ratio according to the network conditions.

[0097] The upload unit can estimate the user's emotions and select the types of data to upload based on the estimated emotions. For example, if the user is stressed, the upload unit will upload only important data. For example, it can analyze the user's facial expressions to determine if they are stressed and select and upload important data. Also, if the user is relaxed, the upload unit can upload detailed data. For example, it can analyze the user's voice to determine if they are relaxed and upload detailed data. Furthermore, if the user is in a hurry, the upload unit can prioritize uploading data with high urgency. For example, it can analyze the user's heart rate to determine if they are in a hurry and select and upload data with high urgency. In this way, the upload unit can select the types of data to upload based on 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.

[0098] The upload unit can enhance security by adjusting the data encryption level during upload. For example, the upload unit can increase the encryption level when uploading important data. For instance, the upload unit can encrypt data using advanced encryption technologies such as AES-256 or RSA to enhance security. The upload unit can also adjust the encryption level when uploading general data. For example, the upload unit can select an encryption level according to the importance of the data and perform appropriate encryption. Furthermore, the upload unit can adjust the encryption level in real time according to the importance of the data. For example, the upload unit can evaluate the importance of the data and adjust the encryption level in real time before uploading. This allows the upload unit to enhance security by adjusting the data encryption level.

[0099] The upload unit can automatically back up data during upload, preventing data loss. For example, the upload unit can automatically create a backup when uploading data. For instance, it can use cloud storage to create a backup, preventing data loss. Furthermore, the upload unit can create a backup before uploading data to prevent data loss. For example, it can create a backup in local storage before uploading data, ensuring data security. Additionally, the upload unit can create a backup after uploading data to ensure data security. For example, it can create a backup in cloud storage after uploading data, preventing data loss. In this way, the upload unit can automatically back up data and prevent data loss.

[0100] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the level of detail of the analysis to provide more accurate data. For example, the analysis unit can analyze the user's facial expressions to determine if they are stressed and adjust the level of detail of the analysis. The analysis unit can also increase the level of detail of the data if the user is relaxed. For example, the analysis unit can analyze the user's voice to determine if they are relaxed and adjust the level of detail of the analysis. Furthermore, if the user is in a hurry, the analysis unit can optimize the level of detail of the analysis to provide data quickly. For example, the analysis unit can analyze the user's heart rate to determine if they are in a hurry and adjust the level of detail of the analysis. In this way, the analysis unit can adjust the level of detail of the analysis based on 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.

[0101] The analysis unit can detect outliers by comparing them with past data during analysis and correct the analysis results. For example, the analysis unit can detect outliers by comparing them with past data and correct the analysis results. For example, the analysis unit can identify outliers by referring to past data and correct the analysis results. The analysis unit can also perform corrections by referring to past data when it detects outliers. For example, when the analysis unit detects outliers, it can perform corrections based on past data. Furthermore, the analysis unit can perform outlier detection and correction in real time to improve the accuracy of the analysis results. For example, the analysis unit can detect outliers in real time and correct them immediately. This allows the analysis unit to detect outliers by comparing them with past data and correct the analysis results.

[0102] The analysis unit can perform multidimensional analysis while considering the correlation between data. For example, the analysis unit can analyze the correlation between vehicle speed and traffic volume. For example, the analysis unit can analyze vehicle speed data and traffic volume data and identify the correlation. The analysis unit can also analyze the correlation between the number of pedestrians and weather. For example, the analysis unit can analyze the number of pedestrians and weather data and identify the correlation. Furthermore, the analysis unit can analyze the correlation between vehicle type and traffic accident rate. For example, the analysis unit can analyze vehicle type data and traffic accident data and identify the correlation. As a result, the analysis unit can perform multidimensional analysis while considering the correlation between data.

[0103] 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 stressed, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can analyze the user's facial expressions to determine if they are stressed and select a simple display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can analyze the user's voice to determine if they are relaxed and select a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can analyze the user's heart rate to determine if they are in a hurry and select a concise display method. In this way, the analysis unit can adjust the display method of the analysis results based on 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.

