system

Abstract

A system is provided.SOLUTION: A system comprising: monitoring camera means for constantly capturing an image in a vehicle; AI means for analyzing the captured image and detecting an abnormality; GPS means for acquiring current position information when an abnormality is detected; and means for transmitting the image and the position information to a server via a communication device.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] Ensuring safety during ride-sharing services is an important issue. Both drivers and users are concerned about the possibility of crime or violent acts occurring during their rides, with the occurrence of injuries being a particularly serious problem. Conventional monitoring systems have difficulty detecting abnormalities in real time and reporting them instantly, making it difficult to respond quickly. For this reason, there is a demand for a system that can respond immediately to accidents or criminal acts during rides. [Means for solving the problem]

[0005] To improve safety inside the vehicle, the present invention includes a surveillance camera that constantly captures video from inside the vehicle and an AI that analyzes the captured video to detect abnormalities. It also includes a GPS that acquires the vehicle's current location when an abnormality is detected, and a communication system that transmits the video and location information to a server. The server then analyzes the abnormality data and location information received and, if necessary, notifies the police. This allows for a swift and accurate response when an abnormality occurs, enhancing the sense of security for the driver and passengers.

[0006] The term "monitoring camera means" refers to a device and its function that constantly captures images inside the vehicle and provides the images to the system.

[0007] "AI means" refers to the artificial intelligence algorithm and its execution environment for analyzing captured video and detecting abnormalities.

[0008] "GPS Means" refers to the Global Positioning System for obtaining the current location within the vehicle, and the equipment and functions for processing and transmitting that information.

[0009] "Communication means" refers to a communication device and its functions for transmitting the images captured by the surveillance camera means and the location information acquired by the GPS means to the server.

[0010] "Server" refers to the central computer system and its functions for analyzing data received from the terminals and reporting to the police if necessary.

[0011] "Abnormal data" refers to abnormal video data and related information detected by AI means.

[0012] "Means for reporting to the police" refers to the communication and notification functions that determine the possibility of an incident occurring based on abnormal data and location information, and convey that information to the police if necessary. [Brief explanation of the drawings]

[0013] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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, a 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), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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), Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0025] 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.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, and communication technology to detect abnormalities and respond quickly.

[0035] System Configuration

[0036] The surveillance camera means installed inside the ride-sharing vehicle constantly captures video footage inside the vehicle, allowing the situation inside the vehicle to be monitored in real time. The AI ​​means analyzes the captured video footage and detects abnormalities (such as violent or fraudulent behavior). When an abnormality is detected, the GPS means acquires the current location information. The communication means then transmits the video data and location information of the abnormality to the server. The server analyzes the received data and, if necessary, notifies the police.

[0037] Program processing overview

[0038] The terminal continuously captures video of the inside of the vehicle using a surveillance camera and transmits it to the AI ​​means. The AI ​​means analyzes the video data and sets a flag if an abnormality is detected. When this flag is set, the terminal obtains current location information using the GPS means and transmits that information and video data of the abnormality to the server via the communication means.

[0039] Based on the received video data and location information, the server performs additional analysis to determine whether there is an abnormality. If an abnormality is confirmed, the server immediately reports the information to the police. This report includes video and location information showing the abnormality, allowing the police to respond quickly and accurately.

[0040] Specific examples

[0041] For example, if a violent act occurs inside a car, the system operates as follows: First, the device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. The device then obtains its current location information using GPS, and sends this location information and video data to the server. The server receives this information, confirms that there is an abnormality, and notifies the police. This notification allows the police to quickly arrive at the scene and take appropriate action.

[0042] The benefits of this system include significantly improved safety for drivers and passengers, and improved reliability and quality of the entire ride-sharing service. It also contributes to preventing or mitigating serious incidents by enabling real-time anomaly detection and immediate response.

[0043] The above is the "mode for carrying out the invention" of the present invention.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The device captures video in real time using a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0047] Step 2:

[0048] The device sends the captured video data to the AI ​​module, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0049] Step 3:

[0050] The device checks whether the AI ​​module detects an anomaly. If so, it sets a flag and proceeds to the next step. If no anomaly is detected, it returns to step 1, where it captures video again.

[0051] Step 4:

[0052] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0053] Step 5:

[0054] The terminal transmits the acquired video data and GPS information to the server by using a communication means to transmit the data to a specific endpoint on the server.

[0055] Step 6:

[0056] The server analyzes the video data and GPS information received from the device, performs additional checks based on the received data to determine if there are any abnormalities, and prepares to notify the police if necessary.

[0057] Step 7:

[0058] The server then reports any data that is deemed abnormal to the police. The report includes the video data and GPS information that has been determined to be abnormal, allowing the police to take prompt action.

[0059] Step 8:

[0060] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0061] Step 9:

[0062] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0063] The above is the specific flow of the program processing.

[0064] Example 1

[0065] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0066] Ensuring the safety of passengers and drivers in ride-sharing vehicles is an important issue. In particular, when violent or fraudulent behavior occurs, it is necessary to quickly grasp the situation on the scene and respond promptly. However, current systems have difficulty detecting abnormalities in real time and responding immediately, so improvements in safety are required.

[0067] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0068] In this invention, the server includes an image capturing means for constantly capturing images of the inside of the vehicle, an analysis means for analyzing the captured images and detecting abnormalities, a location information capturing means for capturing current location information when an abnormality is detected, a means for transmitting the image and location information via a communication device, and a means for reporting the abnormality data and location information received by the server to a reporting agency. This makes it possible to detect abnormalities inside the vehicle in real time and respond quickly.

[0069] The "image acquisition means" is a device for constantly capturing images inside the vehicle.

[0070] "Analysis means" refers to technology for analyzing captured video and detecting abnormalities.

[0071] The "location information acquisition means" is a device that acquires current location information when an abnormality is detected.

[0072] A "communication device" is a device for transmitting video and location information to a server or the like.

[0073] The "server" is a device that receives the transmitted abnormal data and location information, analyzes it, and reports it.

[0074] "Reporting agency" refers to an external agency, such as the police, that responds when an abnormality occurs.

[0075] This invention relates to a system for improving safety inside ride-sharing vehicles. The system combines surveillance cameras, AI technology, GPS, and communication devices to detect abnormalities and respond quickly.

[0076] Hardware and software used

[0077] 1. Video acquisition method

[0078] A surveillance camera installed inside a ride-sharing vehicle. It is a high-resolution camera that can cover the entire interior of the vehicle. The camera is always on and captures video in real time.

[0079] 2. Analysis method

[0080] To analyze the captured video, AI technology is used, specifically machine learning frameworks such as TensorFlow and OpenCV, to detect anomalies such as fraudulent or violent behavior in the video data.

[0081] 3. Location information acquisition means

[0082] The GPS module is used to obtain the current location information when an abnormality is detected, and detailed latitude and longitude can be obtained in real time.

[0083] 4. Communications Equipment

[0084] Video data and location information of abnormalities are sent to a server via a 4G / 5G communication module or Wi-Fi. The data is encrypted and sent securely.

[0085] 5. Server

[0086] The server re-analyzes the received data and further checks for anomalies. It performs the re-analysis using a deep learning model (e.g., YOLO, ResNet). If anomalies are found, it reports them to reporting agencies such as the police.

[0087] Example of operation

[0088] For example, if a violent act occurs in a ride-sharing vehicle, the system operates as follows: First, the device captures the violent act with a surveillance camera and analyzes the video using AI technology (e.g., OpenCV). If the AI ​​detects a violent act, it raises a flag. The device then obtains its current location using its GPS module, encrypts this information and the video data, and sends it to a server. The server reanalyzes the received video data and location information (e.g., using a ResNet model), and if it ultimately confirms abnormal behavior, it notifies reporting agencies such as the police. This allows the police to arrive at the scene quickly and take appropriate action.

[0089] Prompt Sentence Examples

[0090] An example of a prompt to be input to the generative AI model is, "Please explain in natural language how the system will behave if a violent act occurs inside a ride-sharing vehicle. The system will capture video using a surveillance camera and use AI to detect anomalies. If an anomaly is detected, the location information will be obtained using GPS and sent via communication means to the server. The server will reanalyze the data, and if an anomaly is confirmed, it will notify the police." This prompt allows for a detailed explanation of the system's behavior.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] Input: Real-time video from inside a rideshare vehicle.

[0094] How it works: The device constantly captures video from surveillance cameras inside the vehicle. The cameras are high-resolution and are installed to cover the entire interior of the vehicle.

[0095] Output: High-resolution real-time video data.

[0096] Step 2:

[0097] Input: Captured real-time video data.

[0098] How it works: The device sends the captured video data to an AI analysis module. Specifically, the video is analyzed using machine learning frameworks such as TensorFlow and OpenCV. The AI ​​detects abnormal behavior based on pre-trained patterns of misconduct and violence.

[0099] Output: Video data flagged if an anomaly is detected.

[0100] Step 3:

[0101] Input: Flagged anomalous video data.

[0102] How it works: When the device receives video data that flags an anomaly, it uses its built-in GPS module to obtain its current location, which measures latitude and longitude in real time to generate detailed location data.

[0103] Output: Anomaly video data and corresponding real-time location information.

[0104] Step 4:

[0105] Input: Anomaly video data and real-time location information.

[0106] Operation: The device sends the acquired video data and location information to the server using a communication method (e.g., 4G / 5G communication module or Wi-Fi). The data is encrypted and sent securely.

[0107] Output: Anomaly video data and location information sent to the server.

[0108] Step 5:

[0109] Input: Anomaly video data and location information sent to the server.

[0110] Operation: The server re-analyzes the received data. Specifically, it uses a deep learning model (e.g., YOLO, ResNet) to re-check for anomalous behavior. If the re-analysis confirms anomalous behavior, it proceeds to the next step.

[0111] Output: Reviewed anomaly data and location information.

[0112] Step 6:

[0113] Input: Reviewed anomaly data and location information.

[0114] Operation: The server reports the detected anomaly to a reporting agency such as the police. The report includes video data showing the anomaly and location information. The server attaches a text message or image file to the report.

[0115] Output: Anomaly data and location information sent to reporting agencies such as police.

[0116] (Application example 1)

[0117] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] As the use of autonomous vehicles increases, ensuring safety inside the vehicle is extremely important. Conventional ride-sharing services have implemented some safety measures using surveillance cameras and GPS, but these alone make it difficult to respond quickly to sudden acts of violence or fraud. Furthermore, driverless autonomous vehicles require a means to ensure passenger safety in real time. Furthermore, passengers need a way to check the situation themselves and report it immediately.

[0119] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0120] In this invention, the server includes a means for analyzing and determining the abnormal data received, a means for reporting to the police only if the abnormal data meets certain criteria, and an abnormality notification means for issuing an alert when the mobile information terminal detects an abnormality. This enables rapid detection and response to abnormalities in real time through a surveillance camera means for constantly capturing video from inside the vehicle, an AI means for analyzing the captured video and detecting abnormalities, and a location information acquisition means for acquiring current location information when an abnormality is detected. Furthermore, passengers can check the video from inside the vehicle in real time using their own mobile information terminals, allowing them to take self-defense measures and improving overall safety.

[0121] The "monitoring camera means" is a device that constantly captures images of the inside of the vehicle and monitors them in real time.

[0122] "AI means" refers to artificial intelligence technology that analyzes captured video data and automatically detects abnormal behavior or fraudulent activity.

[0123] The "location information acquisition means" is a device or technology for acquiring current vehicle location information when an abnormality is detected.

[0124] The "transmission means" is a system for transmitting the video and location information to a server via a communication device.

[0125] The "reporting means" is a function for notifying the police of the abnormal data and location information received by the server.

[0126] The "remote video viewing means" is a function that allows passengers to view camera footage inside the vehicle in real time using a mobile information terminal.

[0127] The "abnormality notification means" is a function that issues an alert when the mobile information terminal detects an abnormality.

[0128] The "image capturing device" is a camera device for capturing images inside the vehicle at a constant frame rate.

[0129] This invention provides a system for improving safety inside ride-sharing vehicles, which includes a surveillance camera, an AI, a location information acquisition unit, a transmission unit, a reporting unit, a remote video confirmation unit, and an abnormality notification unit.

[0130] Hardware and Software

[0131] The hardware includes the smartphone's camera (video capture device), GPS module (location information acquisition means), and communications device, while the software includes OpenCV (video capture), Geopy (location information acquisition), Requests (data transmission), and generative AI models (video analysis and anomaly detection).

[0132] Processing Overview

[0133] 1. Surveillance camera means:

[0134] A camera installed inside the vehicle constantly captures video in real time, which is then sent to an AI system to analyze for any abnormalities.

[0135] 2. AI means:

[0136] Generative AI models are used to analyze captured video data in real time to detect anomalies, such as violent or fraudulent behavior, which are then flagged.

[0137] 3. Location information acquisition means:

[0138] If an abnormality is detected, the GPS module obtains the current vehicle location information.

[0139] 4. Means of transmission:

[0140] When an abnormality is detected, the terminal transmits the captured video and the acquired location information to the server via the communication device.

[0141] 5. Reporting methods:

[0142] The server analyzes the received abnormal data and location information, and if an abnormality is confirmed, it reports it to the police.

[0143] 6. Remote video confirmation method:

[0144] Passengers can use their mobile devices to view real-time camera footage from inside the vehicle, allowing them to understand the current situation and take self-protection measures.

[0145] 7. Abnormality notification means:

[0146] The mobile information terminal has the ability to issue an alert and notify passengers when an abnormality is detected.

[0147] Specific examples

[0148] This explains what happens when violence occurs inside an autonomous vehicle.

[0149] 1. Surveillance camera means capture footage of violent acts.

[0150] 2. The captured footage is analyzed using AI means to detect any anomalies.

[0151] 3. The location information acquisition means acquires the current vehicle location.

[0152] 4. These data are transmitted to the server via the transmission means.

[0153] 5. The server checks the abnormal data and location information, and if it determines that the damage is serious, it will report it to the police using the reporting means.

[0154] 6. Meanwhile, passengers can use remote video surveillance to monitor the situation and take self-protection measures.

[0155] 7. If an abnormality is detected, the mobile information device will alert passengers via the abnormality notification means.

[0156] Prompt Sentence Examples

[0157] Please capture footage of passengers committing violent acts inside the self-driving vehicle. The footage will be analyzed in real time, and if an abnormality is detected, the current location will be acquired via GPS and sent to the server along with the video of the abnormality. The server will receive this and, if necessary, notify the police.

[0158] In this way, a system is provided that aims to improve safety inside ride-sharing vehicles and can respond quickly and appropriately in the event of an abnormality.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] The terminal constantly captures video using a surveillance camera installed inside the vehicle. The input is camera image data, and the output is the captured video frame. Specifically, the terminal acquires video frames from the camera and temporarily stores them.

[0162] Step 2:

[0163] The terminal transmits the captured video frame to the AI ​​means. The input is the captured video frame, and the output is the input data to the AI ​​means. Specifically, the terminal inputs the video frame to the AI ​​model.

[0164] Step 3:

[0165] The AI ​​means uses a generative AI model to analyze the captured video data and detect anomalies. The input is the transmitted video frame, and the output is an anomaly detection flag. Specifically, the AI ​​model analyzes the video frame and determines whether any abnormal behavior is occurring.

[0166] Step 4:

[0167] When the AI ​​means detects an abnormality, the terminal acquires current location information using the location information acquisition means. The input is an abnormality detection flag, and the output is current location information. Specifically, the terminal acquires location information from the GPS module.

[0168] Step 5:

[0169] The terminal transmits the video data of the anomaly and the acquired location information to the server using the transmission means. The input is the video data of the anomaly and the current location information, and the output is the data to be transmitted to the server. In concrete terms, the terminal uploads this data to the server via the transmission means.

[0170] Step 6:

[0171] The server analyzes the received anomaly data and location information to confirm the anomaly. The input is the transmitted anomaly data and location information, and the output is the anomaly confirmation result. Specifically, the server reanalyzes the anomaly data and determines whether the anomaly is confirmed.

[0172] Step 7:

[0173] If the server detects an abnormality, it will notify the police using the reporting means. The input is the abnormality detection result and location information, and the output is the report data to the police. Specifically, the server sends the necessary information to the police.

[0174] Step 8:

[0175] The user uses the remote video confirmation means to check the camera video in the vehicle in real time. The input is a remote access request, and the output is real-time video data. Specifically, the mobile information terminal accesses the camera video and displays the video in real time.

