System

Abstract

An object of a system according to an embodiment is to detect a road surface deformation in real time and notify a pedestrian of the deformation.SOLUTION: A system includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires an image of a road surface. The analysis unit analyzes the image acquired by the acquisition unit and detects a deformation of the road surface. The notification unit notifies the user of the defect detected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately detect road surface changes in real time and notify pedestrians, so there is room for improvement.

[0005] The system according to the embodiment aims to detect road surface abnormalities in real time and notify pedestrians. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires an image of the road surface. The analysis unit analyzes the image acquired by the acquisition unit and detects a change in the road surface. The notification unit notifies the user of the change detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect road surface changes in real time and notify pedestrians. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A road condition detection system according to an embodiment of the present invention acquires road surface images using a smartphone camera, analyzes them using AI to detect road surface abnormalities, and notifies pedestrians of abnormalities. The road condition detection system acquires road surface images using a smartphone camera, analyzes them using AI to detect road surface abnormalities, and notifies pedestrians of abnormalities. For example, the road condition detection system monitors road surface conditions in real time as pedestrians walk with their smartphones. For example, as pedestrians walk with their smartphones, the camera continuously acquires road surface images. Next, the road condition detection system analyzes the acquired images using AI. The AI ​​uses image analysis technology to detect road surface abnormalities. For example, it can detect abnormalities such as cracks, unevenness, and holes. This allows for real-time understanding of road surface conditions. Next, the road condition detection system notifies pedestrians of the detected abnormalities through a dedicated app. For example, it can display a pop-up notification on the smartphone screen or issue an audio warning. This allows pedestrians to immediately recognize and avoid abnormalities. This helps pedestrians walk safely. This is particularly useful for people who have difficulty checking road conditions, such as the visually impaired and the elderly. For example, if a visually impaired person is walking while holding a smartphone, AI can detect abnormalities in the road surface and issue an audio warning, allowing them to walk safely. In this way, the road condition detection system can help pedestrians walk safely. This is particularly useful for people who have difficulty checking road conditions, such as the visually impaired and the elderly. For example, if a visually impaired person is walking while holding a smartphone, AI can detect abnormalities in the road surface and issue an audio warning, allowing them to walk safely.

[0029] A road surface condition detection system according to an embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires images of the road surface using a smartphone camera. For example, the acquisition unit monitors the road surface condition in real time by, for example, a pedestrian walking with the smartphone. The acquisition unit can also continuously acquire images of the road surface using the smartphone camera. For example, the acquisition unit continuously acquires images of the road surface while a pedestrian is walking with the smartphone. The analysis unit uses AI to analyze the images acquired by the acquisition unit and detect abnormalities in the road surface. The analysis unit detects abnormalities in the road surface using, for example, image analysis technology. For example, the analysis unit detects abnormalities such as cracks, unevenness, and holes. The analysis unit can also use image analysis technology using deep learning. For example, the analysis unit uses deep learning to improve the accuracy of image analysis. The notification unit notifies the user of the abnormalities detected by the analysis unit. For example, the notification unit can display a pop-up notification on the smartphone screen or issue an audio warning. The notification unit can also provide a vibration notification. For example, the notification unit may provide a pop-up notification, a voice warning, or a vibration notification. This allows the road surface condition detection system according to the embodiment to assist pedestrians in walking safely. This system is useful for people who have difficulty checking road surface conditions, such as the visually impaired and elderly. For example, if a visually impaired person is walking with a smartphone, the AI ​​can detect an abnormality in the road surface and issue a voice warning, allowing the person to walk safely.

[0030] The analysis unit can detect abnormalities such as cracks, unevenness, and holes. For example, the analysis unit detects cracks based on the width and depth of the cracks. The analysis unit can also detect unevenness based on the height and width of the unevenness. The analysis unit can also detect holes based on the diameter and depth of the holes. This allows for detailed detection of abnormalities in the road surface, making it possible to provide accurate information to pedestrians. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities using an AI model for detecting abnormalities such as cracks, unevenness, and holes.

[0031] The notification unit can provide a pop-up notification, an audio warning, and a vibration notification. The notification unit can, for example, display a pop-up notification on a smartphone screen. The notification unit can also issue an audio warning. The notification unit can also provide a vibration notification. For example, the notification unit can display a pop-up notification to visually notify the user of an abnormality. The notification unit can also issue an audio warning to audibly notify the user of an abnormality. The notification unit can also provide a vibration notification to tactilely notify the user of an abnormality. This makes it possible to alert pedestrians by notifying them of an abnormality in a variety of ways. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, when the notification unit detects an abnormality, it can select one or more of a pop-up notification, an audio warning, and a vibration notification to notify the user.

[0032] The acquisition unit can monitor the road surface condition in real time as a pedestrian walks with a smartphone. For example, the acquisition unit continuously acquires images of the road surface using a camera while a pedestrian walks with a smartphone. The acquisition unit can also monitor the road surface condition in real time. For example, the acquisition unit can monitor the road surface condition in real time as a pedestrian walks with a smartphone. By monitoring the road surface condition in real time, it is possible to immediately detect and notify abnormalities. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by a smartphone camera into a generation AI to monitor the road surface condition in real time.

