System

Abstract

An object of a system according to an embodiment is to prevent minor accidents during operation of a electric kick scooter.SOLUTION: A system includes an image acquisition unit, an analysis unit, a determination unit, and an alert transmission unit. The image acquisition unit acquires an image using a camera of a smartphone. The analysis unit analyzes the image data acquired by the image acquisition unit. The determination unit determines the distance based on the data analyzed by the analysis unit. The alert transmission unit transmits an alert when the distance determined by the determination unit is not within the safety range.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] With conventional technology, it was difficult to properly grasp the distance between vehicles, people, and objects while driving an electric kick scooter and prevent collision accidents.

[0005] The system according to the embodiment aims to prevent collision accidents while driving an electric kick scooter. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, a determination unit, and an alert transmission unit. The image acquisition unit acquires an image using a smartphone camera. The analysis unit analyzes the image data acquired by the image acquisition unit. The determination unit determines the distance based on the data analyzed by the analysis unit. The alert transmission unit transmits an alert if the distance determined by the determination unit is not within a safe range. [Effects of the Invention]

[0007] The system according to the embodiment can prevent collision accidents while driving an electric kick scooter. [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) The collision accident prevention system according to an embodiment of the present invention is a system that acquires images using a smartphone camera attached to an electric kick scooter, analyzes the images with a generating AI, determines the distance, and issues an alert. As a result, the collision accident prevention system can alert the driver when there is a possibility of collision, thereby preventing collision accidents from occurring.

[0029] The collision accident prevention system according to the embodiment includes an image acquisition unit, an analysis unit, a determination unit, and an alert transmission unit. The image acquisition unit acquires images using a smartphone camera. For example, the image acquisition unit uses a smartphone camera attached to the electric scooter to capture images of vehicles, pedestrians, obstacles, and the like ahead. The image acquisition unit can also cover a wide area using a high-resolution camera. The analysis unit uses a generation AI to analyze the image data acquired by the image acquisition unit. For example, the generation AI recognizes objects in the image using a text generation AI (e.g., LLM) and measures the distance to each object. The generation AI can also analyze the image data using a multimodal generation AI. The generation AI also uses object recognition technology to measure the distance to vehicles, pedestrians, and obstacles ahead. The determination unit determines the distance based on the data analyzed by the analysis unit. For example, the determination unit determines whether the measured distance is within a safe range. The determination unit can determine that there is a possibility of collision if the distance falls within a certain range. The alert issuing unit issues an alert when the distance determined by the determining unit is not within the safe range. For example, the alert issuing unit notifies the driver by voice, vibration, screen display, or the like. The alert issuing unit can also issue different alerts depending on the object with which there is a possibility of contact. As a result, the collision accident prevention system according to the embodiment issues an alert to the driver when there is a possibility of contact, and can prevent collision accidents from occurring. For example, by receiving the alert, the driver can take appropriate action against vehicles, pedestrians, or obstacles ahead. Furthermore, by receiving the alert, the driver's attention can be improved, thereby reducing the risk of accidents.

[0030] The image acquisition unit can automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. The image acquisition unit is equipped with a function to automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. For example, it adjusts to a wide angle on narrow roads and to a standard angle on wide roads. The image acquisition unit also adjusts the camera's viewing angle in real time to optimally provide the information the driver needs. For example, it switches to a wide angle at intersections and to a standard angle on straight roads. The image acquisition unit has also developed an algorithm to automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. For example, it sets to a wide angle at night or in bad weather. This makes it possible to provide the optimal field of view depending on the driving environment.

[0031] The image acquisition unit can equip the camera with a night mode, enabling highly accurate image acquisition even in dark places. The image acquisition unit, for example, equips the camera with a night mode, enabling highly accurate image acquisition even in dark places. For example, noise is reduced in low-light environments, providing clear images. The image acquisition unit also adds a function to automatically switch to the night mode, optimizing image acquisition in dark places. For example, visibility is improved on roads with few streetlights or in tunnels. The image acquisition unit also enhances the night mode, performing highly accurate image analysis even in dark places. For example, an infrared sensor can be used in combination to improve object recognition accuracy in dark places. This makes it possible to acquire images with high accuracy even in dark places.

