system

Abstract

The system according to this embodiment aims to avoid the dangers associated with using a smartphone while walking and to ensure the safety of the user. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit recognizes the forward video in real time. The analysis unit analyzes the video data collected by the data collection unit and detects red lights, stairs, people, and obstacles. The warning unit displays a notification to alert the user based on the hazards detected by the analysis unit, issues a warning if necessary, and forcibly stops the application.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including 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 that responds 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] Conventional technologies cannot sufficiently avoid the risks associated with walking while using a smartphone, and there is room for improvement.

[0005] The system according to the embodiment aims to avoid the risks associated with walking while using a smartphone and ensure the safety of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit recognizes the forward-facing video in real time. The analysis unit analyzes the video data collected by the data collection unit and detects red lights, stairs, people, and obstacles. The warning unit displays a notification to alert the user based on the hazards detected by the analysis unit, issues a warning if necessary, and forcibly stops the application. [Effects of the Invention]

[0007] The system according to this embodiment can avoid the dangers associated with using a smartphone while walking and ensure the safety of the user. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards 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 system for solving the dangers and health problems associated with walking while using a smartphone, according to an embodiment of the present invention, uses the smartphone's rear camera to recognize the image in front in real time, detects red lights, stairs, people, and obstacles, and displays a notification in the center of the screen to alert the user. If the alert is ignored, the system issues a warning and forcibly stops the app being used. Furthermore, to address the problem of difficulty in perceiving hazards ahead when the rear camera is pointed downwards, the system prevents smartphone use in this position and displays a warning to correct posture. In addition, it utilizes AI-generated content to recognize the situation ahead and generate and display "impactful messages," icons, and images tailored to the user. For example, it may alert the user with messages such as "I'm (Oshi) right in front of you! Look!" or "A real monster is approaching!" When the smartphone is started, AI-generated characters and images are displayed using AR technology, and the screen angle can be changed to improve visibility. This mechanism improves the safety of walking while using a smartphone and reduces health problems. It also supports safe and healthy smartphone use for smartphone addicts and enhances entertainment value to attract user interest. This means that a system designed to address the dangers and health problems associated with using smartphones while walking can ensure user safety and reduce health risks.

[0029] The system for solving the dangers and health problems of walking while using a smartphone according to this embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit recognizes forward video in real time. The data collection unit can collect forward video using, for example, the rear camera of a smartphone. The data collection unit can also acquire video data in real time and transmit it to the analysis unit. The analysis unit analyzes the video data collected by the data collection unit and detects red lights, stairs, people, and obstacles. The analysis unit can, for example, use AI to analyze the video data and detect red lights. The analysis unit can also use AI to detect stairs. Furthermore, the analysis unit can also use AI to detect people and obstacles. The warning unit displays a notification to alert the user based on the hazards detected by the analysis unit, issues a warning as needed, and forcibly stops the app. The warning unit can, for example, display a notification in the center of the screen that the user is viewing. The warning unit can also issue a warning if the user ignores the notification. Furthermore, the warning unit can also forcibly stop the app. As a result, the system for solving the dangers and health problems associated with walking while using a smartphone, according to this embodiment, can ensure user safety and reduce health risks.

[0030] The data acquisition unit recognizes forward-facing images in real time. For example, the unit can collect forward-facing images using the rear camera of a smartphone. Specifically, smartphone cameras capture high-resolution images and are equipped with dedicated chips and software for real-time processing. The data acquisition unit can acquire video data in real time and transmit it to the analysis unit. The video data is transmitted efficiently using compression technology, minimizing communication delays. Furthermore, the data acquisition unit has correction functions to collect high-quality images even under challenging shooting conditions such as low light or backlighting. For example, it automatically adjusts the camera sensitivity to provide clear images even at night or on cloudy days. The data acquisition unit also features image stabilization to prevent blurring, minimizing shaking while walking. This allows the data acquisition unit to accurately perceive the situation ahead, even when the user is using their smartphone while walking. Additionally, the data acquisition unit can temporarily store the collected video data and provide the analysis unit with historical data as needed. This allows the analysis unit to perform more accurate analysis based on continuous video data.

[0031] The analysis unit analyzes video data collected by the collection unit to detect red lights, stairs, people, and obstacles. For example, the analysis unit can use AI to analyze video data and detect red lights. Specifically, the AI ​​uses deep learning technology to learn patterns of red lights from a vast amount of video data. This allows the analysis unit to detect red lights with high accuracy from various angles and distances. The analysis unit can also use AI to detect stairs. Edge detection and pattern recognition technologies are used to accurately identify the shape and arrangement of stairs. Furthermore, the analysis unit can also use AI to detect people and obstacles. Face recognition technology and posture estimation technology are used to detect people, and it can also identify moving objects such as pedestrians, cyclists, and wheelchairs. Object detection algorithms are used to detect obstacles, and it can also detect obstacles on roads and barricades at construction sites. The analysis unit processes these detection results in real time and provides immediate feedback to the user. Furthermore, the analysis unit can utilize past data and statistical information to analyze and predict trends in danger at specific times and locations. This allows the analysis unit to provide advanced analysis functions to ensure user safety.

[0032] The warning unit displays notifications to alert the user based on hazards detected by the analysis unit, issuing warnings and forcibly stopping the app as needed. The warning unit can, for example, display notifications in the center of the user's screen. Specifically, it displays warning messages in the most eye-catching location and uses colors and animations for visual emphasis. The warning unit can also issue warnings if the user ignores the notification, for example, using audio alerts or vibration to forcibly draw the user's attention. Furthermore, the warning unit can forcibly stop the app. This is a function to temporarily restrict smartphone use when the user is facing a dangerous situation. For example, if a user continues walking despite a red light, the warning unit will forcibly stop the app and prompt the user to act safely. The warning unit can also record the user's behavior history and provide data for later analysis. This allows the warning unit to provide multi-layered warning functions to ensure user safety and mitigate health risks. Additionally, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning system. For example, the system analyzes the user's behavior after receiving a warning and optimizes the timing and content of the warning. This allows the warning unit to provide users with more effective safety measures and significantly reduce the dangers of walking while using a smartphone.

[0033] The warning unit can display a warning to correct posture if the rear camera is pointing downwards. For example, the warning unit can detect that the rear camera is pointing downwards and display a warning prompting the user to correct their posture. The warning unit can display a text message, for example. The warning unit can also issue an audio alert. Furthermore, the warning unit can display a visual alert. This makes it easier to identify hazards in front of the user even when the rear camera is pointing downwards. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input video data from the rear camera into the AI ​​and have the AI ​​perform the process of detecting the camera's orientation.