[0033] The analysis unit can use image analysis technology using deep learning. The analysis unit improves the accuracy of image analysis by using, for example, deep learning. For example, the analysis unit detects abnormalities in the road surface by using a convolutional neural network (CNN). The analysis unit can also use deep learning to detect abnormalities such as cracks, unevenness, and holes. For example, the analysis unit improves the accuracy of image analysis by using deep learning. In this way, the accuracy of image analysis is improved by using deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities in the road surface by using image analysis technology using deep learning.

[0034] The acquisition unit can automatically adjust image acquisition settings according to different weather conditions. For example, when it is raining, the acquisition unit adjusts the exposure settings of the camera to acquire clear images. The acquisition unit can also set a lower exposure on a snowy day to prevent overexposure. The acquisition unit can also increase contrast to improve image clarity when it is foggy. This allows clear images to be acquired at all times by adjusting the image acquisition settings according to weather conditions. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input weather condition data into a generation AI and automatically adjust the image acquisition settings.

[0035] The acquisition unit can change the frequency of image acquisition according to the walking speed of the pedestrian. For example, if the pedestrian is walking fast, the acquisition unit can increase the frequency of image acquisition to provide road surface information in real time. Furthermore, if the pedestrian is walking slowly, the acquisition unit can reduce the frequency of image acquisition to reduce battery consumption. Furthermore, the acquisition unit can pause image acquisition when the pedestrian stops and resume it when the pedestrian starts walking again. In this way, by adjusting the frequency of image acquisition according to the walking speed, it is possible to provide appropriate information while reducing battery consumption. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's walking speed data into the generation AI and adjust the frequency of image acquisition.

[0036] The acquisition unit can adjust the frequency of image acquisition based on the remaining battery level of the smartphone. For example, when the remaining battery level is low, the acquisition unit reduces the frequency of image acquisition to reduce battery consumption. Furthermore, when the remaining battery level is sufficient, the acquisition unit can increase the frequency of image acquisition to provide detailed road surface information. Furthermore, when the remaining battery level is medium, the acquisition unit can set a balanced image acquisition frequency. This makes it possible to optimize battery consumption by adjusting the frequency of image acquisition according to the remaining battery level. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input remaining battery level data of the smartphone into the generation AI and adjust the frequency of image acquisition.

[0037] The acquisition unit can prioritize acquiring images of a specific area based on the pedestrian's geographical location information. For example, if the pedestrian is in a congested area, the acquisition unit prioritizes acquiring the road surface condition of that area. Furthermore, if the pedestrian is in a tourist spot, the acquisition unit can prioritize acquiring the road surface condition around the tourist spot. Furthermore, if the pedestrian is in a residential area, the acquisition unit can prioritize acquiring the road surface condition of the residential area. In this way, by preferentially acquiring images based on the geographical location information, it is possible to provide information on important areas. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's geographical location information data into the generation AI and prioritize acquiring images of a specific area.

[0038] The acquisition unit can analyze the pedestrian's social media activity and acquire related road surface images. For example, the acquisition unit can prioritize acquiring road surface conditions of locations where the pedestrian has checked in on social media. The acquisition unit can also analyze the pedestrian's social media posts and acquire road surface conditions of related areas. The acquisition unit can also acquire road surface conditions of related areas by referring to the activities of the pedestrian's friends on social media. This makes it possible to provide useful information to users by acquiring related images based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's social media activity data into a generation AI and acquire related road surface images.

[0039] The acquisition unit can customize the image acquisition method by reflecting the pedestrian's past feedback. The acquisition unit, for example, adjusts the frequency and timing of image acquisition based on feedback provided by the pedestrian in the past. The acquisition unit can also preferentially acquire road surface conditions of a specific area based on the pedestrian's past feedback. The acquisition unit can also customize the image acquisition settings by reflecting the pedestrian's past feedback. In this way, by reflecting the past feedback, it is possible to provide the user with an optimal image acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's past feedback data into the generation AI to customize the image acquisition method.

[0040] The analysis unit can adjust the analysis algorithm according to different road surface materials. For example, in the case of an asphalt road surface, the analysis unit uses an algorithm specialized for detecting cracks and holes. In addition, in the case of a concrete road surface, the analysis unit can also use an algorithm that detects unevenness and surface deterioration. In addition, in the case of a tile road surface, the analysis unit can also use an algorithm that detects cracks and chips in the tile. In this way, by adjusting the analysis algorithm according to the road surface material, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different road surface material data into the generation AI and adjust the analysis algorithm.

[0041] The analysis unit can improve the analysis accuracy by referring to past analysis results. For example, the analysis unit stores past analysis results in a database and refers to them when performing a new analysis. The analysis unit can also learn from past analysis results and improve the analysis algorithm. The analysis unit can also detect specific patterns based on past analysis results to improve the analysis accuracy. In this way, by referring to past analysis results, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI to improve the analysis accuracy.