[0176] Step 9:

[0177] When the terminal detects an abnormality, it uses the abnormality notification means to issue an alert to the user. The input is an abnormality detection flag, and the output is an alert notification. Specifically, when the terminal detects an abnormality, a warning message is displayed on the user's mobile information terminal.

[0178] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0179] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, communication technology, and an emotion engine, with the aim of detecting abnormalities and responding quickly.

[0180] System Configuration

[0181] The surveillance camera means installed in the ride-sharing vehicle constantly captures images of the inside of the vehicle, allowing for real-time monitoring of the situation inside the vehicle. The AI ​​means has an algorithm for analyzing the captured images and detecting abnormalities (e.g., violent or fraudulent behavior). Furthermore, by incorporating an emotion engine, the system can also monitor and analyze the user's emotional state, improving the accuracy of abnormality detection.

[0182] User Emotion Recognition

[0183] The emotion engine analyzes emotions from the user's facial expressions and voice. Based on video and audio data, it monitors the user's emotional state (e.g., anger, fear, stress, etc.) in real time. The AI ​​means also takes into account data from the emotion engine to detect anomalies. For this reason, sudden changes in emotions or high stress levels may be detected as anomalies.

[0184] Response in the event of an emergency

[0185] If an abnormality is detected, the device acquires current GPS information and sends it along with video data to the server. This data is sent to the server's designated endpoint via a communication method. The server analyzes the received data and, if it determines that an abnormality exists, immediately notifies the police. This report includes video data and GPS information indicating the abnormality, allowing the police to respond to the scene quickly.

[0186] Program processing overview

[0187] The device continuously captures video of the interior of the vehicle using a surveillance camera and sends it to the AI ​​means. The AI ​​means analyzes the video data and detects abnormalities. It also uses an emotion engine to monitor the user's emotional state in real time to complement the abnormality detection. If an abnormality is detected, the device obtains the current GPS information and sends it to the server along with the video data.

[0188] The server analyzes the video data and GPS information received from the device to check for any abnormalities. If further analysis determines that there is an abnormality, the server immediately notifies the police. This notification includes the video data and GPS information indicating the abnormality. The police can use this information to take swift and appropriate action.

[0189] Specific examples

[0190] For example, if a violent act occurs inside a car, the system works as follows: The device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. At the same time, the emotion engine analyzes the user's emotional state and measures abnormal stress levels. At this time, the device obtains current GPS information and sends this information and video data to the server. The server receives this information, further verifies that it is an abnormality, and then reports it to the police. This allows the police to quickly respond to the scene and provide initial support for the incident.

[0191] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] The device captures video in real time from a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0195] Step 2:

[0196] The device transmits video data captured by the camera to the AI ​​means, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0197] Step 3:

[0198] The device sends the user's facial expressions and voice along with the video data to the emotion engine, which analyzes them and monitors the user's emotional state in real time. The emotion engine detects emotions such as anger, fear, and stress.

[0199] Step 4:

[0200] The device combines the analysis results of the emotion engine and AI means to determine whether an anomaly has been detected. If an anomaly is detected, it sets an anomaly flag. If no anomaly is detected, it returns to step 1 to capture video again.

[0201] Step 5:

[0202] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0203] Step 6:

[0204] The device transmits the captured video data, emotional state data, and GPS information to the server via a communication means, which uses a means for packaging the data and transmitting it to a specific endpoint on the server.

[0205] Step 7:

[0206] The server analyzes the video data, emotional state data, and GPS information received from the device, and further checks whether there are any abnormalities based on the received data, and decides on a response if necessary.

[0207] Step 8:

[0208] The server then reports any abnormal data to the police. The report includes the video data, emotional state data, and GPS information that were determined to be abnormal. This allows the police to take prompt action.

[0209] Step 9:

[0210] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0211] Step 10:

[0212] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0213] The above are the specific processing steps of the system that combines the emotion engine.

[0214] Example 2

[0215] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] In conventional ride-sharing systems, it was difficult to detect abnormal behavior occurring in the vehicle or changes in the user's emotional state in real time and respond quickly. It was also difficult to accurately obtain location information when an abnormality occurred and report it to the appropriate authorities. This led to problems with ensuring safety inside the vehicle.

[0217] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0218] In this invention, the server includes a surveillance camera, an artificial intelligence, an emotion engine, a global positioning system, a data transmission unit via a communication device, and a police reporting unit. This allows for real-time detection of abnormal behavior in the vehicle and changes in the user's emotional state, enabling prompt response. Furthermore, accurate location information can be obtained and immediate reporting to the appropriate authorities can improve safety inside the vehicle.

[0219] The "monitoring camera means" is a device for constantly capturing and recording images inside the vehicle.

[0220] "Artificial intelligence means" refers to algorithms and software used to analyze captured footage and detect abnormal or fraudulent activity.

[0221] The "emotion engine means" is software for analyzing the user's facial expressions and voice and determining their emotional state (for example, anger, fear, stress, etc.).

[0222] The "global positioning system means" is a positioning device for obtaining current location information when an abnormality is detected.

[0223] "Means for transmitting to a server via a communication device" refers to a device for transmitting the captured video and the acquired location information to a server using communication technology.

[0224] "Means for reporting to the police" refers to the reporting system that informs the police of the abnormal data and location information received by the server.

[0225] This invention relates to a system that improves safety in ride-sharing vehicles, combining surveillance cameras, artificial intelligence, global positioning systems, communication technologies, and an emotion engine to detect abnormalities and respond quickly.

[0226] System Configuration

[0227] The devices installed in ride-sharing vehicles are equipped with the following hardware and software:

[0228] 1. Surveillance camera means: A high-resolution camera (e.g., a general-purpose high-resolution camera device) is used. This camera constantly captures and records images inside the vehicle.

[0229] 2. Artificial intelligence means: AI software (e.g., TensorFlow, a general-purpose machine learning framework) equipped with algorithms for analyzing video data and detecting abnormal or fraudulent behavior will be used.

[0230] 3. Emotion engine means: Software (e.g., a general-purpose emotion analysis solution) is used to analyze the user's facial expressions and voice to determine their emotional state.

[0231] 4. Global Positioning System Means: A GPS device (e.g., a general-purpose GPS module) is used to obtain current location information.

[0232] 5. Communication device: A communication means (e.g., a general-purpose 4G LTE modem) is used to transmit video data and location information to the server.

[0233] User Emotion Recognition

[0234] The terminal transmits video data captured using the surveillance camera means to the artificial intelligence means. The artificial intelligence means analyzes the video data and detects abnormal behavior (such as violent behavior or fraud) occurring inside the vehicle. The emotional engine means also analyzes the user's emotional state in real time from their facial expressions and voice to complement the abnormality detection.

[0235] Response in the event of an emergency

[0236] If an anomaly is detected, the device acquires current GPS information and transmits it along with video data to the server. This data is then sent via a communications device to a designated endpoint on the server. The server analyzes the received data to determine whether it is an anomaly. If further analysis determines that an anomaly exists, the server notifies the police. This notification includes video data and GPS information indicating the anomaly, allowing the police to take swift and appropriate action.

[0237] Specific examples

[0238] For example, if a violent act occurs inside a car, the system operates as follows: The device captures the violent act with a surveillance camera, and the artificial intelligence means recognizes it as a violent act. At the same time, the emotion engine means analyzes the user's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then reports it to the police. This allows the police to respond to the scene quickly and provide initial support for the incident.

[0239] Example prompts for generative AI models

[0240] Example prompt:

[0241] "Consider what to do if a violent act occurs in the car and the user's stress level suddenly rises. In this case, AI analyzes the video and audio data captured by the camera and sends the GPS information to a server. Please explain in detail what happens after that."

[0242] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0243] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0244] Step 1:

[0245] The device continuously captures video using a surveillance camera installed inside the vehicle. The input is real-time video from inside the vehicle, and the output is high-resolution video data. This data is buffered for a few seconds and then stored in the internal memory.

[0246] Step 2:

[0247] The device sends the captured video data to an artificial intelligence means. The input is the high-resolution video data obtained in step 1, and the output is a frame-by-frame analysis of potential anomalous behavior. The artificial intelligence means uses a machine learning framework such as TensorFlow to run algorithms to detect violent or fraudulent behavior.

[0248] Step 3:

[0249] The terminal uses an emotion engine means to analyze the user's facial expressions and voice. The input is the video data and voice data obtained in step 1, and the output is the analysis result of the detected emotional state (e.g., anger, fear, stress). The emotion engine analyzes facial features and voice tone to detect abnormal emotional states.

[0250] Step 4:

[0251] When abnormal behavior or abnormal emotions are detected, the device obtains current location information using a global positioning system. The input is the anomaly detection trigger event, and the output is the current latitude and longitude information. The GPS module quickly obtains the location information, which is then processed together with other data.

[0252] Step 5:

[0253] The device transmits the acquired location information and video data indicating the anomaly to the server via a communication device. The input is the analysis results obtained in steps 2 and 3 and the location information obtained in step 4, and the output is a data packet sent to the server. This transmission is performed in real time using a secure protocol such as HTTPS.

[0254] Step 6:

[0255] The server analyzes the data received from the device. The input is the data packet sent in step 5, and the output is the confirmation of abnormal behavior. The AI ​​on the server operates in the cloud and reanalyzes the video data and emotion data to confirm the abnormality.

[0256] Step 7:

[0257] If the server detects a definite anomaly, it notifies the police. The input is the detection result and location information obtained in step 6, and the output is a report message sent to the police. The report includes specific video clips and GPS information, allowing the police to respond quickly and accurately.

[0258] The above is the specific flow of the program processing of this system.

[0259] (Application example 2)

[0260] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] Conventional ride-sharing and self-driving taxis lack sufficient monitoring systems to ensure safety within the vehicle. This makes it difficult to respond appropriately to violent or fraudulent behavior, as well as sudden changes in passenger emotion. Furthermore, even if an abnormality occurs, there is no established system for quickly and accurately reporting it, which can result in delayed emergency response.

[0262] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means that constantly captures video of the inside of the vehicle, an artificial intelligence means that analyzes the captured video and detects abnormalities, an emotion engine that analyzes emotions from the user's facial expressions and voice, a location information acquisition means that acquires current location information when an abnormality is detected, a means for transmitting the video and location information to the server via a communication device, and a transmission means for reporting the abnormality data and location information received by the server to the police. This enables more accurate abnormality detection and faster response.

[0263] The "image acquisition means" is a means for constantly capturing images inside the vehicle, and typically refers to a camera device.

[0264] "Artificial intelligence means" means means for analyzing captured video and detecting anomalies, including AI algorithms and models.

[0265] An "emotion engine" is a means for analyzing emotions from a user's facial expressions and voice, and refers to an engine that uses emotion recognition technology.

[0266] "Location information acquisition means" refers to a means for acquiring current location information when an abnormality is detected, and generally refers to a GPS module.

[0267] A "communication device" is a means for transmitting video and location information to a server, and includes communication technologies such as mobile communication and Wi-Fi.

[0268] The "transmission means" is a means for reporting the abnormal data and location information received by the server to the police, and includes a dedicated communication interface.

[0269] The "criteria" are standard evaluation indicators used by the server when analyzing abnormal data, and are an important factor in determining the accuracy of anomaly detection.

[0270] The "camera device" refers to a camera device that captures images inside a vehicle at a constant frame rate and is used to obtain high-resolution images.

[0271] The present invention relates to a system for ensuring the safety of passengers in autonomous vehicles. This system is composed of a combination of various means for monitoring video images inside the vehicle, detecting abnormalities, and responding quickly.

[0272] 1. System Configuration

[0273] The main components are as follows:

[0274] Surveillance camera means

[0275] To constantly capture images inside the vehicle, a surveillance camera system is used. This system consists of camera devices that capture high-resolution images. The cameras are installed at key locations inside the vehicle and capture passenger behavior in real time.

[0276] Artificial Intelligence Tools

[0277] AI algorithms are used to analyze the captured footage and detect anomalies. This includes deep learning models to automatically detect anomalies such as violent or fraudulent behavior. The analyzed data is then used in conjunction with an emotion engine to enable more accurate anomaly detection.

[0278] Emotion Engine

[0279] The emotion engine is a technology for analyzing emotions from users' facial expressions and voices. This allows for real-time monitoring of passengers' stress levels and sudden changes in their emotions, making it possible to detect abnormal changes in emotions as well as violent and fraudulent behavior.

[0280] Location information acquisition means

[0281] If an abnormality is detected, the GPS module is used to obtain the current location information, which is then sent to the server via the communication means described below.

[0282] communication equipment

[0283] The device includes a communication device for transmitting video and location information to a server. The communication device uses communication technologies such as LTE and Wi-Fi, which enables real-time data transmission.

[0284] 2. Server Processing

[0285] The server receives the abnormal data and location information sent from the device. The received abnormal data is further examined by the server's analysis means. If an abnormality is confirmed as a result of the analysis, the server notifies the police. The report includes video data showing the abnormality and location information, allowing the police to respond to the scene quickly.

[0286] 3. Specific Examples

[0287] For example, if a violent act occurs inside a self-driving taxi, the system operates as follows: The camera captures the violent act, and the AI ​​recognizes it as such. At the same time, the emotion engine analyzes the passenger's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then notifies the police.

[0288] Prompt Sentence Examples

[0289] Below are some example prompts for a generative AI model:

[0290] Image data: base64_encoded_image_string

[0291] Emotional Data:

[0292] Anger: 0.8

[0293] Fear: 0.7

[0294] Stress: 0.9

[0295] The prompt contains image data and emotional state information that is sent to the AI ​​model, allowing it to detect anomalies and take specific corrective action.

[0296] The above is the "Mode for Carrying Out the Invention" of the present invention.

[0297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0298] Step 1:

[0299] The terminal constantly captures images of the inside of the vehicle using a surveillance camera means.

[0300] Input: Real-time video data from a camera device

[0301] Output: Captured image frames

[0302] Specific operation: The camera captures video at a constant frame rate and stores the video data in memory.

[0303] Step 2:

[0304] The terminal transmits the captured video to an artificial intelligence means for analysis.

[0305] Input: Captured image frame

[0306] Output: The result of the anomaly detection algorithm (e.g., no anomalies, violent behavior detected, etc.)

[0307] Specific operation: Analyzes video data using a deep learning model to determine whether or not abnormal behavior is occurring.

[0308] Step 3:

[0309] The device analyzes the user's facial expressions and voice using an emotion engine.

[0310] Input: Captured image frames and audio data

[0311] Output: Emotional state data (e.g., anger 0.8, fear 0.7, stress 0.9)

[0312] Specific operation: Analyzes the user's emotions using an emotion recognition model and generates emotion data.

[0313] Step 4:

[0314] The terminal integrates the data obtained from the artificial intelligence means and the emotion engine to make a comprehensive judgment as to whether an abnormality exists.

[0315] Input: Results of anomaly detection algorithm, emotional state data

[0316] Output: Overall abnormality judgment result (e.g., abnormality present, no abnormality present)

[0317] What it does: Consolidate the analysis results and flag any anomalies detected.

[0318] Step 5:

[0319] When an abnormality is detected, the terminal acquires current location information using the location information acquisition means.

[0320] Input: Abnormality judgment flag

[0321] Output: Current GPS information

[0322] Specific operation: Starts the GPS module and obtains current location information.

[0323] Step 6:

[0324] The terminal transmits the video data and the location information to the server.

[0325] Input: Captured image frame, current GPS information

[0326] Output: Data sent to the server (e.g., image data, location information)

[0327] Specific operations: Using a communication device, the collected data is sent to a specified server endpoint.

[0328] Step 7:

[0329] The server analyzes the received data and reconfirms the accuracy of the anomaly.

[0330] Input: Transmitted image data, location information

[0331] Output: Final anomaly judgment (e.g., confirmed anomaly, no anomaly)

[0332] Specific operation: Analyzes the received data and runs an algorithm to assess the probability of an anomaly.

[0333] Step 8:

[0334] If any abnormalities are detected, the server will notify the police.

[0335] Input: Final anomaly determination

[0336] Output: Report data sent to police (e.g. image data, location information)

[0337] Specific actions: Activate the reporting system and send data to the police.

[0338] The above is a description of the specific steps of the process according to the present invention.

[0339] 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.

[0340] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0341] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0342] [Second embodiment]

[0343] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0344] 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.

[0345] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0346] 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.

[0347] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0348] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0349] 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.

[0350] 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.

[0351] 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 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.

[0352] 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.