[0034] The warning unit can encourage users to use their smartphones upright, thereby reducing the risk of straight neck syndrome. For example, the warning unit can detect if a user is using their smartphone face down and display a warning prompting them to use it upright. The warning unit can display a text message, a voice alert, or a visual alert. This can improve the user's posture and reduce health risks. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input smartphone usage data into AI and have the AI ​​perform posture detection and display warnings.

[0035] The analysis unit can perform video recognition of the situation ahead and generate and display impactful messages, icons, and images tailored to the user. For example, the analysis unit can use AI to perform video recognition of the situation ahead and generate a message tailored to the user. For example, the analysis unit can generate messages such as, "I (Oshi) am right in front of you! Look!" or "A monster is really approaching!" The analysis unit can also generate icons and images. Furthermore, the analysis unit can display the generated messages, icons, and images to the user. This allows for effective warnings to be given to the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input forward video data into a generation AI and have the generation AI generate messages, icons, and images.

[0036] The analysis unit can display characters and images generated by AI using AR technology, making them easier to view by changing the screen angle. For example, the analysis unit generates characters and images using AI and displays them using AR technology. For example, the analysis unit can display characters and images on a smartphone screen, making them easier to view by changing the screen angle. This allows information to be provided in a way that is easy for the user to see. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can have a generation AI generate characters and images, and then display the generated characters and images using AR technology.

[0037] The data collection unit can analyze the user's past walking patterns and select the optimal data collection method when collecting video footage. For example, the data collection unit can set optimal video collection points based on routes the user has frequently traveled in the past. The data collection unit can also analyze the user's past walking speed and determine an appropriate collection interval. Furthermore, the data collection unit can automatically adjust the camera's orientation based on the user's past walking direction. This enables optimal video collection based on the user's past walking patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past walking data into a generating AI and have the generating AI perform the process of selecting the optimal data collection method.

[0038] The data collection unit can dynamically adjust its collection range based on the user's current walking speed and direction during video collection. For example, if the user is walking quickly, the data collection unit can collect a wider range of video to expand the detection range for hazardous objects. Conversely, if the user is walking slowly, the data collection unit can collect a narrower range of video to perform more detailed detection. Furthermore, the data collection unit can automatically adjust the camera's orientation to match the user's walking direction, optimally collecting video from the front. This allows for optimal video collection according to the user's walking speed and direction. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's walking speed and direction data into a generating AI, which can then perform the dynamic adjustment of the collection range.

[0039] The collection unit can prioritize collecting highly relevant videos by considering the user's geographical location information during video collection. For example, if the user is in a specific area, the collection unit will prioritize collecting videos related to that area. Furthermore, if the user is in a tourist area, the collection unit can prioritize collecting videos of tourist spots. Additionally, if the user is in a dangerous area, the collection unit can prioritize collecting videos that detect hazardous objects. This allows for the collection of optimal videos based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the process of prioritizing the collection of highly relevant videos.

[0040] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can collect videos related to places the user has shared on social media. It can also collect videos related to places the user follows on social media. Furthermore, the collection unit can collect videos related to places the user has checked into on social media. This allows for the collection of relevant videos based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI perform the process of collecting relevant videos.

[0041] The analysis unit can optimize the current analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the current analysis algorithm based on past analysis data to improve accuracy. The analysis unit can also extract specific patterns from past analysis data and reflect them in the current analysis. Furthermore, the analysis unit can analyze past analysis data and adjust the parameters of the current analysis algorithm. This allows the current analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis algorithm.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the video during analysis. For example, the analysis unit can apply a specific color recognition algorithm to detect red traffic lights. It can also apply a shape recognition algorithm to detect stairs. Furthermore, it can apply a motion recognition algorithm to detect people or obstacles. This allows the optimal analysis algorithm to be applied according to the category of the video. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the video recording date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent video to detect hazardous materials in real time. The analysis unit can also refer to past video and analyze specific patterns. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the video recording date. This allows for optimal analysis based on the video recording date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video recording date data into a generating AI and have the generating AI perform the process of determining the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the video footage during the analysis. For example, the analysis unit may prioritize analyzing video footage related to the detection of hazardous materials. It can also prioritize analyzing video footage related to the user's current situation. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the video footage. This allows for the determination of the optimal analysis order based on the relevance of the video footage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video relevance data into a generating AI and have the generating AI perform the process of adjusting the order of analysis.

[0045] The warning unit can adjust the level of detail of the warning based on the severity of the hazardous material when a warning is issued. For example, the warning unit displays a detailed warning for highly hazardous materials. It can also display a concise warning for moderately hazardous materials. Furthermore, it can display a mild warning for low-level hazardous materials. This allows for the provision of the most appropriate warning according to the severity of the hazardous material. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material severity data into a generating AI and have the generating AI perform the process of adjusting the level of detail of the warning.

[0046] The warning unit can apply different warning algorithms depending on the category of hazardous material when a warning is issued. For example, the warning unit can apply a specific color recognition algorithm to detect a red light. It can also apply a shape recognition algorithm to detect stairs. Furthermore, it can apply a motion recognition algorithm to detect people or obstacles. This allows the unit to provide the most appropriate warning depending on the category of hazardous material. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material category data into a generating AI and have the generating AI apply different warning algorithms.

[0047] The warning unit can determine the priority of warnings based on when the hazardous material was detected. For example, the warning unit will prioritize warnings for the most recent hazardous material. The warning unit can also refer to past hazardous material detections and issue warnings for specific patterns. Furthermore, the warning unit can dynamically adjust the warning priority based on when the hazardous material was detected. This allows for the provision of optimal warnings based on when the hazardous material was detected. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material detection time data into a generating AI and have the generating AI perform the process of determining the warning priority.

[0048] The warning unit can adjust the order of warnings based on the relevance of the hazardous materials when a warning is issued. For example, the warning unit may prioritize displaying warnings related to the detection of hazardous materials. It may also prioritize displaying warnings related to the user's current situation. Furthermore, the warning unit can dynamically adjust the order of warnings based on the relevance of the hazardous materials. This allows for the determination of the optimal warning order based on the relevance of the hazardous materials. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material relevance data into a generating AI and have the generating AI perform the process of adjusting the order of warnings.

[0049] The posture correction warning system can select the optimal warning method by referring to the user's past posture data when issuing a warning. For example, if the user has had poor posture in the past, the posture correction warning system will display a detailed warning about posture correction. The posture correction warning system can also determine the appropriate timing for posture correction based on the user's past posture data. Furthermore, the posture correction warning system can analyze the user's past posture data and select the most effective warning method for posture correction. This allows the system to provide the optimal posture correction warning based on the user's past posture data. Some or all of the above processes in the posture correction warning system may be performed using AI, for example, or without AI. For example, the posture correction warning system can input the user's past posture data into a generating AI and have the generating AI perform the process of selecting a warning method.