[0104] The analysis unit can improve the accuracy of its analysis by referring to external data sources during the analysis process. For example, the analysis unit can improve the accuracy of traffic volume analysis by referring to weather information. For example, the analysis unit can acquire weather information and perform analysis in combination with traffic volume data. The analysis unit can also correct the analysis results by referring to the operating status of public transportation. For example, the analysis unit can acquire operating data of public transportation and perform analysis in combination with traffic volume data. Furthermore, the analysis unit can improve the accuracy of its analysis results by referring to road construction information. For example, the analysis unit can acquire road construction information and perform analysis in combination with traffic volume data. In this way, the analysis unit can improve the accuracy of its analysis by referring to external data sources.

[0105] The analysis unit can automatically optimize the analysis algorithm by feeding back data in real time during analysis. For example, the analysis unit can adjust the analysis algorithm based on data acquired in real time to improve accuracy. Furthermore, the analysis unit can also adjust the analysis algorithm based on the feedback data. For example, the analysis unit can analyze the feedback data and adjust the algorithm parameters. In addition, the analysis unit can improve the accuracy of the analysis algorithm through real-time data feedback. For example, the analysis unit can improve the accuracy of the algorithm by feeding back data in real time. This allows the analysis unit to automatically optimize the analysis algorithm by feeding back data in real time.

[0106] The report generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, the report generation unit can generate a simple and easy-to-understand report. For example, it can analyze the user's facial expressions to determine if they are stressed and generate a simple report. Furthermore, if the user is relaxed, the report generation unit can generate a detailed report. For example, it can analyze the user's voice to determine if they are relaxed and generate a detailed report. Additionally, if the user is in a hurry, the report generation unit can generate a concise report. For example, it can analyze the user's heart rate to determine if they are in a hurry and generate a concise report. This allows the report generation unit to adjust the report's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The report generation unit can adjust the level of detail in a report based on the importance of the data during report generation. For example, the report generation unit can prioritize and describe important data in detail. For example, the report generation unit can evaluate the importance of the data and describe important data in detail. The report generation unit can also adjust the level of detail in a report based on the importance of the data. For example, the report generation unit can generate a report with adjusted detail based on the importance of the data. Furthermore, the report generation unit can describe highly important data in detail and less important data concisely. For example, the report generation unit can evaluate the importance of the data and describe highly important data in detail and less important data concisely. In this way, the report generation unit can adjust the level of detail in a report based on the importance of the data.

[0108] The report generation unit can generate reports in different formats. For example, it can generate reports in PDF format. For example, it can convert data to PDF format and generate a report. It can also generate reports in Excel format. For example, it can convert data to Excel format and generate a report. Furthermore, the report generation unit can generate reports in different formats according to user specifications. For example, it can generate reports in PDF, Excel, CSV, etc., according to user requests. This allows the report generation unit to generate reports in different formats.

[0109] The report generation unit can estimate the user's emotions and prioritize reports based on the estimated emotions. For example, if the user is stressed, the report generation unit can prioritize generating important reports. For example, it can analyze the user's facial expressions to determine if they are stressed and prioritize generating important reports. Also, if the user is relaxed, the report generation unit can prioritize generating detailed reports. For example, it can analyze the user's voice to determine if they are relaxed and prioritize generating detailed reports. Furthermore, if the user is in a hurry, the report generation unit can prioritize generating urgent reports. For example, it can analyze the user's heart rate to determine if they are in a hurry and prioritize generating urgent reports. In this way, the report generation unit can prioritize reports based on 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.

[0110] The report generation unit can prioritize displaying highly relevant information by referring to the user's past report viewing history when generating reports. For example, the report generation unit can prioritize displaying information from reports the user has previously viewed. For example, the report generation unit can analyze the user's past report viewing history and display highly relevant information. The report generation unit can also display highly relevant information by referring to the user's past report viewing history. For example, the report generation unit can select and display highly relevant information based on the user's past viewing history. Furthermore, the report generation unit can analyze the user's past report viewing history and display the most relevant information. For example, the report generation unit can analyze the user's past viewing history and select and display the most relevant information. As a result, the report generation unit can prioritize displaying highly relevant information by referring to the user's past report viewing history.