[0032] The image acquisition unit adds an infrared sensor to the camera, enabling it to detect obstacles that are not visible to the naked eye. For example, the image acquisition unit adds an infrared sensor to the camera to detect obstacles that are not visible to the naked eye. For example, this improves the accuracy of obstacle detection at night or in fog. The image acquisition unit also uses the infrared sensor to build a system that detects obstacles that are not visible to the naked eye in real time. For example, this improves safety in dark places or in bad weather. The image acquisition unit also integrates the infrared sensor into the camera and develops an algorithm to detect obstacles that are not visible to the naked eye. For example, the infrared data is incorporated into image analysis. This makes it possible to detect obstacles that are not visible to the naked eye.

[0033] The image acquisition unit can simultaneously collect audio data in addition to acquiring images from the camera, and analyze the surrounding sound environment. For example, the image acquisition unit can simultaneously collect audio data in addition to acquiring images from the camera, and build a system that analyzes the surrounding sound environment. For example, it can detect car horns and pedestrian voices. The image acquisition unit also integrates audio data with the camera's image data to more accurately grasp the surrounding situation. For example, it can use audio analysis to detect dangerous situations early. The image acquisition unit also links the camera and microphone to simultaneously collect image and audio data and develop an algorithm that analyzes the surrounding sound environment. For example, it can identify the direction of the sound source. This makes it possible to analyze the surrounding sound environment.

[0034] The analysis unit can predict the movement of objects from image data and evaluate the risk of future contact. For example, the analysis unit develops an algorithm that allows the generation AI to predict the movement of objects from image data and evaluate the risk of future contact. For example, it analyzes the movement of vehicles ahead and predicts the risk of contact. The analysis unit also builds a system that allows the generation AI to predict the movement of objects in real time based on image data and evaluate the risk of contact. For example, it analyzes the movement of pedestrians and predicts the risk of contact. The analysis unit also has a function that allows the generation AI to predict the movement of objects from image data and evaluate the risk of future contact. For example, it analyzes the movement of obstacles and predicts the risk of contact. This makes it possible to evaluate the risk of future contact.

[0035] The analysis unit can analyze weather and road conditions from image data and perform risk assessment according to driving conditions. For example, the analysis unit constructs a system in which the generation AI analyzes weather and road conditions from image data and performs risk assessment according to driving conditions. For example, it evaluates slipperiness in rainy weather. The analysis unit also uses the image data to analyze weather and road conditions in real time and perform risk assessment. For example, it detects the presence of snow or ice and evaluates risk. The analysis unit also develops an algorithm in which the generation AI analyzes weather and road conditions from image data and performs risk assessment according to driving conditions. For example, it evaluates fog and poor visibility. This makes it possible to perform risk assessment according to driving conditions.

[0036] The analysis unit can recognize traffic signs and signals from image data and provide appropriate instructions to the driver. The analysis unit, for example, builds a system in which the generation AI recognizes traffic signs and signals from image data and provides appropriate instructions to the driver. For example, it recognizes the color of a traffic light and instructs the driver to stop or proceed. The analysis unit also enables the generation AI to recognize traffic signs and signals in real time based on image data and provide appropriate instructions to the driver. For example, it recognizes speed limit signs and prompts the driver to adjust their speed. The analysis unit also develops an algorithm in which the generation AI recognizes traffic signs and signals from image data and provides appropriate instructions to the driver. For example, it recognizes a stop sign and instructs the driver to stop. This makes it possible to recognize traffic signs and signals and provide appropriate instructions to the driver.

[0037] The analysis unit can infer the intentions of traffic participants from image data and issue a warning to the driver. For example, the analysis unit constructs a system in which the generation AI infers the intentions of traffic participants from image data and issues a warning to the driver. For example, it predicts the possibility that a vehicle ahead will make a sudden stop and issues a warning to the driver. The analysis unit also uses the generation AI to infer the intentions of traffic participants in real time based on image data and issues a warning to the driver. For example, it predicts the possibility that a pedestrian will cross the street and issues a warning to the driver. The analysis unit also develops an algorithm in which the generation AI infers the intentions of traffic participants from image data and issues a warning to the driver. For example, it predicts the possibility that a vehicle behind will attempt to overtake and issues a warning to the driver. This makes it possible to infer the intentions of traffic participants and issue a warning to the driver.