[0050] The posture correction warning system can select the optimal warning method by considering the user's geographical location information when issuing a posture correction warning. For example, if the user is in a specific area, the system can select a posture correction warning method appropriate for that area. Furthermore, if the user is indoors, the system can select a posture correction warning method appropriate for indoor use. Additionally, if the user is outdoors, the system can select a posture correction warning method appropriate for outdoor use. This allows the system to provide optimal posture correction warnings based on the user's geographical location information. Some or all of the above-described processes in the posture correction warning system may be performed using AI, for example, or without AI. For instance, the posture correction warning system can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting a warning method.

[0051] Straight neck risk reduction can select the optimal method by referring to the user's past posture data when reducing the risk of straight neck. For example, if the user has had a high risk of straight neck in the past, straight neck risk reduction will present detailed risk reduction methods. Straight neck risk reduction can also determine the appropriate timing for risk reduction based on the user's past posture data. Furthermore, straight neck risk reduction can analyze the user's past posture data and select the most effective risk reduction method. This allows for the provision of the optimal straight neck risk reduction method based on the user's past posture data. Some or all of the above processes in straight neck risk reduction may be performed using AI, for example, or without AI. For example, straight neck risk reduction can input the user's past posture data into a generating AI and have the generating AI perform the process of selecting a risk reduction method.

[0052] Straight neck risk mitigation can select the optimal method considering the user's geographical location information during the process. For example, if the user is in a specific area, straight neck risk mitigation can select a risk mitigation method suitable for that area. Furthermore, if the user is indoors, straight neck risk mitigation can select a risk mitigation method suitable for indoors. Additionally, if the user is outdoors, straight neck risk mitigation can select a risk mitigation method suitable for outdoors. This allows for the provision of the optimal straight neck risk mitigation method based on the user's geographical location information. Some or all of the above-described processes in straight neck risk mitigation may be performed using AI, for example, or without AI. For example, straight neck risk mitigation can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting a risk mitigation method.

[0053] The "impactful message generation" process can generate the optimal message by referencing the user's past response data during message generation. For example, it can generate the optimal message based on messages that the user has responded to favorably in the past. It can also analyze the user's past response data to generate effective messages. Furthermore, it can refer to the user's past response data and generate messages based on specific patterns. This allows for the provision of the optimal message based on the user's past response data. Some or all of the above processes in impactful message generation may be performed using AI, for example, or without AI. For example, impactful message generation can input the user's past response data into a generation AI and have the generation AI execute the message generation process.

[0054] The "Impactful Message Generation" system can generate the most relevant message by considering the user's geographical location during the message generation process. For example, if the user is in a specific area, it can generate a message appropriate for that area. It can also generate messages related to tourist attractions if the user is in a tourist destination. Furthermore, if the user is in a dangerous area, it can generate messages related to dangerous objects. This allows for the provision of the most relevant message based on the user's geographical location. Some or all of the above-described processes in Impactful Message Generation may be performed using AI, for example, or without AI. For instance, Impactful Message Generation can input the user's geographical location data into a generation AI and have the generation AI perform the message generation process.

[0055] AR technology allows for the selection of the optimal display method by referencing the user's past usage data during AR display. For example, AR technology can perform optimal AR display based on display methods that the user has previously responded favorably to. Furthermore, AR technology can analyze the user's past usage data to perform effective AR display. Additionally, AR technology can refer to the user's past usage data and perform AR display based on specific patterns. This allows for the provision of optimal AR display based on the user's past usage data. Some or all of the above processes in AR technology display may be performed using AI, for example, or without AI. For example, AR technology display can input the user's past usage data into a generating AI and have the generating AI perform the process of selecting a display method.

[0056] AR technology allows for the selection of the optimal display method by considering the user's geographical location information during AR display. For example, if the user is in a specific area, AR technology can display AR content appropriate for that area. Furthermore, if the user is in a tourist destination, AR technology can display AR content related to tourist spots. Additionally, if the user is in a dangerous area, AR technology can display AR content related to dangerous objects. This allows for the provision of optimal AR content based on the user's geographical location information. Some or all of the above-described processes in AR technology display may be performed using AI, for example, or without AI. For instance, AR technology display can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting the display method.

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

[0058] The data collection unit can analyze the user's walking pattern in real time and dynamically adjust the range of video collection according to the walking speed and direction. For example, if the user is walking quickly, it can collect a wider range of video to expand the detection range for hazardous objects. Conversely, if the user is walking slowly, it can collect a narrower range of video for more detailed detection. Furthermore, the data collection unit can automatically adjust the camera's orientation to match the user's walking direction, optimally collecting video from the front. This allows for optimal video collection according to the user's walking speed and direction.

[0059] The collection unit can prioritize collecting highly relevant footage by considering the user's geographical location. For example, if the user is in a specific area, it will prioritize collecting footage related to that area. Similarly, if the user is in a tourist area, it can prioritize collecting footage of tourist spots. Furthermore, if the user is in a dangerous area, it can prioritize collecting footage of hazardous objects. This allows for the collection of optimal footage based on the user's geographical location.

[0060] The analysis unit can apply different analysis algorithms depending on the category of the video. For example, a specific color recognition algorithm can be applied to detect red traffic lights. A shape recognition algorithm can also be applied to detect stairs. Furthermore, a motion recognition algorithm can be applied to detect people and obstacles. This allows the system to apply the most suitable analysis algorithm for each category of video.

[0061] The warning unit can adjust the level of detail of the warning based on the severity of the hazardous material. For example, a detailed warning is displayed for highly hazardous materials. A concise warning can be displayed for moderately hazardous materials. Furthermore, a mild warning can be displayed for less hazardous materials. This allows for the provision of the most appropriate warning according to the severity of the hazardous material.

[0062] The analysis unit can optimize the current analysis algorithm by referring to past analysis data. For example, it can optimize the current analysis algorithm based on past analysis data to improve accuracy. It can also extract specific patterns from past analysis data and reflect them in the current analysis. Furthermore, it can analyze past analysis data and adjust the parameters of the current analysis algorithm. This allows the current analysis algorithm to be optimized based on past analysis data.

[0063] The analysis unit can determine the priority of analysis based on when the video was captured. For example, it can prioritize the analysis of the most recent video to detect hazardous materials in real time. It can also refer to past video footage to analyze specific patterns. Furthermore, it can dynamically adjust the analysis priority based on when the video was captured. This allows for optimal analysis based on the video's capture date.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit recognizes the forward-facing image in real time. For example, it uses the rear camera of a smartphone to collect the forward-facing image, acquires the image data in real time, and transmits it to the analysis unit. Step 2: The analysis unit analyzes the video data collected by the collection unit to detect red lights, stairs, people, and obstacles. For example, AI is used to analyze the video data and detect red lights, stairs, people, and obstacles. Step 3: The warning unit displays a notification to the user based on the hazardous materials detected by the analysis unit, issues a warning if necessary, and forcibly stops the app. For example, it displays a notification in the center of the screen the user is viewing, issues a warning if the notification is ignored, and forcibly stops the app.