[0353] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0354] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0355] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, and communication technology to detect abnormalities and respond quickly.

[0356] System Configuration

[0357] The surveillance camera means installed inside the ride-sharing vehicle constantly captures video footage inside the vehicle, allowing the situation inside the vehicle to be monitored in real time. The AI ​​means analyzes the captured video footage and detects abnormalities (such as violent or fraudulent behavior). When an abnormality is detected, the GPS means acquires the current location information. The communication means then transmits the video data and location information of the abnormality to the server. The server analyzes the received data and, if necessary, notifies the police.

[0358] Program processing overview

[0359] The terminal continuously captures video of the inside of the vehicle using a surveillance camera and transmits it to the AI ​​means. The AI ​​means analyzes the video data and sets a flag if an abnormality is detected. When this flag is set, the terminal obtains current location information using the GPS means and transmits that information and video data of the abnormality to the server via the communication means.

[0360] Based on the received video data and location information, the server performs additional analysis to determine whether there is an abnormality. If an abnormality is confirmed, the server immediately reports the information to the police. This report includes video and location information showing the abnormality, allowing the police to respond quickly and accurately.

[0361] Specific examples

[0362] For example, if a violent act occurs inside a car, the system operates as follows: First, the device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. The device then obtains its current location information using GPS, and sends this location information and video data to the server. The server receives this information, confirms that there is an abnormality, and notifies the police. This notification allows the police to quickly arrive at the scene and take appropriate action.

[0363] The benefits of this system include significantly improved safety for drivers and passengers, and improved reliability and quality of the entire ride-sharing service. It also contributes to preventing or mitigating serious incidents by enabling real-time anomaly detection and immediate response.

[0364] The above is the "mode for carrying out the invention" of the present invention.

[0365] The processing flow will be explained below.

[0366] Step 1:

[0367] The device captures video in real time using a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0368] Step 2:

[0369] The device sends the captured video data to the AI ​​module, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0370] Step 3:

[0371] The device checks whether the AI ​​module detects an anomaly. If so, it sets a flag and proceeds to the next step. If no anomaly is detected, it returns to step 1, where it captures video again.

[0372] Step 4:

[0373] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0374] Step 5:

[0375] The terminal transmits the acquired video data and GPS information to the server by using a communication means to transmit the data to a specific endpoint on the server.

[0376] Step 6:

[0377] The server analyzes the video data and GPS information received from the device, performs additional checks based on the received data to determine if there are any abnormalities, and prepares to notify the police if necessary.

[0378] Step 7:

[0379] The server then reports any data that is deemed abnormal to the police. The report includes the video data and GPS information that has been determined to be abnormal, allowing the police to take prompt action.

[0380] Step 8:

[0381] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0382] Step 9:

[0383] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0384] The above is the specific flow of the program processing.

[0385] Example 1

[0386] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0387] Ensuring the safety of passengers and drivers in ride-sharing vehicles is an important issue. In particular, when violent or fraudulent behavior occurs, it is necessary to quickly grasp the situation on the scene and respond promptly. However, current systems have difficulty detecting abnormalities in real time and responding immediately, so improvements in safety are required.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0389] In this invention, the server includes an image capturing means for constantly capturing images of the inside of the vehicle, an analysis means for analyzing the captured images and detecting abnormalities, a location information capturing means for capturing current location information when an abnormality is detected, a means for transmitting the image and location information via a communication device, and a means for reporting the abnormality data and location information received by the server to a reporting agency. This makes it possible to detect abnormalities inside the vehicle in real time and respond quickly.

[0390] The "image acquisition means" is a device for constantly capturing images inside the vehicle.

[0391] "Analysis means" refers to technology for analyzing captured video and detecting abnormalities.

[0392] The "location information acquisition means" is a device that acquires current location information when an abnormality is detected.

[0393] A "communication device" is a device for transmitting video and location information to a server or the like.

[0394] The "server" is a device that receives the transmitted abnormal data and location information, analyzes it, and reports it.

[0395] "Reporting agency" refers to an external agency, such as the police, that responds when an abnormality occurs.

[0396] This invention relates to a system for improving safety inside ride-sharing vehicles. The system combines surveillance cameras, AI technology, GPS, and communication devices to detect abnormalities and respond quickly.

[0397] Hardware and software used

[0398] 1. Video acquisition method

[0399] A surveillance camera installed inside a ride-sharing vehicle. It is a high-resolution camera that can cover the entire interior of the vehicle. The camera is always on and captures video in real time.

[0400] 2. Analysis method

[0401] To analyze the captured video, AI technology is used, specifically machine learning frameworks such as TensorFlow and OpenCV, to detect anomalies such as fraudulent or violent behavior in the video data.

[0402] 3. Location information acquisition means

[0403] The GPS module is used to obtain the current location information when an abnormality is detected, and detailed latitude and longitude can be obtained in real time.

[0404] 4. Communications Equipment

[0405] Video data and location information of abnormalities are sent to a server via a 4G / 5G communication module or Wi-Fi. The data is encrypted and sent securely.

[0406] 5. Server

[0407] The server re-analyzes the received data and further checks for anomalies. It performs the re-analysis using a deep learning model (e.g., YOLO, ResNet). If anomalies are found, it reports them to reporting agencies such as the police.

[0408] Example of operation

[0409] For example, if a violent act occurs in a ride-sharing vehicle, the system operates as follows: First, the device captures the violent act with a surveillance camera and analyzes the video using AI technology (e.g., OpenCV). If the AI ​​detects a violent act, it raises a flag. The device then obtains its current location using its GPS module, encrypts this information and the video data, and sends it to a server. The server reanalyzes the received video data and location information (e.g., using a ResNet model), and if it ultimately confirms abnormal behavior, it notifies reporting agencies such as the police. This allows the police to arrive at the scene quickly and take appropriate action.

[0410] Prompt Sentence Examples

[0411] An example of a prompt to be input to the generative AI model is, "Please explain in natural language how the system will behave if a violent act occurs inside a ride-sharing vehicle. The system will capture video using a surveillance camera and use AI to detect anomalies. If an anomaly is detected, the location information will be obtained using GPS and sent via communication means to the server. The server will reanalyze the data, and if an anomaly is confirmed, it will notify the police." This prompt allows for a detailed explanation of the system's behavior.

[0412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0413] Step 1:

[0414] Input: Real-time video from inside a rideshare vehicle.

[0415] How it works: The device constantly captures video from surveillance cameras inside the vehicle. The cameras are high-resolution and are installed to cover the entire interior of the vehicle.

[0416] Output: High-resolution real-time video data.

[0417] Step 2:

[0418] Input: Captured real-time video data.

[0419] How it works: The device sends the captured video data to an AI analysis module. Specifically, the video is analyzed using machine learning frameworks such as TensorFlow and OpenCV. The AI ​​detects abnormal behavior based on pre-trained patterns of misconduct and violence.

[0420] Output: Video data flagged if an anomaly is detected.

[0421] Step 3:

[0422] Input: Flagged anomalous video data.

[0423] How it works: When the device receives video data that flags an anomaly, it uses its built-in GPS module to obtain its current location, which measures latitude and longitude in real time to generate detailed location data.

[0424] Output: Anomaly video data and corresponding real-time location information.

[0425] Step 4:

[0426] Input: Anomaly video data and real-time location information.

[0427] Operation: The device sends the acquired video data and location information to the server using a communication method (e.g., 4G / 5G communication module or Wi-Fi). The data is encrypted and sent securely.

[0428] Output: Anomaly video data and location information sent to the server.

[0429] Step 5:

[0430] Input: Anomaly video data and location information sent to the server.

[0431] Operation: The server re-analyzes the received data. Specifically, it uses a deep learning model (e.g., YOLO, ResNet) to re-check for anomalous behavior. If the re-analysis confirms anomalous behavior, it proceeds to the next step.

[0432] Output: Reviewed anomaly data and location information.

[0433] Step 6:

[0434] Input: Reviewed anomaly data and location information.

[0435] Operation: The server reports the detected anomaly to a reporting agency such as the police. The report includes video data showing the anomaly and location information. The server attaches a text message or image file to the report.

[0436] Output: Anomaly data and location information sent to reporting agencies such as police.

[0437] (Application example 1)

[0438] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0439] As the use of autonomous vehicles increases, ensuring safety inside the vehicle is extremely important. Conventional ride-sharing services have implemented some safety measures using surveillance cameras and GPS, but these alone make it difficult to respond quickly to sudden acts of violence or fraud. Furthermore, driverless autonomous vehicles require a means to ensure passenger safety in real time. Furthermore, passengers need a way to check the situation themselves and report it immediately.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0441] In this invention, the server includes a means for analyzing and determining the abnormal data received, a means for reporting to the police only if the abnormal data meets certain criteria, and an abnormality notification means for issuing an alert when the mobile information terminal detects an abnormality. This enables rapid detection and response to abnormalities in real time through a surveillance camera means for constantly capturing video from inside the vehicle, an AI means for analyzing the captured video and detecting abnormalities, and a location information acquisition means for acquiring current location information when an abnormality is detected. Furthermore, passengers can check the video from inside the vehicle in real time using their own mobile information terminals, allowing them to take self-defense measures and improving overall safety.

[0442] The "monitoring camera means" is a device that constantly captures images of the inside of the vehicle and monitors them in real time.

[0443] "AI means" refers to artificial intelligence technology that analyzes captured video data and automatically detects abnormal behavior or fraudulent activity.

[0444] The "location information acquisition means" is a device or technology for acquiring current vehicle location information when an abnormality is detected.

[0445] The "transmission means" is a system for transmitting the video and location information to a server via a communication device.

[0446] The "reporting means" is a function for notifying the police of the abnormal data and location information received by the server.

[0447] The "remote video viewing means" is a function that allows passengers to view camera footage inside the vehicle in real time using a mobile information terminal.

[0448] The "abnormality notification means" is a function that issues an alert when the mobile information terminal detects an abnormality.

[0449] The "image capturing device" is a camera device for capturing images inside the vehicle at a constant frame rate.

[0450] This invention provides a system for improving safety inside ride-sharing vehicles, which includes a surveillance camera, an AI, a location information acquisition unit, a transmission unit, a reporting unit, a remote video confirmation unit, and an abnormality notification unit.

[0451] Hardware and Software

[0452] The hardware includes the smartphone's camera (video capture device), GPS module (location information acquisition means), and communications device, while the software includes OpenCV (video capture), Geopy (location information acquisition), Requests (data transmission), and generative AI models (video analysis and anomaly detection).

[0453] Processing Overview

[0454] 1. Surveillance camera means:

[0455] A camera installed inside the vehicle constantly captures video in real time, which is then sent to an AI system to analyze for any abnormalities.

[0456] 2. AI means:

[0457] Generative AI models are used to analyze captured video data in real time to detect anomalies, such as violent or fraudulent behavior, which are then flagged.

[0458] 3. Location information acquisition means:

[0459] If an abnormality is detected, the GPS module obtains the current vehicle location information.

[0460] 4. Means of transmission:

[0461] When an abnormality is detected, the terminal transmits the captured video and the acquired location information to the server via the communication device.

[0462] 5. Reporting methods:

[0463] The server analyzes the received abnormal data and location information, and if an abnormality is confirmed, it reports it to the police.

[0464] 6. Remote video confirmation method:

[0465] Passengers can use their mobile devices to view real-time camera footage from inside the vehicle, allowing them to understand the current situation and take self-protection measures.

[0466] 7. Abnormality notification means:

[0467] The mobile information terminal has the ability to issue an alert and notify passengers when an abnormality is detected.

[0468] Specific examples

[0469] This explains what happens when violence occurs inside an autonomous vehicle.

[0470] 1. Surveillance camera means capture footage of violent acts.

[0471] 2. The captured footage is analyzed using AI means to detect any anomalies.

[0472] 3. The location information acquisition means acquires the current vehicle location.

[0473] 4. These data are transmitted to the server via the transmission means.

[0474] 5. The server checks the abnormal data and location information, and if it determines that the damage is serious, it will report it to the police using the reporting means.

[0475] 6. Meanwhile, passengers can use remote video surveillance to monitor the situation and take self-protection measures.

[0476] 7. If an abnormality is detected, the mobile information device will alert passengers via the abnormality notification means.

[0477] Prompt Sentence Examples

[0478] Please capture footage of passengers committing violent acts inside the self-driving vehicle. The footage will be analyzed in real time, and if an abnormality is detected, the current location will be acquired via GPS and sent to the server along with the video of the abnormality. The server will receive this and, if necessary, notify the police.

[0479] In this way, a system is provided that aims to improve safety inside ride-sharing vehicles and can respond quickly and appropriately in the event of an abnormality.

[0480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0481] Step 1:

[0482] The terminal constantly captures video using a surveillance camera installed inside the vehicle. The input is camera image data, and the output is the captured video frame. Specifically, the terminal acquires video frames from the camera and temporarily stores them.

[0483] Step 2:

[0484] The terminal transmits the captured video frame to the AI ​​means. The input is the captured video frame, and the output is the input data to the AI ​​means. Specifically, the terminal inputs the video frame to the AI ​​model.

[0485] Step 3:

[0486] The AI ​​means uses a generative AI model to analyze the captured video data and detect anomalies. The input is the transmitted video frame, and the output is an anomaly detection flag. Specifically, the AI ​​model analyzes the video frame and determines whether any abnormal behavior is occurring.

[0487] Step 4:

[0488] When the AI ​​means detects an abnormality, the terminal acquires current location information using the location information acquisition means. The input is an abnormality detection flag, and the output is current location information. Specifically, the terminal acquires location information from the GPS module.

[0489] Step 5:

[0490] The terminal transmits the video data of the anomaly and the acquired location information to the server using the transmission means. The input is the video data of the anomaly and the current location information, and the output is the data to be transmitted to the server. In concrete terms, the terminal uploads this data to the server via the transmission means.

[0491] Step 6:

[0492] The server analyzes the received anomaly data and location information to confirm the anomaly. The input is the transmitted anomaly data and location information, and the output is the anomaly confirmation result. Specifically, the server reanalyzes the anomaly data and determines whether the anomaly is confirmed.

[0493] Step 7:

[0494] If the server detects an abnormality, it will notify the police using the reporting means. The input is the abnormality detection result and location information, and the output is the report data to the police. Specifically, the server sends the necessary information to the police.

[0495] Step 8:

[0496] The user uses the remote video confirmation means to check the camera video in the vehicle in real time. The input is a remote access request, and the output is real-time video data. Specifically, the mobile information terminal accesses the camera video and displays the video in real time.

[0497] Step 9:

[0498] When the terminal detects an abnormality, it uses the abnormality notification means to issue an alert to the user. The input is an abnormality detection flag, and the output is an alert notification. Specifically, when the terminal detects an abnormality, a warning message is displayed on the user's mobile information terminal.

[0499] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0500] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, communication technology, and an emotion engine, with the aim of detecting abnormalities and responding quickly.

[0501] System Configuration

[0502] The surveillance camera means installed in the ride-sharing vehicle constantly captures images of the inside of the vehicle, allowing for real-time monitoring of the situation inside the vehicle. The AI ​​means has an algorithm for analyzing the captured images and detecting abnormalities (e.g., violent or fraudulent behavior). Furthermore, by incorporating an emotion engine, the system can also monitor and analyze the user's emotional state, improving the accuracy of abnormality detection.

[0503] User Emotion Recognition

[0504] The emotion engine analyzes emotions from the user's facial expressions and voice. Based on video and audio data, it monitors the user's emotional state (e.g., anger, fear, stress, etc.) in real time. The AI ​​means also takes into account data from the emotion engine to detect anomalies. For this reason, sudden changes in emotions or high stress levels may be detected as anomalies.

[0505] Response in the event of an emergency

[0506] If an abnormality is detected, the device acquires current GPS information and sends it along with video data to the server. This data is sent to the server's designated endpoint via a communication method. The server analyzes the received data and, if it determines that an abnormality exists, immediately notifies the police. This report includes video data and GPS information indicating the abnormality, allowing the police to respond to the scene quickly.

[0507] Program processing overview

[0508] The device continuously captures video of the interior of the vehicle using a surveillance camera and sends it to the AI ​​means. The AI ​​means analyzes the video data and detects abnormalities. It also uses an emotion engine to monitor the user's emotional state in real time to complement the abnormality detection. If an abnormality is detected, the device obtains the current GPS information and sends it to the server along with the video data.