[0042] The analysis unit can change the analysis method depending on different time periods. For example, during the day, the analysis unit uses an analysis method that takes brightness into account. Furthermore, the analysis unit can also use an analysis method that is suitable for low-light environments at night. Furthermore, the analysis unit can also use an analysis method that takes congestion into account during rush hour. In this way, by changing the analysis method depending on the time period, it is possible to always provide appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input time period data into a generation AI and change the analysis method.

[0043] The analysis unit can improve the analysis accuracy based on road surface temperature and humidity information. For example, when the road surface temperature is high, the analysis unit uses an algorithm to detect cracks caused by thermal expansion. The analysis unit can also use an algorithm to detect deterioration due to moisture when the road surface humidity is high. The analysis unit can also improve the analysis accuracy by taking into account changes in road surface temperature and humidity. In this way, by taking temperature and humidity information into account, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input road surface temperature and humidity data into the generation AI to improve the analysis accuracy.

[0044] The analysis unit can improve the analysis accuracy by integrating data from other sensors. For example, the analysis unit can integrate data from a vibration sensor to more accurately detect unevenness in the road surface. The analysis unit can also integrate data from a sound sensor to detect cracks in the road surface. The analysis unit can also integrate data from other sensors to improve the overall analysis accuracy. In this way, by integrating data from other sensors, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from a vibration sensor or sound sensor into a generation AI to improve the analysis accuracy.

[0045] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining a crack detection algorithm and an unevenness detection algorithm. The analysis unit can also improve analysis accuracy by combining different deep learning models. The analysis unit can also integrate multiple analysis algorithms to improve overall analysis accuracy. In this way, analysis accuracy can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI to improve analysis accuracy.

[0046] The notification unit can change the notification method depending on the pedestrian's current activity. For example, if the pedestrian is on a call, the notification unit can prioritize vibration notification. Furthermore, if the pedestrian is playing music, the notification unit can also prioritize voice notification. Furthermore, if the pedestrian is using an app, the notification unit can also prioritize pop-up notification. In this way, by changing the notification method depending on the pedestrian's activity, notifications can be provided at appropriate times. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's current activity data into the generation AI and change the notification method.

[0047] The notification unit can select the optimal notification method by referring to the pedestrian's past notification history. For example, the notification unit prioritizes the notification method that the pedestrian has previously preferred. The notification unit can also select the optimal notification method from the pedestrian's past notification history. The notification unit can also customize the notification method based on the pedestrian's past notification history. In this way, by referring to the past notification history, the optimal notification method can be provided to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's past notification history data into a generation AI and select the optimal notification method.

[0048] The notification unit can adjust the notification method based on the pedestrian's device settings. For example, the notification unit can adjust the volume of the audio notification based on the volume setting of the pedestrian's device. The notification unit can also adjust the intensity of the vibration notification based on the vibration intensity setting of the pedestrian's device. The notification unit can also provide an optimal notification method based on the pedestrian's device settings. By adjusting the notification method based on the device settings, it is possible to provide an optimal notification for the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's device setting data into a generation AI to adjust the notification method.

[0049] The notification unit can prioritize notifications for specific areas by taking into account the pedestrian's geographical location information. For example, if the pedestrian is in a dangerous area, the notification unit can prioritize displaying notifications related to that area. Furthermore, if the pedestrian is in a tourist spot, the notification unit can prioritize displaying notifications related to tourist spots. Furthermore, if the pedestrian is in a residential area, the notification unit can prioritize displaying notifications related to road conditions in the residential area. This makes it possible to provide information about important areas by prioritizing notifications based on geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's geographical location information data into a generation AI and prioritize notifications for specific areas.

[0050] The notification unit can analyze the pedestrian's social media activity and provide related notifications. For example, the notification unit can provide notifications about places where the pedestrian has checked in on social media. The notification unit can also analyze the content of the pedestrian's social media posts and provide related notifications. The notification unit can also provide related notifications based on the pedestrian's social media friends' activities. In this way, useful information can be provided to the user by providing related notifications based on social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the pedestrian's social media activity data into a generation AI and provide related notifications.

[0051] The notification unit can customize the notification method by reflecting the pedestrian's past feedback. The notification unit can adjust the notification method based on, for example, feedback provided by the pedestrian in the past. The notification unit can also prioritize a specific notification method based on the pedestrian's past feedback. The notification unit can also customize the notification method by reflecting the pedestrian's past feedback. In this way, by reflecting past feedback, it is possible to provide the optimal notification method for the user. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's past feedback data into the generation AI to customize the notification method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The acquisition unit can learn the user's walking pattern and automatically adjust the optimal image acquisition timing. For example, when the user is walking normally, the acquisition unit acquires images at regular intervals. Furthermore, when the user is in a hurry, the acquisition unit can increase the frequency of image acquisition. Furthermore, when the user stops, the acquisition unit can pause image acquisition and resume it when the user starts walking again. In this way, by adjusting the image acquisition timing according to the user's walking pattern, it is possible to provide appropriate information while reducing battery consumption.