[0066] (Example of form 2) The system for solving the dangers and health problems associated with walking while using a smartphone, according to an embodiment of the present invention, uses the smartphone's rear camera to recognize the image in front in real time, detects red lights, stairs, people, and obstacles, and displays a notification in the center of the screen to alert the user. If the alert is ignored, the system issues a warning and forcibly stops the app being used. Furthermore, to address the problem of difficulty in perceiving hazards ahead when the rear camera is pointed downwards, the system prevents smartphone use in this position and displays a warning to correct posture. In addition, it utilizes AI-generated content to recognize the situation ahead and generate and display "impactful messages," icons, and images tailored to the user. For example, it may alert the user with messages such as "I'm (Oshi) right in front of you! Look!" or "A real monster is approaching!" When the smartphone is started, AI-generated characters and images are displayed using AR technology, and the screen angle can be changed to improve visibility. This mechanism improves the safety of walking while using a smartphone and reduces health problems. It also supports safe and healthy smartphone use for smartphone addicts and enhances entertainment value to attract user interest. This means that a system designed to address the dangers and health problems associated with using smartphones while walking can ensure user safety and reduce health risks.

[0067] The system for solving the dangers and health problems of walking while using a smartphone according to this embodiment comprises a data collection unit, an analysis unit, and a warning unit. The data collection unit recognizes forward video in real time. The data collection unit can collect forward video using, for example, the rear camera of a smartphone. The data collection unit can also acquire video data in real time and transmit it to the analysis unit. The analysis unit analyzes the video data collected by the data collection unit and detects red lights, stairs, people, and obstacles. The analysis unit can, for example, use AI to analyze the video data and detect red lights. The analysis unit can also use AI to detect stairs. Furthermore, the analysis unit can also use AI to detect people and obstacles. The warning unit displays a notification to alert the user based on the hazards detected by the analysis unit, issues a warning as needed, and forcibly stops the app. The warning unit can, for example, display a notification in the center of the screen that the user is viewing. The warning unit can also issue a warning if the user ignores the notification. Furthermore, the warning unit can also forcibly stop the app. As a result, the system for solving the dangers and health problems associated with walking while using a smartphone, according to this embodiment, can ensure user safety and reduce health risks.

[0068] The data acquisition unit recognizes forward-facing images in real time. For example, the unit can collect forward-facing images using the rear camera of a smartphone. Specifically, smartphone cameras capture high-resolution images and are equipped with dedicated chips and software for real-time processing. The data acquisition unit can acquire video data in real time and transmit it to the analysis unit. The video data is transmitted efficiently using compression technology, minimizing communication delays. Furthermore, the data acquisition unit has correction functions to collect high-quality images even under challenging shooting conditions such as low light or backlighting. For example, it automatically adjusts the camera sensitivity to provide clear images even at night or on cloudy days. The data acquisition unit also features image stabilization to prevent blurring, minimizing shaking while walking. This allows the data acquisition unit to accurately perceive the situation ahead, even when the user is using their smartphone while walking. Additionally, the data acquisition unit can temporarily store the collected video data and provide the analysis unit with historical data as needed. This allows the analysis unit to perform more accurate analysis based on continuous video data.

[0069] The analysis unit analyzes video data collected by the collection unit to detect red lights, stairs, people, and obstacles. For example, the analysis unit can use AI to analyze video data and detect red lights. Specifically, the AI ​​uses deep learning technology to learn patterns of red lights from a vast amount of video data. This allows the analysis unit to detect red lights with high accuracy from various angles and distances. The analysis unit can also use AI to detect stairs. Edge detection and pattern recognition technologies are used to accurately identify the shape and arrangement of stairs. Furthermore, the analysis unit can also use AI to detect people and obstacles. Face recognition technology and posture estimation technology are used to detect people, and it can also identify moving objects such as pedestrians, cyclists, and wheelchairs. Object detection algorithms are used to detect obstacles, and it can also detect obstacles on roads and barricades at construction sites. The analysis unit processes these detection results in real time and provides immediate feedback to the user. Furthermore, the analysis unit can utilize past data and statistical information to analyze and predict trends in danger at specific times and locations. This allows the analysis unit to provide advanced analysis functions to ensure user safety.

[0070] The warning unit displays notifications to alert the user based on hazards detected by the analysis unit, issuing warnings and forcibly stopping the app as needed. The warning unit can, for example, display notifications in the center of the user's screen. Specifically, it displays warning messages in the most eye-catching location and uses colors and animations for visual emphasis. The warning unit can also issue warnings if the user ignores the notification, for example, using audio alerts or vibration to forcibly draw the user's attention. Furthermore, the warning unit can forcibly stop the app. This is a function to temporarily restrict smartphone use when the user is facing a dangerous situation. For example, if a user continues walking despite a red light, the warning unit will forcibly stop the app and prompt the user to act safely. The warning unit can also record the user's behavior history and provide data for later analysis. This allows the warning unit to provide multi-layered warning functions to ensure user safety and mitigate health risks. Additionally, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning system. For example, the system analyzes the user's behavior after receiving a warning and optimizes the timing and content of the warning. This allows the warning unit to provide users with more effective safety measures and significantly reduce the dangers of walking while using a smartphone.

[0071] The warning unit can display a warning to correct posture if the rear camera is pointing downwards. For example, the warning unit can detect that the rear camera is pointing downwards and display a warning prompting the user to correct their posture. The warning unit can display a text message, for example. The warning unit can also issue an audio alert. Furthermore, the warning unit can display a visual alert. This makes it easier to identify hazards in front of the user even when the rear camera is pointing downwards. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input video data from the rear camera into the AI ​​and have the AI ​​perform the process of detecting the camera's orientation.

[0072] The warning unit can encourage users to use their smartphones upright, thereby reducing the risk of straight neck syndrome. For example, the warning unit can detect if a user is using their smartphone face down and display a warning prompting them to use it upright. The warning unit can display a text message, a voice alert, or a visual alert. This can improve the user's posture and reduce health risks. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input smartphone usage data into AI and have the AI ​​perform posture detection and display warnings.

[0073] The analysis unit can perform video recognition of the situation ahead and generate and display impactful messages, icons, and images tailored to the user. For example, the analysis unit can use AI to perform video recognition of the situation ahead and generate a message tailored to the user. For example, the analysis unit can generate messages such as, "I (Oshi) am right in front of you! Look!" or "A monster is really approaching!" The analysis unit can also generate icons and images. Furthermore, the analysis unit can display the generated messages, icons, and images to the user. This allows for effective warnings to be given to the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input forward video data into a generation AI and have the generation AI generate messages, icons, and images.