[0509] The server analyzes the video data and GPS information received from the device to check for any abnormalities. If further analysis determines that there is an abnormality, the server immediately notifies the police. This notification includes the video data and GPS information indicating the abnormality. The police can use this information to take swift and appropriate action.

[0510] Specific examples

[0511] For example, if a violent act occurs inside a car, the system works as follows: The device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. At the same time, the emotion engine analyzes the user's emotional state and measures abnormal stress levels. At this time, the device obtains current GPS information and sends this information and video data to the server. The server receives this information, further verifies that it is an abnormality, and then reports it to the police. This allows the police to quickly respond to the scene and provide initial support for the incident.

[0512] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] The device captures video in real time from a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0516] Step 2:

[0517] The device transmits video data captured by the camera to the AI ​​means, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0518] Step 3:

[0519] The device sends the user's facial expressions and voice along with the video data to the emotion engine, which analyzes them and monitors the user's emotional state in real time. The emotion engine detects emotions such as anger, fear, and stress.

[0520] Step 4:

[0521] The device combines the analysis results of the emotion engine and AI means to determine whether an anomaly has been detected. If an anomaly is detected, it sets an anomaly flag. If no anomaly is detected, it returns to step 1 to capture video again.

[0522] Step 5:

[0523] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0524] Step 6:

[0525] The device transmits the captured video data, emotional state data, and GPS information to the server via a communication means, which uses a means for packaging the data and transmitting it to a specific endpoint on the server.

[0526] Step 7:

[0527] The server analyzes the video data, emotional state data, and GPS information received from the device, and further checks whether there are any abnormalities based on the received data, and decides on a response if necessary.

[0528] Step 8:

[0529] The server then reports any abnormal data to the police. The report includes the video data, emotional state data, and GPS information that were determined to be abnormal. This allows the police to take prompt action.

[0530] Step 9:

[0531] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0532] Step 10:

[0533] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0534] The above are the specific processing steps of the system that combines the emotion engine.

[0535] Example 2

[0536] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] In conventional ride-sharing systems, it was difficult to detect abnormal behavior occurring in the vehicle or changes in the user's emotional state in real time and respond quickly. It was also difficult to accurately obtain location information when an abnormality occurred and report it to the appropriate authorities. This led to problems with ensuring safety inside the vehicle.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0539] In this invention, the server includes a surveillance camera, an artificial intelligence, an emotion engine, a global positioning system, a data transmission unit via a communication device, and a police reporting unit. This allows for real-time detection of abnormal behavior in the vehicle and changes in the user's emotional state, enabling prompt response. Furthermore, accurate location information can be obtained and immediate reporting to the appropriate authorities can improve safety inside the vehicle.

[0540] The "monitoring camera means" is a device for constantly capturing and recording images inside the vehicle.

[0541] "Artificial intelligence means" refers to algorithms and software used to analyze captured footage and detect abnormal or fraudulent activity.

[0542] The "emotion engine means" is software for analyzing the user's facial expressions and voice and determining their emotional state (for example, anger, fear, stress, etc.).

[0543] The "global positioning system means" is a positioning device for obtaining current location information when an abnormality is detected.

[0544] "Means for transmitting to a server via a communication device" refers to a device for transmitting the captured video and the acquired location information to a server using communication technology.

[0545] "Means for reporting to the police" refers to the reporting system that informs the police of the abnormal data and location information received by the server.

[0546] This invention relates to a system that improves safety in ride-sharing vehicles, combining surveillance cameras, artificial intelligence, global positioning systems, communication technologies, and an emotion engine to detect abnormalities and respond quickly.

[0547] System Configuration

[0548] The devices installed in ride-sharing vehicles are equipped with the following hardware and software:

[0549] 1. Surveillance camera means: A high-resolution camera (e.g., a general-purpose high-resolution camera device) is used. This camera constantly captures and records images inside the vehicle.

[0550] 2. Artificial intelligence means: AI software (e.g., TensorFlow, a general-purpose machine learning framework) equipped with algorithms for analyzing video data and detecting abnormal or fraudulent behavior will be used.

[0551] 3. Emotion engine means: Software (e.g., a general-purpose emotion analysis solution) is used to analyze the user's facial expressions and voice to determine their emotional state.

[0552] 4. Global Positioning System Means: A GPS device (e.g., a general-purpose GPS module) is used to obtain current location information.

[0553] 5. Communication device: A communication means (e.g., a general-purpose 4G LTE modem) is used to transmit video data and location information to the server.

[0554] User Emotion Recognition

[0555] The terminal transmits video data captured using the surveillance camera means to the artificial intelligence means. The artificial intelligence means analyzes the video data and detects abnormal behavior (such as violent behavior or fraud) occurring inside the vehicle. The emotional engine means also analyzes the user's emotional state in real time from their facial expressions and voice to complement the abnormality detection.

[0556] Response in the event of an emergency

[0557] If an anomaly is detected, the device acquires current GPS information and transmits it along with video data to the server. This data is then sent via a communications device to a designated endpoint on the server. The server analyzes the received data to determine whether it is an anomaly. If further analysis determines that an anomaly exists, the server notifies the police. This notification includes video data and GPS information indicating the anomaly, allowing the police to take swift and appropriate action.

[0558] Specific examples

[0559] For example, if a violent act occurs inside a car, the system operates as follows: The device captures the violent act with a surveillance camera, and the artificial intelligence means recognizes it as a violent act. At the same time, the emotion engine means analyzes the user's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then reports it to the police. This allows the police to respond to the scene quickly and provide initial support for the incident.

[0560] Example prompts for generative AI models

[0561] Example prompt:

[0562] "Consider what to do if a violent act occurs in the car and the user's stress level suddenly rises. In this case, AI analyzes the video and audio data captured by the camera and sends the GPS information to a server. Please explain in detail what happens after that."

[0563] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] The device continuously captures video using a surveillance camera installed inside the vehicle. The input is real-time video from inside the vehicle, and the output is high-resolution video data. This data is buffered for a few seconds and then stored in the internal memory.

[0567] Step 2:

[0568] The device sends the captured video data to an artificial intelligence means. The input is the high-resolution video data obtained in step 1, and the output is a frame-by-frame analysis of potential anomalous behavior. The artificial intelligence means uses a machine learning framework such as TensorFlow to run algorithms to detect violent or fraudulent behavior.

[0569] Step 3:

[0570] The terminal uses an emotion engine means to analyze the user's facial expressions and voice. The input is the video data and voice data obtained in step 1, and the output is the analysis result of the detected emotional state (e.g., anger, fear, stress). The emotion engine analyzes facial features and voice tone to detect abnormal emotional states.

[0571] Step 4:

[0572] When abnormal behavior or abnormal emotions are detected, the device obtains current location information using a global positioning system. The input is the anomaly detection trigger event, and the output is the current latitude and longitude information. The GPS module quickly obtains the location information, which is then processed together with other data.

[0573] Step 5:

[0574] The device transmits the acquired location information and video data indicating the anomaly to the server via a communication device. The input is the analysis results obtained in steps 2 and 3 and the location information obtained in step 4, and the output is a data packet sent to the server. This transmission is performed in real time using a secure protocol such as HTTPS.

[0575] Step 6:

[0576] The server analyzes the data received from the device. The input is the data packet sent in step 5, and the output is the confirmation of abnormal behavior. The AI ​​on the server operates in the cloud and reanalyzes the video data and emotion data to confirm the abnormality.

[0577] Step 7:

[0578] If the server detects a definite anomaly, it notifies the police. The input is the detection result and location information obtained in step 6, and the output is a report message sent to the police. The report includes specific video clips and GPS information, allowing the police to respond quickly and accurately.

[0579] The above is the specific flow of the program processing of this system.

[0580] (Application example 2)

[0581] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0582] Conventional ride-sharing and self-driving taxis lack sufficient monitoring systems to ensure safety within the vehicle. This makes it difficult to respond appropriately to violent or fraudulent behavior, as well as sudden changes in passenger emotion. Furthermore, even if an abnormality occurs, there is no established system for quickly and accurately reporting it, which can result in delayed emergency response.

[0583] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means that constantly captures video of the inside of the vehicle, an artificial intelligence means that analyzes the captured video and detects abnormalities, an emotion engine that analyzes emotions from the user's facial expressions and voice, a location information acquisition means that acquires current location information when an abnormality is detected, a means for transmitting the video and location information to the server via a communication device, and a transmission means for reporting the abnormality data and location information received by the server to the police. This enables more accurate abnormality detection and faster response.

[0584] The "image acquisition means" is a means for constantly capturing images inside the vehicle, and typically refers to a camera device.

[0585] "Artificial intelligence means" means means for analyzing captured video and detecting anomalies, including AI algorithms and models.

[0586] An "emotion engine" is a means for analyzing emotions from a user's facial expressions and voice, and refers to an engine that uses emotion recognition technology.

[0587] "Location information acquisition means" refers to a means for acquiring current location information when an abnormality is detected, and generally refers to a GPS module.

[0588] A "communication device" is a means for transmitting video and location information to a server, and includes communication technologies such as mobile communication and Wi-Fi.

[0589] The "transmission means" is a means for reporting the abnormal data and location information received by the server to the police, and includes a dedicated communication interface.

[0590] The "criteria" are standard evaluation indicators used by the server when analyzing abnormal data, and are an important factor in determining the accuracy of anomaly detection.

[0591] The "camera device" refers to a camera device that captures images inside a vehicle at a constant frame rate and is used to obtain high-resolution images.

[0592] The present invention relates to a system for ensuring the safety of passengers in autonomous vehicles. This system is composed of a combination of various means for monitoring video images inside the vehicle, detecting abnormalities, and responding quickly.

[0593] 1. System Configuration

[0594] The main components are as follows:

[0595] Surveillance camera means

[0596] To constantly capture images inside the vehicle, a surveillance camera system is used. This system consists of camera devices that capture high-resolution images. The cameras are installed at key locations inside the vehicle and capture passenger behavior in real time.

[0597] Artificial Intelligence Tools

[0598] AI algorithms are used to analyze the captured footage and detect anomalies. This includes deep learning models to automatically detect anomalies such as violent or fraudulent behavior. The analyzed data is then used in conjunction with an emotion engine to enable more accurate anomaly detection.

[0599] Emotion Engine

[0600] The emotion engine is a technology for analyzing emotions from users' facial expressions and voices. This allows for real-time monitoring of passengers' stress levels and sudden changes in their emotions, making it possible to detect abnormal changes in emotions as well as violent and fraudulent behavior.

[0601] Location information acquisition means

[0602] If an abnormality is detected, the GPS module is used to obtain the current location information, which is then sent to the server via the communication means described below.

[0603] communication equipment

[0604] The device includes a communication device for transmitting video and location information to a server. The communication device uses communication technologies such as LTE and Wi-Fi, which enables real-time data transmission.

[0605] 2. Server Processing

[0606] The server receives the abnormal data and location information sent from the device. The received abnormal data is further examined by the server's analysis means. If an abnormality is confirmed as a result of the analysis, the server notifies the police. The report includes video data showing the abnormality and location information, allowing the police to respond to the scene quickly.

[0607] 3. Specific Examples

[0608] For example, if a violent act occurs inside a self-driving taxi, the system operates as follows: The camera captures the violent act, and the AI ​​recognizes it as such. At the same time, the emotion engine analyzes the passenger's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then notifies the police.

[0609] Prompt Sentence Examples

[0610] Below are some example prompts for a generative AI model:

[0611] Image data: base64_encoded_image_string

[0612] Emotional Data:

[0613] Anger: 0.8

[0614] Fear: 0.7

[0615] Stress: 0.9

[0616] The prompt contains image data and emotional state information that is sent to the AI ​​model, allowing it to detect anomalies and take specific corrective action.

[0617] The above is the "Mode for Carrying Out the Invention" of the present invention.

[0618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0619] Step 1:

[0620] The terminal constantly captures images of the inside of the vehicle using a surveillance camera means.

[0621] Input: Real-time video data from a camera device

[0622] Output: Captured image frames

[0623] Specific operation: The camera captures video at a constant frame rate and stores the video data in memory.

[0624] Step 2:

[0625] The terminal transmits the captured video to an artificial intelligence means for analysis.

[0626] Input: Captured image frame

[0627] Output: The result of the anomaly detection algorithm (e.g., no anomalies, violent behavior detected, etc.)

[0628] Specific operation: Analyzes video data using a deep learning model to determine whether or not abnormal behavior is occurring.

[0629] Step 3:

[0630] The device analyzes the user's facial expressions and voice using an emotion engine.

[0631] Input: Captured image frames and audio data

[0632] Output: Emotional state data (e.g., anger 0.8, fear 0.7, stress 0.9)

[0633] Specific operation: Analyzes the user's emotions using an emotion recognition model and generates emotion data.

[0634] Step 4:

[0635] The terminal integrates the data obtained from the artificial intelligence means and the emotion engine to make a comprehensive judgment as to whether an abnormality exists.

[0636] Input: Results of anomaly detection algorithm, emotional state data

[0637] Output: Overall abnormality judgment result (e.g., abnormality present, no abnormality present)

[0638] What it does: Consolidate the analysis results and flag any anomalies detected.

[0639] Step 5:

[0640] When an abnormality is detected, the terminal acquires current location information using the location information acquisition means.

[0641] Input: Abnormality judgment flag

[0642] Output: Current GPS information

[0643] Specific operation: Starts the GPS module and obtains current location information.

[0644] Step 6:

[0645] The terminal transmits the video data and the location information to the server.

[0646] Input: Captured image frame, current GPS information

[0647] Output: Data sent to the server (e.g., image data, location information)

[0648] Specific operations: Using a communication device, the collected data is sent to a specified server endpoint.

[0649] Step 7:

[0650] The server analyzes the received data and reconfirms the accuracy of the anomaly.

[0651] Input: Transmitted image data, location information

[0652] Output: Final anomaly judgment (e.g., confirmed anomaly, no anomaly)

[0653] Specific operation: Analyzes the received data and runs an algorithm to assess the probability of an anomaly.

[0654] Step 8:

[0655] If any abnormalities are detected, the server will notify the police.

[0656] Input: Final anomaly determination

[0657] Output: Report data sent to police (e.g. image data, location information)

[0658] Specific actions: Activate the reporting system and send data to the police.

[0659] The above is a description of the specific steps of the process according to the present invention.

[0660] 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.

[0661] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0662] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0663] [Third embodiment]

[0664] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0665] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0666] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0667] 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.

[0668] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0669] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0670] 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.

[0671] 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.

[0672] 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 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.

[0673] 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.

[0674] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0675] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0676] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, and communication technology to detect abnormalities and respond quickly.

[0677] System Configuration

[0678] The surveillance camera means installed inside the ride-sharing vehicle constantly captures video footage inside the vehicle, allowing the situation inside the vehicle to be monitored in real time. The AI ​​means analyzes the captured video footage and detects abnormalities (such as violent or fraudulent behavior). When an abnormality is detected, the GPS means acquires the current location information. The communication means then transmits the video data and location information of the abnormality to the server. The server analyzes the received data and, if necessary, notifies the police.

[0679] Program processing overview

[0680] The terminal continuously captures video of the inside of the vehicle using a surveillance camera and transmits it to the AI ​​means. The AI ​​means analyzes the video data and sets a flag if an abnormality is detected. When this flag is set, the terminal obtains current location information using the GPS means and transmits that information and video data of the abnormality to the server via the communication means.

[0681] Based on the received video data and location information, the server performs additional analysis to determine whether there is an abnormality. If an abnormality is confirmed, the server immediately reports the information to the police. This report includes video and location information showing the abnormality, allowing the police to respond quickly and accurately.

[0682] Specific examples

[0683] For example, if a violent act occurs inside a car, the system operates as follows: First, the device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. The device then obtains its current location information using GPS, and sends this location information and video data to the server. The server receives this information, confirms that there is an abnormality, and notifies the police. This notification allows the police to quickly arrive at the scene and take appropriate action.

[0684] The benefits of this system include significantly improved safety for drivers and passengers, and improved reliability and quality of the entire ride-sharing service. It also contributes to preventing or mitigating serious incidents by enabling real-time anomaly detection and immediate response.

[0685] The above is the "mode for carrying out the invention" of the present invention.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The device captures video in real time using a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0689] Step 2:

[0690] The device sends the captured video data to the AI ​​module, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0691] Step 3:

[0692] The device checks whether the AI ​​module detects an anomaly. If so, it sets a flag and proceeds to the next step. If no anomaly is detected, it returns to step 1, where it captures video again.