[0038] The judgment unit can refer to past driving data when judging distance and issue alerts according to individual driving styles. For example, the judgment unit builds a system in which the generation AI refers to past driving data when judging distance and issues alerts according to individual driving styles. For example, it adjusts alerts based on the frequency of past sudden braking. The judgment unit also allows the generation AI to issue alerts according to individual driving styles based on past driving data. For example, it adjusts the timing of alerts taking into account the driver's reaction time. The judgment unit also develops an algorithm in which the generation AI analyzes past driving data and issues alerts according to individual driving styles. For example, it customizes alerts based on the driver's acceleration and deceleration patterns. This makes it possible to issue alerts according to individual driving styles.

[0039] The judgment unit can analyze surrounding traffic conditions in real time when judging distance and dynamically adjust the alert threshold. For example, the judgment unit builds a system in which the generation AI analyzes surrounding traffic conditions in real time when judging distance and dynamically adjusts the alert threshold. For example, the alert threshold is set low when traffic volume is heavy. The judgment unit also analyzes surrounding traffic conditions in real time and the generation AI dynamically adjusts the alert threshold. For example, the frequency of alerts is increased at intersections and congested roads. The judgment unit also develops an algorithm in which the generation AI analyzes surrounding traffic conditions and dynamically adjusts the alert threshold. For example, the alert is adjusted taking into account changes in traffic signals and the movement of other vehicles. This makes it possible to dynamically adjust the alert threshold according to the surrounding traffic conditions.

[0040] The alert sending unit can cooperate with the driver's smartwatch or headset when sending an alert and send notifications on multiple devices. For example, the alert sending unit cooperates with the driver's smartwatch or headset when sending an alert, and builds a system that sends notifications on multiple devices. For example, the alert is sent by vibration from the smartwatch and audio from the headset. The alert sending unit also cooperates with the smartwatch or headset to send alerts on multiple devices simultaneously. For example, the alert is sent by displaying the alert on the smartwatch and audio from the headset. The alert sending unit also develops an algorithm that cooperates with the driver's smartwatch or headset when sending an alert and sends notifications on multiple devices. For example, the alert is sent by vibration from the smartwatch and audio from the headset. This allows notifications to be sent on multiple devices.

[0041] The alert sending unit can select the most effective alert method based on the driver's past response data when sending an alert. For example, the alert sending unit builds a system that selects the most effective alert method based on the driver's past response data when sending an alert. For example, the alert timing is adjusted based on past response times. The alert sending unit also selects the most effective alert method using a generation AI based on past response data. For example, if the driver responds most quickly to a voice alert, it will prioritize a voice alert. The alert sending unit also analyzes the driver's past response data when sending an alert and develops an algorithm that selects the most effective alert method. For example, if the driver responds most quickly to a vibration alert, it will prioritize a vibration alert. This makes it possible to select the most effective alert method.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The image acquisition unit can add a temperature sensor to the camera to detect changes in the surrounding temperature. For example, a temperature sensor can be used to measure the temperature of the road surface and detect the possibility of icing. The temperature sensor can also be used to monitor the temperature of the engine or battery and warn of the risk of overheating. Furthermore, the temperature sensor can be used to adjust the temperature inside the vehicle to provide a comfortable driving environment. This enables safety measures to be taken in response to temperature changes.

[0044] The analysis unit can track the driver's line of sight from image data and issue a warning if the driver's gaze is averted. For example, it can issue a warning if the driver is using a smartphone. It can also issue a warning if the driver is not looking ahead. It can also issue a warning if the driver is feeling drowsy. This makes it possible to implement safety measures based on the driver's line of sight.

[0045] When sending an alert, the alert sending unit works in conjunction with the driver's smartphone and can adjust the timing of the alert based on the driver's schedule. For example, the timing of the alert can be advanced if the driver is heading to an important meeting. The timing of the alert can also be delayed when the driver is relaxing. Furthermore, the content of the alert can be customized based on the driver's schedule. This makes it possible to adjust the alert according to the driver's schedule.

[0046] The image acquisition unit can add a UV sensor to the camera to measure the intensity of UV rays. For example, the UV sensor can be used to warn of the risk of sunburn. The UV sensor can also be used to strengthen UV protection inside the vehicle. Furthermore, the UV sensor can be used to monitor the driver's health and encourage them to take a break if necessary. This enables safety measures against UV rays.

[0047] When issuing an alert, the alert sending unit can select the most effective alert method based on the driver's past driving data. For example, the timing of the alert can be adjusted based on past reaction times. Also, if the driver responds most quickly to a voice alert, it can prioritize a voice alert. Furthermore, if the driver responds most quickly to a vibration alert, it can prioritize a vibration alert. This allows the most effective alert method to be selected.