[0054] The analysis unit can adjust the analysis algorithm according to different seasons. For example, in winter, the analysis unit uses an algorithm that detects changes in the road surface due to snow and ice. In summer, the analysis unit can also use an algorithm that detects cracks caused by thermal expansion. In spring and autumn, the analysis unit can also use an algorithm that detects changes in the road surface due to fallen leaves and mud. In this way, by adjusting the analysis algorithm according to the season, the analysis accuracy can be improved.

[0055] The notification unit can adjust the notification method taking into account the user's health condition. For example, the notification unit can prioritize voice notification when the user is tired. The notification unit can also prioritize vibration notification when the user is concentrating. The notification unit can also prioritize pop-up notification when the user is relaxed. In this way, by adjusting the notification method according to the user's health condition, notifications can be provided at appropriate times.

[0056] The acquisition unit can change the resolution of image acquisition according to the user's walking speed. For example, if a pedestrian is walking fast, detailed images with high resolution are acquired. On the other hand, if a pedestrian is walking slowly, battery consumption can be reduced by using low resolution. Also, if a pedestrian stops, image acquisition can be paused and resumed when the pedestrian starts walking again. In this way, by adjusting the resolution of image acquisition according to walking speed, it is possible to provide appropriate information while reducing battery consumption.

[0057] The acquisition unit can learn the user's past behavioral history and automatically adjust the optimal image acquisition timing. For example, if the user has walked during a specific time period in the past, the acquisition unit can adjust the frequency of image acquisition to match that time period. Also, if the user has frequently walked in a specific area in the past, the acquisition unit can increase the frequency of image acquisition in that area. Furthermore, the acquisition unit can customize image acquisition settings based on the user's past behavioral history. This makes it possible to provide the user with an optimal image acquisition method by reflecting the user's past behavioral history.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The acquisition unit acquires images of the road surface using the smartphone camera. For example, a pedestrian walking with a smartphone can monitor the road surface condition in real time. The acquisition unit can also continuously acquire images of the road surface using the smartphone camera. Step 2: The analysis unit uses AI to analyze the images acquired by the acquisition unit and detect abnormalities in the road surface. For example, image analysis technology is used to detect abnormalities such as cracks, unevenness, and holes. Image analysis technology using deep learning can also be used, which improves the accuracy of image analysis. Step 3: The notification unit notifies pedestrians of the abnormalities detected by the analysis unit. For example, it can display a pop-up notification on the smartphone screen or issue an audio warning. It can also provide a vibration notification. This helps pedestrians walk safely.

[0060] (Example 2) A road condition detection system according to an embodiment of the present invention acquires road surface images using a smartphone camera, analyzes them using AI to detect road surface abnormalities, and notifies pedestrians of abnormalities. The road condition detection system acquires road surface images using a smartphone camera, analyzes them using AI to detect road surface abnormalities, and notifies pedestrians of abnormalities. For example, the road condition detection system monitors road surface conditions in real time as pedestrians walk with their smartphones. For example, as pedestrians walk with their smartphones, the camera continuously acquires road surface images. Next, the road condition detection system analyzes the acquired images using AI. The AI ​​uses image analysis technology to detect road surface abnormalities. For example, it can detect abnormalities such as cracks, unevenness, and holes. This allows for real-time understanding of road surface conditions. Next, the road condition detection system notifies pedestrians of the detected abnormalities through a dedicated app. For example, it can display a pop-up notification on the smartphone screen or issue an audio warning. This allows pedestrians to immediately recognize and avoid abnormalities. This helps pedestrians walk safely. This is particularly useful for people who have difficulty checking road conditions, such as the visually impaired and the elderly. For example, if a visually impaired person is walking while holding a smartphone, AI can detect abnormalities in the road surface and issue an audio warning, allowing them to walk safely. In this way, the road condition detection system can help pedestrians walk safely. This is particularly useful for people who have difficulty checking road conditions, such as the visually impaired and the elderly. For example, if a visually impaired person is walking while holding a smartphone, AI can detect abnormalities in the road surface and issue an audio warning, allowing them to walk safely.

[0061] A road surface condition detection system according to an embodiment includes an acquisition unit, an analysis unit, and a notification unit. The acquisition unit acquires images of the road surface using a smartphone camera. For example, the acquisition unit monitors the road surface condition in real time by, for example, a pedestrian walking with the smartphone. The acquisition unit can also continuously acquire images of the road surface using the smartphone camera. For example, the acquisition unit continuously acquires images of the road surface while a pedestrian is walking with the smartphone. The analysis unit uses AI to analyze the images acquired by the acquisition unit and detect abnormalities in the road surface. The analysis unit detects abnormalities in the road surface using, for example, image analysis technology. For example, the analysis unit detects abnormalities such as cracks, unevenness, and holes. The analysis unit can also use image analysis technology using deep learning. For example, the analysis unit uses deep learning to improve the accuracy of image analysis. The notification unit notifies the user of the abnormalities detected by the analysis unit. For example, the notification unit can display a pop-up notification on the smartphone screen or issue an audio warning. The notification unit can also provide a vibration notification. For example, the notification unit may provide a pop-up notification, a voice warning, or a vibration notification. This allows the road surface condition detection system according to the embodiment to assist pedestrians in walking safely. This system is useful for people who have difficulty checking road surface conditions, such as the visually impaired and elderly. For example, if a visually impaired person is walking with a smartphone, the AI ​​can detect an abnormality in the road surface and issue a voice warning, allowing the person to walk safely.