[0111] The report generation unit can add an automatic report distribution function when generating reports, allowing reports to be sent at a specified time. For example, the report generation unit can automatically distribute reports at a specified time. For instance, the report generation unit can generate and automatically distribute reports at a time specified by the user. Furthermore, the report generation unit can automatically distribute reports according to user specifications. For example, the report generation unit can generate and distribute reports at a specified time in response to user requests. Additionally, the report generation unit can add an automatic report distribution function, allowing reports to be sent at a specified time. For example, the report generation unit can generate and automatically distribute reports at a time specified by the user. This allows the report generation unit to add an automatic report distribution function and send reports at a specified time.

[0112] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is stressed, the customization unit can provide simple customization options. For example, it can analyze the user's facial expressions to determine if they are stressed and provide simple customization options. The customization unit can also provide detailed customization options if the user is relaxed. For example, it can analyze the user's voice to determine if they are relaxed and provide detailed customization options. Furthermore, if the user is in a hurry, the customization unit can provide options for quick customization. For example, it can analyze the user's heart rate to determine if they are in a hurry and provide options for quick customization. In this way, the customization unit can adjust the customization content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The customization unit can propose the optimal customization by referring to the user's past customization history during the customization process. For example, the customization unit can propose the optimal customization by referring to the user's past customization history. For example, the customization unit can analyze the user's past customization history and propose the optimal customization. Furthermore, the customization unit can propose the optimal customization based on the customization options the user has previously selected. For example, the customization unit can propose the optimal customization options based on the user's past customization history. In addition, the customization unit can analyze the user's past customization history and propose the most suitable customization. For example, the customization unit can analyze the user's past customization history and propose the most suitable customization options. This allows the customization unit to propose the optimal customization by referring to the user's past customization history.

[0114] The customization unit can estimate the user's emotions and prioritize customizations based on those emotions. For example, if the user is stressed, the customization unit will prioritize providing important customizations. For instance, it can analyze the user's facial expressions to determine if they are stressed and prioritize providing important customizations. Similarly, if the user is relaxed, the customization unit can prioritize providing detailed customizations. For example, it can analyze the user's voice to determine if they are relaxed and prioritize providing detailed customizations. Furthermore, if the user is in a hurry, the customization unit can prioritize providing options that allow for quick customization. For example, it can analyze the user's heart rate to determine if they are in a hurry and prioritize providing options that allow for quick customizations. This allows the customization unit to prioritize customizations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The customization unit can provide optimal customization by considering the user's device information during the customization process. For example, if the user is using a smartphone, the customization unit can provide the optimal customization for a smartphone. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a smartphone. Furthermore, if the user is using a tablet, the customization unit can provide the optimal customization for a tablet. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a tablet. In addition, if the user is using a smartwatch, the customization unit can provide the optimal customization for a smartwatch. For example, the customization unit can obtain the user's device information and provide the optimal customization options for a smartwatch. This allows the customization unit to provide optimal customization by considering the user's device information.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The projection unit can track the user's eye movements and prioritize projecting important traffic information in their line of sight. For example, if the user is looking in a specific direction, it will prioritize projecting traffic information in that direction. Using an eye-tracking device, it can track the user's eye movements and display traffic information in their line of sight. Furthermore, when the user moves their gaze, it can instantly project new traffic information in their line of sight. Using a camera, it can detect the user's eye movements and display new traffic information. Additionally, if the user fixates their gaze, it can project detailed traffic information in their line of sight. Using an eye-tracking device, it can detect when the user's gaze is fixed and display detailed traffic information. This allows the projection unit to track the user's eye movements and prioritize projecting important traffic information in their line of sight.

[0118] The counting unit can improve its accuracy by considering the speed and direction of vehicles and pedestrians. For example, it can perform accurate counting by considering the speed of vehicles. By using radar to measure the speed of vehicles, the accuracy of the counting can be improved. It can also perform accurate counting by considering the direction of pedestrians. By using a camera to detect the direction of pedestrians, the accuracy of the counting can be improved. Furthermore, it can improve the accuracy of the counting by considering the speed and direction of vehicles and pedestrians simultaneously. By combining radar and a camera to measure the speed and direction of vehicles and pedestrians, the accuracy of the counting can be improved. As a result, the counting unit can improve its accuracy by considering the speed and direction of vehicles and pedestrians.