[0048] The image acquisition unit can add a humidity sensor to the camera to measure the ambient humidity. For example, the humidity sensor can be used to predict the occurrence of fog and issue a warning to the driver. The humidity sensor can also be used to adjust the humidity inside the vehicle to provide a comfortable driving environment. Furthermore, the humidity sensor can also be used to monitor the driver's health and encourage them to take a break as necessary. This enables safety measures against humidity.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The image acquisition unit acquires images using the smartphone camera. For example, the image acquisition unit uses the smartphone camera attached to the electric kick scooter to capture images of vehicles, pedestrians, obstacles, etc. ahead. The image acquisition unit can also cover a wide area by using a high-resolution camera. Step 2: In the analysis unit, the generation AI analyzes the image data acquired by the image acquisition unit. For example, the generation AI uses a text generation AI (e.g., LLM) to recognize objects in the image and measure the distance to each. The generation AI can also use a multimodal generation AI to analyze the image data. The generation AI also uses object recognition technology to measure the distance to vehicles, pedestrians, and obstacles ahead. Step 3: The determination unit determines the distance based on the data analyzed by the analysis unit. For example, the determination unit determines whether the measured distance is within a safe range. Furthermore, the determination unit can determine that there is a possibility of contact if the distance falls within a certain range. Step 4: The alert sending unit sends an alert if the distance determined by the determination unit is not within the safe range. For example, the alert sending unit notifies the driver by voice, vibration, screen display, etc. The alert sending unit can also send different alerts depending on the object that may be in contact.

[0051] (Example 2) The collision accident prevention system according to an embodiment of the present invention is a system that acquires images using a smartphone camera attached to an electric kick scooter, analyzes the images with a generating AI, determines the distance, and issues an alert. As a result, the collision accident prevention system can alert the driver when there is a possibility of collision, thereby preventing collision accidents from occurring.

[0052] The collision accident prevention system according to the embodiment includes an image acquisition unit, an analysis unit, a determination unit, and an alert transmission unit. The image acquisition unit acquires images using a smartphone camera. For example, the image acquisition unit uses a smartphone camera attached to the electric scooter to capture images of vehicles, pedestrians, obstacles, and the like ahead. The image acquisition unit can also cover a wide area using a high-resolution camera. The analysis unit uses a generation AI to analyze the image data acquired by the image acquisition unit. For example, the generation AI recognizes objects in the image using a text generation AI (e.g., LLM) and measures the distance to each object. The generation AI can also analyze the image data using a multimodal generation AI. The generation AI also uses object recognition technology to measure the distance to vehicles, pedestrians, and obstacles ahead. The determination unit determines the distance based on the data analyzed by the analysis unit. For example, the determination unit determines whether the measured distance is within a safe range. The determination unit can determine that there is a possibility of collision if the distance falls within a certain range. The alert issuing unit issues an alert when the distance determined by the determining unit is not within the safe range. For example, the alert issuing unit notifies the driver by voice, vibration, screen display, or the like. The alert issuing unit can also issue different alerts depending on the object with which there is a possibility of contact. As a result, the collision accident prevention system according to the embodiment issues an alert to the driver when there is a possibility of contact, and can prevent collision accidents from occurring. For example, by receiving the alert, the driver can take appropriate action against vehicles, pedestrians, or obstacles ahead. Furthermore, by receiving the alert, the driver's attention can be improved, thereby reducing the risk of accidents.

[0053] The image acquisition unit can automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. The image acquisition unit is equipped with a function to automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. For example, it adjusts to a wide angle on narrow roads and to a standard angle on wide roads. The image acquisition unit also adjusts the camera's viewing angle in real time to optimally provide the information the driver needs. For example, it switches to a wide angle at intersections and to a standard angle on straight roads. The image acquisition unit has also developed an algorithm to automatically adjust the camera's viewing angle to provide the optimal field of view depending on the driving environment. For example, it sets to a wide angle at night or in bad weather. This makes it possible to provide the optimal field of view depending on the driving environment.

[0054] The image acquisition unit can equip the camera with a night mode, enabling highly accurate image acquisition even in dark places. The image acquisition unit, for example, equips the camera with a night mode, enabling highly accurate image acquisition even in dark places. For example, noise is reduced in low-light environments, providing clear images. The image acquisition unit also adds a function to automatically switch to the night mode, optimizing image acquisition in dark places. For example, visibility is improved on roads with few streetlights or in tunnels. The image acquisition unit also enhances the night mode, performing highly accurate image analysis even in dark places. For example, an infrared sensor can be used in combination to improve object recognition accuracy in dark places. This makes it possible to acquire images with high accuracy even in dark places.