[0062] The analysis unit can detect abnormalities such as cracks, unevenness, and holes. For example, the analysis unit detects cracks based on the width and depth of the cracks. The analysis unit can also detect unevenness based on the height and width of the unevenness. The analysis unit can also detect holes based on the diameter and depth of the holes. This allows for detailed detection of abnormalities in the road surface, making it possible to provide accurate information to pedestrians. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities using an AI model for detecting abnormalities such as cracks, unevenness, and holes.

[0063] The notification unit can provide a pop-up notification, an audio warning, and a vibration notification. The notification unit can, for example, display a pop-up notification on a smartphone screen. The notification unit can also issue an audio warning. The notification unit can also provide a vibration notification. For example, the notification unit can display a pop-up notification to visually notify the user of an abnormality. The notification unit can also issue an audio warning to audibly notify the user of an abnormality. The notification unit can also provide a vibration notification to tactilely notify the user of an abnormality. This makes it possible to alert pedestrians by notifying them of an abnormality in a variety of ways. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, when the notification unit detects an abnormality, it can select one or more of a pop-up notification, an audio warning, and a vibration notification to notify the user.

[0064] The acquisition unit can monitor the road surface condition in real time as a pedestrian walks with a smartphone. For example, the acquisition unit continuously acquires images of the road surface using a camera while a pedestrian walks with a smartphone. The acquisition unit can also monitor the road surface condition in real time. For example, the acquisition unit can monitor the road surface condition in real time as a pedestrian walks with a smartphone. By monitoring the road surface condition in real time, it is possible to immediately detect and notify abnormalities. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by a smartphone camera into a generation AI to monitor the road surface condition in real time.

[0065] The analysis unit can use image analysis technology using deep learning. The analysis unit improves the accuracy of image analysis by using, for example, deep learning. For example, the analysis unit detects abnormalities in the road surface by using a convolutional neural network (CNN). The analysis unit can also use deep learning to detect abnormalities such as cracks, unevenness, and holes. For example, the analysis unit improves the accuracy of image analysis by using deep learning. In this way, the accuracy of image analysis is improved by using deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can detect abnormalities in the road surface by using image analysis technology using deep learning.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of image acquisition based on the estimated emotions. For example, if the user is nervous, the acquisition unit can increase the frequency of image acquisition to provide detailed road surface information. Furthermore, if the user is relaxed, the acquisition unit can reduce the frequency of image acquisition to reduce battery consumption. Furthermore, if the user is in a hurry, the acquisition unit can shorten the timing of image acquisition to provide quick information. Thus, by adjusting the timing of image acquisition according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and adjust the timing of image acquisition.

[0067] The acquisition unit can automatically adjust image acquisition settings according to different weather conditions. For example, when it is raining, the acquisition unit adjusts the exposure settings of the camera to acquire clear images. The acquisition unit can also set a lower exposure on a snowy day to prevent overexposure. The acquisition unit can also increase contrast to improve image clarity when it is foggy. This allows clear images to be acquired at all times by adjusting the image acquisition settings according to weather conditions. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input weather condition data into a generation AI and automatically adjust the image acquisition settings.

[0068] The acquisition unit can change the frequency of image acquisition according to the walking speed of the pedestrian. For example, if the pedestrian is walking fast, the acquisition unit can increase the frequency of image acquisition to provide road surface information in real time. Furthermore, if the pedestrian is walking slowly, the acquisition unit can reduce the frequency of image acquisition to reduce battery consumption. Furthermore, the acquisition unit can pause image acquisition when the pedestrian stops and resume it when the pedestrian starts walking again. In this way, by adjusting the frequency of image acquisition according to the walking speed, it is possible to provide appropriate information while reducing battery consumption. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's walking speed data into the generation AI and adjust the frequency of image acquisition.

[0069] The acquisition unit can adjust the frequency of image acquisition based on the remaining battery level of the smartphone. For example, when the remaining battery level is low, the acquisition unit reduces the frequency of image acquisition to reduce battery consumption. Furthermore, when the remaining battery level is sufficient, the acquisition unit can increase the frequency of image acquisition to provide detailed road surface information. Furthermore, when the remaining battery level is medium, the acquisition unit can set a balanced image acquisition frequency. This makes it possible to optimize battery consumption by adjusting the frequency of image acquisition according to the remaining battery level. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input remaining battery level data of the smartphone into the generation AI and adjust the frequency of image acquisition.

[0070] The acquisition unit can estimate the user's emotions and determine the priority of images to be acquired based on the estimated user's emotions. For example, if the user is nervous, the acquisition unit can prioritize acquiring dangerous defects such as cracks and holes. Furthermore, if the user is relaxed, the acquisition unit can evenly acquire the overall road surface condition. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring only major defects. This allows important information to be provided preferentially by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotion data into the generation AI to determine the priority of images to be acquired.