[0074] The analysis unit can display characters and images generated by AI using AR technology, making them easier to view by changing the screen angle. For example, the analysis unit generates characters and images using AI and displays them using AR technology. For example, the analysis unit can display characters and images on a smartphone screen, making them easier to view by changing the screen angle. This allows information to be provided in a way that is easy for the user to see. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can have a generation AI generate characters and images, and then display the generated characters and images using AR technology.

[0075] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. The collection unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the collection unit can collect video more frequently to enhance the detection of hazardous materials. Conversely, if the user is relaxed, the collection unit can reduce the frequency of video collection to conserve battery power. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of video collection to quickly detect hazardous materials. This allows for video collection at the optimal timing according to the user's emotions. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the timing of video collection.

[0076] The data collection unit can analyze the user's past walking patterns and select the optimal data collection method when collecting video footage. For example, the data collection unit can set optimal video collection points based on routes the user has frequently traveled in the past. The data collection unit can also analyze the user's past walking speed and determine an appropriate collection interval. Furthermore, the data collection unit can automatically adjust the camera's orientation based on the user's past walking direction. This enables optimal video collection based on the user's past walking patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past walking data into a generating AI and have the generating AI perform the process of selecting the optimal data collection method.

[0077] The data collection unit can dynamically adjust its collection range based on the user's current walking speed and direction during video collection. For example, if the user is walking quickly, the data collection unit can collect a wider range of video to expand the detection range for hazardous objects. Conversely, if the user is walking slowly, the data collection unit can collect a narrower range of video to perform more detailed detection. Furthermore, the data collection unit can automatically adjust the camera's orientation to match the user's walking direction, optimally collecting video from the front. This allows for optimal video collection according to the user's walking speed and direction. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's walking speed and direction data into a generating AI, which can then perform the dynamic adjustment of the collection range.

[0078] The collection unit can estimate the user's emotions and determine the priority of the video to collect based on the estimated user emotions. The collection unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the collection unit may prioritize the detection of hazardous objects and collect important video. Also, if the user is relaxed, the collection unit may prioritize the collection of scenery and surrounding video. Furthermore, if the user is in a hurry, the collection unit may prioritize the collection of video related to the shortest route. This allows for the priority collection of important video according to the user's emotions. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority of video.

[0079] The collection unit can prioritize collecting highly relevant videos by considering the user's geographical location information during video collection. For example, if the user is in a specific area, the collection unit will prioritize collecting videos related to that area. Furthermore, if the user is in a tourist area, the collection unit can prioritize collecting videos of tourist spots. Additionally, if the user is in a dangerous area, the collection unit can prioritize collecting videos that detect hazardous objects. This allows for the collection of optimal videos based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the process of prioritizing the collection of highly relevant videos.

[0080] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can collect videos related to places the user has shared on social media. It can also collect videos related to places the user follows on social media. Furthermore, the collection unit can collect videos related to places the user has checked into on social media. This allows for the collection of relevant videos based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI perform the process of collecting relevant videos.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the analysis unit can increase the accuracy of the analysis to enhance the detection of hazardous materials. Also, if the user is relaxed, the analysis unit can moderately adjust the accuracy of the analysis to reduce battery consumption. Furthermore, if the user is in a hurry, the analysis unit can quickly adjust the accuracy of the analysis to detect hazardous materials in real time. This allows the accuracy of the analysis to be optimized according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the accuracy of the analysis.

[0082] The analysis unit can optimize the current analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the current analysis algorithm based on past analysis data to improve accuracy. The analysis unit can also extract specific patterns from past analysis data and reflect them in the current analysis. Furthermore, the analysis unit can analyze past analysis data and adjust the parameters of the current analysis algorithm. This allows the current analysis algorithm to be optimized based on past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the current analysis algorithm.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the video during analysis. For example, the analysis unit can apply a specific color recognition algorithm to detect red traffic lights. It can also apply a shape recognition algorithm to detect stairs. Furthermore, it can apply a motion recognition algorithm to detect people or obstacles. This allows the optimal analysis algorithm to be applied according to the category of the video. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the system to provide the optimal display method according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0085] The analysis unit can determine the priority of analysis based on the video recording date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent video to detect hazardous materials in real time. The analysis unit can also refer to past video and analyze specific patterns. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the video recording date. This allows for optimal analysis based on the video recording date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video recording date data into a generating AI and have the generating AI perform the process of determining the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the video footage during the analysis. For example, the analysis unit may prioritize analyzing video footage related to the detection of hazardous materials. It can also prioritize analyzing video footage related to the user's current situation. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the video footage. This allows for the determination of the optimal analysis order based on the relevance of the video footage. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video relevance data into a generating AI and have the generating AI perform the process of adjusting the order of analysis.

[0087] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. The warning unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the warning unit can display a warning in a calm manner. The warning unit can also display a warning in a cheerful manner if the user is relaxed. Furthermore, if the user is in a hurry, the warning unit can display a warning in a quick and concise manner. This allows the system to provide the most appropriate warning according to the user's emotions. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the way the warning is expressed.

[0088] The warning unit can adjust the level of detail of the warning based on the severity of the hazardous material when a warning is issued. For example, the warning unit displays a detailed warning for highly hazardous materials. It can also display a concise warning for moderately hazardous materials. Furthermore, it can display a mild warning for low-level hazardous materials. This allows for the provision of the most appropriate warning according to the severity of the hazardous material. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material severity data into a generating AI and have the generating AI perform the process of adjusting the level of detail of the warning.

[0089] The warning unit can apply different warning algorithms depending on the category of hazardous material when a warning is issued. For example, the warning unit can apply a specific color recognition algorithm to detect a red light. It can also apply a shape recognition algorithm to detect stairs. Furthermore, it can apply a motion recognition algorithm to detect people or obstacles. This allows the unit to provide the most appropriate warning depending on the category of hazardous material. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material category data into a generating AI and have the generating AI apply different warning algorithms.

[0090] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated emotions. The warning unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, the warning unit displays a short, to-the-point warning. The warning unit can also display a detailed warning if the user is relaxed. Furthermore, if the user is in a hurry, the warning unit can display a quick and concise warning. This allows the system to provide the optimal warning length according to the user's emotions. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the length of the warning.

[0091] The warning unit can determine the priority of warnings based on when the hazardous material was detected. For example, the warning unit will prioritize warnings for the most recent hazardous material. The warning unit can also refer to past hazardous material detections and issue warnings for specific patterns. Furthermore, the warning unit can dynamically adjust the warning priority based on when the hazardous material was detected. This allows for the provision of optimal warnings based on when the hazardous material was detected. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material detection time data into a generating AI and have the generating AI perform the process of determining the warning priority.