[0693] Step 4:

[0694] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0695] Step 5:

[0696] The terminal transmits the acquired video data and GPS information to the server by using a communication means to transmit the data to a specific endpoint on the server.

[0697] Step 6:

[0698] The server analyzes the video data and GPS information received from the device, performs additional checks based on the received data to determine if there are any abnormalities, and prepares to notify the police if necessary.

[0699] Step 7:

[0700] The server then reports any data that is deemed abnormal to the police. The report includes the video data and GPS information that has been determined to be abnormal, allowing the police to take prompt action.

[0701] Step 8:

[0702] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0703] Step 9:

[0704] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0705] The above is the specific flow of the program processing.

[0706] Example 1

[0707] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0708] Ensuring the safety of passengers and drivers in ride-sharing vehicles is an important issue. In particular, when violent or fraudulent behavior occurs, it is necessary to quickly grasp the situation on the scene and respond promptly. However, current systems have difficulty detecting abnormalities in real time and responding immediately, so improvements in safety are required.

[0709] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0710] In this invention, the server includes an image capturing means for constantly capturing images of the inside of the vehicle, an analysis means for analyzing the captured images and detecting abnormalities, a location information capturing means for capturing current location information when an abnormality is detected, a means for transmitting the image and location information via a communication device, and a means for reporting the abnormality data and location information received by the server to a reporting agency. This makes it possible to detect abnormalities inside the vehicle in real time and respond quickly.

[0711] The "image acquisition means" is a device for constantly capturing images inside the vehicle.

[0712] "Analysis means" refers to technology for analyzing captured video and detecting abnormalities.

[0713] The "location information acquisition means" is a device that acquires current location information when an abnormality is detected.

[0714] A "communication device" is a device for transmitting video and location information to a server or the like.

[0715] The "server" is a device that receives the transmitted abnormal data and location information, analyzes it, and reports it.

[0716] "Reporting agency" refers to an external agency, such as the police, that responds when an abnormality occurs.

[0717] This invention relates to a system for improving safety inside ride-sharing vehicles. The system combines surveillance cameras, AI technology, GPS, and communication devices to detect abnormalities and respond quickly.

[0718] Hardware and software used

[0719] 1. Video acquisition method

[0720] A surveillance camera installed inside a ride-sharing vehicle. It is a high-resolution camera that can cover the entire interior of the vehicle. The camera is always on and captures video in real time.

[0721] 2. Analysis method

[0722] To analyze the captured video, AI technology is used, specifically machine learning frameworks such as TensorFlow and OpenCV, to detect anomalies such as fraudulent or violent behavior in the video data.

[0723] 3. Location information acquisition means

[0724] The GPS module is used to obtain the current location information when an abnormality is detected, and detailed latitude and longitude can be obtained in real time.

[0725] 4. Communications Equipment

[0726] Video data and location information of abnormalities are sent to a server via a 4G / 5G communication module or Wi-Fi. The data is encrypted and sent securely.

[0727] 5. Server

[0728] The server re-analyzes the received data and further checks for anomalies. It performs the re-analysis using a deep learning model (e.g., YOLO, ResNet). If anomalies are found, it reports them to reporting agencies such as the police.

[0729] Example of operation

[0730] For example, if a violent act occurs in a ride-sharing vehicle, the system operates as follows: First, the device captures the violent act with a surveillance camera and analyzes the video using AI technology (e.g., OpenCV). If the AI ​​detects a violent act, it raises a flag. The device then obtains its current location using its GPS module, encrypts this information and the video data, and sends it to a server. The server reanalyzes the received video data and location information (e.g., using a ResNet model), and if it ultimately confirms abnormal behavior, it notifies reporting agencies such as the police. This allows the police to arrive at the scene quickly and take appropriate action.

[0731] Prompt Sentence Examples

[0732] An example of a prompt to be input to the generative AI model is, "Please explain in natural language how the system will behave if a violent act occurs inside a ride-sharing vehicle. The system will capture video using a surveillance camera and use AI to detect anomalies. If an anomaly is detected, the location information will be obtained using GPS and sent via communication means to the server. The server will reanalyze the data, and if an anomaly is confirmed, it will notify the police." This prompt allows for a detailed explanation of the system's behavior.

[0733] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0734] Step 1:

[0735] Input: Real-time video from inside a rideshare vehicle.

[0736] How it works: The device constantly captures video from surveillance cameras inside the vehicle. The cameras are high-resolution and are installed to cover the entire interior of the vehicle.

[0737] Output: High-resolution real-time video data.

[0738] Step 2:

[0739] Input: Captured real-time video data.

[0740] How it works: The device sends the captured video data to an AI analysis module. Specifically, the video is analyzed using machine learning frameworks such as TensorFlow and OpenCV. The AI ​​detects abnormal behavior based on pre-trained patterns of misconduct and violence.

[0741] Output: Video data flagged if an anomaly is detected.

[0742] Step 3:

[0743] Input: Flagged anomalous video data.

[0744] How it works: When the device receives video data that flags an anomaly, it uses its built-in GPS module to obtain its current location, which measures latitude and longitude in real time to generate detailed location data.

[0745] Output: Anomaly video data and corresponding real-time location information.

[0746] Step 4:

[0747] Input: Anomaly video data and real-time location information.

[0748] Operation: The device sends the acquired video data and location information to the server using a communication method (e.g., 4G / 5G communication module or Wi-Fi). The data is encrypted and sent securely.

[0749] Output: Anomaly video data and location information sent to the server.

[0750] Step 5:

[0751] Input: Anomaly video data and location information sent to the server.

[0752] Operation: The server re-analyzes the received data. Specifically, it uses a deep learning model (e.g., YOLO, ResNet) to re-check for anomalous behavior. If the re-analysis confirms anomalous behavior, it proceeds to the next step.

[0753] Output: Reviewed anomaly data and location information.

[0754] Step 6:

[0755] Input: Reviewed anomaly data and location information.

[0756] Operation: The server reports the detected anomaly to a reporting agency such as the police. The report includes video data showing the anomaly and location information. The server attaches a text message or image file to the report.

[0757] Output: Anomaly data and location information sent to reporting agencies such as police.

[0758] (Application example 1)

[0759] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0760] As the use of autonomous vehicles increases, ensuring safety inside the vehicle is extremely important. Conventional ride-sharing services have implemented some safety measures using surveillance cameras and GPS, but these alone make it difficult to respond quickly to sudden acts of violence or fraud. Furthermore, driverless autonomous vehicles require a means to ensure passenger safety in real time. Furthermore, passengers need a way to check the situation themselves and report it immediately.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0762] In this invention, the server includes a means for analyzing and determining the abnormal data received, a means for reporting to the police only if the abnormal data meets certain criteria, and an abnormality notification means for issuing an alert when the mobile information terminal detects an abnormality. This enables rapid detection and response to abnormalities in real time through a surveillance camera means for constantly capturing video from inside the vehicle, an AI means for analyzing the captured video and detecting abnormalities, and a location information acquisition means for acquiring current location information when an abnormality is detected. Furthermore, passengers can check the video from inside the vehicle in real time using their own mobile information terminals, allowing them to take self-defense measures and improving overall safety.

[0763] The "monitoring camera means" is a device that constantly captures images of the inside of the vehicle and monitors them in real time.

[0764] "AI means" refers to artificial intelligence technology that analyzes captured video data and automatically detects abnormal behavior or fraudulent activity.

[0765] The "location information acquisition means" is a device or technology for acquiring current vehicle location information when an abnormality is detected.

[0766] The "transmission means" is a system for transmitting the video and location information to a server via a communication device.

[0767] The "reporting means" is a function for notifying the police of the abnormal data and location information received by the server.

[0768] The "remote video viewing means" is a function that allows passengers to view camera footage inside the vehicle in real time using a mobile information terminal.

[0769] The "abnormality notification means" is a function that issues an alert when the mobile information terminal detects an abnormality.

[0770] The "image capturing device" is a camera device for capturing images inside the vehicle at a constant frame rate.

[0771] This invention provides a system for improving safety inside ride-sharing vehicles, which includes a surveillance camera, an AI, a location information acquisition unit, a transmission unit, a reporting unit, a remote video confirmation unit, and an abnormality notification unit.

[0772] Hardware and Software

[0773] The hardware includes the smartphone's camera (video capture device), GPS module (location information acquisition means), and communications device, while the software includes OpenCV (video capture), Geopy (location information acquisition), Requests (data transmission), and generative AI models (video analysis and anomaly detection).

[0774] Processing Overview

[0775] 1. Surveillance camera means:

[0776] A camera installed inside the vehicle constantly captures video in real time, which is then sent to an AI system to analyze for any abnormalities.

[0777] 2. AI means:

[0778] Generative AI models are used to analyze captured video data in real time to detect anomalies, such as violent or fraudulent behavior, which are then flagged.

[0779] 3. Location information acquisition means:

[0780] If an abnormality is detected, the GPS module obtains the current vehicle location information.

[0781] 4. Means of transmission:

[0782] When an abnormality is detected, the terminal transmits the captured video and the acquired location information to the server via the communication device.

[0783] 5. Reporting methods:

[0784] The server analyzes the received abnormal data and location information, and if an abnormality is confirmed, it reports it to the police.

[0785] 6. Remote video confirmation method:

[0786] Passengers can use their mobile devices to view real-time camera footage from inside the vehicle, allowing them to understand the current situation and take self-protection measures.

[0787] 7. Abnormality notification means:

[0788] The mobile information terminal has the ability to issue an alert and notify passengers when an abnormality is detected.

[0789] Specific examples

[0790] This explains what happens when violence occurs inside an autonomous vehicle.

[0791] 1. Surveillance camera means capture footage of violent acts.

[0792] 2. The captured footage is analyzed using AI means to detect any anomalies.

[0793] 3. The location information acquisition means acquires the current vehicle location.

[0794] 4. These data are transmitted to the server via the transmission means.

[0795] 5. The server checks the abnormal data and location information, and if it determines that the damage is serious, it will report it to the police using the reporting means.

[0796] 6. Meanwhile, passengers can use remote video surveillance to monitor the situation and take self-protection measures.

[0797] 7. If an abnormality is detected, the mobile information device will alert passengers via the abnormality notification means.

[0798] Prompt Sentence Examples

[0799] Please capture footage of passengers committing violent acts inside the self-driving vehicle. The footage will be analyzed in real time, and if an abnormality is detected, the current location will be acquired via GPS and sent to the server along with the video of the abnormality. The server will receive this and, if necessary, notify the police.

[0800] In this way, a system is provided that aims to improve safety inside ride-sharing vehicles and can respond quickly and appropriately in the event of an abnormality.

[0801] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0802] Step 1:

[0803] The terminal constantly captures video using a surveillance camera installed inside the vehicle. The input is camera image data, and the output is the captured video frame. Specifically, the terminal acquires video frames from the camera and temporarily stores them.

[0804] Step 2:

[0805] The terminal transmits the captured video frame to the AI ​​means. The input is the captured video frame, and the output is the input data to the AI ​​means. Specifically, the terminal inputs the video frame to the AI ​​model.

[0806] Step 3:

[0807] The AI ​​means uses a generative AI model to analyze the captured video data and detect anomalies. The input is the transmitted video frame, and the output is an anomaly detection flag. Specifically, the AI ​​model analyzes the video frame and determines whether any abnormal behavior is occurring.

[0808] Step 4:

[0809] When the AI ​​means detects an abnormality, the terminal acquires current location information using the location information acquisition means. The input is an abnormality detection flag, and the output is current location information. Specifically, the terminal acquires location information from the GPS module.

[0810] Step 5:

[0811] The terminal transmits the video data of the anomaly and the acquired location information to the server using the transmission means. The input is the video data of the anomaly and the current location information, and the output is the data to be transmitted to the server. In concrete terms, the terminal uploads this data to the server via the transmission means.

[0812] Step 6:

[0813] The server analyzes the received anomaly data and location information to confirm the anomaly. The input is the transmitted anomaly data and location information, and the output is the anomaly confirmation result. Specifically, the server reanalyzes the anomaly data and determines whether the anomaly is confirmed.

[0814] Step 7:

[0815] If the server detects an abnormality, it will notify the police using the reporting means. The input is the abnormality detection result and location information, and the output is the report data to the police. Specifically, the server sends the necessary information to the police.

[0816] Step 8:

[0817] The user uses the remote video confirmation means to check the camera video in the vehicle in real time. The input is a remote access request, and the output is real-time video data. Specifically, the mobile information terminal accesses the camera video and displays the video in real time.

[0818] Step 9:

[0819] When the terminal detects an abnormality, it uses the abnormality notification means to issue an alert to the user. The input is an abnormality detection flag, and the output is an alert notification. Specifically, when the terminal detects an abnormality, a warning message is displayed on the user's mobile information terminal.

[0820] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0821] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, communication technology, and an emotion engine, with the aim of detecting abnormalities and responding quickly.

[0822] System Configuration

[0823] The surveillance camera means installed in the ride-sharing vehicle constantly captures images of the inside of the vehicle, allowing for real-time monitoring of the situation inside the vehicle. The AI ​​means has an algorithm for analyzing the captured images and detecting abnormalities (e.g., violent or fraudulent behavior). Furthermore, by incorporating an emotion engine, the system can also monitor and analyze the user's emotional state, improving the accuracy of abnormality detection.

[0824] User Emotion Recognition

[0825] The emotion engine analyzes emotions from the user's facial expressions and voice. Based on video and audio data, it monitors the user's emotional state (e.g., anger, fear, stress, etc.) in real time. The AI ​​means also takes into account data from the emotion engine to detect anomalies. For this reason, sudden changes in emotions or high stress levels may be detected as anomalies.

[0826] Response in the event of an emergency

[0827] If an abnormality is detected, the device acquires current GPS information and sends it along with video data to the server. This data is sent to the server's designated endpoint via a communication method. The server analyzes the received data and, if it determines that an abnormality exists, immediately notifies the police. This report includes video data and GPS information indicating the abnormality, allowing the police to respond to the scene quickly.

[0828] Program processing overview

[0829] The device continuously captures video of the interior of the vehicle using a surveillance camera and sends it to the AI ​​means. The AI ​​means analyzes the video data and detects abnormalities. It also uses an emotion engine to monitor the user's emotional state in real time to complement the abnormality detection. If an abnormality is detected, the device obtains the current GPS information and sends it to the server along with the video data.

[0830] The server analyzes the video data and GPS information received from the device to check for any abnormalities. If further analysis determines that there is an abnormality, the server immediately notifies the police. This notification includes the video data and GPS information indicating the abnormality. The police can use this information to take swift and appropriate action.

[0831] Specific examples

[0832] For example, if a violent act occurs inside a car, the system works as follows: The device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. At the same time, the emotion engine analyzes the user's emotional state and measures abnormal stress levels. At this time, the device obtains current GPS information and sends this information and video data to the server. The server receives this information, further verifies that it is an abnormality, and then reports it to the police. This allows the police to quickly respond to the scene and provide initial support for the incident.

[0833] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] The device captures video in real time from a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[0837] Step 2:

[0838] The device transmits video data captured by the camera to the AI ​​means, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[0839] Step 3:

[0840] The device sends the user's facial expressions and voice along with the video data to the emotion engine, which analyzes them and monitors the user's emotional state in real time. The emotion engine detects emotions such as anger, fear, and stress.

[0841] Step 4:

[0842] The device combines the analysis results of the emotion engine and AI means to determine whether an anomaly has been detected. If an anomaly is detected, it sets an anomaly flag. If no anomaly is detected, it returns to step 1 to capture video again.

[0843] Step 5:

[0844] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[0845] Step 6:

[0846] The device transmits the captured video data, emotional state data, and GPS information to the server via a communication means, which uses a means for packaging the data and transmitting it to a specific endpoint on the server.

[0847] Step 7:

[0848] The server analyzes the video data, emotional state data, and GPS information received from the device, and further checks whether there are any abnormalities based on the received data, and decides on a response if necessary.

[0849] Step 8:

[0850] The server then reports any abnormal data to the police. The report includes the video data, emotional state data, and GPS information that were determined to be abnormal. This allows the police to take prompt action.

[0851] Step 9:

[0852] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[0853] Step 10:

[0854] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[0855] The above are the specific processing steps of the system that combines the emotion engine.