[0071] The acquisition unit can prioritize acquiring images of a specific area based on the pedestrian's geographical location information. For example, if the pedestrian is in a congested area, the acquisition unit prioritizes acquiring the road surface condition of that area. Furthermore, if the pedestrian is in a tourist spot, the acquisition unit can prioritize acquiring the road surface condition around the tourist spot. Furthermore, if the pedestrian is in a residential area, the acquisition unit can prioritize acquiring the road surface condition of the residential area. In this way, by preferentially acquiring images based on the geographical location information, it is possible to provide information on important areas. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's geographical location information data into the generation AI and prioritize acquiring images of a specific area.

[0072] The acquisition unit can analyze the pedestrian's social media activity and acquire related road surface images. For example, the acquisition unit can prioritize acquiring road surface conditions of locations where the pedestrian has checked in on social media. The acquisition unit can also analyze the pedestrian's social media posts and acquire road surface conditions of related areas. The acquisition unit can also acquire road surface conditions of related areas by referring to the activities of the pedestrian's friends on social media. This makes it possible to provide useful information to users by acquiring related images based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's social media activity data into a generation AI and acquire related road surface images.

[0073] The acquisition unit can customize the image acquisition method by reflecting the pedestrian's past feedback. The acquisition unit, for example, adjusts the frequency and timing of image acquisition based on feedback provided by the pedestrian in the past. The acquisition unit can also preferentially acquire road surface conditions of a specific area based on the pedestrian's past feedback. The acquisition unit can also customize the image acquisition settings by reflecting the pedestrian's past feedback. In this way, by reflecting the past feedback, it is possible to provide the user with an optimal image acquisition method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the pedestrian's past feedback data into the generation AI to customize the image acquisition method.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the presentation method of the analysis results according to the user's emotions, more understandable information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the presentation method of the analysis results.

[0075] The analysis unit can adjust the analysis algorithm according to different road surface materials. For example, in the case of an asphalt road surface, the analysis unit uses an algorithm specialized for detecting cracks and holes. In addition, in the case of a concrete road surface, the analysis unit can also use an algorithm that detects unevenness and surface deterioration. In addition, in the case of a tile road surface, the analysis unit can also use an algorithm that detects cracks and chips in the tile. In this way, by adjusting the analysis algorithm according to the road surface material, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different road surface material data into the generation AI and adjust the analysis algorithm.

[0076] The analysis unit can improve the analysis accuracy by referring to past analysis results. For example, the analysis unit stores past analysis results in a database and refers to them when performing a new analysis. The analysis unit can also learn from past analysis results and improve the analysis algorithm. The analysis unit can also detect specific patterns based on past analysis results to improve the analysis accuracy. In this way, by referring to past analysis results, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI to improve the analysis accuracy.

[0077] The analysis unit can change the analysis method depending on different time periods. For example, during the day, the analysis unit uses an analysis method that takes brightness into account. Furthermore, the analysis unit can also use an analysis method that is suitable for low-light environments at night. Furthermore, the analysis unit can also use an analysis method that takes congestion into account during rush hour. In this way, by changing the analysis method depending on the time period, it is possible to always provide appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input time period data into a generation AI and change the analysis method.

[0078] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. By adjusting the level of detail of the analysis results according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the level of detail of the analysis results.

[0079] The analysis unit can improve the analysis accuracy based on road surface temperature and humidity information. For example, when the road surface temperature is high, the analysis unit uses an algorithm to detect cracks caused by thermal expansion. The analysis unit can also use an algorithm to detect deterioration due to moisture when the road surface humidity is high. The analysis unit can also improve the analysis accuracy by taking into account changes in road surface temperature and humidity. In this way, by taking temperature and humidity information into account, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input road surface temperature and humidity data into the generation AI to improve the analysis accuracy.

[0080] The analysis unit can improve the analysis accuracy by integrating data from other sensors. For example, the analysis unit can integrate data from a vibration sensor to more accurately detect unevenness in the road surface. The analysis unit can also integrate data from a sound sensor to detect cracks in the road surface. The analysis unit can also integrate data from other sensors to improve the overall analysis accuracy. In this way, by integrating data from other sensors, the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from a vibration sensor or sound sensor into a generation AI to improve the analysis accuracy.

[0081] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining a crack detection algorithm and an unevenness detection algorithm. The analysis unit can also improve analysis accuracy by combining different deep learning models. The analysis unit can also integrate multiple analysis algorithms to improve overall analysis accuracy. In this way, analysis accuracy can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI to improve analysis accuracy.

[0082] The notification unit can estimate the user's emotions and adjust the notification presentation method based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide a simple, highly visible notification. Furthermore, if the user is relaxed, the notification unit can provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a notification that focuses on the main points. This allows for more appropriate notifications to be provided by adjusting the notification presentation method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into the generation AI and adjust the notification presentation method.