[0092] The warning unit can adjust the order of warnings based on the relevance of the hazardous materials when a warning is issued. For example, the warning unit may prioritize displaying warnings related to the detection of hazardous materials. It may also prioritize displaying warnings related to the user's current situation. Furthermore, the warning unit can dynamically adjust the order of warnings based on the relevance of the hazardous materials. This allows for the determination of the optimal warning order based on the relevance of the hazardous materials. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input hazardous material relevance data into a generating AI and have the generating AI perform the process of adjusting the order of warnings.

[0093] Posture correction warnings can estimate the user's emotions and adjust the warning method based on those emotions. For example, a posture correction warning system might use an emotion engine or generative AI to estimate the user's emotions. For instance, if the user is tense, the system might display a calm warning. Conversely, if the user is relaxed, it might display a cheerful warning. Furthermore, if the user is in a hurry, it might display a quick and concise warning. This allows for the provision of optimal posture correction warnings according to the user's emotions. Some or all of the above processing in posture correction warnings may be performed using AI, or not. For example, the posture correction warning system could input user emotion data into a generative AI and have the generative AI perform the process of adjusting the warning method.

[0094] The posture correction warning system can select the optimal warning method by referring to the user's past posture data when issuing a warning. For example, if the user has had poor posture in the past, the posture correction warning system will display a detailed warning about posture correction. The posture correction warning system can also determine the appropriate timing for posture correction based on the user's past posture data. Furthermore, the posture correction warning system can analyze the user's past posture data and select the most effective warning method for posture correction. This allows the system to provide the optimal posture correction warning based on the user's past posture data. Some or all of the above processes in the posture correction warning system may be performed using AI, for example, or without AI. For example, the posture correction warning system can input the user's past posture data into a generating AI and have the generating AI perform the process of selecting a warning method.

[0095] Posture correction warnings can estimate the user's emotions and determine the priority of posture correction based on those emotions. For example, posture correction warnings estimate the user's emotions using an emotion engine or generative AI. For instance, if the user is tense, posture correction warnings may set a higher priority for posture correction. Conversely, if the user is relaxed, posture correction warnings may set a lower priority. Furthermore, posture correction warnings can dynamically adjust the priority of posture correction if the user is in a hurry. This allows for the provision of optimal posture correction priorities according to the user's emotions. Some or all of the above processing in posture correction warnings may be performed using AI, or not. For example, posture correction warnings can input user emotion data into a generative AI and have the generative AI perform the process of determining priorities.

[0096] The posture correction warning system can select the optimal warning method by considering the user's geographical location information when issuing a posture correction warning. For example, if the user is in a specific area, the system can select a posture correction warning method appropriate for that area. Furthermore, if the user is indoors, the system can select a posture correction warning method appropriate for indoor use. Additionally, if the user is outdoors, the system can select a posture correction warning method appropriate for outdoor use. This allows the system to provide optimal posture correction warnings based on the user's geographical location information. Some or all of the above-described processes in the posture correction warning system may be performed using AI, for example, or without AI. For instance, the posture correction warning system can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting a warning method.

[0097] Straight neck risk reduction can estimate the user's emotions and adjust the method of straight neck risk reduction based on the estimated emotions. Straight neck risk reduction estimates the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is tense, straight neck risk reduction can present the method of straight neck risk reduction in calm language. Also, if the user is relaxed, straight neck risk reduction can present the method of straight neck risk reduction in cheerful language. Furthermore, if the user is in a hurry, straight neck risk reduction can present the method of straight neck risk reduction in quick and concise language. This makes it possible to provide the optimal straight neck risk reduction method according to the user's emotions. Some or all of the above processing in straight neck risk reduction may be performed using, for example, AI, or not using AI. For example, straight neck risk reduction can input user emotion data into a generative AI and have the generative AI perform the processing of adjusting the risk reduction method.

[0098] Straight neck risk reduction can select the optimal method by referring to the user's past posture data when reducing the risk of straight neck. For example, if the user has had a high risk of straight neck in the past, straight neck risk reduction will present detailed risk reduction methods. Straight neck risk reduction can also determine the appropriate timing for risk reduction based on the user's past posture data. Furthermore, straight neck risk reduction can analyze the user's past posture data and select the most effective risk reduction method. This allows for the provision of the optimal straight neck risk reduction method based on the user's past posture data. Some or all of the above processes in straight neck risk reduction may be performed using AI, for example, or without AI. For example, straight neck risk reduction can input the user's past posture data into a generating AI and have the generating AI perform the process of selecting a risk reduction method.

[0099] Straight neck risk reduction can estimate the user's emotions and determine the priority of straight neck risk reduction based on the estimated emotions. Straight neck risk reduction estimates the user's emotions using, for example, an emotion engine or generative AI. Straight neck risk reduction sets a higher priority for risk reduction when the user is tense. It can also set a lower priority for risk reduction when the user is relaxed. Furthermore, straight neck risk reduction can dynamically adjust the priority for risk reduction when the user is in a hurry. This allows for the provision of the optimal straight neck risk reduction priority according to the user's emotions. Some or all of the above processing in straight neck risk reduction may be performed using, for example, AI, or not using AI. For example, straight neck risk reduction can input user emotion data into a generative AI and have the generative AI perform the process of determining the priority.

[0100] Straight neck risk mitigation can select the optimal method considering the user's geographical location information during the process. For example, if the user is in a specific area, straight neck risk mitigation can select a risk mitigation method suitable for that area. Furthermore, if the user is indoors, straight neck risk mitigation can select a risk mitigation method suitable for indoors. Additionally, if the user is outdoors, straight neck risk mitigation can select a risk mitigation method suitable for outdoors. This allows for the provision of the optimal straight neck risk mitigation method based on the user's geographical location information. Some or all of the above-described processes in straight neck risk mitigation may be performed using AI, for example, or without AI. For example, straight neck risk mitigation can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting a risk mitigation method.

[0101] Impactful message generation can estimate the user's emotions and adjust the message's expression based on those emotions. Impactful message generation estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, if the user is tense, impactful message generation can display a message in a calm tone. Conversely, if the user is relaxed, impactful message generation can display a message in a cheerful tone. Furthermore, if the user is in a hurry, impactful message generation can display a message in a quick and concise tone. This allows for the provision of the most appropriate message according to the user's emotions. Some or all of the above processes in impactful message generation may be performed using, for example, AI, or not. For example, impactful message generation can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the message's expression.