[0856] Example 2

[0857] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0858] In conventional ride-sharing systems, it was difficult to detect abnormal behavior occurring in the vehicle or changes in the user's emotional state in real time and respond quickly. It was also difficult to accurately obtain location information when an abnormality occurred and report it to the appropriate authorities. This led to problems with ensuring safety inside the vehicle.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0860] In this invention, the server includes a surveillance camera, an artificial intelligence, an emotion engine, a global positioning system, a data transmission unit via a communication device, and a police reporting unit. This allows for real-time detection of abnormal behavior in the vehicle and changes in the user's emotional state, enabling prompt response. Furthermore, accurate location information can be obtained and immediate reporting to the appropriate authorities can improve safety inside the vehicle.

[0861] The "monitoring camera means" is a device for constantly capturing and recording images inside the vehicle.

[0862] "Artificial intelligence means" refers to algorithms and software used to analyze captured footage and detect abnormal or fraudulent activity.

[0863] The "emotion engine means" is software for analyzing the user's facial expressions and voice and determining their emotional state (for example, anger, fear, stress, etc.).

[0864] The "global positioning system means" is a positioning device for obtaining current location information when an abnormality is detected.

[0865] "Means for transmitting to a server via a communication device" refers to a device for transmitting the captured video and the acquired location information to a server using communication technology.

[0866] "Means for reporting to the police" refers to the reporting system that informs the police of the abnormal data and location information received by the server.

[0867] This invention relates to a system that improves safety in ride-sharing vehicles, combining surveillance cameras, artificial intelligence, global positioning systems, communication technologies, and an emotion engine to detect abnormalities and respond quickly.

[0868] System Configuration

[0869] The devices installed in ride-sharing vehicles are equipped with the following hardware and software:

[0870] 1. Surveillance camera means: A high-resolution camera (e.g., a general-purpose high-resolution camera device) is used. This camera constantly captures and records images inside the vehicle.

[0871] 2. Artificial intelligence means: AI software (e.g., TensorFlow, a general-purpose machine learning framework) equipped with algorithms for analyzing video data and detecting abnormal or fraudulent behavior will be used.

[0872] 3. Emotion engine means: Software (e.g., a general-purpose emotion analysis solution) is used to analyze the user's facial expressions and voice to determine their emotional state.

[0873] 4. Global Positioning System Means: A GPS device (e.g., a general-purpose GPS module) is used to obtain current location information.

[0874] 5. Communication device: A communication means (e.g., a general-purpose 4G LTE modem) is used to transmit video data and location information to the server.

[0875] User Emotion Recognition

[0876] The terminal transmits video data captured using the surveillance camera means to the artificial intelligence means. The artificial intelligence means analyzes the video data and detects abnormal behavior (such as violent behavior or fraud) occurring inside the vehicle. The emotional engine means also analyzes the user's emotional state in real time from their facial expressions and voice to complement the abnormality detection.

[0877] Response in the event of an emergency

[0878] If an anomaly is detected, the device acquires current GPS information and transmits it along with video data to the server. This data is then sent via a communications device to a designated endpoint on the server. The server analyzes the received data to determine whether it is an anomaly. If further analysis determines that an anomaly exists, the server notifies the police. This notification includes video data and GPS information indicating the anomaly, allowing the police to take swift and appropriate action.

[0879] Specific examples

[0880] For example, if a violent act occurs inside a car, the system operates as follows: The device captures the violent act with a surveillance camera, and the artificial intelligence means recognizes it as a violent act. At the same time, the emotion engine means analyzes the user's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then reports it to the police. This allows the police to respond to the scene quickly and provide initial support for the incident.

[0881] Example prompts for generative AI models

[0882] Example prompt:

[0883] "Consider what to do if a violent act occurs in the car and the user's stress level suddenly rises. In this case, AI analyzes the video and audio data captured by the camera and sends the GPS information to a server. Please explain in detail what happens after that."

[0884] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[0885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0886] Step 1:

[0887] The device continuously captures video using a surveillance camera installed inside the vehicle. The input is real-time video from inside the vehicle, and the output is high-resolution video data. This data is buffered for a few seconds and then stored in the internal memory.

[0888] Step 2:

[0889] The device sends the captured video data to an artificial intelligence means. The input is the high-resolution video data obtained in step 1, and the output is a frame-by-frame analysis of potential anomalous behavior. The artificial intelligence means uses a machine learning framework such as TensorFlow to run algorithms to detect violent or fraudulent behavior.

[0890] Step 3:

[0891] The terminal uses an emotion engine means to analyze the user's facial expressions and voice. The input is the video data and voice data obtained in step 1, and the output is the analysis result of the detected emotional state (e.g., anger, fear, stress). The emotion engine analyzes facial features and voice tone to detect abnormal emotional states.

[0892] Step 4:

[0893] When abnormal behavior or abnormal emotions are detected, the device obtains current location information using a global positioning system. The input is the anomaly detection trigger event, and the output is the current latitude and longitude information. The GPS module quickly obtains the location information, which is then processed together with other data.

[0894] Step 5:

[0895] The device transmits the acquired location information and video data indicating the anomaly to the server via a communication device. The input is the analysis results obtained in steps 2 and 3 and the location information obtained in step 4, and the output is a data packet sent to the server. This transmission is performed in real time using a secure protocol such as HTTPS.

[0896] Step 6:

[0897] The server analyzes the data received from the device. The input is the data packet sent in step 5, and the output is the confirmation of abnormal behavior. The AI ​​on the server operates in the cloud and reanalyzes the video data and emotion data to confirm the abnormality.

[0898] Step 7:

[0899] If the server detects a definite anomaly, it notifies the police. The input is the detection result and location information obtained in step 6, and the output is a report message sent to the police. The report includes specific video clips and GPS information, allowing the police to respond quickly and accurately.

[0900] The above is the specific flow of the program processing of this system.

[0901] (Application example 2)

[0902] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0903] Conventional ride-sharing and self-driving taxis lack sufficient monitoring systems to ensure safety within the vehicle. This makes it difficult to respond appropriately to violent or fraudulent behavior, as well as sudden changes in passenger emotion. Furthermore, even if an abnormality occurs, there is no established system for quickly and accurately reporting it, which can result in delayed emergency response.

[0904] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means that constantly captures video of the inside of the vehicle, an artificial intelligence means that analyzes the captured video and detects abnormalities, an emotion engine that analyzes emotions from the user's facial expressions and voice, a location information acquisition means that acquires current location information when an abnormality is detected, a means for transmitting the video and location information to the server via a communication device, and a transmission means for reporting the abnormality data and location information received by the server to the police. This enables more accurate abnormality detection and faster response.

[0905] The "image acquisition means" is a means for constantly capturing images inside the vehicle, and typically refers to a camera device.

[0906] "Artificial intelligence means" means means for analyzing captured video and detecting anomalies, including AI algorithms and models.

[0907] An "emotion engine" is a means for analyzing emotions from a user's facial expressions and voice, and refers to an engine that uses emotion recognition technology.

[0908] "Location information acquisition means" refers to a means for acquiring current location information when an abnormality is detected, and generally refers to a GPS module.

[0909] A "communication device" is a means for transmitting video and location information to a server, and includes communication technologies such as mobile communication and Wi-Fi.

[0910] The "transmission means" is a means for reporting the abnormal data and location information received by the server to the police, and includes a dedicated communication interface.

[0911] The "criteria" are standard evaluation indicators used by the server when analyzing abnormal data, and are an important factor in determining the accuracy of anomaly detection.

[0912] The "camera device" refers to a camera device that captures images inside a vehicle at a constant frame rate and is used to obtain high-resolution images.

[0913] The present invention relates to a system for ensuring the safety of passengers in autonomous vehicles. This system is composed of a combination of various means for monitoring video images inside the vehicle, detecting abnormalities, and responding quickly.

[0914] 1. System Configuration

[0915] The main components are as follows:

[0916] Surveillance camera means

[0917] To constantly capture images inside the vehicle, a surveillance camera system is used. This system consists of camera devices that capture high-resolution images. The cameras are installed at key locations inside the vehicle and capture passenger behavior in real time.

[0918] Artificial Intelligence Tools

[0919] AI algorithms are used to analyze the captured footage and detect anomalies. This includes deep learning models to automatically detect anomalies such as violent or fraudulent behavior. The analyzed data is then used in conjunction with an emotion engine to enable more accurate anomaly detection.

[0920] Emotion Engine

[0921] The emotion engine is a technology for analyzing emotions from users' facial expressions and voices. This allows for real-time monitoring of passengers' stress levels and sudden changes in their emotions, making it possible to detect abnormal changes in emotions as well as violent and fraudulent behavior.

[0922] Location information acquisition means

[0923] If an abnormality is detected, the GPS module is used to obtain the current location information, which is then sent to the server via the communication means described below.

[0924] communication equipment

[0925] The device includes a communication device for transmitting video and location information to a server. The communication device uses communication technologies such as LTE and Wi-Fi, which enables real-time data transmission.

[0926] 2. Server Processing

[0927] The server receives the abnormal data and location information sent from the device. The received abnormal data is further examined by the server's analysis means. If an abnormality is confirmed as a result of the analysis, the server notifies the police. The report includes video data showing the abnormality and location information, allowing the police to respond to the scene quickly.

[0928] 3. Specific Examples

[0929] For example, if a violent act occurs inside a self-driving taxi, the system operates as follows: The camera captures the violent act, and the AI ​​recognizes it as such. At the same time, the emotion engine analyzes the passenger's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then notifies the police.

[0930] Prompt Sentence Examples

[0931] Below are some example prompts for a generative AI model:

[0932] Image data: base64_encoded_image_string

[0933] Emotional Data:

[0934] Anger: 0.8

[0935] Fear: 0.7

[0936] Stress: 0.9

[0937] The prompt contains image data and emotional state information that is sent to the AI ​​model, allowing it to detect anomalies and take specific corrective action.

[0938] The above is the "Mode for Carrying Out the Invention" of the present invention.

[0939] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0940] Step 1:

[0941] The terminal constantly captures images of the inside of the vehicle using a surveillance camera means.

[0942] Input: Real-time video data from a camera device

[0943] Output: Captured image frames

[0944] Specific operation: The camera captures video at a constant frame rate and stores the video data in memory.

[0945] Step 2:

[0946] The terminal transmits the captured video to an artificial intelligence means for analysis.

[0947] Input: Captured image frame

[0948] Output: The result of the anomaly detection algorithm (e.g., no anomalies, violent behavior detected, etc.)

[0949] Specific operation: Analyzes video data using a deep learning model to determine whether or not abnormal behavior is occurring.

[0950] Step 3:

[0951] The device analyzes the user's facial expressions and voice using an emotion engine.

[0952] Input: Captured image frames and audio data

[0953] Output: Emotional state data (e.g., anger 0.8, fear 0.7, stress 0.9)

[0954] Specific operation: Analyzes the user's emotions using an emotion recognition model and generates emotion data.

[0955] Step 4:

[0956] The terminal integrates the data obtained from the artificial intelligence means and the emotion engine to make a comprehensive judgment as to whether an abnormality exists.

[0957] Input: Results of anomaly detection algorithm, emotional state data

[0958] Output: Overall abnormality judgment result (e.g., abnormality present, no abnormality present)

[0959] What it does: Consolidate the analysis results and flag any anomalies detected.

[0960] Step 5:

[0961] When an abnormality is detected, the terminal acquires current location information using the location information acquisition means.

[0962] Input: Abnormality judgment flag

[0963] Output: Current GPS information

[0964] Specific operation: Starts the GPS module and obtains current location information.

[0965] Step 6:

[0966] The terminal transmits the video data and the location information to the server.

[0967] Input: Captured image frame, current GPS information

[0968] Output: Data sent to the server (e.g., image data, location information)

[0969] Specific operations: Using a communication device, the collected data is sent to a specified server endpoint.

[0970] Step 7:

[0971] The server analyzes the received data and reconfirms the accuracy of the anomaly.

[0972] Input: Transmitted image data, location information

[0973] Output: Final anomaly judgment (e.g., confirmed anomaly, no anomaly)

[0974] Specific operation: Analyzes the received data and runs an algorithm to assess the probability of an anomaly.

[0975] Step 8:

[0976] If any abnormalities are detected, the server will notify the police.

[0977] Input: Final anomaly determination

[0978] Output: Report data sent to police (e.g. image data, location information)

[0979] Specific actions: Activate the reporting system and send data to the police.

[0980] The above is a description of the specific steps of the process according to the present invention.

[0981] 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.

[0982] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0983] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0984] [Fourth embodiment]

[0985] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0986] 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.

[0987] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0988] 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.

[0989] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0990] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0991] 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.

[0992] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0993] 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.

[0994] 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 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.

[0995] 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.

[0996] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0997] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0998] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, and communication technology to detect abnormalities and respond quickly.

[0999] System Configuration

[1000] The surveillance camera means installed inside the ride-sharing vehicle constantly captures video footage inside the vehicle, allowing the situation inside the vehicle to be monitored in real time. The AI ​​means analyzes the captured video footage and detects abnormalities (such as violent or fraudulent behavior). When an abnormality is detected, the GPS means acquires the current location information. The communication means then transmits the video data and location information of the abnormality to the server. The server analyzes the received data and, if necessary, notifies the police.

[1001] Program processing overview

[1002] The terminal continuously captures video of the inside of the vehicle using a surveillance camera and transmits it to the AI ​​means. The AI ​​means analyzes the video data and sets a flag if an abnormality is detected. When this flag is set, the terminal obtains current location information using the GPS means and transmits that information and video data of the abnormality to the server via the communication means.

[1003] Based on the received video data and location information, the server performs additional analysis to determine whether there is an abnormality. If an abnormality is confirmed, the server immediately reports the information to the police. This report includes video and location information showing the abnormality, allowing the police to respond quickly and accurately.

[1004] Specific examples

[1005] For example, if a violent act occurs inside a car, the system operates as follows: First, the device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. The device then obtains its current location information using GPS, and sends this location information and video data to the server. The server receives this information, confirms that there is an abnormality, and notifies the police. This notification allows the police to quickly arrive at the scene and take appropriate action.

[1006] The benefits of this system include significantly improved safety for drivers and passengers, and improved reliability and quality of the entire ride-sharing service. It also contributes to preventing or mitigating serious incidents by enabling real-time anomaly detection and immediate response.

[1007] The above is the "mode for carrying out the invention" of the present invention.

[1008] The processing flow will be explained below.

[1009] Step 1:

[1010] The device captures video in real time using a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[1011] Step 2:

[1012] The device sends the captured video data to the AI ​​module, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[1013] Step 3:

[1014] The device checks whether the AI ​​module detects an anomaly. If so, it sets a flag and proceeds to the next step. If no anomaly is detected, it returns to step 1, where it captures video again.

[1015] Step 4:

[1016] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[1017] Step 5:

[1018] The terminal transmits the acquired video data and GPS information to the server by using a communication means to transmit the data to a specific endpoint on the server.

[1019] Step 6:

[1020] The server analyzes the video data and GPS information received from the device, performs additional checks based on the received data to determine if there are any abnormalities, and prepares to notify the police if necessary.

[1021] Step 7:

[1022] The server then reports any data that is deemed abnormal to the police. The report includes the video data and GPS information that has been determined to be abnormal, allowing the police to take prompt action.

[1023] Step 8:

[1024] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[1025] Step 9:

[1026] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[1027] The above is the specific flow of the program processing.

[1028] Example 1

[1029] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1030] Ensuring the safety of passengers and drivers in ride-sharing vehicles is an important issue. In particular, when violent or fraudulent behavior occurs, it is necessary to quickly grasp the situation on the scene and respond promptly. However, current systems have difficulty detecting abnormalities in real time and responding immediately, so improvements in safety are required.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1032] In this invention, the server includes an image capturing means for constantly capturing images of the inside of the vehicle, an analysis means for analyzing the captured images and detecting abnormalities, a location information capturing means for capturing current location information when an abnormality is detected, a means for transmitting the image and location information via a communication device, and a means for reporting the abnormality data and location information received by the server to a reporting agency. This makes it possible to detect abnormalities inside the vehicle in real time and respond quickly.

[1033] The "image acquisition means" is a device for constantly capturing images inside the vehicle.

[1034] "Analysis means" refers to technology for analyzing captured video and detecting abnormalities.

[1035] The "location information acquisition means" is a device that acquires current location information when an abnormality is detected.

[1036] A "communication device" is a device for transmitting video and location information to a server or the like.

[1037] The "server" is a device that receives the transmitted abnormal data and location information, analyzes it, and reports it.

[1038] "Reporting agency" refers to an external agency, such as the police, that responds when an abnormality occurs.