[0119] The analysis unit can improve the accuracy of its analysis by referring to external data sources. For example, it can improve the accuracy of traffic volume analysis by referring to weather information. Weather information can be acquired and combined with traffic volume data for analysis. It can also correct the analysis results by referring to the operating status of public transportation. Public transportation operation data can be acquired and combined with traffic volume data for analysis. Furthermore, it can improve the accuracy of the analysis results by referring to road construction information. Road construction information can be acquired and combined with traffic volume data for analysis. In this way, the analysis unit can improve the accuracy of its analysis by referring to external data sources.

[0120] The upload unit can adjust the data compression ratio according to network conditions. For example, if the network is congested, it can increase the data compression ratio before uploading. It monitors network bandwidth and compresses data before uploading when congested. Conversely, if the network is not congested, it can upload high-quality data with a lower compression ratio. It monitors network conditions and uploads data without compression when the network is not congested. Furthermore, it can adjust the data compression ratio in real time according to network conditions. It monitors network conditions in real time and selects an appropriate compression ratio before uploading data. In this way, the upload unit can adjust the data compression ratio according to network conditions.

[0121] The report generation unit can generate reports in different formats. For example, it can generate reports in PDF format. It can convert data to PDF format and generate a report. It can also generate reports in Excel format. It can convert data to Excel format and generate a report. Furthermore, it can generate reports in different formats according to user specifications. It can generate reports in PDF, Excel, CSV, etc., according to user requests. In this way, the report generation unit can generate reports in different formats.

[0122] The projection unit can estimate the user's emotions and adjust the type of information projected based on those emotions. For example, if the user is stressed, it can project only essential traffic information, reducing the amount of information displayed. It can analyze the user's facial expressions to determine if they are stressed and display only essential information. Conversely, if the user is relaxed, it can project detailed traffic information, increasing the amount of information displayed. It can analyze the user's voice to determine if they are relaxed and display detailed information. Furthermore, if the user is in a hurry, it can prioritize projecting the most important traffic information. It can analyze the user's heart rate to determine if they are in a hurry and prioritize displaying essential information. In this way, the projection unit can adjust the type of information projected based on the user's emotions.

[0123] The counting unit can estimate the user's emotions and adjust the accuracy of the count based on those emotions. For example, if the user is stressed, the counting accuracy can be increased to provide more accurate data. The unit can analyze the user's facial expressions to determine if they are stressed and adjust the counting accuracy accordingly. Similarly, if the user is relaxed, the counting accuracy can be adjusted to increase the level of detail in the data. The unit can analyze the user's voice to determine if they are relaxed and adjust the counting accuracy accordingly. Furthermore, if the user is in a hurry, the counting accuracy can be optimized to provide data more quickly. The unit can analyze the user's heart rate to determine if they are in a hurry and adjust the counting accuracy accordingly. In this way, the counting unit can adjust the accuracy of the count based on the user's emotions.

[0124] The upload unit can estimate the user's emotions and adjust the upload frequency based on those emotions. For example, if the user is stressed, the upload frequency can be reduced to alleviate their burden. The system can analyze the user's facial expressions to determine if they are stressed and adjust the upload frequency accordingly. Conversely, if the user is relaxed, the upload frequency can be increased to provide more detailed data. The system can analyze the user's voice to determine if they are relaxed and adjust the upload frequency accordingly. Furthermore, if the user is in a hurry, the upload frequency can be optimized to provide data quickly. The system can analyze the user's heart rate to determine if they are in a hurry and adjust the upload frequency accordingly. In this way, the upload unit can adjust the upload frequency based on the user's emotions.