[0055] The image acquisition unit can use the emotion estimation function to increase the camera's image acquisition frequency when the driver's attention is declining. The image acquisition unit, for example, uses the emotion estimation function to increase the camera's image acquisition frequency when the driver's attention is declining. For example, the image acquisition unit analyzes the driver's facial expressions and movements to detect a decline in attention. The image acquisition unit also monitors the driver's emotional state in real time and automatically increases the camera's image acquisition frequency when the driver's attention is declining. For example, it detects fatigue and stress. The image acquisition unit also dynamically adjusts the camera's image acquisition frequency when the driver's attention is declining based on the emotion estimation data. For example, it shortens the image acquisition interval when the driver's attention is declining. This makes it possible to increase the image acquisition frequency when the driver's attention is declining.

[0056] The image acquisition unit adds an infrared sensor to the camera, enabling it to detect obstacles that are not visible to the naked eye. For example, the image acquisition unit adds an infrared sensor to the camera to detect obstacles that are not visible to the naked eye. For example, this improves the accuracy of obstacle detection at night or in fog. The image acquisition unit also uses the infrared sensor to build a system that detects obstacles that are not visible to the naked eye in real time. For example, this improves safety in dark places or in bad weather. The image acquisition unit also integrates the infrared sensor into the camera and develops an algorithm to detect obstacles that are not visible to the naked eye. For example, the infrared data is incorporated into image analysis. This makes it possible to detect obstacles that are not visible to the naked eye.

[0057] The image acquisition unit can simultaneously collect audio data in addition to acquiring images from the camera, and analyze the surrounding sound environment. For example, the image acquisition unit can simultaneously collect audio data in addition to acquiring images from the camera, and build a system that analyzes the surrounding sound environment. For example, it can detect car horns and pedestrian voices. The image acquisition unit also integrates audio data with the camera's image data to more accurately grasp the surrounding situation. For example, it can use audio analysis to detect dangerous situations early. The image acquisition unit also links the camera and microphone to simultaneously collect image and audio data and develop an algorithm that analyzes the surrounding sound environment. For example, it can identify the direction of the sound source. This makes it possible to analyze the surrounding sound environment.

[0058] The image acquisition unit can use the emotion estimation function to automatically adjust the image acquisition settings of the camera according to the driver's stress level. The image acquisition unit, for example, uses the emotion estimation function to automatically adjust the image acquisition settings of the camera according to the driver's stress level. For example, if stress is high, the image acquisition frequency is increased. The image acquisition unit also monitors the driver's stress level in real time and builds a system that dynamically adjusts the image acquisition settings of the camera. For example, if stress is low, the image acquisition frequency is decreased. The image acquisition unit also develops an algorithm that automatically changes the image acquisition settings of the camera according to the driver's stress level based on the emotion estimation data. For example, if stress is high, the camera is set to a wide angle. This makes it possible to automatically adjust the image acquisition settings according to the driver's stress level.

[0059] The analysis unit can predict the movement of objects from image data and evaluate the risk of future contact. For example, the analysis unit develops an algorithm that allows the generation AI to predict the movement of objects from image data and evaluate the risk of future contact. For example, it analyzes the movement of vehicles ahead and predicts the risk of contact. The analysis unit also builds a system that allows the generation AI to predict the movement of objects in real time based on image data and evaluate the risk of contact. For example, it analyzes the movement of pedestrians and predicts the risk of contact. The analysis unit also has a function that allows the generation AI to predict the movement of objects from image data and evaluate the risk of future contact. For example, it analyzes the movement of obstacles and predicts the risk of contact. This makes it possible to evaluate the risk of future contact.

[0060] The analysis unit can analyze weather and road conditions from image data and perform risk assessment according to driving conditions. For example, the analysis unit constructs a system in which the generation AI analyzes weather and road conditions from image data and performs risk assessment according to driving conditions. For example, it evaluates slipperiness in rainy weather. The analysis unit also uses the image data to analyze weather and road conditions in real time and perform risk assessment. For example, it detects the presence of snow or ice and evaluates risk. The analysis unit also develops an algorithm in which the generation AI analyzes weather and road conditions from image data and performs risk assessment according to driving conditions. For example, it evaluates fog and poor visibility. This makes it possible to perform risk assessment according to driving conditions.