[1039] This invention relates to a system for improving safety inside ride-sharing vehicles. The system combines surveillance cameras, AI technology, GPS, and communication devices to detect abnormalities and respond quickly.

[1040] Hardware and software used

[1041] 1. Video acquisition method

[1042] A surveillance camera installed inside a ride-sharing vehicle. It is a high-resolution camera that can cover the entire interior of the vehicle. The camera is always on and captures video in real time.

[1043] 2. Analysis method

[1044] To analyze the captured video, AI technology is used, specifically machine learning frameworks such as TensorFlow and OpenCV, to detect anomalies such as fraudulent or violent behavior in the video data.

[1045] 3. Location information acquisition means

[1046] The GPS module is used to obtain the current location information when an abnormality is detected, and detailed latitude and longitude can be obtained in real time.

[1047] 4. Communications Equipment

[1048] Video data and location information of abnormalities are sent to a server via a 4G / 5G communication module or Wi-Fi. The data is encrypted and sent securely.

[1049] 5. Server

[1050] The server re-analyzes the received data and further checks for anomalies. It performs the re-analysis using a deep learning model (e.g., YOLO, ResNet). If anomalies are found, it reports them to reporting agencies such as the police.

[1051] Example of operation

[1052] For example, if a violent act occurs in a ride-sharing vehicle, the system operates as follows: First, the device captures the violent act with a surveillance camera and analyzes the video using AI technology (e.g., OpenCV). If the AI ​​detects a violent act, it raises a flag. The device then obtains its current location using its GPS module, encrypts this information and the video data, and sends it to a server. The server reanalyzes the received video data and location information (e.g., using a ResNet model), and if it ultimately confirms abnormal behavior, it notifies reporting agencies such as the police. This allows the police to arrive at the scene quickly and take appropriate action.

[1053] Prompt Sentence Examples

[1054] An example of a prompt to be input to the generative AI model is, "Please explain in natural language how the system will behave if a violent act occurs inside a ride-sharing vehicle. The system will capture video using a surveillance camera and use AI to detect anomalies. If an anomaly is detected, the location information will be obtained using GPS and sent via communication means to the server. The server will reanalyze the data, and if an anomaly is confirmed, it will notify the police." This prompt allows for a detailed explanation of the system's behavior.

[1055] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] Input: Real-time video from inside a rideshare vehicle.

[1058] How it works: The device constantly captures video from surveillance cameras inside the vehicle. The cameras are high-resolution and are installed to cover the entire interior of the vehicle.

[1059] Output: High-resolution real-time video data.

[1060] Step 2:

[1061] Input: Captured real-time video data.

[1062] How it works: The device sends the captured video data to an AI analysis module. Specifically, the video is analyzed using machine learning frameworks such as TensorFlow and OpenCV. The AI ​​detects abnormal behavior based on pre-trained patterns of misconduct and violence.

[1063] Output: Video data flagged if an anomaly is detected.

[1064] Step 3:

[1065] Input: Flagged anomalous video data.

[1066] How it works: When the device receives video data that flags an anomaly, it uses its built-in GPS module to obtain its current location, which measures latitude and longitude in real time to generate detailed location data.

[1067] Output: Anomaly video data and corresponding real-time location information.

[1068] Step 4:

[1069] Input: Anomaly video data and real-time location information.

[1070] Operation: The device sends the acquired video data and location information to the server using a communication method (e.g., 4G / 5G communication module or Wi-Fi). The data is encrypted and sent securely.

[1071] Output: Anomaly video data and location information sent to the server.

[1072] Step 5:

[1073] Input: Anomaly video data and location information sent to the server.

[1074] Operation: The server re-analyzes the received data. Specifically, it uses a deep learning model (e.g., YOLO, ResNet) to re-check for anomalous behavior. If the re-analysis confirms anomalous behavior, it proceeds to the next step.

[1075] Output: Reviewed anomaly data and location information.

[1076] Step 6:

[1077] Input: Reviewed anomaly data and location information.

[1078] Operation: The server reports the detected anomaly to a reporting agency such as the police. The report includes video data showing the anomaly and location information. The server attaches a text message or image file to the report.

[1079] Output: Anomaly data and location information sent to reporting agencies such as police.

[1080] (Application example 1)

[1081] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1082] As the use of autonomous vehicles increases, ensuring safety inside the vehicle is extremely important. Conventional ride-sharing services have implemented some safety measures using surveillance cameras and GPS, but these alone make it difficult to respond quickly to sudden acts of violence or fraud. Furthermore, driverless autonomous vehicles require a means to ensure passenger safety in real time. Furthermore, passengers need a way to check the situation themselves and report it immediately.

[1083] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1084] In this invention, the server includes a means for analyzing and determining the abnormal data received, a means for reporting to the police only if the abnormal data meets certain criteria, and an abnormality notification means for issuing an alert when the mobile information terminal detects an abnormality. This enables rapid detection and response to abnormalities in real time through a surveillance camera means for constantly capturing video from inside the vehicle, an AI means for analyzing the captured video and detecting abnormalities, and a location information acquisition means for acquiring current location information when an abnormality is detected. Furthermore, passengers can check the video from inside the vehicle in real time using their own mobile information terminals, allowing them to take self-defense measures and improving overall safety.

[1085] The "monitoring camera means" is a device that constantly captures images of the inside of the vehicle and monitors them in real time.

[1086] "AI means" refers to artificial intelligence technology that analyzes captured video data and automatically detects abnormal behavior or fraudulent activity.

[1087] The "location information acquisition means" is a device or technology for acquiring current vehicle location information when an abnormality is detected.

[1088] The "transmission means" is a system for transmitting the video and location information to a server via a communication device.

[1089] The "reporting means" is a function for notifying the police of the abnormal data and location information received by the server.

[1090] The "remote video viewing means" is a function that allows passengers to view camera footage inside the vehicle in real time using a mobile information terminal.

[1091] The "abnormality notification means" is a function that issues an alert when the mobile information terminal detects an abnormality.

[1092] The "image capturing device" is a camera device for capturing images inside the vehicle at a constant frame rate.

[1093] This invention provides a system for improving safety inside ride-sharing vehicles, which includes a surveillance camera, an AI, a location information acquisition unit, a transmission unit, a reporting unit, a remote video confirmation unit, and an abnormality notification unit.

[1094] Hardware and Software

[1095] The hardware includes the smartphone's camera (video capture device), GPS module (location information acquisition means), and communications device, while the software includes OpenCV (video capture), Geopy (location information acquisition), Requests (data transmission), and generative AI models (video analysis and anomaly detection).

[1096] Processing Overview

[1097] 1. Surveillance camera means:

[1098] A camera installed inside the vehicle constantly captures video in real time, which is then sent to an AI system to analyze for any abnormalities.

[1099] 2. AI means:

[1100] Generative AI models are used to analyze captured video data in real time to detect anomalies, such as violent or fraudulent behavior, which are then flagged.

[1101] 3. Location information acquisition means:

[1102] If an abnormality is detected, the GPS module obtains the current vehicle location information.

[1103] 4. Means of transmission:

[1104] When an abnormality is detected, the terminal transmits the captured video and the acquired location information to the server via the communication device.

[1105] 5. Reporting methods:

[1106] The server analyzes the received abnormal data and location information, and if an abnormality is confirmed, it reports it to the police.

[1107] 6. Remote video confirmation method:

[1108] Passengers can use their mobile devices to view real-time camera footage from inside the vehicle, allowing them to understand the current situation and take self-protection measures.

[1109] 7. Abnormality notification means:

[1110] The mobile information terminal has the ability to issue an alert and notify passengers when an abnormality is detected.

[1111] Specific examples

[1112] This explains what happens when violence occurs inside an autonomous vehicle.

[1113] 1. Surveillance camera means capture footage of violent acts.

[1114] 2. The captured footage is analyzed using AI means to detect any anomalies.

[1115] 3. The location information acquisition means acquires the current vehicle location.

[1116] 4. These data are transmitted to the server via the transmission means.

[1117] 5. The server checks the abnormal data and location information, and if it determines that the damage is serious, it will report it to the police using the reporting means.

[1118] 6. Meanwhile, passengers can use remote video surveillance to monitor the situation and take self-protection measures.

[1119] 7. If an abnormality is detected, the mobile information device will alert passengers via the abnormality notification means.

[1120] Prompt Sentence Examples

[1121] Please capture footage of passengers committing violent acts inside the self-driving vehicle. The footage will be analyzed in real time, and if an abnormality is detected, the current location will be acquired via GPS and sent to the server along with the video of the abnormality. The server will receive this and, if necessary, notify the police.

[1122] In this way, a system is provided that aims to improve safety inside ride-sharing vehicles and can respond quickly and appropriately in the event of an abnormality.

[1123] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1124] Step 1:

[1125] The terminal constantly captures video using a surveillance camera installed inside the vehicle. The input is camera image data, and the output is the captured video frame. Specifically, the terminal acquires video frames from the camera and temporarily stores them.

[1126] Step 2:

[1127] The terminal transmits the captured video frame to the AI ​​means. The input is the captured video frame, and the output is the input data to the AI ​​means. Specifically, the terminal inputs the video frame to the AI ​​model.

[1128] Step 3:

[1129] The AI ​​means uses a generative AI model to analyze the captured video data and detect anomalies. The input is the transmitted video frame, and the output is an anomaly detection flag. Specifically, the AI ​​model analyzes the video frame and determines whether any abnormal behavior is occurring.

[1130] Step 4:

[1131] When the AI ​​means detects an abnormality, the terminal acquires current location information using the location information acquisition means. The input is an abnormality detection flag, and the output is current location information. Specifically, the terminal acquires location information from the GPS module.

[1132] Step 5:

[1133] The terminal transmits the video data of the anomaly and the acquired location information to the server using the transmission means. The input is the video data of the anomaly and the current location information, and the output is the data to be transmitted to the server. In concrete terms, the terminal uploads this data to the server via the transmission means.

[1134] Step 6:

[1135] The server analyzes the received anomaly data and location information to confirm the anomaly. The input is the transmitted anomaly data and location information, and the output is the anomaly confirmation result. Specifically, the server reanalyzes the anomaly data and determines whether the anomaly is confirmed.

[1136] Step 7:

[1137] If the server detects an abnormality, it will notify the police using the reporting means. The input is the abnormality detection result and location information, and the output is the report data to the police. Specifically, the server sends the necessary information to the police.

[1138] Step 8:

[1139] The user uses the remote video confirmation means to check the camera video in the vehicle in real time. The input is a remote access request, and the output is real-time video data. Specifically, the mobile information terminal accesses the camera video and displays the video in real time.

[1140] Step 9:

[1141] When the terminal detects an abnormality, it uses the abnormality notification means to issue an alert to the user. The input is an abnormality detection flag, and the output is an alert notification. Specifically, when the terminal detects an abnormality, a warning message is displayed on the user's mobile information terminal.

[1142] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1143] This invention relates to a system that improves safety inside ride-sharing vehicles, combining surveillance cameras, AI, GPS, communication technology, and an emotion engine, with the aim of detecting abnormalities and responding quickly.

[1144] System Configuration

[1145] The surveillance camera means installed in the ride-sharing vehicle constantly captures images of the inside of the vehicle, allowing for real-time monitoring of the situation inside the vehicle. The AI ​​means has an algorithm for analyzing the captured images and detecting abnormalities (e.g., violent or fraudulent behavior). Furthermore, by incorporating an emotion engine, the system can also monitor and analyze the user's emotional state, improving the accuracy of abnormality detection.

[1146] User Emotion Recognition

[1147] The emotion engine analyzes emotions from the user's facial expressions and voice. Based on video and audio data, it monitors the user's emotional state (e.g., anger, fear, stress, etc.) in real time. The AI ​​means also takes into account data from the emotion engine to detect anomalies. For this reason, sudden changes in emotions or high stress levels may be detected as anomalies.

[1148] Response in the event of an emergency

[1149] If an abnormality is detected, the device acquires current GPS information and sends it along with video data to the server. This data is sent to the server's designated endpoint via a communication method. The server analyzes the received data and, if it determines that an abnormality exists, immediately notifies the police. This report includes video data and GPS information indicating the abnormality, allowing the police to respond to the scene quickly.

[1150] Program processing overview

[1151] The device continuously captures video of the interior of the vehicle using a surveillance camera and sends it to the AI ​​means. The AI ​​means analyzes the video data and detects abnormalities. It also uses an emotion engine to monitor the user's emotional state in real time to complement the abnormality detection. If an abnormality is detected, the device obtains the current GPS information and sends it to the server along with the video data.

[1152] The server analyzes the video data and GPS information received from the device to check for any abnormalities. If further analysis determines that there is an abnormality, the server immediately notifies the police. This notification includes the video data and GPS information indicating the abnormality. The police can use this information to take swift and appropriate action.

[1153] Specific examples

[1154] For example, if a violent act occurs inside a car, the system works as follows: The device captures the violent act with its camera, and the AI ​​means recognizes it as a violent act. At the same time, the emotion engine analyzes the user's emotional state and measures abnormal stress levels. At this time, the device obtains current GPS information and sends this information and video data to the server. The server receives this information, further verifies that it is an abnormality, and then reports it to the police. This allows the police to quickly respond to the scene and provide initial support for the incident.

[1155] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[1156] The processing flow will be explained below.

[1157] Step 1:

[1158] The device captures video in real time from a camera installed inside the vehicle, which monitors the entire interior and constantly acquires video data.

[1159] Step 2:

[1160] The device transmits video data captured by the camera to the AI ​​means, which analyzes the video data and runs algorithms to detect anomalies (e.g., violent or fraudulent behavior).

[1161] Step 3:

[1162] The device sends the user's facial expressions and voice along with the video data to the emotion engine, which analyzes them and monitors the user's emotional state in real time. The emotion engine detects emotions such as anger, fear, and stress.

[1163] Step 4:

[1164] The device combines the analysis results of the emotion engine and AI means to determine whether an anomaly has been detected. If an anomaly is detected, it sets an anomaly flag. If no anomaly is detected, it returns to step 1 to capture video again.

[1165] Step 5:

[1166] When an anomaly is detected, the device retrieves the current GPS information and uses the GPS module to determine the current location of the car.

[1167] Step 6:

[1168] The device transmits the captured video data, emotional state data, and GPS information to the server via a communication means, which uses a means for packaging the data and transmitting it to a specific endpoint on the server.

[1169] Step 7:

[1170] The server analyzes the video data, emotional state data, and GPS information received from the device, and further checks whether there are any abnormalities based on the received data, and decides on a response if necessary.

[1171] Step 8:

[1172] The server then reports any abnormal data to the police. The report includes the video data, emotional state data, and GPS information that were determined to be abnormal. This allows the police to take prompt action.

[1173] Step 9:

[1174] Once the report is complete, the server stores a record of the incident in a database, allowing it to be reviewed and used as evidence later.

[1175] Step 10:

[1176] Users (drivers and passengers) are notified of the situation: through the application, the current status and warnings are displayed and they can take action if necessary.

[1177] The above are the specific processing steps of the system that combines the emotion engine.

[1178] Example 2

[1179] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1180] In conventional ride-sharing systems, it was difficult to detect abnormal behavior occurring in the vehicle or changes in the user's emotional state in real time and respond quickly. It was also difficult to accurately obtain location information when an abnormality occurred and report it to the appropriate authorities. This led to problems with ensuring safety inside the vehicle.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1182] In this invention, the server includes a surveillance camera, an artificial intelligence, an emotion engine, a global positioning system, a data transmission unit via a communication device, and a police reporting unit. This allows for real-time detection of abnormal behavior in the vehicle and changes in the user's emotional state, enabling prompt response. Furthermore, accurate location information can be obtained and immediate reporting to the appropriate authorities can improve safety inside the vehicle.

[1183] The "monitoring camera means" is a device for constantly capturing and recording images inside the vehicle.

[1184] "Artificial intelligence means" refers to algorithms and software used to analyze captured footage and detect abnormal or fraudulent activity.

[1185] The "emotion engine means" is software for analyzing the user's facial expressions and voice and determining their emotional state (for example, anger, fear, stress, etc.).

[1186] The "global positioning system means" is a positioning device for obtaining current location information when an abnormality is detected.

[1187] "Means for transmitting to a server via a communication device" refers to a device for transmitting the captured video and the acquired location information to a server using communication technology.

[1188] "Means for reporting to the police" refers to the reporting system that informs the police of the abnormal data and location information received by the server.