[0102] The "impactful message generation" process can generate the optimal message by referencing the user's past response data during message generation. For example, it can generate the optimal message based on messages that the user has responded to favorably in the past. It can also analyze the user's past response data to generate effective messages. Furthermore, it can refer to the user's past response data and generate messages based on specific patterns. This allows for the provision of the optimal message based on the user's past response data. Some or all of the above processes in impactful message generation may be performed using AI, for example, or without AI. For example, impactful message generation can input the user's past response data into a generation AI and have the generation AI execute the message generation process.

[0103] Impactful message generation can estimate the user's emotions and prioritize messages based on those emotions. Impactful message generation estimates the user's emotions using, for example, an emotion engine or generative AI. For example, if the user is stressed, impactful message generation will prioritize important messages. It can also prioritize lighter messages if the user is relaxed. Furthermore, if the user is in a hurry, impactful message generation can prioritize quick and concise messages. This allows for optimal message prioritization according to the user's emotions. Some or all of the above processes in impactful message generation may be performed using, for example, AI, or not. For example, impactful message generation can input user emotion data into a generative AI and have the generative AI perform the process of determining message prioritization.

[0104] The "Impactful Message Generation" system can generate the most relevant message by considering the user's geographical location during the message generation process. For example, if the user is in a specific area, it can generate a message appropriate for that area. It can also generate messages related to tourist attractions if the user is in a tourist destination. Furthermore, if the user is in a dangerous area, it can generate messages related to dangerous objects. This allows for the provision of the most relevant message based on the user's geographical location. Some or all of the above-described processes in Impactful Message Generation may be performed using AI, for example, or without AI. For instance, Impactful Message Generation can input the user's geographical location data into a generation AI and have the generation AI perform the message generation process.

[0105] AR technology can estimate the user's emotions and adjust the AR display method based on those emotions. For example, AR technology can estimate the user's emotions using an emotion engine or generative AI. For instance, if the user is tense, the AR display can display in a calm manner. If the user is relaxed, it can display in a bright manner. Furthermore, if the user is in a hurry, it can display in a quick and concise manner. This allows for the provision of an optimal AR display according to the user's emotions. Some or all of the above processing in AR technology may be performed using AI, or not. For example, AR technology can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the AR display method.

[0106] AR technology allows for the selection of the optimal display method by referencing the user's past usage data during AR display. For example, AR technology can perform optimal AR display based on display methods that the user has previously responded favorably to. Furthermore, AR technology can analyze the user's past usage data to perform effective AR display. Additionally, AR technology can refer to the user's past usage data and perform AR display based on specific patterns. This allows for the provision of optimal AR display based on the user's past usage data. Some or all of the above processes in AR technology display may be performed using AI, for example, or without AI. For example, AR technology display can input the user's past usage data into a generating AI and have the generating AI perform the process of selecting a display method.

[0107] AR technology can estimate the user's emotions and determine the priority of AR displays based on those emotions. For example, AR technology can estimate the user's emotions using an emotion engine or generative AI. For instance, if the user is stressed, AR technology can prioritize important AR displays. Conversely, if the user is relaxed, it can prioritize lighter AR displays. Furthermore, if the user is in a hurry, it can prioritize quick and concise AR displays. This allows for the provision of optimal AR display priorities according to the user's emotions. Some or all of the above processing in AR technology may be performed using AI, or without AI. For example, AR technology can input user emotion data into a generative AI and have the generative AI perform the process of determining priorities.

[0108] AR technology allows for the selection of the optimal display method by considering the user's geographical location information during AR display. For example, if the user is in a specific area, AR technology can display AR content appropriate for that area. Furthermore, if the user is in a tourist destination, AR technology can display AR content related to tourist spots. Additionally, if the user is in a dangerous area, AR technology can display AR content related to dangerous objects. This allows for the provision of optimal AR content based on the user's geographical location information. Some or all of the above-described processes in AR technology display may be performed using AI, for example, or without AI. For instance, AR technology display can input the user's geographical location data into a generating AI and have the generating AI perform the process of selecting the display method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The data collection unit can analyze the user's walking pattern in real time and dynamically adjust the range of video collection according to the walking speed and direction. For example, if the user is walking quickly, it can collect a wider range of video to expand the detection range for hazardous objects. Conversely, if the user is walking slowly, it can collect a narrower range of video for more detailed detection. Furthermore, the data collection unit can automatically adjust the camera's orientation to match the user's walking direction, optimally collecting video from the front. This allows for optimal video collection according to the user's walking speed and direction.

[0111] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is stressed, the accuracy of the analysis can be increased to enhance the detection of hazardous materials. If the user is relaxed, the accuracy of the analysis can be adjusted appropriately to reduce battery consumption. Furthermore, if the user is in a hurry, the accuracy of the analysis can be quickly adjusted to detect hazardous materials in real time. This allows for the optimization of analysis accuracy according to the user's emotions.

[0112] The warning unit can estimate the user's emotions and adjust the way the warning is presented based on those emotions. For example, if the user is tense, the warning can be displayed in a calm tone. If the user is relaxed, the warning can be displayed in a cheerful tone. Furthermore, if the user is in a hurry, the warning can be displayed in a quick and concise tone. This allows the system to provide the most appropriate warning according to the user's emotions.

[0113] The collection unit can prioritize collecting highly relevant footage by considering the user's geographical location. For example, if the user is in a specific area, it will prioritize collecting footage related to that area. Similarly, if the user is in a tourist area, it can prioritize collecting footage of tourist spots. Furthermore, if the user is in a dangerous area, it can prioritize collecting footage of hazardous objects. This allows for the collection of optimal footage based on the user's geographical location.

[0114] The analysis unit can apply different analysis algorithms depending on the category of the video. For example, a specific color recognition algorithm can be applied to detect red traffic lights. A shape recognition algorithm can also be applied to detect stairs. Furthermore, a motion recognition algorithm can be applied to detect people and obstacles. This allows the system to apply the most suitable analysis algorithm for each category of video.

[0115] The warning unit can adjust the level of detail of the warning based on the severity of the hazardous material. For example, a detailed warning is displayed for highly hazardous materials. A concise warning can be displayed for moderately hazardous materials. Furthermore, a mild warning can be displayed for less hazardous materials. This allows for the provision of the most appropriate warning according to the severity of the hazardous material.

[0116] The data collection unit can estimate the user's emotions and determine the priority of the footage to collect based on those emotions. For example, if the user is stressed, it can prioritize the detection of hazardous objects and collect important footage. If the user is relaxed, it can prioritize the collection of scenery and surrounding footage. Furthermore, if the user is in a hurry, it can prioritize the collection of footage related to the shortest route. This allows for the collection of important footage in accordance with the user's emotions.