[0061] The analysis unit can use the emotion estimation function to adjust the priority of analysis results based on the driver's emotional state. The analysis unit, for example, uses the emotion estimation function to build a system that adjusts the priority of analysis results based on the driver's emotional state. For example, if the driver is nervous, important information is displayed preferentially. The analysis unit also monitors the driver's emotional state in real time and dynamically adjusts the priority of analysis results. For example, if the driver is relaxed, normal information is displayed. The analysis unit also develops an algorithm that automatically changes the priority of analysis results according to the driver's emotional state based on the emotion estimation data. For example, important information is emphasized if the driver is tired. This makes it possible to adjust the priority of analysis results based on the driver's emotional state.

[0062] The analysis unit can recognize traffic signs and signals from image data and provide appropriate instructions to the driver. The analysis unit, for example, builds a system in which the generation AI recognizes traffic signs and signals from image data and provides appropriate instructions to the driver. For example, it recognizes the color of a traffic light and instructs the driver to stop or proceed. The analysis unit also enables the generation AI to recognize traffic signs and signals in real time based on image data and provide appropriate instructions to the driver. For example, it recognizes speed limit signs and prompts the driver to adjust their speed. The analysis unit also develops an algorithm in which the generation AI recognizes traffic signs and signals from image data and provides appropriate instructions to the driver. For example, it recognizes a stop sign and instructs the driver to stop. This makes it possible to recognize traffic signs and signals and provide appropriate instructions to the driver.

[0063] The analysis unit can infer the intentions of traffic participants from image data and issue a warning to the driver. For example, the analysis unit constructs a system in which the generation AI infers the intentions of traffic participants from image data and issues a warning to the driver. For example, it predicts the possibility that a vehicle ahead will make a sudden stop and issues a warning to the driver. The analysis unit also uses the generation AI to infer the intentions of traffic participants in real time based on image data and issues a warning to the driver. For example, it predicts the possibility that a pedestrian will cross the street and issues a warning to the driver. The analysis unit also develops an algorithm in which the generation AI infers the intentions of traffic participants from image data and issues a warning to the driver. For example, it predicts the possibility that a vehicle behind will attempt to overtake and issues a warning to the driver. This makes it possible to infer the intentions of traffic participants and issue a warning to the driver.

[0064] The analysis unit can use the emotion estimation function to customize the display method of the analysis results according to the driver's emotional state. For example, the analysis unit uses the emotion estimation function to build a system that customizes the display method of the analysis results according to the driver's emotional state. For example, if the driver is nervous, a simple display is provided. The analysis unit also monitors the driver's emotional state in real time and dynamically customizes the display method of the analysis results. For example, if the driver is relaxed, detailed information is displayed. The analysis unit also develops an algorithm that automatically changes the display method of the analysis results according to the driver's emotional state based on the emotion estimation data. For example, if the driver is tired, important information is emphasized. This makes it possible to customize the display method of the analysis results according to the driver's emotional state.

[0065] The judgment unit can refer to past driving data when judging distance and issue alerts according to individual driving styles. For example, the judgment unit builds a system in which the generation AI refers to past driving data when judging distance and issues alerts according to individual driving styles. For example, it adjusts alerts based on the frequency of past sudden braking. The judgment unit also allows the generation AI to issue alerts according to individual driving styles based on past driving data. For example, it adjusts the timing of alerts taking into account the driver's reaction time. The judgment unit also develops an algorithm in which the generation AI analyzes past driving data and issues alerts according to individual driving styles. For example, it customizes alerts based on the driver's acceleration and deceleration patterns. This makes it possible to issue alerts according to individual driving styles.

[0066] The judgment unit can analyze surrounding traffic conditions in real time when judging distance and dynamically adjust the alert threshold. For example, the judgment unit builds a system in which the generation AI analyzes surrounding traffic conditions in real time when judging distance and dynamically adjusts the alert threshold. For example, the alert threshold is set low when traffic volume is heavy. The judgment unit also analyzes surrounding traffic conditions in real time and the generation AI dynamically adjusts the alert threshold. For example, the frequency of alerts is increased at intersections and congested roads. The judgment unit also develops an algorithm in which the generation AI analyzes surrounding traffic conditions and dynamically adjusts the alert threshold. For example, the alert is adjusted taking into account changes in traffic signals and the movement of other vehicles. This makes it possible to dynamically adjust the alert threshold according to the surrounding traffic conditions.