[0083] The notification unit can change the notification method depending on the pedestrian's current activity. For example, if the pedestrian is on a call, the notification unit can prioritize vibration notification. Furthermore, if the pedestrian is playing music, the notification unit can also prioritize voice notification. Furthermore, if the pedestrian is using an app, the notification unit can also prioritize pop-up notification. In this way, by changing the notification method depending on the pedestrian's activity, notifications can be provided at appropriate times. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's current activity data into the generation AI and change the notification method.

[0084] The notification unit can select the optimal notification method by referring to the pedestrian's past notification history. For example, the notification unit prioritizes the notification method that the pedestrian has previously preferred. The notification unit can also select the optimal notification method from the pedestrian's past notification history. The notification unit can also customize the notification method based on the pedestrian's past notification history. In this way, by referring to the past notification history, the optimal notification method can be provided to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's past notification history data into a generation AI and select the optimal notification method.

[0085] The notification unit can adjust the notification method based on the pedestrian's device settings. For example, the notification unit can adjust the volume of the audio notification based on the volume setting of the pedestrian's device. The notification unit can also adjust the intensity of the vibration notification based on the vibration intensity setting of the pedestrian's device. The notification unit can also provide an optimal notification method based on the pedestrian's device settings. By adjusting the notification method based on the device settings, it is possible to provide an optimal notification for the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's device setting data into a generation AI to adjust the notification method.

[0086] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize displaying important notifications. Furthermore, if the user is relaxed, the notification unit can also display all notifications equally. Furthermore, if the user is in a hurry, the notification unit can prioritize displaying notifications with high urgency. This allows important information to be provided preferentially by determining the priority of notifications according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's emotion data into the generation AI to determine the priority of notifications.

[0087] The notification unit can prioritize notifications for specific areas by taking into account the pedestrian's geographical location information. For example, if the pedestrian is in a dangerous area, the notification unit can prioritize displaying notifications related to that area. Furthermore, if the pedestrian is in a tourist spot, the notification unit can prioritize displaying notifications related to tourist spots. Furthermore, if the pedestrian is in a residential area, the notification unit can prioritize displaying notifications related to road conditions in the residential area. This makes it possible to provide information about important areas by prioritizing notifications based on geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's geographical location information data into a generation AI and prioritize notifications for specific areas.

[0088] The notification unit can analyze the pedestrian's social media activity and provide related notifications. For example, the notification unit can provide notifications about places where the pedestrian has checked in on social media. The notification unit can also analyze the content of the pedestrian's social media posts and provide related notifications. The notification unit can also provide related notifications based on the pedestrian's social media friends' activities. In this way, useful information can be provided to the user by providing related notifications based on social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the pedestrian's social media activity data into a generation AI and provide related notifications.

[0089] The notification unit can customize the notification method by reflecting the pedestrian's past feedback. The notification unit can adjust the notification method based on, for example, feedback provided by the pedestrian in the past. The notification unit can also prioritize a specific notification method based on the pedestrian's past feedback. The notification unit can also customize the notification method by reflecting the pedestrian's past feedback. In this way, by reflecting past feedback, it is possible to provide the optimal notification method for the user. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the pedestrian's past feedback data into the generation AI to customize the notification method. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires an image of the road surface using the camera 42 of the smart device 14. For example, the acquisition unit may also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit may be realized by the control unit 46A of the smart device 14, and AI analyzes the acquired image to detect abnormalities in the road surface. For example, the analysis unit may also be realized by the specific processing unit 290 of the data processing device 12. The notification unit issues a pop-up notification or a voice warning using the output device 40 of the smart device 14, for example. For example, the notification unit may also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, analysis unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image of the road surface using the camera 42 of the smart glasses 214. For example, the acquisition unit may also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit may be realized by the control unit 46A of the smart glasses 214, and AI analyzes the acquired image to detect a change in the road surface. For example, the analysis unit may also be realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit issues an audio warning using the speaker 240 of the smart glasses 214. For example, the notification unit may also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires an image of the road surface using the camera 42 of the headset type terminal 314. For example, the acquisition unit may also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314, and AI analyzes the acquired image to detect a change in the road surface. For example, the analysis unit may also be realized by the specific processing unit 290 of the data processing device 12. The notification unit issues an audio warning using, for example, the speaker 240 of the headset type terminal 314. For example, the notification unit may also be realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires images of the road surface using the camera 42 of the robot 414. For example, the acquisition unit may also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414, and AI analyzes the acquired images to detect abnormalities in the road surface. For example, the analysis unit may also be realized by the specific processing unit 290 of the data processing device 12. The notification unit issues an audio warning using, for example, the speaker 240 of the robot 414. For example, the notification unit may also be realized by the specific processing unit 290 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The acquisition unit can learn the user's walking pattern and automatically adjust the optimal image acquisition timing. For example, when the user is walking normally, the acquisition unit acquires images at regular intervals. Furthermore, when the user is in a hurry, the acquisition unit can increase the frequency of image acquisition. Furthermore, when the user stops, the acquisition unit can pause image acquisition and resume it when the user starts walking again. In this way, by adjusting the image acquisition timing according to the user's walking pattern, it is possible to provide appropriate information while reducing battery consumption.