[1189] This invention relates to a system that improves safety in ride-sharing vehicles, combining surveillance cameras, artificial intelligence, global positioning systems, communication technologies, and an emotion engine to detect abnormalities and respond quickly.

[1190] System Configuration

[1191] The devices installed in ride-sharing vehicles are equipped with the following hardware and software:

[1192] 1. Surveillance camera means: A high-resolution camera (e.g., a general-purpose high-resolution camera device) is used. This camera constantly captures and records images inside the vehicle.

[1193] 2. Artificial intelligence means: AI software (e.g., TensorFlow, a general-purpose machine learning framework) equipped with algorithms for analyzing video data and detecting abnormal or fraudulent behavior will be used.

[1194] 3. Emotion engine means: Software (e.g., a general-purpose emotion analysis solution) is used to analyze the user's facial expressions and voice to determine their emotional state.

[1195] 4. Global Positioning System Means: A GPS device (e.g., a general-purpose GPS module) is used to obtain current location information.

[1196] 5. Communication device: A communication means (e.g., a general-purpose 4G LTE modem) is used to transmit video data and location information to the server.

[1197] User Emotion Recognition

[1198] The terminal transmits video data captured using the surveillance camera means to the artificial intelligence means. The artificial intelligence means analyzes the video data and detects abnormal behavior (such as violent behavior or fraud) occurring inside the vehicle. The emotional engine means also analyzes the user's emotional state in real time from their facial expressions and voice to complement the abnormality detection.

[1199] Response in the event of an emergency

[1200] If an anomaly is detected, the device acquires current GPS information and transmits it along with video data to the server. This data is then sent via a communications device to a designated endpoint on the server. The server analyzes the received data to determine whether it is an anomaly. If further analysis determines that an anomaly exists, the server notifies the police. This notification includes video data and GPS information indicating the anomaly, allowing the police to take swift and appropriate action.

[1201] Specific examples

[1202] For example, if a violent act occurs inside a car, the system operates as follows: The device captures the violent act with a surveillance camera, and the artificial intelligence means recognizes it as a violent act. At the same time, the emotion engine means analyzes the user's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then reports it to the police. This allows the police to respond to the scene quickly and provide initial support for the incident.

[1203] Example prompts for generative AI models

[1204] Example prompt:

[1205] "Consider what to do if a violent act occurs in the car and the user's stress level suddenly rises. In this case, AI analyzes the video and audio data captured by the camera and sends the GPS information to a server. Please explain in detail what happens after that."

[1206] The above is the "mode for carrying out the invention" of the present invention in which an emotion engine is combined.

[1207] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1208] Step 1:

[1209] The device continuously captures video using a surveillance camera installed inside the vehicle. The input is real-time video from inside the vehicle, and the output is high-resolution video data. This data is buffered for a few seconds and then stored in the internal memory.

[1210] Step 2:

[1211] The device sends the captured video data to an artificial intelligence means. The input is the high-resolution video data obtained in step 1, and the output is a frame-by-frame analysis of potential anomalous behavior. The artificial intelligence means uses a machine learning framework such as TensorFlow to run algorithms to detect violent or fraudulent behavior.

[1212] Step 3:

[1213] The terminal uses an emotion engine means to analyze the user's facial expressions and voice. The input is the video data and voice data obtained in step 1, and the output is the analysis result of the detected emotional state (e.g., anger, fear, stress). The emotion engine analyzes facial features and voice tone to detect abnormal emotional states.

[1214] Step 4:

[1215] When abnormal behavior or abnormal emotions are detected, the device obtains current location information using a global positioning system. The input is the anomaly detection trigger event, and the output is the current latitude and longitude information. The GPS module quickly obtains the location information, which is then processed together with other data.

[1216] Step 5:

[1217] The device transmits the acquired location information and video data indicating the anomaly to the server via a communication device. The input is the analysis results obtained in steps 2 and 3 and the location information obtained in step 4, and the output is a data packet sent to the server. This transmission is performed in real time using a secure protocol such as HTTPS.

[1218] Step 6:

[1219] The server analyzes the data received from the device. The input is the data packet sent in step 5, and the output is the confirmation of abnormal behavior. The AI ​​on the server operates in the cloud and reanalyzes the video data and emotion data to confirm the abnormality.

[1220] Step 7:

[1221] If the server detects a definite anomaly, it notifies the police. The input is the detection result and location information obtained in step 6, and the output is a report message sent to the police. The report includes specific video clips and GPS information, allowing the police to respond quickly and accurately.

[1222] The above is the specific flow of the program processing of this system.

[1223] (Application example 2)

[1224] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1225] Conventional ride-sharing and self-driving taxis lack sufficient monitoring systems to ensure safety within the vehicle. This makes it difficult to respond appropriately to violent or fraudulent behavior, as well as sudden changes in passenger emotion. Furthermore, even if an abnormality occurs, there is no established system for quickly and accurately reporting it, which can result in delayed emergency response.

[1226] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means that constantly captures video of the inside of the vehicle, an artificial intelligence means that analyzes the captured video and detects abnormalities, an emotion engine that analyzes emotions from the user's facial expressions and voice, a location information acquisition means that acquires current location information when an abnormality is detected, a means for transmitting the video and location information to the server via a communication device, and a transmission means for reporting the abnormality data and location information received by the server to the police. This enables more accurate abnormality detection and faster response.

[1227] The "image acquisition means" is a means for constantly capturing images inside the vehicle, and typically refers to a camera device.

[1228] "Artificial intelligence means" means means for analyzing captured video and detecting anomalies, including AI algorithms and models.

[1229] An "emotion engine" is a means for analyzing emotions from a user's facial expressions and voice, and refers to an engine that uses emotion recognition technology.

[1230] "Location information acquisition means" refers to a means for acquiring current location information when an abnormality is detected, and generally refers to a GPS module.

[1231] A "communication device" is a means for transmitting video and location information to a server, and includes communication technologies such as mobile communication and Wi-Fi.

[1232] The "transmission means" is a means for reporting the abnormal data and location information received by the server to the police, and includes a dedicated communication interface.

[1233] The "criteria" are standard evaluation indicators used by the server when analyzing abnormal data, and are an important factor in determining the accuracy of anomaly detection.

[1234] The "camera device" refers to a camera device that captures images inside a vehicle at a constant frame rate and is used to obtain high-resolution images.

[1235] The present invention relates to a system for ensuring the safety of passengers in autonomous vehicles. This system is composed of a combination of various means for monitoring video images inside the vehicle, detecting abnormalities, and responding quickly.

[1236] 1. System Configuration

[1237] The main components are as follows:

[1238] Surveillance camera means

[1239] To constantly capture images inside the vehicle, a surveillance camera system is used. This system consists of camera devices that capture high-resolution images. The cameras are installed at key locations inside the vehicle and capture passenger behavior in real time.

[1240] Artificial Intelligence Tools

[1241] AI algorithms are used to analyze the captured footage and detect anomalies. This includes deep learning models to automatically detect anomalies such as violent or fraudulent behavior. The analyzed data is then used in conjunction with an emotion engine to enable more accurate anomaly detection.

[1242] Emotion Engine

[1243] The emotion engine is a technology for analyzing emotions from users' facial expressions and voices. This allows for real-time monitoring of passengers' stress levels and sudden changes in their emotions, making it possible to detect abnormal changes in emotions as well as violent and fraudulent behavior.

[1244] Location information acquisition means

[1245] If an abnormality is detected, the GPS module is used to obtain the current location information, which is then sent to the server via the communication means described below.

[1246] communication equipment

[1247] The device includes a communication device for transmitting video and location information to a server. The communication device uses communication technologies such as LTE and Wi-Fi, which enables real-time data transmission.

[1248] 2. Server Processing

[1249] The server receives the abnormal data and location information sent from the device. The received abnormal data is further examined by the server's analysis means. If an abnormality is confirmed as a result of the analysis, the server notifies the police. The report includes video data showing the abnormality and location information, allowing the police to respond to the scene quickly.

[1250] 3. Specific Examples

[1251] For example, if a violent act occurs inside a self-driving taxi, the system operates as follows: The camera captures the violent act, and the AI ​​recognizes it as such. At the same time, the emotion engine analyzes the passenger's emotional state and detects an abnormal stress level. At this time, the device acquires current GPS information and sends this information and video data to the server. The server receives this information, confirms that it is an abnormality, and then notifies the police.

[1252] Prompt Sentence Examples

[1253] Below are some example prompts for a generative AI model:

[1254] Image data: base64_encoded_image_string

[1255] Emotional Data:

[1256] Anger: 0.8

[1257] Fear: 0.7

[1258] Stress: 0.9

[1259] The prompt contains image data and emotional state information that is sent to the AI ​​model, allowing it to detect anomalies and take specific corrective action.

[1260] The above is the "Mode for Carrying Out the Invention" of the present invention.

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The terminal constantly captures images of the inside of the vehicle using a surveillance camera means.

[1264] Input: Real-time video data from a camera device

[1265] Output: Captured image frames

[1266] Specific operation: The camera captures video at a constant frame rate and stores the video data in memory.

[1267] Step 2:

[1268] The terminal transmits the captured video to an artificial intelligence means for analysis.

[1269] Input: Captured image frame

[1270] Output: The result of the anomaly detection algorithm (e.g., no anomalies, violent behavior detected, etc.)

[1271] Specific operation: Analyzes video data using a deep learning model to determine whether or not abnormal behavior is occurring.

[1272] Step 3:

[1273] The device analyzes the user's facial expressions and voice using an emotion engine.

[1274] Input: Captured image frames and audio data

[1275] Output: Emotional state data (e.g., anger 0.8, fear 0.7, stress 0.9)

[1276] Specific operation: Analyzes the user's emotions using an emotion recognition model and generates emotion data.

[1277] Step 4:

[1278] The terminal integrates the data obtained from the artificial intelligence means and the emotion engine to make a comprehensive judgment as to whether an abnormality exists.

[1279] Input: Results of anomaly detection algorithm, emotional state data

[1280] Output: Overall abnormality judgment result (e.g., abnormality present, no abnormality present)

[1281] What it does: Consolidate the analysis results and flag any anomalies detected.

[1282] Step 5:

[1283] When an abnormality is detected, the terminal acquires current location information using the location information acquisition means.

[1284] Input: Abnormality judgment flag

[1285] Output: Current GPS information

[1286] Specific operation: Starts the GPS module and obtains current location information.

[1287] Step 6:

[1288] The terminal transmits the video data and the location information to the server.

[1289] Input: Captured image frame, current GPS information

[1290] Output: Data sent to the server (e.g., image data, location information)

[1291] Specific operations: Using a communication device, the collected data is sent to a specified server endpoint.

[1292] Step 7:

[1293] The server analyzes the received data and reconfirms the accuracy of the anomaly.

[1294] Input: Transmitted image data, location information

[1295] Output: Final anomaly judgment (e.g., confirmed anomaly, no anomaly)

[1296] Specific operation: Analyzes the received data and runs an algorithm to assess the probability of an anomaly.

[1297] Step 8:

[1298] If any abnormalities are detected, the server will notify the police.

[1299] Input: Final anomaly determination

[1300] Output: Report data sent to police (e.g. image data, location information)

[1301] Specific actions: Activate the reporting system and send data to the police.

[1302] The above is a description of the specific steps of the process according to the present invention.

[1303] 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.

[1304] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1305] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1306] 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.

[1307] 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 includes both affect 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.

[1308] 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.

[1309] 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).

[1310] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1311] 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."

[1312] 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.

[1313] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1314] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1319] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] The following is further disclosed regarding the above embodiment.

[1325] (Claim 1)

[1326] a surveillance camera means for constantly capturing images of the interior of the vehicle;

[1327] AI means to analyze captured video and detect abnormalities,

[1328] a GPS means for acquiring current location information when an anomaly is detected;

[1329] means for transmitting the video and position information to a server via a communication device;

[1330] means for reporting the abnormal data and location information received by the server to the police;

[1331] A system including:

[1332] (Claim 2)

[1333] means for analyzing and determining abnormal data received by the server;

[1334] a means for reporting to the police only when the abnormal data meets a certain standard;

[1335] 10. The system of claim 1.

[1336] (Claim 3)

[1337] The monitoring camera means includes a camera device that captures images of the inside of the vehicle at a constant frame rate.

[1338] 10. The system of claim 1.

[1339] "Example 1"

[1340] (Claim 1)

[1341] An image acquisition means for constantly capturing images inside the vehicle;

[1342] an analysis means for analyzing the captured video and detecting abnormalities;

[1343] a location information acquisition means for acquiring current location information when an abnormality is detected;

[1344] means for transmitting the video and position information via a communication device;

[1345] means for reporting the abnormal data and location information received by the server to a reporting organization;

[1346] A system including:

[1347] (Claim 2)

[1348] means for reanalyzing and determining abnormal data received by the server;

[1349] a means for reporting to a reporting organization only when the abnormal data meets a certain standard;

[1350] 10. The system of claim 1.

[1351] (Claim 3)

[1352] The image acquisition means includes a device that captures images of the interior of the vehicle at a constant speed.

[1353] 10. The system of claim 1.

[1354] "Application Example 1"

[1355] (Claim 1)

[1356] a surveillance camera means for constantly capturing images of the interior of the vehicle;

[1357] AI means to analyze captured video and detect abnormalities,

[1358] a location information acquisition means for acquiring current location information when an abnormality is detected;

[1359] a transmitting means for transmitting the video and the position information to a server via a communication device;

[1360] a reporting means for reporting the abnormal data and location information received by the server to a police agency;

[1361] A remote video confirmation means that allows passengers to check the camera footage in the car in real time from their mobile information terminals;

[1362] A system including:

[1363] (Claim 2)

[1364] means for analyzing and determining abnormal data received by the server;

[1365] a means for reporting to a police agency only when the abnormal data meets a certain standard;

[1366] an abnormality notification means for issuing an alert when the mobile information terminal detects an abnormality;

[1367] 10. The system of claim 1.

[1368] (Claim 3)

[1369] The monitoring camera means includes an image capturing device that captures images of the interior of the vehicle at a constant frame rate.

[1370] 10. The system of claim 1.

[1371] "Example 2: Combining Emotion Engines"

[1372] (Claim 1)

[1373] a surveillance camera means for constantly capturing images of the interior of the vehicle;

[1374] An artificial intelligence means for analyzing the captured video and detecting abnormalities;

[1375] emotion engine means for analyzing the user's emotional state;

[1376] Global positioning system means for obtaining current location information when an anomaly is detected;

[1377] means for transmitting the video and position information to a server via a communication device;

[1378] means for reporting the abnormal data and location information received by the server to the police;

[1379] A system including:

[1380] (Claim 2)

[1381] means for analyzing and determining abnormal data received by the server;

[1382] a means for reporting to the police only when the abnormal data meets a certain standard;

[1383] 10. The system of claim 1.

[1384] (Claim 3)

[1385] The monitoring camera means includes a camera device that captures images of the inside of the vehicle at a constant frame rate.

[1386] 10. The system of claim 1.

[1387] "Application example 2 when combining emotion engines"

[1388] (Claim 1)

[1389] an image acquisition means for constantly capturing images inside the vehicle;

[1390] An artificial intelligence means for analyzing the captured video and detecting abnormalities;

[1391] An emotion engine that analyzes emotions from the user's facial expressions and voice,

[1392] a location information acquisition means for acquiring current location information when an abnormality is detected;

[1393] means for transmitting the video and position information to a server via a communication device;

[1394] a transmitting means for reporting the abnormal data and location information received by the server to the police;

[1395] A system including:

[1396] (Claim 2)

[1397] an analysis means for analyzing and determining abnormal data received by the server;

[1398] a reporting means for reporting to the police only when the abnormal data meets a certain standard;

[1399] 10. The system of claim 1, comprising:

[1400] (Claim 3)

[1401] The image acquisition means includes a camera device that captures images of the interior of the vehicle at a constant frame rate.

[1402] 10. The system of claim 1. [Explanation of symbols]

[1403] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a surveillance camera means for constantly capturing images of the interior of the vehicle; AI means to analyze captured video and detect abnormalities, a GPS means for acquiring current location information when an anomaly is detected; means for transmitting the video and position information to a server via a communication device; means for reporting the abnormal data and location information received by the server to the police; A system including:

2. means for analyzing and determining abnormal data received by the server; a means for reporting to the police only when the abnormal data meets a certain standard; The system of claim 1 .

3. The monitoring camera means includes a camera device that captures images of the inside of the vehicle at a constant frame rate. The system of claim 1 .

Images