[0125] 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 stressed, it can provide a simple and highly visible display method. By analyzing the user's facial expressions, it can determine whether the user is stressed and select a simple display method. If the user is relaxed, it can also provide a display method that includes detailed information. By analyzing the user's voice, it can determine whether the user is relaxed and select a detailed display method. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. By analyzing the user's heart rate, it can determine whether the user is in a hurry and select a display method that focuses on the essentials. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions.

[0126] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, it can generate a simple and easy-to-understand report. It can analyze the user's facial expressions to determine if they are stressed and generate a simple report. If the user is relaxed, it can generate a report with more detailed information. It can analyze the user's voice to determine if they are relaxed and generate a detailed report. Furthermore, if the user is in a hurry, it can generate a concise report. It can analyze the user's heart rate to determine if they are in a hurry and generate a concise report. In this way, the report generation unit can adjust the report's presentation based on the user's emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The projection unit projects traffic conditions into the user's field of view. For example, smart glasses can be used to display real-time traffic conditions in the user's field of view. The projection unit can display traffic signal status, vehicle flow, pedestrian movement, and more. Step 2: The counting unit counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit. For example, it automatically counts the number of vehicles and pedestrians using image recognition technology, sensors, and generative AI. Step 3: The upload unit uploads the data collected by the counting unit to the cloud in real time. For example, it uploads the data to the cloud using wireless communication technology, wired communication technology, or generative AI. Step 4: The analysis unit analyzes the data uploaded by the upload unit. For example, it analyzes the data using data mining techniques, statistical analysis techniques, and generative AI. Step 5: The report generation unit generates a report based on the data analyzed by the analysis unit. For example, it can generate reports in text format, graph format, or using AI generation.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the projection unit, counting unit, upload unit, analysis unit, report generation unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the projection unit is implemented by the display 40A of the smart device 14, displaying traffic conditions in real time within the user's field of view. The counting unit is implemented by the camera 42 and control unit 46A of the smart device 14, automatically counting the number of vehicles and pedestrians. The upload unit uploads data to the cloud using the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the uploaded data. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a report based on the analyzed data. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, customizing, extracting, and analyzing specific information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the projection unit, counting unit, upload unit, analysis unit, report generation unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the projection unit is implemented by the display of the smart glasses 214, displaying traffic conditions in real time within the user's field of view. The counting unit is implemented by the camera 42 and control unit 46A of the smart glasses 214, automatically counting the number of vehicles and pedestrians. The upload unit uploads data to the cloud using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the uploaded data. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a report based on the analyzed data. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, customizing, extracting, and analyzing specific information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the projection unit, counting unit, upload unit, analysis unit, report generation unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the projection unit is implemented by the display 343 of the headset terminal 314, displaying traffic conditions in real time within the user's field of view. The counting unit is implemented by the camera 42 and control unit 46A of the headset terminal 314, automatically counting the number of vehicles and pedestrians. The upload unit uploads data to the cloud using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the uploaded data. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a report based on the analyzed data. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, customizing, extracting, and analyzing specific information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the projection unit, counting unit, upload unit, analysis unit, report generation unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the projection unit is implemented by the display of the robot 414, displaying traffic conditions in real time within the user's field of view. The counting unit is implemented by the camera 42 and control unit 46A of the robot 414, automatically counting the number of vehicles and pedestrians. The upload unit uploads data to the cloud using the communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the uploaded data. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates a report based on the analyzed data. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12, and customizes, extracts, and analyzes specific information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A projection unit that projects traffic conditions into the user's field of view, A counting unit that counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit, An upload unit uploads the data collected by the counting unit to the cloud in real time. An analysis unit analyzes the data uploaded by the aforementioned upload unit, The system includes a report generation unit that generates a report based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) It includes a customization unit that allows for the extraction and analysis of specific information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customization unit is Extract and analyze information such as specific gender, specific age group, specific vehicle type, and specific time of day. The system described in Appendix 2, characterized by the features described herein. (Note 4) The projection unit is Real-time traffic information displayed within the user's field of view. The system described in Appendix 1, characterized by the features described herein. (Note 5) The counting unit is Automatically counts the number of vehicles and pedestrians. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze data on the cloud The system described in Appendix 1, characterized by the features described herein. (Note 7) The projection unit is It estimates the user's emotions and adjusts the type of information projected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The projection unit is During projection, the system tracks the user's eye movements and prioritizes projecting important traffic information that is in their line of sight. The system described in Appendix 1, characterized by the features described herein. (Note 9) The projection unit is During projection, the brightness and contrast of the projection are automatically adjusted according to the ambient light. The system described in Appendix 1, characterized by the features described herein. (Note 10) The projection unit is It estimates the user's emotions and determines the priority of information to project based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The projection unit is During projection, region-specific traffic information is projected based on the user's location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The projection unit is During projection, the system references the user's past behavioral history to project highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The counting unit is It estimates the user's emotions and adjusts the accuracy of the count based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The counting unit is When counting, the accuracy of the counting is improved by taking into account the speed and direction of vehicles and pedestrians. The system described in Appendix 1, characterized by the features described herein. (Note 15) The counting unit is When counting, adjust the timing of the count based on specific events. The system described in Appendix 1, characterized by the features described herein. (Note 16) The counting unit is It estimates the user's emotions and determines the priority of items to count based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The counting unit is When counting, the system takes into account the attribute information of vehicles and pedestrians. The system described in Appendix 1, characterized by the features described herein. (Note 18) The counting unit is During counting, the system analyzes ambient sound information and corrects the count based on specific sounds. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned upload unit, It estimates user sentiment and adjusts upload frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned upload unit, During the upload process, the system prioritizes uploads based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned upload unit, During upload, the data compression ratio is adjusted according to the network conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned upload unit, The system estimates the user's emotions and selects the type of data to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned upload unit, Enhance security by adjusting the data encryption level during upload. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned upload unit, During upload, data backups are automatically performed to prevent data loss. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, outliers are detected by comparing them with past data, and the analysis results are corrected. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, multidimensional analysis is performed while considering the correlation between the data. The system described in Appendix 1, characterized by the features described herein. (Note 28) 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 29) The aforementioned analysis unit, During analysis, external data sources are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, data is fed back in real time, and the analysis algorithm is automatically optimized. The system described in Appendix 1, characterized by the features described herein. (Note 31) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The report generation unit, When generating a report, adjust the level of detail in the report based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The report generation unit, When generating a report, generate the report in a different format. The system described in Appendix 1, characterized by the features described herein. (Note 34) The report generation unit, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The report generation unit, When generating reports, the system prioritizes displaying highly relevant information by referencing the user's past report viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The report generation unit, When generating a report, an automated report distribution function will be added, sending the report at a specified time. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned customization unit is During customization, we refer to the user's past customization history to suggest the optimal customization. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned customization unit is When customizing, we provide optimal customization by taking into account the user's device information. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0201] 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 projection unit that projects traffic conditions into the user's field of view, A counting unit that counts the number of vehicles and pedestrians based on the traffic conditions projected by the projection unit, An upload unit uploads the data collected by the counting unit to the cloud in real time. An analysis unit analyzes the data uploaded by the aforementioned upload unit, The system includes a report generation unit that generates a report based on the data analyzed by the analysis unit. A system characterized by the following features.

2. It includes a customization unit that customizes, extracts, and analyzes specific information. The system according to feature 1.

3. The aforementioned customization unit is Extract and analyze information such as specific gender, specific age group, specific vehicle type, and specific time of day. The system according to feature 2.

4. The projection unit is Real-time traffic information displayed within the user's field of view. The system according to feature 1.

5. The counting unit is Automatically counts the number of vehicles and pedestrians. The system according to feature 1.

6. The aforementioned analysis unit, Analyze data on the cloud The system according to feature 1.

7. The projection unit is It estimates the user's emotions and adjusts the type of information projected based on those estimated emotions. The system according to feature 1.

8. The projection unit is During projection, the system tracks the user's eye movements and prioritizes projecting important traffic information that is in their line of sight. The system according to feature 1.

9. The projection unit is During projection, the brightness and contrast of the projection are automatically adjusted according to the ambient light. The system according to feature 1.

Images