[0067] The determination unit can use the emotion estimation function to adjust the intensity and frequency of alerts according to the driver's emotional state. For example, the determination unit uses the emotion estimation function to build a system that adjusts the intensity and frequency of alerts according to the driver's emotional state. For example, if the driver is nervous, the intensity of the alerts is set low. The determination unit also monitors the driver's emotional state in real time and dynamically adjusts the intensity and frequency of alerts. For example, if the driver is relaxed, the frequency of alerts is reduced. The determination unit also develops an algorithm that automatically changes the intensity and frequency of alerts according to the driver's emotional state based on the emotion estimation data. For example, if the driver is tired, the frequency of alerts is increased. This makes it possible to adjust the intensity and frequency of alerts according to the driver's emotional state.

[0068] The alert sending unit can cooperate with the driver's smartwatch or headset when sending an alert and send notifications on multiple devices. For example, the alert sending unit cooperates with the driver's smartwatch or headset when sending an alert, and builds a system that sends notifications on multiple devices. For example, the alert is sent by vibration from the smartwatch and audio from the headset. The alert sending unit also cooperates with the smartwatch or headset to send alerts on multiple devices simultaneously. For example, the alert is sent by displaying the alert on the smartwatch and audio from the headset. The alert sending unit also develops an algorithm that cooperates with the driver's smartwatch or headset when sending an alert and sends notifications on multiple devices. For example, the alert is sent by vibration from the smartwatch and audio from the headset. This allows notifications to be sent on multiple devices.

[0069] The alert sending unit can select the most effective alert method based on the driver's past response data when sending an alert. For example, the alert sending unit builds a system that selects the most effective alert method based on the driver's past response data when sending an alert. For example, the alert timing is adjusted based on past response times. The alert sending unit also selects the most effective alert method using a generation AI based on past response data. For example, if the driver responds most quickly to a voice alert, it will prioritize a voice alert. The alert sending unit also analyzes the driver's past response data when sending an alert and develops an algorithm that selects the most effective alert method. For example, if the driver responds most quickly to a vibration alert, it will prioritize a vibration alert. This makes it possible to select the most effective alert method.

[0070] The alert sending unit can use the emotion estimation function to customize the content and timing of alerts according to the driver's emotional state. For example, the alert sending unit uses the emotion estimation function to build a system that customizes the content and timing of alerts according to the driver's emotional state. For example, if the driver is nervous, the content of the alert is simplified. The alert sending unit also monitors the driver's emotional state in real time and dynamically customizes the content and timing of alerts. For example, if the driver is relaxed, the timing of the alert is delayed. The alert sending unit also develops an algorithm that automatically changes the content and timing of alerts according to the driver's emotional state based on the emotion estimation data. For example, if the driver is tired, the timing of the alert is advanced. This makes it possible to customize the content and timing of alerts according to the driver's emotional state.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The image acquisition unit can add a temperature sensor to the camera to detect changes in the surrounding temperature. For example, a temperature sensor can be used to measure the temperature of the road surface and detect the possibility of icing. The temperature sensor can also be used to monitor the temperature of the engine or battery and warn of the risk of overheating. Furthermore, the temperature sensor can be used to adjust the temperature inside the vehicle to provide a comfortable driving environment. This enables safety measures to be taken in response to temperature changes.

[0073] The analysis unit can track the driver's line of sight from image data and issue a warning if the driver's gaze is averted. For example, it can issue a warning if the driver is using a smartphone. It can also issue a warning if the driver is not looking ahead. It can also issue a warning if the driver is feeling drowsy. This makes it possible to implement safety measures based on the driver's line of sight.

[0074] The determination unit can use the emotion estimation function to automatically switch driving modes based on the driver's emotional state. For example, if the driver is feeling stressed, the system can switch to relaxation mode. If the driver is concentrating, the system can switch to sport mode. Furthermore, if the driver is tired, the system can switch to eco mode. This makes it possible to switch driving modes according to the driver's emotional state.

[0075] When sending an alert, the alert sending unit works in conjunction with the driver's smartphone and can adjust the timing of the alert based on the driver's schedule. For example, the timing of the alert can be advanced if the driver is heading to an important meeting. The timing of the alert can also be delayed when the driver is relaxing. Furthermore, the content of the alert can be customized based on the driver's schedule. This makes it possible to adjust the alert according to the driver's schedule.