[0117] The analysis unit can optimize the current analysis algorithm by referring to past analysis data. For example, it can optimize the current analysis algorithm based on past analysis data to improve accuracy. It can also extract specific patterns from past analysis data and reflect them in the current analysis. Furthermore, it can analyze past analysis data and adjust the parameters of the current analysis algorithm. This allows the current analysis algorithm to be optimized based on past analysis data.

[0118] The warning system can estimate the user's emotions and adjust the length of the warning based on that estimation. For example, if the user is stressed, a short, concise warning can be displayed. If the user is relaxed, a more detailed warning can be shown. Furthermore, if the user is in a hurry, a quick and brief warning can be displayed. This allows the system to provide the optimal warning length according to the user's emotions.

[0119] The analysis unit can determine the priority of analysis based on when the video was captured. For example, it can prioritize the analysis of the most recent video to detect hazardous materials in real time. It can also refer to past video footage to analyze specific patterns. Furthermore, it can dynamically adjust the analysis priority based on when the video was captured. This allows for optimal analysis based on the video's capture date.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The collection unit recognizes the forward-facing image in real time. For example, it uses the rear camera of a smartphone to collect the forward-facing image, acquires the image data in real time, and transmits it to the analysis unit. Step 2: The analysis unit analyzes the video data collected by the collection unit to detect red lights, stairs, people, and obstacles. For example, AI is used to analyze the video data and detect red lights, stairs, people, and obstacles. Step 3: The warning unit displays a notification to the user based on the hazardous materials detected by the analysis unit, issues a warning if necessary, and forcibly stops the app. For example, it displays a notification in the center of the screen the user is viewing, issues a warning if the notification is ignored, and forcibly stops the app.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect forward-facing video using the rear camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses AI to analyze video data and detect red lights, stairs, people, and obstacles. The warning unit is implemented in the control unit 46A of the smart device 14, which displays a notification in the center of the screen viewed by the user, issues a warning as needed, and forcibly stops the application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect forward-facing images using the camera 42 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the image data and detect red lights, stairs, people, and obstacles. The warning unit is implemented in the control unit 46A of the smart glasses 214, which displays a notification in the center of the screen the user is viewing, issues a warning if necessary, and forcibly stops the application. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect forward-facing video using the camera 42 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses AI to analyze video data and detect red lights, stairs, people, and obstacles. The warning unit is implemented in the control unit 46A of the headset terminal 314, which displays a notification in the center of the screen viewed by the user, issues a warning as needed, and forcibly stops the application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, and warning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect forward-facing video using the camera 42 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the video data and detect red lights, stairs, people, and obstacles. The warning unit is implemented in the control unit 46A of the robot 414, which displays a notification in the center of the screen viewed by the user, issues a warning as needed, and forcibly stops the application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A data collection unit that recognizes the image in front in real time, The video data collected by the aforementioned collection unit is analyzed by an analysis unit that detects red lights, stairs, people, and obstacles, The system includes a warning unit that displays a notification to the user to alert them based on the hazardous materials detected by the analysis unit, issues a warning if necessary, and forcibly stops the application. A system characterized by the following features. (Note 2) The aforementioned warning unit is A warning will be displayed to correct your posture if the rear camera is pointing downwards. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned warning unit is Encourage users to use their smartphones upright to reduce the risk of straight neck syndrome. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system uses video recognition to identify the situation ahead and generates and displays impactful messages, icons, and images tailored to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, AI-generated characters and images are displayed using AR technology, and the viewing angle can be changed to improve visibility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During video collection, the system analyzes the user's past walking patterns to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During video acquisition, the acquisition range is dynamically adjusted based on the user's current walking speed and direction. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting video footage, the system prioritizes collecting highly relevant footage by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting video footage, the system analyzes users' social media activity and collects relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the current analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the video. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the video was filmed. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the video footage. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is When issuing a warning, adjust the level of detail based on the severity of the hazardous material. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, different warning algorithms are applied depending on the category of the hazardous material. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is It estimates the user's emotions and adjusts the length of the warning based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When a warning is issued, the priority of the warning is determined based on when the hazardous material was detected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, adjust the order of warnings based on the relevance of the hazardous material. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned posture correction warning is, The system estimates the user's emotions and adjusts the posture correction warning method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned posture correction warning is, When issuing a posture correction warning, the system selects the optimal warning method by referring to the user's past posture data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned posture correction warning is, The system estimates the user's emotions and determines the priority of posture correction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned posture correction warning is, When issuing a posture correction warning, the system selects the optimal warning method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reduction of the risk of straight neck is The system estimates the user's emotions and adjusts the method of reducing the risk of straight neck based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reduction of the risk of straight neck is When mitigating the risk of straight neck, the optimal method is selected by referring to the user's past posture data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reduction of the risk of straight neck is The system estimates the user's emotions and determines the priority of straight neck risk reduction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reduction of the risk of straight neck is When mitigating the risk of straight neck syndrome, the optimal method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned message generation that resonates is, It estimates the user's emotions and adjusts the way messages are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned message generation that resonates is, When generating a message, the system refers to the user's past response data to generate the most suitable message. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned message generation that resonates is, It estimates the user's emotions and prioritizes messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned message generation that resonates is, When generating a message, the system considers the user's geographical location to generate the most appropriate message. The system described in Appendix 1, characterized by the features described herein. (Note 36) The display using the aforementioned AR technology is It estimates the user's emotions and adjusts the AR display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The display using the aforementioned AR technology is When displaying AR content, the system selects the optimal display method by referring to the user's past usage data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The display using the aforementioned AR technology is It estimates the user's emotions and determines the priority of AR displays based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The display using the aforementioned AR technology is When displaying AR content, the optimal display method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that recognizes the image in front in real time, The video data collected by the aforementioned collection unit is analyzed by an analysis unit that detects red lights, stairs, people, and obstacles, The system includes a warning unit that displays a notification to the user to alert them based on the hazardous materials detected by the analysis unit, issues a warning if necessary, and forcibly stops the application. A system characterized by the following features.

2. The aforementioned warning unit is A warning will be displayed to correct your posture if the rear camera is pointing downwards. The system according to feature 1.

3. The aforementioned warning unit is Encourage users to use their smartphones upright to reduce the risk of straight neck syndrome. The system according to feature 1.

4. The aforementioned analysis unit, The system uses video recognition to identify the situation ahead and generates and displays icons and images tailored to the user. The system according to feature 1.

5. The aforementioned analysis unit, AI-generated characters and images are displayed using AR technology, and the viewing angle can be changed to improve visibility. The system according to feature 1.

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

7. The aforementioned collection unit is During video collection, the system analyzes the user's past walking patterns to select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During video acquisition, the acquisition range is dynamically adjusted based on the user's current walking speed and direction. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting video footage, the system prioritizes collecting highly relevant footage by considering the user's geographical location. The system according to feature 1.

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