[0092] The analysis unit can adjust the analysis algorithm according to different seasons. For example, in winter, the analysis unit uses an algorithm that detects changes in the road surface due to snow and ice. In summer, the analysis unit can also use an algorithm that detects cracks caused by thermal expansion. In spring and autumn, the analysis unit can also use an algorithm that detects changes in the road surface due to fallen leaves and mud. In this way, by adjusting the analysis algorithm according to the season, the analysis accuracy can be improved.

[0093] The notification unit can adjust the notification method taking into account the user's health condition. For example, the notification unit can prioritize voice notification when the user is tired. The notification unit can also prioritize vibration notification when the user is concentrating. The notification unit can also prioritize pop-up notification when the user is relaxed. In this way, by adjusting the notification method according to the user's health condition, notifications can be provided at appropriate times.

[0094] The acquisition unit can estimate the user's emotion and adjust the image acquisition resolution based on the estimated emotion. For example, if the user is nervous, detailed images can be acquired at high resolution. If the user is relaxed, battery consumption can be reduced by using low resolution. If the user is in a hurry, information can be provided quickly at medium resolution. In this way, by adjusting the image acquisition resolution according to the user's emotion, more appropriate information can be provided.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, simple, highly visible analysis results can be provided. If the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, analysis results that focus on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide information that is easier to understand.

[0096] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated emotion. For example, if the user is nervous, the notification can be sent immediately. If the user is relaxed, the notification can be sent with a slight delay. If the user is in a hurry, the notification can be sent quickly. In this way, by adjusting the timing of the notification according to the user's emotion, the notification can be provided at a more appropriate time.

[0097] The acquisition unit can change the resolution of image acquisition according to the user's walking speed. For example, if a pedestrian is walking fast, detailed images with high resolution are acquired. On the other hand, if a pedestrian is walking slowly, battery consumption can be reduced by using low resolution. Also, if a pedestrian stops, image acquisition can be paused and resumed when the pedestrian starts walking again. In this way, by adjusting the resolution of image acquisition according to walking speed, it is possible to provide appropriate information while reducing battery consumption.

[0098] The analysis unit can estimate the user's emotions and select an analysis algorithm based on the estimated emotions. For example, if the user is nervous, an algorithm that performs a quick analysis can be used. If the user is relaxed, an algorithm that performs a detailed analysis can be used. If the user is in a hurry, an algorithm that performs an analysis that focuses on the main points can be used. In this way, by selecting an analysis algorithm according to the user's emotions, more appropriate analysis results can be provided.

[0099] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is nervous, concise but important information can be notified. If the user is relaxed, detailed information can be notified. If the user is in a hurry, information that focuses on the main points can be notified. In this way, by adjusting the content of the notification according to the user's emotions, more appropriate information can be provided.

[0100] The acquisition unit can learn the user's past behavioral history and automatically adjust the optimal image acquisition timing. For example, if the user has walked during a specific time period in the past, the acquisition unit can adjust the frequency of image acquisition to match that time period. Also, if the user has frequently walked in a specific area in the past, the acquisition unit can increase the frequency of image acquisition in that area. Furthermore, the acquisition unit can customize image acquisition settings based on the user's past behavioral history. This makes it possible to provide the user with an optimal image acquisition method by reflecting the user's past behavioral history.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit acquires images of the road surface using the smartphone camera. For example, a pedestrian walking with a smartphone can monitor the road surface condition in real time. The acquisition unit can also continuously acquire images of the road surface using the smartphone camera. Step 2: The analysis unit uses AI to analyze the images acquired by the acquisition unit and detect abnormalities in the road surface. For example, image analysis technology is used to detect abnormalities such as cracks, unevenness, and holes. Image analysis technology using deep learning can also be used, which improves the accuracy of image analysis. Step 3: The notification unit notifies pedestrians of the abnormalities detected by the analysis unit. For example, it can display a pop-up notification on the smartphone screen or issue an audio warning. It can also provide a vibration notification. This helps pedestrians walk safely.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires an image of a road surface; an analysis unit that analyzes the image acquired by the acquisition unit and detects a change in the road surface; a notification unit that notifies the user of the abnormality detected by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Detects cracks, irregularities, holes, and other abnormalities 2. The system of claim 1.

3. The notification unit Pop-up notification, sound alert, vibration notification 2. The system of claim 1.

4. The acquisition unit Pedestrians walking with their smartphones can instantly monitor road conditions.

2. The system of claim 1.

5. The analysis unit Use deep learning image analysis technology 2. The system of claim 1.

6. The acquisition unit Estimate the user's emotions and adjust the timing of image capture based on the estimated emotions.

2. The system of claim 1.

7. The acquisition unit Automatically adjusts image acquisition settings for different weather conditions 2. The system of claim 1.

8. The acquisition unit Change the frequency of image acquisition according to the walking speed of pedestrians 2. The system of claim 1.

Images