[0076] The image acquisition unit can add a UV sensor to the camera to measure the intensity of UV rays. For example, the UV sensor can be used to warn of the risk of sunburn. The UV sensor can also be used to strengthen UV protection inside the vehicle. Furthermore, the UV sensor can be used to monitor the driver's health and encourage them to take a break if necessary. This enables safety measures against UV rays.

[0077] The analysis unit can use the emotion estimation function to automatically adjust the music selection based on the driver's emotional state. For example, if the driver is feeling stressed, it can select relaxing music. If the driver is concentrating, it can select upbeat music. Furthermore, if the driver is tired, it can select refreshing music. This makes it possible to select music according to the driver's emotional state.

[0078] The determination unit can use the emotion estimation function to automatically adjust the navigation route based on the driver's emotional state. For example, if the driver is feeling stressed, a route that avoids congestion can be selected. Also, if the driver is relaxed, a route with good scenery can be selected. Furthermore, if the driver is in a hurry, the shortest route can be selected. This makes it possible to adjust the navigation route according to the driver's emotional state.

[0079] When issuing an alert, the alert sending unit can select the most effective alert method based on the driver's past driving data. For example, the timing of the alert can be adjusted based on past reaction times. Also, if the driver responds most quickly to a voice alert, it can prioritize a voice alert. Furthermore, if the driver responds most quickly to a vibration alert, it can prioritize a vibration alert. This allows the most effective alert method to be selected.

[0080] The image acquisition unit can add a humidity sensor to the camera to measure the ambient humidity. For example, the humidity sensor can be used to predict the occurrence of fog and issue a warning to the driver. The humidity sensor can also be used to adjust the humidity inside the vehicle to provide a comfortable driving environment. Furthermore, the humidity sensor can also be used to monitor the driver's health and encourage them to take a break as necessary. This enables safety measures against humidity.

[0081] The analysis unit can use the emotion estimation function to automatically adjust the intervention level of the driving assistance system based on the driver's emotional state. For example, if the driver is feeling stressed, the intervention level can be increased. If the driver is relaxed, the intervention level can also be decreased. Furthermore, if the driver is tired, the intervention level can be maximized. This makes it possible to adjust the intervention level of the driving assistance system according to the driver's emotional state.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The image acquisition unit acquires images using the smartphone camera. For example, the image acquisition unit uses the smartphone camera attached to the electric kick scooter to capture images of vehicles, pedestrians, obstacles, etc. ahead. The image acquisition unit can also cover a wide area by using a high-resolution camera. Step 2: In the analysis unit, the generation AI analyzes the image data acquired by the image acquisition unit. For example, the generation AI uses a text generation AI (e.g., LLM) to recognize objects in the image and measure the distance to each. The generation AI can also use a multimodal generation AI to analyze the image data. The generation AI also uses object recognition technology to measure the distance to vehicles, pedestrians, and obstacles ahead. Step 3: The determination unit determines the distance based on the data analyzed by the analysis unit. For example, the determination unit determines whether the measured distance is within a safe range. Furthermore, the determination unit can determine that there is a possibility of contact if the distance falls within a certain range. Step 4: The alert sending unit sends an alert if the distance determined by the determination unit is not within the safe range. For example, the alert sending unit notifies the driver by voice, vibration, screen display, etc. The alert sending unit can also send different alerts depending on the object that may be in contact.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 robot 414 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 image acquisition unit that acquires an image using a camera of a smartphone; an analysis unit that analyzes the image data acquired by the image acquisition unit; a determination unit that determines a distance based on the data analyzed by the analysis unit; an alert issuing unit that issues an alert when the distance determined by the determining unit is not within a safe range. A system characterized by:

2. The image acquisition unit The camera's viewing angle is automatically adjusted to provide the optimal field of view depending on the driving environment.

2. The system of claim 1.

3. The image acquisition unit The camera is equipped with a night mode, enabling high-precision image capture even in dark places.

2. The system of claim 1.

4. The image acquisition unit Increasing the frequency of image acquisition by the camera when the driver's attention is reduced 2. The system of claim 1.

5. The image acquisition unit An infrared sensor is added to the camera to detect invisible obstacles.

2. The system of claim 1.

6. The image acquisition unit In addition to capturing images from the camera, audio data is also collected at the same time to analyze the sound environment.

2. The system of claim 1.

Images